Dual-configuration grid-connectable renewable energy power plant delivering high capacity factors to controllable loads

Abstract

A renewable energy power system with a dual configurable architecture is described. The system includes a renewable energy source (RES), an energy storage system (ESS), and at least one controllable load (CL) (e.g., an AI training / data center). The system can be used as a base-load or half-base-load power plant for CL (s) and / or as a peak-shaving or half-peak-shaving power plant for an electrical grid, or vice versa, and optionally in parallel, and can also provide auxiliary services for the electrical grid and / or for CL (s). In certain embodiments, e.g., solar photovoltaic (PV) RES (s), the system can have a capacity factor of at least about 60% and up to 100% compared to known CL-free systems, higher asset utilization, better economic benefits of RES-ESS, improved system performance, and lower energy costs. By varying the load and making it part of the system, complex resource allocation policies, including the AI algorithm, can be developed, which are previously impossible in known CL-free systems.

Description

[0001] Cross Reference to Related Applications

[0002] This application claims priority to U.S. Patent Application No. 19 / 069,820, filed March 4, 2025, and titled “Twin-Configurable Architecture Renewable Power Plant for High Capacity Factor Servicing of Controllable Loads,” which is a continuation of U.S. Patent Application No. 18 / 792,847, filed August 2, 2024, and titled “Twin-Configurable Architecture Renewable Power Plant for High Capacity Factor Servicing of Controllable Loads,” now U.S. Patent No. 12,244,147, which in turn claims priority and benefit to U.S. Provisional Patent Application No. 63 / 645,837, filed May 10, 2024, and titled “Twin Mode Renewable Electric Generation Resources and Energy Storage Systems Serving High Capacity Factor Controllable Loads,” and this application also claims priority to: U.S. Provisional Patent Application No. 63 / 568,397, filed March 21, 2024, and titled “Networked Energy Generation, Storage, and Distribution,” U.S. Provisional Patent Application No. 63 / 574,733, filed April 4, 2024, and titled “Smart Seasonal Electrical Resource Allocation with Controllable Loads,” U.S. Provisional Patent Application No. 63 / 574,733, filed April 4, 2024, and titled “Smart Seasonal Electrical Resource Allocation with Controllable Loads,” U.S. Provisional Patent Application No. 63 / 574,733, filed April 4, 2024, and titled “Smart Seasonal Electrical Resource Allocation with Controllable Loads,” U.S. Provisional Patent Application No. 63 / 574,733, filed April 4, 2024, and titled “Smart Seasonal Electrical Resource Allocation with Controllable Loads,” U.S. Provisional Patent Application No. 63 / 574,733, filed April 4, 2024, and titled “Smart Seasonal Electrical Resource Allocation with Controllable Loads,” U.S. Provisional Patent Application No. 63 / 574,733, filed April 4, 2024, and titled “Smart Seasonal Electrical Resource Allocation with Controllable Loads,” U.S. Provisional Patent Application No. 63 / 574,733, filed April 4, 2024, and titled “Smart Seasonal Electrical Resource Allocation with Controllable Loads,” U.S. Provisional Patent Application No. 63 / 574,733, filed April 4, 2024, and titled “Smart Seasonal Electrical Resource Allocation with Controllable Loads,” U.S. Provisional Patent Application No. 63 / 574,733, filed April 4, 2024, and titled “Smart Seasonal Electrical Resource Allocation with Controllable Loads,” U.S. Provisional Patent Application No. 63 / 574,733, filed April 4, 2024, and titled “Smart Seasonal Electrical Resource Allocation with Controllable Loads,” U.S. Provisional63 / 639,474, and U.S. Provisional Patent Application No. 63 / 642,490, filed May 3, 2024, and titled “Systems and Methods Utilizing AC Overbuilt Renewable Electric Generation Resource with Storage Device and Controllable Loads,” the entire contents of each of the foregoing are hereby incorporated by reference herein in their entireties for all purposes. TECHNICAL FIELD

[0003] The present disclosure relates generally to energy generation, storage, and distribution. The present disclosure relates to, for example, configurable renewable energy power plants that provide efficient power generation and asset utilization for post- and pre-metered loads that benefit from high capacity factors or peaking power with limited grid connections. The present disclosure also relates to, for example, renewable energy power plants that serve multiple loads, and renewable energy power plants that are overbuilt for their grid connections. BACKGROUND

[0004] The global shift towards renewable energy has spurred the development of innovations in energy generation, storage, and distribution that aim to reduce greenhouse gas emissions and promote sustainable energy practices. Known energy grids rely on fossil fuel power generation, which presents challenges in terms of environmental impact, slow response times, and resource depletion. In contrast, renewable energy technologies such as solar, wind, and hydroelectric power offer abundant and environmentally friendly alternatives. However, many renewable energy sources are intermittent, often requiring efficient storage and distribution systems to address fluctuations in supply and demand. As a result, there is an urgent need to improve renewable energy storage and grid and load management to optimize the integration of renewable energy with existing power infrastructure. Furthermore, after decades of near-stagnant growth in developed countries’ electricity demand, the demand for clean and affordable power is growing substantially in two ways: (i) the transition from fossil fuels to clean energy provides a substantial amount of power to the grid, real estate, transportation, and commercial / industrial processes; and (ii) the digital transformation of our economy, such as data centers and now AI training. This growth puts additional pressure on existing grid infrastructure, creating a demand for improvements and new methods by integrating these new loads into a more flexible power plant system architecture operated by smart / AI controllers that have overall control of the entire energy system.

[0005] Energy generated from renewable energy sources (RES) can vary with the seasons. In some cases, energy generated from solar RES can be higher during summer months when the period of sunlight is longer and daytime peak power is higher, while lower during winter months when the period of sunlight is shorter and daytime peak power is lower. Similarly, wind power generation is typically stronger during winter months than summer months.

[0006] The demand for electrical energy or power from the grid can also exhibit variability. For example, in hot desert regions such as the southwestern United States, the demand for electrical energy from the grid can be highest during summer months due to air conditioning loads. Conversely, in other regions, the demand for electrical energy from the grid can be highest during winter due to heating loads. Depending on the geographic location, climate, industry, and / or culture of the location where the grid operates, the demand for energy or power from the grid can exhibit a variety of patterns.

[0007] In some cases, the variability of energy generation from RES can not match the variability of the required energy from the grid. SUMMARY

[0008] The following is a non-exhaustive list of some aspects of the present technology. These and other aspects are described in the following disclosure.

[0009] In some embodiments, a system includes at least one renewable energy source (RES), at least one energy storage system (ESS) (also referred to herein as a power storage system), and a controller. The at least one RES is configured to be electrically coupled to a point of grid interconnection of an electric grid. The aggregate alternating current (AC) power output capacity of the at least one RES exceeds a point of grid interconnection (POGI) limit (also referred to herein as a "POGI limit" or "POGI capacity") of the point of grid interconnection (POGI) of the electric grid (e.g., by at least about 1.3 times, or by a factor between about 3 and about 6). In some cases, the point of grid interconnection at the POGI can specify different capacities / values for the POGI capacity depending on whether the system is a net load of the electric grid or a net power generation resource of the electric grid. Accordingly, where applicable, the POGI capacity numbers discussed herein and used in calculations herein should be understood to refer to the capacity of the POGI under the corresponding load or power generation scenario. The at least one ESS is electrically coupled to the point of grid interconnection and the at least one RES. The at least one ESS has an aggregate power capacity that is less than or equal to the aggregate power output capacity (e.g., AC power output capacity) of the at least one RES. The controller is communicatively coupled with at least one controllable load, the at least one ESS, and the at least one RES. The at least one controllable load can be positioned / located pre-meter (e.g., energy-related activities occurring on the utility company / entity side of the electric grid) and / or post-meter (e.g., energy-related activities occurring on the customer side of the electric grid, optionally at a customer premises / site, and / or energy-related activities occurring on the electric grid but involving one or more independent power producers (IPPs), utilities, customer-specific tariff(s), and / or energy service providers (ESPs), as discussed further herein. Pre-meter operations can include, but are not limited to, direct access, pseudo-association (e.g., involving one or more balancing authorities), and / or special tariffs. As used herein, "direct access" can refer to a power service option (e.g., a retail power service option) in which a customer can purchase power from a competitive, non-utility entity, such as an ESP (or a utility with customer- or customer group-specific tariffs), optionally within the service territory of the utility that is still responsible for transmission and distribution for the direct access customers. It is noted that different utility service territories can use different terminology to refer to "direct access," but nonetheless can have the common ability to provide customers with the option to purchase power service(s) directly from energy provider(s).

[0010] The controller is configured to control a net load curve of the at least one CL such that the net load curve of the at least one CL includes at least one value between a maximum net load value and a minimum net load value of the at least one CL. As used herein, the “net load curve” of the CL(s) can refer to the total load / gross load minus RES generation allocated to the CL(s), while the “net load curve” of the grid can refer to the total load / gross load minus renewable energy generation allocated to the grid. Controlling the net load curve of the at least one CL can include controlling one or more subsystems of the at least one CL, such as performing “pre-cooling” on the data center. The controller is further configured to provide first instructions to at least one of the at least one RES or the at least one ESS to provide a first portion of power generated by the at least one RES or stored by the at least one ESS to the at least one controllable load until the aggregate power demand is reached. The controller is further configured to provide second instructions to at least one of the at least one RES or the at least one ESS to provide power to the grid in response to (A) power generated by the at least one RES exceeding the aggregate power capacity of the ESS and the aggregate power demand, or (B) the controller determining, using a predictive algorithm and power data, that grid conditions exist in the power system forecast. The controller is further configured to provide third instructions to the at least one controllable load to reduce or increase power demand at the at least one controllable load in response to determining that grid conditions exist and that power generated by the at least one RES does not exceed the aggregate power capacity of the ESS and the aggregate power demand. Alternatively or additionally, the controller can be configured to provide fourth instructions to the at least one controllable load to increase power demand at the at least one controllable load in response to detecting / determining that the ESS has reached a storage limit, and / or in response to a prediction that the ESS will reach a storage limit at a future time (e.g., at a time predicted to be when the ESS will next reach a storage limit without providing energy to the at least one controllable load), and / or in response to determining that it is more desirable to do so operationally or economically, such that excess energy can be used by the at least one controllable load (e.g., to perform pre-cooling for the data center).

[0011] In some embodiments, a method of providing power on a RES-ESS-CL system includes providing power at a first time and by at least one of a renewable energy source (RES) or an energy storage system (ESS) to a grid point of interconnection (POGI) associated with a grid, the POGI being disposed between at least one controllable load and the grid. The method further includes providing power at a second time and by the at least one of the RES or the ESS to the at least one controllable load. The method further includes providing power at a third time received from the grid at the POGI to the ESS. The method further includes providing power at a fourth time received from the grid at the POGI to the at least one controllable load. The method further includes providing power at a fifth time without the POGI, and instead at least one of providing power from the ESS to the at least one controllable load, providing power from the RES to the at least one controllable load, or providing power from the RES to the ESS.

[0012] In some embodiments, a non-transitory processor-readable medium stores instructions that, when executed by a processor, cause the processor to cause at least one of a renewable energy source (RES) or an energy storage system (ESS) to supply power to a controllable load without using a grid. The processor-readable medium further stores instructions that, when executed by the processor, cause the processor to cause the at least one of the RES or the ESS to supply power to the grid in response to determining that (A) power generated by the at least one RES exceeds an energy storage capacity associated with the ESS and a power demand associated with the controllable load, or (B) a grid condition associated with the grid exists. The processor-readable medium further stores instructions that, when executed by the processor, cause the processor to cause the controllable load to reduce or increase the power demand associated with the controllable load when the grid condition exists and the power generated by the at least one RES does not exceed the local storage capacity and the local power demand.

[0013] Some embodiments of the present disclosure include a system comprising a point of grid interconnection (POGI) located on a power grid and having a POGI limit; at least one renewable energy source (RES) electrically coupled to the POGI, wherein a collective AC power output capacity of the at least one RES significantly exceeds the POGI limit (e.g., by at least about 1.3 times); at least one ESS electrically coupled to the POGI and the at least one RES, wherein the at least one ESS has a collective power capacity that is less than the total power output capacity; at least one controllable load electrically coupled to at least one of the at least one RES or the at least one ESS, wherein the at least one controllable load has a collective power demand that is less than the total power output capacity; and a controller communicatively coupled to the at least one controllable load, the at least one ESS, and the at least one RES, wherein the controller is configured to: provide first instructions to at least one of the at least one RES or the at least one ESS to provide a first portion of power generated by the at least one RES or stored by the at least one ESS to the at least one controllable load up to the collective power demand; provide second instructions to at least one of the at least one RES or the at least one ESS to provide power to the power grid via the POGI only when (1) power generated by the at least one RES exceeds the collective power capacity and the collective power demand or (2) the controller determines that power grid conditions exist in a power system forecast using a predictive algorithm and power data obtained from a power data source; and provide third instructions to the at least one controllable load to reduce or increase power demand at the at least one controllable load if the power grid conditions exist and power generated by the at least one RES does not exceed the collective power capacity and the collective power demand. Alternatively or additionally, the controller can be configured to provide fourth instructions to the at least one controllable load to increase power demand at the at least one controllable load in response to detecting / determining that the ESS has reached a storage limit, and / or in response to a prediction that the ESS will reach a storage limit at a future time (e.g., at a time predicted that the ESS will next reach a storage limit without providing energy to the at least one controllable load), or is otherwise more desirable operationally or economically, so that excess energy can be used by the at least one controllable load.

[0014] Some embodiments of the present disclosure include a tangible, non-transitory machine-readable medium storing instructions that, when executed by a data processing apparatus, cause the data processing apparatus to perform operations comprising the processing(s) described above.

[0015] Some embodiments of the present disclosure include a system having one or more processors and a memory storing instructions that, when executed by the one or more processors, cause the one or more processors to implement operations of the processing(s) described above. BRIEF DESCRIPTION OF DRAWINGS

[0016] FIG. 1is a schematic diagram illustrating an embodiment of a dual-mode power generation, storage, and distribution system according to some embodiments of the disclosure;

[0017] FIG. 2 is a schematic diagram illustrating an embodiment of a dual-mode power generation, storage, and distribution system according to some embodiments of the disclosure; FIG. 1

[0018] FIG. 3 is a flow diagram illustrating a dual-mode power generation, storage, and distribution system according to some embodiments of the disclosure, acting as a base load for one or more controllable loads and as a peaking power plant for a grid; FIG. 1

[0019] FIG. 4 shows an example of a computing device through which the present technology can be implemented according to some embodiments of the disclosure;

[0020] FIG. 5 is a flow diagram showing a first method for controlling a power generation, storage, and distribution system according to some embodiments;

[0021] FIG. 6 is a flow diagram showing a second method for controlling a power generation, storage, and distribution system according to some embodiments;

[0022] FIG. 7 is a flow diagram showing a third method for controlling a power generation, storage, and distribution system according to some embodiments;

[0023] FIG. 8 is a flow diagram showing a fourth method for controlling a power generation, storage, and distribution system according to some embodiments;

[0024] FIG. 9A is a first example set of graphs comparing a controllable load to a net load curve of a grid according to some embodiments;

[0025] FIG. 9B is a second example set of graphs comparing a controllable load to a net load curve of a grid according to some embodiments;

[0026] FIG. 9C is a third example set of graphs comparing a controllable load to a net load curve of a grid according to some embodiments;

[0027] FIG. 10 is a schematic diagram illustrating an embodiment of a networked energy generation, storage, and controllable load system according to some embodiments of the disclosure;

[0028] FIG. 11 ​​is a schematic diagram illustrating an embodiment of an energy management system used in a FIG. 10

[0029] FIG. 12 is a flowchart of an embodiment of a method of networked energy generation, energy storage, and energy distribution to controllable loads according to some embodiments of the disclosure;

[0030] FIG. 13 is a block diagram of an example renewable energy power plant (REPP) according to one or more embodiments according to some embodiments of the disclosure;

[0031] FIG. 14 is a block diagram of a REPP of FIG. 13 having a switch connecting a second energy storage system (ESS) with controllable loads according to some embodiments of the disclosure;

[0032] FIG. 15 is a block diagram of a REPP of FIG. 13 having a switch connecting a second ESS with a second meter according to some embodiments of the disclosure;

[0033] FIG. 16 is a block diagram of a REPP of FIG. 13 having a switch connecting a second ESS with a first meter according to some embodiments of the disclosure;

[0034] FIG. 17 is a block diagram of a REPP of FIG. 13 having a second switch connecting a first ESS with a second meter according to some embodiments of the disclosure;

[0035] FIG. 18 is a schematic diagram illustrating an embodiment of an energy management system used in a FIG. 13 REPP according to some embodiments of the disclosure;

[0036] FIG. 19 is a flowchart illustrating a renewable energy power plant serving a plurality of controllable and uncontrollable loads according to some embodiments of the disclosure; and

[0037] FIG. 20 is a schematic diagram illustrating an embodiment of a RES-ESS system according to some embodiments of the disclosure;

[0038] FIG. 21 is a schematic diagram illustrating an embodiment of a RES-ESS controller used in a FIG. 20 RES-ESS system according to some embodiments of the disclosure;

[0039] ​FIG. 22 Figure illustrates a RES-ESS system serving one or more controllable processes according to some embodiments of the present disclosure; and

[0040] While the technology can take many different forms, specific embodiments thereof are shown in the drawings and will be described herein in detail. The drawings can not be to scale. It should be understood that the drawings and detailed description thereto are not intended to limit the technology to the particular form disclosed but on the contrary, the technology is to cover all modifications, equivalents and alternatives falling within the spirit and scope of the technology as defined by the appended claims. DETAILED DESCRIPTION

[0041] Dual configurable architecture renewable energy power plant for high capacity factor service of controllable loads

[0042] To alleviate the problems described herein, the inventors not only had to invent solutions, but in some cases it was equally important to recognize problems that were overlooked (or not yet foreseen) by others in the following areas: energy generation, storage, and distribution and renewable energy power plants, grid connected loads, controllable and non-controllable and / or related, partially related, and unrelated, and controllable and / or non-controllable loads behind the meter and controllable and / or non-controllable loads in front of the meter. Indeed, the inventors wish to emphasize the difficulty of recognizing these problems, which are nascent, and will become more apparent in the future if industry trends continue to develop as the inventors expect, such as for example, large load growth from industries such as data centers, artificial intelligence (AI) training, vertical farming, carbon capture, electric vehicle charging, smelters, water treatment plants (including desalination and purification), industrial or real estate processing energy conversion to electricity, hydrogen production, cryptocurrency mining, etc. Additionally, because multiple problems are to be solved, it should be understood that some embodiments are specific to a problem, and not all embodiments solve every problem of known systems described herein or provide every benefit described herein. That said, various permutations of improvements that solve these problems are described below.

[0043] In recent years, new loads are being introduced into the grid. These loads often have large power demands. For example, loads such as vertical farming, data centers, AI training, crypto mining, smelters, water treatment plants (including desalination and purification), electric vehicle charging, industrial or real estate energy conversion to electricity, hydrogen production, or other loads can include high energy intensive processes. Moreover, these loads can be unrelated to the known grid, where the power curve or net load curve is different from the typical power consumption of the grid, which typically follows the HVAC schedule in hot regions, heating in cold climates, established industrial processes in the region, daily time periods when commercial and residential areas typically require power, etc. These new loads often desire clean renewable energy as well as cheap energy, as energy often makes up a large portion of their operating expenses.

[0044] However, renewable energy generation sources, especially solar photovoltaic (PV) and wind turbines, are variable due to natural and weather conditions. This variability presents challenges to grid stability, which includes frequency and voltage deviation. As renewable power generation resources begin to supply a larger portion of the grid and replace known base load units such as coal and nuclear power plants, a series of technical challenges follow. These challenges include grid interconnection, power quality, reliability, stability, protection, and generation dispatch and control. The intermittent nature of solar and wind generation, coupled with rapid fluctuations in output, has led to the integration of energy storage systems (ESS) with energy storage devices such as battery energy storage systems (BESS). This integration aims to enhance grid compatibility by smoothing fluctuations and improving the predictability of energy supply from renewable energy sources, as known renewable energy sources typically exhibit low capacity factors, often ranging between 15% to 40%, depending on the resource, location, and weather patterns.

[0045] Typically, these RES-ESS systems can be linked with the transmission resources of the grid at a point of grid interconnection (POGI), which is typically operated at the optimal voltage for long distance transmission of power with minimal transmission losses. To maintain reliability and protect the transmission resources, a POGI limit is established for each electrical energy generation resource, which defines the maximum power that can be supplied to the transmission resources.

[0046] To enhance the revenue potential of photovoltaic energy generation resources and associated transmission resources of predetermined costs, there has been a recent introduction of over-provisioning of aggregate output of photovoltaic arrays or other renewable energy sources (RES) relative to POGI limits. This strategic move is inspired by occasional peak photovoltaic generation due to various factors such as inclement weather conditions, light conditions, panel cleanliness, PV panel aging, and environmental temperature rise leading to reduced PV panel output. ESS can be used in conjunction with over-provisioned RES to help absorb excess power generation over POGI limits during peak energy generation times, and provide power to the grid during times when RES is not generating power or generating less power than the POGI limit. It should be noted that while ESS can be charged during peak energy generation times, in some scenarios, ESS can also be charged when distribution to the grid is below the POGI limit to provide fuller ESS capacity, or ESS can be charged by the grid via POGI. While over-provisioned photovoltaic arrays increase the amount of power sold throughout the year and ESS absorbs excess power generation during peak RES generation times, these systems often still require curtailment of excess power during peak irradiance / wind periods and when ESS is full, which is typically achieved through regulatory requirements or designated inverter peak shaving to shield the grid from potential failures caused by circuit overload, transmission line overload, transformer strain, or the need for breakers to disconnect overproducing facilities. This power curtailment is often undesirable. Furthermore, while the new grid loads discussed above see more renewable power in the grid and can contract with renewable energy sources to deliver power on the grid, cost and reliable renewable power is still an issue as the systems still require or involve grid transmission fees and some reliance on non-renewable energy sources.

[0047] Another issue with current power plants is that when power demand is high, the grid often relies on peaking power plants (also known as peaker plants). Peaking power plants can often include low, high-emission power plants that are called upon by grid operators when demand is high. Gas turbines or diesel generators are common peaker plants because they are able to start up and ramp up when demand is high. These power plants are often undesirable due to high operating and maintenance costs and use of fossil fuels, but are a necessity for the grid to provide additional power to meet any potential power shortfall. It is estimated that peaker plants make up 10% of grid infrastructure to supply energy during dangerous peak demand that occurs only 1% of the time.

[0048] In view of these challenges, there is an urgent need to improve renewable power generation resources, energy storage, and distribution infrastructure. In addition, there is a need for sophisticated control methods to effectively manage these facilities. Further, there is a need for simplified processes to facilitate power delivery transactions of output generated by such facilities. Still further, there is a need to provide reliable, inexpensive renewable energy for certain industries, and to provide power to the grid quickly during peak times without the use of fossil fuels, and without the need for peaking plant infrastructure needed (or provided) for grid stability.

[0049] The systems and methods of the present disclosure provide a dual-mode power generation, storage, and distribution system for providing high capacity factor base load for controllable loads and peaking power for grid connection. The dual-mode power generation, storage, and distribution system can include a networked renewable energy source (“RES”) (e.g., solar, wind, etc.), an energy storage system (“ESS”), and a controllable load (“CL”) facility or power plant, where the combination can be referred to herein as a RES-ESS-CL or RES-ESS-CL facility (a photovoltaic plus storage or “PV+S” facility is a subset thereof). In various embodiments, the RES-ESS can be coupled directly with one or more controllable loads. Thus, the one or more controllable loads can be defined as being behind the meter. The controllable loads can or can not be related to loads on the grid. In some embodiments, the controllable load(s) can be on the grid, or can be on and off the grid (e.g., one or more controllable loads can be behind the meter, and one or more controllable loads can be in front of the meter). In various embodiments, the networked RES-ESS-CL system defaults as base load for supplying power to controllable loads for which the RES and ESS are built. As used herein, the phrase “capacity factor” refers to the ratio of electrical energy produced by a power generation system (e.g., a system including one or more RES and / or one or more ESS) to the load (e.g., the load in one or more controllable loads, as to its maximum rated load, or the load of the grid via its POGI capacity) served by the power generation system.

[0050] In one or more embodiments, the RES-ESS-CL system or facility can be configured to reduce the correlation of one or more controllable loads with the grid. For example, the load curve of one or more controllable loads ((one or more) CLs) can be shifted (e.g., in time or load), adjusted, or modified in a de-correlated manner relative to the load curve of the grid (e.g., the "net load" curve of the grid, which can refer to the total load / gross load of the grid less the RES generation allocated to the grid) or the power curve of the grid (e.g., the "net power" curve) such that the performance associated with the RES-ESS-CL system (e.g., the financial performance of the RES-ESS-CL system, asset utilization associated with the RES and / or ESS, etc.) is improved. For example, the de-correlation can include causing one or more peaks of the load curve of one or more controllable loads to no longer overlap, or to overlap less, with one or more peaks of the load curve (e.g., the net load curve), the net power curve, or the energy service price curve of the grid, or to be substantially opposite, or otherwise different (e.g., flatter or less flat, or time-shifted) from these peaks, for example as discussed below FIGS. 9A-9C

[0051] In contrast to POGI limits, the RES in the present disclosure can be overconfigured more than other overconfigured RESs because the RESs are used as the baseload for the controllable loads rather than the baseload for the grid. For example, the RESs of the RES-ESS-CL system of the present disclosure can be overconfigured many times above the power limit of the POGI without suffering from low efficiency or potentially wasted energy, as compared to what can be reasonably overconfigured in other overconfigured systems. As discussed above, known overconfigured systems are configured as the baseload for the grid. Thus, the RESs of the overconfigured systems are typically capped at the sum of the power limits of the POGI and the ESS, where the ESS is sized up to or equal to the POGI, resulting in the RES being capped at twice the POGI, which avoids curtailment of energy. For example, if the POGI limit is 100 MW, then the ESS is also sized to deliver 100 MW to the POGI, to equal (within the limits allowable by the grid operator) or be less than the POGI for maximum efficiency, while using the maximum available transfer capability to the grid via the POGI. Thus, the RES is limited to 200 MW (i.e., twice the POGI), because any additional power from the system cannot go anywhere, so energy will be wasted.

[0052] ​In contrast, the RES of the RES-ESS-CL system of the present disclosure is not limited by the POGI. More specifically, the RES can have a capacity that is based on the capacity of the controllable load behind the meter. Thus, the RES can be three, four, five, or more times the POGI limit. For example, the POGI limit can be 100 MW, but the controllable load (which can include multiple controllable loads) can have a total capacity of 100 MW and the ESS can have a capacity of 300 MW, and the RES can have a capacity of 500 MW or other power output capacity. Thus, the RES can have five (or more) times the power capacity of the POGI limit due to the controllable load behind the meter included in the RES-ESS-CL system. Thus, the RES-ESS-CL system can be overbuilt on a large scale when providing base load for the controllable load rather than for the grid. Thus, economies of scale can be achieved for the RES and ESS, making the system more efficient, with higher asset utilization. Moreover, the RES-ESS-CL system of the foregoing example can provide very high capacity factors (in this example, the same) for the CL and the grid, values far beyond what known systems without CL can achieve. This again demonstrates that the asset utilization of the PGI grid connection and / or the CL is very high, with capacity factors that can exceed 80% and can even approach 100%. While the overbuilding of the RES relative to the POGI limit is one benefit of the RES-ESS-CL system, another benefit is that the dual configurable architecture (also referred to herein as “dual mode,” “dual configuration,” and / or “dual configuration system”) of the RES-ESS-CL system can operate as a base load for the controllable load or as a peaking plant, and can also operate as a peaking plant or base load for the grid or microgrid or micro utility grid (e.g., islanded grid) or geographically limited utility grid.

[0053] For example, when the RES-ESS of a RES-ESS-CL system acts as a baseload for a controllable load, the RES-ESS-CL can be in dual mode, e.g., where the RES-ESS-CL can also operate as a peaking plant and supply power to the grid when conditions are met to do so. In some embodiments, a dual configurable architecture system as described herein can be implemented in / as a single standalone power plant and can be configured to operate in a first mode in which the system operates as a baseload or semi-baseload plant (i.e., between pure baseload and pure peaker) and / or provides ancillary services and (optionally concurrently, in parallel, or overlapping in time) in a second mode in which the system operates as a peaking or semi-peaking plant (between pure peaker and pure baseload) and / or provides ancillary services to a customer or group of customers. The ancillary services provided in the first mode can be the same, overlapping, or different from the ancillary services provided in the second mode. In various embodiments, the condition can be based on power demand on the grid, such as when the power demand on the grid meets a predetermined threshold or when the increment of available power supply meets a predetermined threshold of power demand. During times when grid demand is high, power generation is low, and the RES-generated power is insufficient to meet both the grid connection power capacity and the controllable load power capacity, the RES-ESS-CL can control the load at the controllable load by communicating with the controllable load to reduce power consumption, such that power can be redirected from the controllable load to the grid interconnection point. This can include redirecting power provided by the ESS or RES from the controllable load to the grid interconnection.

[0054] In other embodiments, an overbuilt high capacity factor RES-ESS-CL system can experience times when the RES is generating too much power for the ESS and CL to consume. During these peak power generation times, the RES-ESS-CL system can provide the excess power generation to the grid. Thus, when conditions are good on the grid, the grid acts as a source to remove the excess generated power by transferring power to the grid or subsidize the capital cost of building the overbuilt RES-ESS system, such that power can be provided to the controllable load more efficiently, more economically. In contrast, recent RES-ESS systems are designed to be built to serve the grid only.

[0055] In one or more embodiments of the present disclosure, in addition to the RES, ESS, and CL, the RES-ESS-CL system can be configured to control (e.g., using one or more controllers of the RES-ESS-CL system and / or using communications via one or more communication networks described herein) one or more “old fashioned” (e.g., non-renewable) power generators, such as a gas turbine(s) or diesel generator(s). These non-renewable energy sources (herein and FIG. 1A grid (e.g., a utility grid) can be coupled to one or more CLs of the RES-ESS-CL system, one or more ESSs of the RES-ESS-CL system, and / or the RES-ESS-CL system.

[0056] In one or more embodiments of the present disclosure, the RES-ESS-CL system can be configured to operate / run in multiple modes (e.g., more than two modes), each mode including two or more of: operating as a base load (e.g., at one or more different output levels), operating as a peaking plant for a grid (e.g., at one or more different output levels), operating as a peaking plant for a microgrid or micro utility (e.g., at one or more different output levels), or operating as a provider of one or more ancillary services for a grid. As used herein, an “ancillary service” can refer to a service that helps maintain or supplement the integrity, stability, and / or power quality associated with an electric power transmission and / or distribution system. As non-limiting examples, an ancillary service can refer to one or more of: reactive power compensation, regulation including voltage regulation, flicker control, active power filtering, harmonic cancellation, frequency control including inertia support, frequency control reserves / primary control, frequency restoration reserves / secondary control, and / or replacement reserves / tertiary control, performing synchronous regulation (e.g., correcting / compensating for changes in electrical imbalances that can affect the stability of a power system), ramp up service, ramp down service, providing emergency reserves (e.g., supplying power to cope with unexpected power outages or failures of electrical elements or system components such as generators, transmission lines, circuit breakers, switches, etc.), black start regulation (e.g., providing power for system restoration when an entire grid or a subsystem thereof is powered down), or flexibility reserves (e.g., supplying power to compensate for variability and / or uncertainty over longer time scales than typically involved in emergency reserves, synchronous regulation, and / or black start regulation), day-ahead dispatch reserves, loss compensation, congestion management, or oscillation damping.

[0057] Accordingly, aspects of the present disclosure provide a smart network of controllable loads, ESS, and RES (e.g., solar and wind energy sharing a grid connection) behind and in front of the meter. The RES-ESS-CL system can be “networked” to center around a single node (if a node is defined as one connection to the grid), which optimizes the cost and capacity factor of controllable loads (and maximizes revenue / profitability / emergency needs) by increasing the utilization of assets such as ESS and RES. Accordingly, aspects of the present disclosure provide RES-ESS-CL systems with higher efficiency and better economic benefits than overbuilt RES-ESS systems, as RES-ESS is built as a base load for controllable loads, which can benefit from lower power costs and better capacity factors when employing a system approach. Having “controllable” loads means that the system architecture is not limited to generation, storage, and distribution, but also incorporates loads and uses some unrelated “grid loads” to subsidize economic benefits by making RES-ESS a peaking power plant through better asset utilization. The grid can also provide flexibility and become a source of power when the grid price is low, allowing for the extension of the ESS’s useful life by reducing charge / discharge cycles. Controllers driven by AI algorithms work to optimally exploit the synergies.

[0058] As discussed above, controllable loads can be new loads (e.g., AI training, data centers, vertical farming, smelters, EV charging, hydrogen production, water treatment plants (including desalination and purification), crypto mining, etc.) and can have different characteristics than known loads on the grid (HVAC in hot climates, heating in cold climates, industry, etc.). To take advantage of their high capital expenditure, these new loads need to operate at high utilization, which conflicts with the low capacity factor of known renewable energy. But at the same time, these new loads are very dependent on finding cheap power, as their economic benefits are dominated by power costs. While renewable energy is now typically the cheapest form of power, their capacity factor is usually low— to increase the capacity factor, storage is needed, which costs additional capital. With embodiments of the present disclosure, controllers can cross-subsidize storage costs and other capital costs by selling power to the grid when those prices are high (typically requiring ESS, as well as when renewable energy generation is not cheap), effectively turning power plants into peaking plants for the grid, which can also provide valuable ancillary services to the grid or customers. Accordingly, by designing RES and ESS with one or more controllable loads, synergies can be explored and large ESS can be owned that are used both to increase capacity factor and to create a rich revenue when grid prices are high. Being behind the meter helps to avoid grid fees, which can dominate economic benefits.

[0059] Depending on the grid load curve (and price curve) and the controllable load curve, the operation (and design) of the RES can be optimized. The operation simulation of the RES-ESS-CL system shows that the combination of RES-ESS with controllable (and unrelated) loads gives better results at lower cost. This is because the ESS is better utilized and the solar farm or wind turbine (EOS) is larger. The controllable loads can be located pre- and / or post-meter - post-meter has the additional advantage of maximizing interconnection / grid access (which is a constraint), reducing losses, avoiding transmission fees, avoiding grid curtailment, avoiding grid congestion and associated fees, and overhead and management costs for the grid operator. Grid connection has valuable, expensive, and fixed costs, so having more energy flow through the entire system also reduces the cost per MWh.

[0060] In some embodiments, the system can include multiple controllable loads (one or more post-meter (e.g., AI training and vertical farming or cooling for data centers) as a focus of cost optimization, and one or more pre-meter, which can be used to optimize economic benefits and utilization). Grid connection also allows the controllable load(s) to be operated when there is cheap electricity on the grid (e.g., wind at night), further reducing costs, or providing or receiving power from the ESS or RES on the grid. Again, the controller with machine learning / AI can predict and manage the system accordingly. When the RES-ESS-CL system produces too much at the RES and the remaining post-meter load and ESS capacity are insufficient or the controller determines that it is best to keep some ESS capacity unused, the ESS on the grid can be controlled to store energy, and the controller can push power to the grid. Similarly, when the grid net load is low (e.g., the grid is close to overproduction from renewable energy) or the energy price is low, and in order to save battery life or the power stored on the ESS of the RES-ESS-CL system, the controller can obtain power from the RES on the grid and / or from the grid market.

[0061] In various embodiments, a load that is a controllable load can include a load that the controller described herein can change the demand of by either increasing or decreasing the demand for power at that load. Thus, the present disclosure contemplates adjusting both the allocation of energy from the RES and the demand for energy from one or more of the controllable loads, which can be located on the grid or behind the meter. The controllable loads of the present disclosure can be controlled such that their energy demand / load has a value between 0 and its maximum value, and can be dynamically adjusted or regulated over time, e.g., by the controller and / or in response to user input, AI model output, etc., as compared to known / non-controllable loads. In various embodiments, controllable loads behind the meter allow energy producers to increase the size and performance of the RES. For example, the RES can be built to generate a greater capacity than the capacity that the RES can provide to the grid. This provides cost and performance advantages of economies of scale as compared to an over-provisioned system without controllable loads behind the meter. When generation is not at peak (e.g., cloudy or early or late in the day or less windy, etc.) or when the energy storage system included within the RES is full, the excess energy is absorbed by the grid in addition to the ESS and controllable loads. Also, when the RES is generating low and the gap between supply and demand on the grid is small, the over-provisioned system can use the stored amount of electricity on the ESS to deliver more power to more critical or more valuable loads on the grid and meet the bandwidth that the RES can provide to the grid. Thus, the RES-ESS-CL system can be designed for better performance and lower cost, i.e., the overall system performance is better such that by acting as a peaking power plant, it provides more consistent energy supply, capacity, or other ancillary services to new loads behind the meter and the grid.

[0062] In various embodiments of the present disclosure, the controller can include a predictive algorithm such as, for example, model predictive control (MPC), model-based reinforcement learning (MBRL), adaptive model predictive control (AMPC), or other predictive algorithm / machine learning algorithm. MPC can be implemented with long short-term memory (LSTM), state space models, or transformer architectures. Some embodiments can use a multi-modal time series prediction model (e.g., considering values for weather, wind power, solar power, grid demand, and metered behind-the-meter load output), examples including: autoregressive moving average (ARMA) models (e.g., seasonal ARIMA); autoregressive integrated moving average (ARIMA) models; generalized autoregressive conditional heteroskedasticity (GARCH) models; vector autoregression models, Holt-Winters exponential smoothing; state space models; and Kalman filters. The predictive algorithm can predict priorities for future time intervals, and based on the prioritization and predicted total energy storage and generation, the predictive algorithm can determine any demand adjustments to controllable loads and allocate energy and power to various loads (on or off the grid) or ESS (on or off the grid) based on the prioritization and other constraints.

[0063] The controller can include predictive and machine learning algorithms for balancing energy allocation of controllable loads. For example, the energy generation and allocation controller can ingest data from various data sources (e.g., weather forecasts, event schedules, calendars, historical energy usage data, sensor data, or other data sources apparent to one of ordinary skill in the art having the present disclosure). In other embodiments, the data sources can include power state data or analytical values of other RES and their ESS or standalone ESS. These other RES can include energy storage systems that are not on the network and can be competitors or grid resources. Thus, predicting how much energy storage another RES is providing can be beneficial to foresee how much energy will be available on the grid at a particular time, such that control of ESS, controllable loads, or even controllable RES (e.g., hydroelectric power plants) can be managed.

[0064] The controller, using predictive / ML algorithms trained based on historical data or simulator data, can foresee the energy demand of uncontrollable loads on the grid, grid operating parameters, and energy supply of power plants. Based on the foreseen energy demand and energy supply and operating parameters of the grid, the controller can determine whether one or more energy balance conditions associated with a respective controllable load are satisfied to increase or decrease the power allocation to that controllable load. For example, in exchange for a more favorable rate of its energy price or some other energy allocation factor desired by the controllable load, the controllable load can allow the controller to decrease the energy consumption at that controllable load to reallocate the energy supply of RES or ESS to loads that are uncontrollable and can pay a higher premium or have a higher priority according to various factors (e.g., more essential / high priority infrastructure, such as hospitals, water supply plants, critical communication infrastructure, etc.). Furthermore, the controllable load itself can be adjusted to decrease or increase energy consumption.

[0065] In some embodiments where the controllable loads include multiple controllable loads, the controller and its machine learning / predictive algorithms can efficiently balance load control when power capacity from the controllable loads is needed to charge the ESS or provide power to the grid. For example, the controllable loads can include an Al training data center, a cryptocurrency mining center, and / or a vertical farm. These controllable loads can have load curves (e.g., net load curves) that can be operated / controlled in a manner that further optimizes the operation and efficiency of each individual CL, while the combined load of the CLs presents a more favorable (e.g., in terms of grid stability, POGI utilization efficiency, combined RES-ESS-CL system asset utilization behind the meter, efficiency and effectiveness of grid ancillary services provided by the RES-ESS-CL, energy price minimization, etc.) load curve to the grid than would exist if each CL was controlled individually and independently. For example, cryptocurrency mining can fluctuate with the weather, as the computers performing the mining can run continuously, while the cooling of the computers can fluctuate with the outside temperature. Data centers can experience a similar curve to that of the cryptocurrency miners, while vertical farms experience a curve of low energy demand for several hours when needed to provide dark cycles to the factory. By anticipating the amount of power reduction needed (or to be) by the aggregate controllable loads and when the reduction is needed, the controller can intelligently select which controllable load or loads to send instructions to reduce power demand. In some embodiments, the controller can be aware of various processes occurring at the controllable loads. For example, a data center can be performing time-consuming processes that take hours or days to complete, as well as processes that take less than a second, a few seconds, a few minutes, or other short time intervals relative to grid demand, where the machines completing those processes can be instructed to idle or consume less power, while the machines performing the “long” processes remain running. However, in other embodiments, the controller can determine at a high level which controllable loads should reduce or increase their power consumption, provide instructions to those controllable loads, and the controllable loads themselves can have intelligent algorithms to determine which processes running on those controllable loads can be reduced or increased based on the parameters provided by the controller of the RES-ESS-CL system.

[0066] Similarly, the controllable load can include an energy storage system, where the controller can increase or decrease the allocation of power to the energy storage devices. Further, more optimal decisions can be made as to which energy storage devices in the ESS to use for storing energy. For example, zinc-air batteries, thermal batteries, pumped hydro, gravity energy storage, or hydrogen production facilities can be charged / powered when there is cheap power available; while lithium-ion batteries can be charged when there is more expensive power available, faster response times are foreseen, higher round trip efficiency is beneficial, or other benefits that will be apparent to those skilled in the art upon mastery of the present disclosure. Thus, the type of energy storage device or other factors associated with the energy storage device can be used to determine when a particular energy storage device is charged or how much power a particular energy storage device is to receive.

[0067] As mentioned above, the controllable load can include its own ESS. In some embodiments, those ESS can include a BESS system. However, in other embodiments, the controllable load can include other ESS, such as, for example, thermal batteries or thermal storage batteries, pumped hydro, gravity energy storage devices, hydrogen production facilities, etc. In one example, the controllable load can be a data center, an AI training center, a cryptocurrency miner, etc. that generates a large amount of heat during the operation of the servers performing the operations. To cool the servers, these controllable loads also use power from the RES / ESS to cool the servers. In some cases, the controllable load can include a system that can convert waste heat into cold air or ice that can be stored and then used to cool the servers when power at the controllable load is reduced. The controllable load can reduce air conditioners used to cool the servers and allow the stored cooling medium to transfer heat from the servers to the cooling medium.

[0068] In other embodiments, the controller can also use the foreseen energy demand and energy supply to balance the storage of energy generated by the RES on the associated batteries. For example, the controller can determine the amount of energy stored on each battery, and how those batteries in the power plant will distribute energy in an optimized manner. For example, to maintain the expected life of the batteries, under normal conditions, the batteries cannot be fully charged or fully depleted (e.g., the batteries can be placed in a battery protection mode) because fully charging and / or fully depleting the batteries reduces the expected useful life of the batteries. However, if the foreseen energy supply and demand indicates that it is more beneficial to fully charge or fully deplete the batteries than to consider the expected life of the batteries, then the controller can fully charge the batteries to account for future events. For example, if there is a foreseen event that requires (or involves) a high energy demand, then the energy generation and distribution controller can fully charge the batteries. In other embodiments, the controller can tier the batteries such that a first battery distributes energy based on a first condition, a second battery distributes energy based on a second condition, and a third battery distributes energy based on a third condition. These conditions can be prioritized based on different tiers. For example, the third battery can only distribute energy when the price of energy is above a certain threshold or when the discharge period is long.

[0069] In other embodiments of the present disclosure, the energy generation and distribution controller can determine when to provide energy storage to power plants that are not included in the RES, such as power plants on the grid. The energy generation and distribution controller can determine the conditions under which off-grid power plants can store energy on the batteries or other ESS of the RES. Using the foreseen energy demand and energy storage decisions made by the machine learning algorithm of the RES-ESS-CL controller, the RES-ESS-CL controller can determine when to purchase power from power plants on the grid, from the grid itself (e.g., via an energy market), or when to provide storage for contracted off-grid power plants. The RES-ESS-CL controller can communicate with applications located at the off-grid power plants, similar to the applications provided at the controllable loads and storage devices of the networked power plants. Thus, the systems and methods of the present disclosure provide more optimized and consistent energy generation, storage, and distribution of energy generated by the RES by providing base load to one or more controllable loads and acting as a peaking power plant for the grid, which increases grid reliability while providing a more consistent / higher capacity factor for the “new” load seen by the grid as it enters.

[0070] FIG. 1An example dual-mode power generation, storage, and distribution system 100 is illustrated in accordance with one or more embodiments. While it is described herein as a "dual" mode, the inventors of the present disclosure recognize that additional modes can be included, optionally running simultaneously or overlapping in time, or fewer than two modes can be running simultaneously. The energy generation, storage, and dual-mode energy generation, storage, and distribution system 100 can include a controller 102; a network 104; a RES-ESS-CL system 106 including one or more RESs 109, one or more ESSs 107, one or more non-renewable energy sources (NRESs) 113, one or more controllable loads 108, one or more inverters 116, one or more inverters 118, and optionally one or more inverters 119 (e.g., when the NRES 113 does not have an inbuilt inverter(s) or direct output AC power); a grid 110; one or more sources of power data 111; one or more known loads (e.g., load 114a and / or load 114b); one or more controllable loads 114c located pre-meter; one or more RESs 120; and one or more ESSs 122. The one or more NRESs 113 can include, for example, one or more diesel, gasoline, hydrogen, heavy oil, aviation fuel, or other types of fuel generators (which can or can not include their own associated inbuilt inverters) and / or one or more gas turbine generators (which can or can not include their own associated inbuilt inverters). While some components are listed and illustrated in quantity as one or more, other components illustrated as individual components can include more than one of those components. Also, in this document, while components can include one or more, for ease of discussion, components can be described as one component (e.g., one or more controllable loads 108 can simply be described as a controllable load for the purposes of discussion).

[0071] The loads 114a, 114b, controllable load(s) 114c, one or more NRESs 124, RES 120, and ESS 122 can be electrically coupled to the grid 110. The one or more NRESs 124 can include, for example, one or more gas turbines and / or one or more diesel generators. The controllable load(s) 114c can be paired / electrically coupled to the one or more NRESs 124 (e.g., such that the one or more NRESs can act as a backup energy source for the controllable load(s) 114c, for example). The loads 114a, 114b, one or more NRESs 124, and / or controllable load(s) 114c can be remote from each other and have separate power demands. The loads 114a can have a first power delivery profile that details the power demand of the loads 114a at different times. The loads 114b can have a second power delivery profile that details the power demand of the loads 114b at different times. The controllable load(s) 114c can have a third power delivery profile that details the power demand of the controllable load(s) 114c at different times. In some embodiments, the grid 110 can be a utility grid owned and operated by a single utility or system operator. In other embodiments, the grid 110 can be a plurality of electrical connections allowing for the transmission of power from the RES-ESS-CL system 106 to the loads 114a, 114b, and controllable load(s) 114c. In some embodiments, the grid 110 can include a microgrid or micro-utility (e.g., a self-sufficient grid) that is co-created with customers to create their own grid. For example, a village or island in Africa can own its own utility and paying customers.

[0072] The RES 109 can include a first renewable energy power plant (REPP). Examples of REPPs include, but are not limited to, solar power plants, wind power plants, geothermal power plants, and biomass power plants. However, the RES 109 can include multiple REPPs. A portion of the multiple REPPs can be a first type of REPP (e.g., multiple solar power plants), another portion of the multiple REPPs can be a second type of REPP (e.g., multiple wind turbines), yet another portion of the multiple REPPs can be a third type, and so on up to an nth type. The RES-ESS can include an energy storage system (ESS) 107. An example of an ESS is a battery. A battery-based ESS can be referred to as a battery ESS or BESS. As discussed above, the ESS can include a thermal battery or thermal storage battery, pumped hydro, gravity energy storage, hydrogen production facilities, or other energy storage systems apparent to those of ordinary skill in the art having the benefit of the disclosure. The RES 109 can have a first power output that varies over time. The multiple different types of REPPs can share an ESS, or have separate ESSs or a combination of shared ESSs and dedicated ESSs. In various embodiments, the ratio of power generated by the RES 109 to the power limit of the POGI can be any ratio greater than 2 (e.g., the ratio can be 3, 4, 5, or 6). For example, because the metered behind-the-meter controllable load 108 can allow the RES 109 to scale up, the size of the RES 109 is not limited to the POGI, such that the power generated by the RES 109 can be between about 3 times and about 6 times the power limit of the POGI. The ratio can be optimized based on one or more types of controllable loads, RESs, and ESSs, as well as grid energy consumption and generation, such that a high capacity factor is achieved with minimal energy curtailment for the RES-ESS-CL system 106. As an example, for a solar PV RES in a sunny region, where the natural capacity factor of the sun is between about 15% (e.g., Northern Europe or Canada) and about 30% (e.g., North Africa or the American Southwest desert), the higher ratio described herein allows the RES-ESS-CL system to have a higher asset utilization and substantially increase the capacity factor (e.g., as measured relative to the POGI capacity, i.e., the capacity factor of the POGI utilization) when compared to a RES-ESS system with a POGI ratio of about 2 and a capacity factor of the POGI utilization of about 35% to about 55%.Therefore, although RES109 is constructed as the baseload of controllable load 108, the entire RES-ESS-CL system 106 can operate as a dual-mode system with dual uses: (1) as the baseload of controllable load 108 or, in some cases, as the baseload of controllable load 114c; and (2) as a peaking power plant for grid 110 to provide power to the grid during periods of high demand and low supply, and / or ancillary services, and as an outlet for the generation of excess power when ESS107 and controllable load 108 cannot consume any additional power. These modes can operate concurrently or independently.

[0073] In some embodiments, RES 109 may be coupled to inverter 116. Inverter 116 may convert the DC power generated by RES 109 into AC power supplied to grid 110 at a grid interconnection point. The grid interconnection point has a Grid Interconnection Point (POGI) limit. Inverter 116 may have an AC power output limit greater than the POGI limit. RES-ESS-CL system 106 may include inverter 118 that may be coupled between ESS 107 and grid 110 and between inverter 116 and grid 110. Inverter 118 may be bidirectional, such that it converts the AC power output from inverter 116 into DC power that can charge ESS 107. Similarly, inverter 118 may convert the DC power of ESS into AC power that can be output to grid 110. The RES-ESS-CL system 106 may further include an inverter 119 that can be coupled between NRES 113 and the grid 110, and NRES 113 may be directly electrically coupled to ESS 107 (e.g., so that NRES 113 can be used to charge ESS 107) and (one or more) controllable loads 108 (e.g., so that NRES 113 can be used as a backup power source for controllable loads 108). In various embodiments, inverter 118 may optionally be configured to have an AC power output greater than POGI. In some embodiments, inverter 118 may be a bidirectional inverter that receives grid AC power from grid 110 and converts the grid AC power into DC power for charging ESS 107 or powering controllable loads (or both). Controllable loads 108 may be coupled between inverter 116 and grid 110, and between inverter 118 and grid 110. In some embodiments, the controllable load 108 may be directly electrically coupled to RES 109 or ESS 107, allowing it to receive DC power from RES 109 or ESS without requiring an inverter to convert the DC power to AC power and then back. In some embodiments, the grid 110 may supply power to the controllable load 108, so another inverter, a bidirectional inverter (not shown), may be used to convert AC power from the grid 110 to DC power and supply it directly to the controllable load 108. However, it is conceivable that the controllable load may operate using either AC or DC power and require (or use) a bidirectional inverter. The controllable load 108 may use grid power when the net load or electricity price on the grid 108 is below a threshold. Therefore, by using ESS 107 only when conditions require it, the use of cheap electricity on grid 110 can save electricity on ESS 107 or the lifespan of ESS 107, and excess renewable energy or cheap energy or potentially negative-priced energy (i.e., when customers pay for electricity consumption) available on grid 110 can also be used to charge ESS.

[0074] In various embodiments, the controllable load 108 and ESS 107 can have similar demand ratios. For example, ESS 107 can be sized to at least serve the power interconnection of the controllable load 108. RES 109 can be sized such that, during peak generation, RES 109 can supply its power to the controllable load 108, ESS 107, and grid 110. For example, the POGI limit of grid 110 can be 100MW, the power interconnection limit of controllable load 108 can be 200MW, and the power interconnection of ESS 107 can be 300MW, allowing ESS 107 to supply power to grid 110 and controllable load 108. Therefore, RES 109 can be over-configured to up to 600MW, which is six times the over-configuration relative to POGI.

[0075] The RES-ESS-CL system 106 can communicate with the networked energy RES-ESS-CL controller 102 via network 104. Similarly, one or more controllable loads 108 and 114c, RES 109 and 120, and ESS 107 and 122 can communicate with the RES-ESS-CL controller 102 via network 104. Furthermore, ESS 122, ESS 107, RES 120, and / or RES 109 can communicate with the power grid 110 via network 104, for example, using one or more monitoring and data acquisition (SCADA) systems, which may optionally reside on, be accessible from, and / or be operatively coupled to one or more of ESS 122, ESS 107, RES 120, and / or RES 109. Additionally, NRES 113 and / or NRES 124 can communicate with the power grid 110 via network 104. Furthermore, inverters 116, 118, and / or 119 can communicate with RES 120, RES 109, ESS 107, and / or ESS 122 via network 104. Additionally, controller 102 can communicate with power data source 111 via network 104. The data source may include sensor data, weather data, local timetables, or any other system data or third-party information that is obvious to those skilled in the art possessing this disclosure. Network 104 can be any local area network (LAN), wide area network (WAN), and / or satellite-based network. In some embodiments, network 104 is the Internet. In other embodiments, network 104 is a private communication network. RES-ESS-CL controller 102 may include a processor and memory.

[0076] The RES-ESS-CL controller 102 can control RES 109 and direct power from RES 109 to ESS 107, controllable load 108, and the grid 110. The RES-ESS-CL controller 102 can also control when ESS 107 charges or discharges power received from inverter 118 from RES 109 or, in some embodiments, from the grid 110. The RES-ESS-CL controller 102 can also control the power demand at controllable load(s) 108 and 114c. While a specific system has been described, those skilled in the art, possessing this disclosure, will recognize that other variations, components, multiple RES, ESS, and controllable loads can be considered without departing from the scope of this disclosure.

[0077] Although FIG. 1 Not explicitly shown, but multiple switches (e.g., electronic switches, low-voltage, medium-voltage, or high-voltage switches, or intelligent controllable circuit breakers) may be placed at appropriate locations throughout the system 100 to facilitate selection and control (e.g., via controller 102) of various operating modes, which may include, but are not limited to, one or more of the following: supplying power to the grid 110 from inverter 116 (or directly if NRES has AC output), supplying power to the grid 110 from inverter 118, supplying power to the grid 110 from inverter 119, supplying power to the grid 110 from RES 120, supplying power to the grid 110 from ESS 122, supplying power to the grid 110 from NRES 113, and so on. 124 supplies power to grid 110, uses grid 110 to supply power to (one or more) controllable loads 108, uses grid 110 to supply power to (one or more) controllable loads 114c, uses grid 110 to supply power to (one or more) loads 114a, and / or uses grid 110 to supply power to (one or more) loads 114b.

[0078] exist FIG. 1 In one or more embodiments of system 100, controller 102 can be configured to dynamically control FIG. 1One or more other components of the system 100, for example, automatically, in a time-varying manner and / or in response to / based on one or more user-provided instructions and / or AI model outputs. For example, controller 102 can be programmed / configured to perform one or more of the following in various ways: control (e.g., increase, decrease, segment modification, etc.) the net load profile of (one or more) controllable loads 108 (including their subsystems, such as a data center cooling system); modify the operating mode of (one or more) controllable loads 108; modify the number of controllable loads 108 operating within a given predefined time interval; modify the load distribution across multiple controllable loads 108 within a given predefined time interval (e.g., in a uniform or non-uniform manner); cause / control the operation of (one or more) controllable loads 108 (and optionally NRES 113) while charging ESS 107 (e.g., with a predefined, modifiable charging rate / profile) and / or operating RES 109 (and / or RES 120) (supplying or not supplying power to grid 110); discharge ESS 107 (e.g., with a predefined, modifiable discharge rate / profile) and / or operating RES 109 (and / or RES 120) While receiving power from grid 110, the system initiates / controls the operation of one or more controllable loads 108 (and optionally NRES 113); while charging ESS 107 (e.g., with a statically or dynamically adjusted charging rate), operating NRES 113 (if present), and / or operating RES 109 (and / or RES 120) (supplying or not supplying power to grid 110), the system initiates / controls the operation of one or more controllable loads 108; while discharging ESS 107 (e.g., with a predefined, modifiable discharge rate / curve), operating NRES 113 (if present), and / or operating RES 109 (and / or RES 120) and receiving power from grid 110, the system initiates / controls the operation of one or more controllable loads 108; while operating NRES 113 (if present) and reducing RES 109 (and / or RES 120), the system initiates / controls the operation of one or more controllable loads 108; while operating NRES 113 (if present) and reducing RES 109 (and / or RES 120), the system initiates / controls the operation of NRES 113 (if present) and / or reducing RES 120 (and / or RES 120)... While operating (supplying or not supplying power to grid 110) 120, the operation of controllable load 108 and ESS 107 is caused / controlled; while putting part or all of ESS 107 into energy-saving mode (e.g., reducing parasitic loads and / or HVAC systems associated with ESS 107), and while operating NRES 113 (if present) and / or RES 109 (and / or RES 120) and receiving power from grid 110, the operation of controllable load 108 is caused / controlled;While ESS 107 is in frequency regulation service mode (supplying or not supplying power to grid 110 from NRES 113 and / or RES 109), the operation of one or more controllable loads 108 is initiated / controlled; while NRES 113 (if present) is running and ESS 107 is charging (e.g., with a predefined, modifiable charging rate / curve) (supplying or not supplying power to grid 110), the operation of one or more controllable loads 108 and RES 109 (and / or RES 120) is initiated / controlled; while RES 109 is blocked and NRES 113 is optionally running and ESS 107 is optionally charging or discharging (supplying or not supplying power to grid 110), the operation of one or more controllable loads 108 is initiated / controlled; or while NRES 113 (if present) is running and ESS 108 is charging or discharging (supplying or not supplying power to grid 110), the operation of NRES 113 is initiated / controlled; while NRES 113 is running and ESS 108 is charging or discharging (supplying or not supplying power to grid 110), the operation of NRES 108 is initiated / controlled; 107 discharges (e.g., using a predefined, modifiable discharge rate / curve) and receives power from the grid 110 while simultaneously initiating / controlling the operation of (one or more) controllable loads 108 and RES 109.

[0079] In some embodiments, any two or more of the foregoing system operation mechanisms can be combined or cascaded, for example, such that they are executed sequentially by controller 102, e.g., as part of a power resource deployment schedule. Moreover, the ordering of such combinations of system operation mechanisms(one or more) can vary over time (e.g., automatically, optionally dynamically, and / or in response to the output of AI model(one or more) via controller 102). The resource deployment schedule can be specific to / unique to the individual power plants within a system (e.g., a networked system), each power plant including the systems disclosed herein (e.g., FIG. 1 The system 100 in the system, and the resource allocation strategy implemented by each resource deployment schedule (for each power plant) can be different from each other, making the overall resource allocation strategy reflected by the power plant system diverse.

[0080] In some embodiments, the controller 102 may select one or more of the aforementioned system operation mechanisms based on predictive analysis performed by one or more AI models. For example, when generating and / or modifying the aforementioned system operation mechanisms and / or associated resource allocation strategies, predictions related to one or more of weather, grid conditions, electricity demand information, load response, and / or market pricing of electricity services (e.g., including ancillary services) on / via the grid may be considered.

[0081] According to one or more embodiments of this disclosure, by setting "load" as a variable (via the use and control of one or more CLs, as described herein), resource allocation strategies (e.g., energy resources and / or financial resources) are more sophisticated and / or complex than those associated with known systems lacking (one or more) CLs, for example, facilitating finer adjustments and optimizations. Moreover, resource allocation strategies previously impossible (e.g., in terms of flexibility, energy efficiency, cost efficiency, size, scale, etc.) for known systems lacking (one or more) CLs can be developed / implemented. By adding (one or more) CLs to a RES-ESS system, the ability to optimize the overall performance of the system is enhanced / improved, moving from a single dimension of the RES-ESS system (e.g., where the ESS may be controllable) to a multidimensional control mechanism that allows for multidimensional optimization strategies. This can superlinearly improve system performance and facilitate the economically feasible construction and operation of larger, more efficient power systems / plants. For embodiments including (one or more) NRESs, the overall system controllability can be further increased, enabling further enhancement of system performance and allowing for the construction and operation of larger-scale power systems / plants.

[0082] In some embodiments, FIG. 1 One or more functions of system 100 can be combined with FIG. 4 System 400 FIG. 10 System 1000 FIGS. 13-17 System 1300 and / or FIG. 20 The system 2000 may combine or replace one or more functions of the system. Alternatively or additionally, FIG. 1 One or more functions of the controller 102 can be used FIG. 2 Controller 200 FIG. 11 Controller 1102 FIG. 18 Controller 1802 and / or FIG. 21 Implemented by one or more functions / features of the controller 2102. Alternatively or additionally, FIG. 1 System 100 can be configured to execute FIG. 3 Method 300 FIG. 5 Method 500 FIG. 6 Method 600 FIG. 7 Method 700 FIG. 8 Method 800 FIG. 12 Method 1200 FIG. 19 Method 1900 or FIG. 22 One or more of methods 2200.

[0083] This is publicly available. FIG. 2 An embodiment of the RES-ESS-CL controller 200 is illustrated, which can be the controller described in the above reference. FIG. 1The RES-ESS-CL controller 202 is discussed. Although described as a standalone system, those skilled in the art will recognize that the RES-ESS-CL controller 200 can be distributed across many computing devices, such as in a cloud environment. In the illustrated embodiment, the RES-ESS-CL controller 200 includes a chassis 202 housing the components of the RES-ESS-CL controller 200. FIG. 2 Only some of the components are illustrated. For example, chassis 202 may house a processing system (not shown) and a non-transitory memory system (not shown) containing instructions that, when executed by the processing system, cause the processing system to provide a RES-ESS-CL engine 204 configured to perform the functions of the RES-ESS-CL engine or RES-ESS-CL controller discussed below. FIG. 2 In the specific examples shown, the RES-ESS-CL engine 204 may include a RES-ESS-CL predictive algorithm 205 configured to perform the functions of the RES-ESS-CL predictive algorithms discussed herein. In various embodiments, the RES-ESS-CL predictive algorithm 205 may ingest data provided by a data source and predict energy demand and supply, grid conditions, or any other functions discussed herein. In various embodiments, the RES-ESS-CL predictive algorithm 205 may include a network simulator to model behavior, which, due to a lack of historical data, can predict components to be integrated into the grid by running simulations. In other examples, the RES-ESS-CL predictive algorithm 205 may include model predictive control or other predictive / machine learning algorithms that are obvious to those skilled in the art with this disclosure.

[0084] The chassis 202 may also house a communication system 206 coupled to the energy generation, storage, and distribution engine 204 (e.g., coupling with the processing system via the communication system 206) and configured to provide communication via the communication network 104, as described in detail below. The chassis 202 may also house a storage system 208 coupled to the RES-ESS-CL engine 204 via the processing system and configured to store rules or other data used by the RES-ESS-CL engine 204 to provide the functions discussed below. While the RES-ESS-CL controller 200 has been illustrated, those skilled in the art with this disclosure will recognize that other RES-ESS-CL controllers (or other devices operating in a manner similar to that described below for the RES-ESS-CL controller 200 according to the teachings of this disclosure) may include various components and / or component configurations for providing known computing device functions and the functions discussed below, while still remaining within the scope of this disclosure.

[0085] FIG. 3Embodiments of a method 300 for generating, storing, and distributing renewable energy are described, in some embodiments of which the method may utilize the methods discussed above. FIG. 1 and FIG. 2 At least some of the components are used to implement this. As discussed below, some embodiments technically improve the RES-ESS system by over-constructing (e.g., by which one or more ESS components can store excess capacity generated by one or more RES and not otherwise consumed, supplied to the grid, etc.), and / or otherwise serving as a "dual-mode" system of high-capacity baseload (optionally combined with ancillary services) to controllable loads and more as a peaking power plant for the grid (optionally combined with ancillary services). Some or all of the steps of method 300 may be performed by other participants in the energy generation, storage, and dual-mode power generation, storage, and distribution system 100 and still fall within the scope of this disclosure. Furthermore, and as mentioned above, the RES-ESS-CL controller 102 / 200 may include one or more processors or one or more servers, so method 300 may be distributed across those one or more processors or one or more servers.

[0086] Method 300 may begin at block 302, wherein a first instruction is provided to at least one of at least one RES or at least one of at least one ESS to provide a first portion of the power generated by at least one RES or stored by at least one ESS to at least one controllable load until the aggregate power demand is reached.

[0087] Method 300 may proceed to block 304, wherein a second instruction is provided to at least one of at least one RES or at least one ESS, so that power is supplied to the grid via the grid interconnection point only if the power generated by at least one RES exceeds the aggregated power capacity of the ESS and the aggregated power demand or the controller determines, using a predictive algorithm and power data obtained from the power data source, that grid conditions exist in the power system forecast (e.g., indicating one or more of the following forecasts or predictions: power demand, power availability, grid capacity, grid availability and grid congestion, or power pricing or ancillary service pricing during a given time / predefined time period).

[0088] Method 300 can proceed to block 306, wherein, if grid conditions exist, a third instruction is provided to at least one controllable load to reduce (or optionally increase) the power demand at that at least one controllable load. Thus, the dual-mode power generation, storage, and distribution system can, in a first time, supply power to at least one of the POGIs connected to the grid 110 or to at least one controllable load 108 located downstream of a meter via at least one of RES 109 or ESS 107. In a second time, power can be received from the POGIs to at least one of the ESS 107 or at least one of the at least one controllable loads. In the second time, power can also be supplied to the controllable load 108 from at least one of RES 109 or ESS 107, or from RES 109 to ESS 107. In a third time, power is not supplied or received via the POGIs, and power can be supplied to the controllable load 108 from at least one of the ESS 107, to the controllable load 108 from RES 109, or to ESS 107 from RES 109.

[0089] Therefore, when there is high electricity demand on the grid (typically coinciding with peak electricity prices), electricity is typically supplied to grid 110. This may occur only for 1%-10% of the time, but other percentages must also be considered. Thus, the RES-ESS-CL system 106 can act as a peaking power plant and serve as the baseload of the controllable load 108 in some cases. Therefore, grid 110 can be used solely to generate profit during peak prices or to avoid energy cuts within the RES-ESS-CL system 106. When prices are low on grid 110, the RES-ESS-CL controller 102 can instruct the controllable load 108 to consume potentially cheaper electricity from grid 110, such as wind power generated at night. Therefore, the systems and methods of this disclosure provide post-meter loads for key customers to achieve high capacity factors. Now, by increasing grid connection, approximately 3 times or even more excess capacity can be built. This overcomes the typical 2 times excess buildup in overbuilt RES-ESS systems, allowing for a more efficient system with dual-mode baseload and peaking power plant configurations.

[0090] FIG. 4 This is a diagram illustrating an exemplary computing system 400 according to an embodiment of the present technology. Various parts of the systems and methods described herein may include or execute on one or more computer systems similar to computing system 400. For example, networked energy generation, storage, and distribution RES-ESS-CL controllers 102 / 200, power plants 106a and 106b, controllable loads 108a, 108b, 112, and (one or more) controllable loads 114c may include computing system 400. In another example, FIG. 13The EMS controllers 1305 / 1800, RES 1335, inverters 1315, 1340 and 1360, ESS 1310 and 1365, loads 1370 and 1375 or meters 1320 and 1350 may include computing system 400. Additionally, the processes, operations, services, and modules described herein may be executed by one or more processing systems similar to computing system 400.

[0091] The computing system 400 may include one or more processors (e.g., processors 410a-410n) coupled to system memory 420, input / output I / O device interface 430, and network interface 440 via input / output (I / O) interface 450. The processors may include a single processor or multiple processors (e.g., a distributed processor). The processor may be any suitable processor capable of executing or otherwise performing instructions. The processor may include a central processing unit (CPU) that executes program instructions to perform arithmetic, logical, and input / output operations of the computing system 400. The processor may execute code that creates an execution environment for the program instructions (e.g., processor firmware, protocol stack, database management system, operating system, or a combination thereof). The processor may include a programmable processor. The processor may include a general-purpose or special-purpose microprocessor. The processor may receive instructions and data from memory (e.g., system memory 420). The computing system 400 may be a single-processor system including one processor (e.g., processor 410a) or a multiprocessor system including any number of suitable processors (e.g., 410a-410n). Multiple processors can be employed to provide parallel or sequential execution of one or more portions of the techniques described herein. The processes described herein (such as logical flows) can be executed by one or more programmable processors that execute one or more computer programs to perform functions by manipulating input data and generating corresponding outputs. The processes described herein can be executed by a dedicated logic circuit system (e.g., a FPGA (Field-Programmable Gate Array) or an ASIC (Application-Specific Integrated Circuit)), and the apparatus can also be implemented as a dedicated logic circuit system (e.g., a FPGA or an ASIC). Computing system 400 may include multiple computing devices (e.g., a distributed computer system) to implement various processing functions.

[0092] I / O device interface 430 can provide an interface for connecting one or more I / O devices 460 to computer system 400. I / O devices may include devices that receive input (e.g., from a user) or output information (e.g., to a user). I / O devices 460 may include, for example, graphical user interfaces displayed on a monitor (e.g., a cathode ray tube (CRT) or liquid crystal display (LCD)), pointing devices (e.g., a computer mouse or trackball), keyboards, keypads, touchpads, scanning devices, voice recognition devices, gesture recognition devices, printers, audio speakers, microphones, cameras, etc. I / O devices 460 can be connected to computer system 400 via wired or wireless connections. I / O devices 460 can be connected to computer system 400 from remote locations. For example, I / O devices 460 located on a remote computer system can be connected to computer system 400 via a network and network interface 440.

[0093] Network interface 440 may include a network adapter that provides connection between computer system 400 and a network. Network interface 440 facilitates data exchange between computer system 400 and other devices connected to the network. Network interface 440 may support wired or wireless communication. The network may include electronic communication networks such as the Internet, local area network (LAN), wide area network (WAN), cellular communication network, etc.

[0094] System memory 420 may be configured to store program instructions 401 or data 402. Program instructions 401 may be executable by a processor (e.g., one or more of processors 410a-410n) to implement one or more embodiments of the present technology. Instructions 401 may include modules of computer program instructions for implementing one or more technologies described herein with respect to various processing modules. Program instructions may include computer programs (referred to in some forms as programs, software, software applications, scripts, or code). Computer programs may be written in programming languages, including compiled or interpreted languages, or declarative or procedural languages. Computer programs may include units suitable for use in a computing environment, including standalone programs, modules, components, or subroutines. Computer programs may or may not correspond to files in a file system. Programs may be stored in a portion of a file holding other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple coordinating files (e.g., portions of files storing one or more modules, subroutines, or code). Computer programs may be deployed to execute on one or more computer processors located locally at a site or distributed across multiple remote sites interconnected by a communication network.

[0095] System memory 420 may include a tangible program carrier on which program instructions are stored. The tangible program carrier may include a non-transitory computer-readable storage medium. A non-transitory computer-readable storage medium may include a machine-readable storage device, a machine-readable storage substrate, a memory device, or any combination thereof. A non-transitory computer-readable storage medium may include non-volatile memory (e.g., flash memory, ROM, PROM, EPROM, EEPROM memory), volatile memory (e.g., random access memory (RAM), static random access memory (SRAM), synchronous dynamic RAM (SDRAM)), mass storage memory (e.g., CD-ROM and / or DVD-ROM, hard disk drive), etc. System memory 420 may include a non-transitory computer-readable storage medium on which program instructions executable by a computer processor (e.g., one or more processors 410a-410n) to cause the operation of the subjects and functions described herein. Memory (e.g., system memory 420) may include a single memory device and / or multiple memory devices (e.g., distributed memory devices). Instructions or other program code providing the functions described herein may be stored on a tangible non-transitory computer-readable medium. In some cases, the entire instruction set can be stored concurrently on the medium, or in other cases, different parts of the instruction can be stored on the same medium at different times.

[0096] I / O interface 450 can be configured to coordinate I / O traffic between processors 410a-410n, system memory 420, network interface 440, I / O devices 460, and / or other peripheral devices. I / O interface 450 can perform protocol, timing, or other data transformations to convert data signals from one component (e.g., system memory 420) into a format suitable for use by another component (e.g., processors 410a-410n). I / O interface 450 may include support for devices attached via various types of peripheral buses, such as the Peripheral Component Interconnect (PCI) bus standard or variants of the Universal Serial Bus (USB) standard.

[0097] Embodiments of the techniques described herein can be implemented using a single instance of computer system 400 or multiple computer systems 400 configured to carry different portions or instances of the embodiments. Multiple computer systems 400 can provide parallel or sequential processing / execution of one or more portions of the techniques described herein.

[0098] Those skilled in the art will recognize that computer system 400 is merely illustrative and not intended to limit the scope of the techniques described herein. Computer system 400 may include any combination of devices or software capable of performing or otherwise providing the performance of the techniques described herein. For example, computer system 400 may include cloud computing systems, data centers, server racks, servers, virtual servers, desktop computers, laptop computers, tablet computers, server equipment, client devices, mobile phones, personal digital assistants (PDAs), mobile audio or video players, game consoles, in-vehicle computers, or global positioning systems (GPS), or combinations thereof. Computer system 400 may also connect to other devices not shown, or may operate as a standalone system. Furthermore, in some embodiments, the functionality provided by the illustrated components may be combined into fewer components or distributed across additional components. Similarly, in some embodiments, the functionality of some of the illustrated components may not be provided, or other additional functionality may be available.

[0099] In some embodiments, FIG. 4 One or more functions of system 400 can be combined with FIG. 1 System 100 FIG. 10 System 1000 FIGS. 13-17 System 1300 and / or FIG. 20 The system 2000 may use one or more of its functions in combination or as a substitute. Alternatively or additionally, FIG. 4 System 400 can be configured to execute FIG. 3 Method 300 FIG. 5 Method 500 FIG. 6 Method 600 FIG. 7 Method 700 FIG. 8 Method 800 FIG. 12 Method 1200 FIG. 19 Method 1900 or FIG. 22 One or more of methods 2200.

[0100] FIG. 5 This is a flowchart illustrating a first method for controlling a power generation, storage and distribution system according to some embodiments. FIG. 5 Method 500 can be used FIG. 1 System 100 FIG. 2 System 200 and / or FIG. 4 The computer system 400 is used to execute this. For example, FIG. 5Method 500 can be performed using a system comprising at least one renewable energy source (RES), at least one energy storage system (ESS), and a controller. The at least one RES can be configured to be electrically coupled to a grid interconnection point, and the aggregate power output capacity of the at least one RES can exceed the grid interconnection point (POGI) limit of that interconnection point. The at least one ESS can be electrically coupled to both the grid interconnection point and the at least one RES. The at least one ESS can have an aggregate power capacity smaller than the aggregate power output capacity of the at least one RES. The controller can be communicatively coupled to at least one controllable load, at least one ESS, and at least one RES. The controller can be configured to perform the following operations. FIG. 5 Method 500: At 502, a first instruction is provided to at least one of at least one RES or at least one of at least one ESS to provide a first portion of the power generated by at least one RES or stored by at least one ESS to at least one controllable load until the aggregated power demand is reached. The controller may also be configured, at 504, in response to (A) the power generated by at least one RES exceeding the aggregated power capacity and aggregated power demand; or (B) the controller using predictive algorithms and power data to determine that grid conditions exist in the power system forecast, to provide a second instruction to at least one of at least one RES or at least one of at least one ESS to provide a second portion of the power to the grid. The controller may also be configured, at 506 and in response to determining that grid conditions exist and the power generated by at least one RES does not exceed the aggregated power capacity and aggregated power demand, to provide a third instruction to at least one controllable load to reduce the power demand at at least one controllable load (or alternatively increase the power demand).

[0101] In some embodiments of method 500, the total AC power output capacity of at least one RES exceeds the POGI limit by at least approximately 1.3 times.

[0102] In some embodiments of method 500, grid conditions are associated with at least one of the following: the price of electricity associated with the grid, the demand for electricity from the grid, the temperature associated with the grid, the operating conditions associated with the grid, the price of ancillary services associated with the grid, a reduction order associated with the grid, a congestion price associated with the grid, or a congestion mitigation value associated with the grid. Alternatively or additionally, grid conditions may include or be associated with a relative energy value for a given location, date, and / or time (e.g., the energy value for powering an air conditioner in hot weather may be higher than the energy value for powering an air conditioner in cool weather). Grid conditions can be determined to exist when one or more of the following exceed a predefined maximum threshold value, fall below a predefined minimum threshold value, fall within a predefined threshold range, or exceed a predefined threshold range: the price of electricity associated with the grid, the demand for electricity from the grid, the temperature associated with the grid, the operating conditions associated with the grid, the price of ancillary services associated with the grid, a reduction order associated with the grid, a congestion price associated with the grid, or a congestion mitigation value associated with the grid. As used herein, grid-related cuts can refer to the deliberate reduction of power output below a productive level, and as a non-limiting example, can occur in response to an emergency, according to a predefined schedule, or as a measure to balance energy supply and demand due to transmission or generation restrictions.

[0103] In some embodiments of method 500, the controller is further configured to operate at least one RES or at least one ESS as at least one of a peaking power plant of the grid or a provider of ancillary services of the grid, and the grid conditions are associated with at least one of the following: the price of electricity associated with the grid, the price of ancillary services associated with the grid, grid-related reductions, grid-related congestion prices, or grid-related congestion mitigation values.

[0104] In some embodiments of method 500, at least one controllable load includes multiple controllable loads, and the controller is further configured to provide instructions to the multiple controllable loads to balance the energy distribution associated with the multiple controllable loads.

[0105] In some embodiments of method 500, at least one controllable load (CL) includes a data center. Alternatively or additionally, at least one controllable load may include one or more of the following: an artificial intelligence (AI) training center, a cryptocurrency mining machine, an electric vehicle (EV) charging station, a vertical farm, a hydrogen production facility (e.g., an electrolyzer), a smelter, a water treatment plant (including seawater desalination and purification), an industrial process heater, or a thermal battery.

[0106] In some embodiments of method 500, the controller is configured to select a first instruction such that, in response to the first instruction or in response to at least one controllable load, the correlation between at least one controllable load and the power grid decreases or increases. For example, the first instruction may cause the controllable load to consume more energy when the net load on the power grid is below a certain threshold (e.g., the power grid is close to an over-generation state, making the power grid close to an unstable state), or when the energy price is negative. Alternatively or additionally, in some embodiments of method 500, the controller is configured to select a first instruction such that, in response to the first instruction or in response to at least one controllable load, (1) the correlation between at least one peak of the net load curve associated with at least one controllable load and (2) at least one peak of the net load curve associated with the power grid decreases.

[0107] In some embodiments of method 500, the first instruction is configured to execute in response to a first instruction or in response to at least one controllable load, such that the correlation between the at least one controllable load and the power grid is reduced. Optionally, one or more correlations described herein are associated with a predefined time period.

[0108] In some embodiments of method 500, the controller is also configured to deliver power from the grid to at least one controllable load.

[0109] In some embodiments of method 500, the load curve of at least one controllable load can be controlled to have a smaller correlation with the load curve of the power grid. In some embodiments of method 500, the load curve of at least one controllable load differs from the load curve of at least one additional load electrically coupled to the power grid.

[0110] In some embodiments of method 500, the correlation between the net load curve of at least one controllable load and the net load curve of the grid may decrease or otherwise change during peak price periods and increase during periods of low grid energy prices.

[0111] In some embodiments of method 500, the system is configured to: (1) operate in a first mode as one of a baseload, half-baseload, or peak-shaving power plant of at least one controllable load; and (2) operate concurrently with operation in the first mode as a peak-shaving power plant of the grid in a second mode.

[0112] In some embodiments of method 500, the system (e.g., FIG. 1 System 100 FIG. 2 System 200 and / or FIG. 4The computer system 400) has an associated capacity factor of at least approximately 60%, or at least approximately 65%, or at least approximately 70%, or at least approximately 75%, or at least approximately 80%, or at least approximately 80%, or at least approximately 85%, or at least approximately 90%, or at least approximately 95%, or close to approximately 100%, or between approximately 60% and approximately 90%, or between approximately 50% and approximately 80%, or between approximately 70% and approximately 90%, or between approximately 75% and approximately 95%, or between 80% and approximately 100%.

[0113] In some embodiments of method 500, the ratio of the power generated by at least one RES to the total load of at least one controllable load is between about 3 and about 6, or between about 4 and about 7, or between about 3 and about 9, or between about 6 and about 9, or has a value of about 3, or has a value of about 4, or has a value of about 5, or has a value of about 6, or has a value of about 7, or has a value of about 8, or has a value of about 9 or has a value of about 10.

[0114] In some embodiments of method 500, the controller is also configured to provide a fourth instruction to at least one non-renewable energy source (NRES) to cause the at least one NRES to provide a third portion of the power generated by the at least one NRES to at least one controllable load in a post-meter manner and / or via direct access.

[0115] FIG. 6 This is a flowchart illustrating a second method for controlling a power generation, storage, and distribution system, according to some embodiments. FIG. 6 Method 600 can be used FIG. 1 System 100 FIG. 2 System 200 and / or FIG. 4 The computer system 400 is used to execute this. For example... FIG. 6As shown, method 600 includes, at 602, providing power at a first time via at least one of a renewable energy source (RES) or an energy storage system (ESS) to a point of connection (POGI) associated with the grid, the POGI being deployed between at least one controllable load and the grid. Method 600 further includes, at 604, providing power at a second time via at least one of the RES or ESS. Method 600 further includes, at 606, at a third time, providing power received from the grid at the POGI to the ESS. Method 600 further includes, at 608, at a fourth time, providing power received from the grid at the POGI to at least one controllable load. Method 600 further includes, at 610, at a fifth time, providing power not via the POGI, but rather providing power from the ESS to at least one controllable load, from the RES to at least one controllable load, or from the RES to the ESS.

[0116] In some implementations, method 600 further includes supplying power from at least one of the RES or ESS to at least one controllable load at a fourth time.

[0117] In some implementations, method 600 further includes providing power from RES to at least one of ESS or at least one controllable load at a fourth time.

[0118] In some embodiments of method 600, providing power at the fifth time includes: (1) providing power from the ESS to at least one controllable load, and (2) one of the following: providing power from the RES to at least one controllable load or providing power from the RES to the ESS.

[0119] In some embodiments of method 600, providing power at the fifth time includes: (1) providing power from RES to at least one controllable load, and (2) one of the following: providing power from ESS to at least one controllable load or providing power from RES to ESS.

[0120] In some embodiments of method 600, the method further includes supplying power from at least one non-renewable energy source (NRES) to at least one controllable load at a sixth time.

[0121] In some embodiments of method 600, providing power at the fifth time includes: (1) providing power from RES to ESS, (2) providing power from RES to at least one controllable load, and (3) providing power from ESS to at least one controllable load.

[0122] FIG. 7 This is a flowchart illustrating a third method for controlling a power generation, storage, and distribution system according to some embodiments. FIG. 7Method 700 can be used FIG. 1 System 100 FIG. 2 System 200 and / or FIG. 4 The computer system 400 is used to execute this. For example... FIG. 7 As shown, method 700, which can be implemented via processor-executable instructions stored in / on a non-transitory processor-readable medium, includes, at 702, causing at least one of a renewable energy source (RES) or an energy storage system (ESS) to supply power to a controllable load without using the power grid. Method 700 further includes, at 704, causing at least one of the RES or ESS to supply power to the power grid in response to determining that (A) the power generated by at least one RES exceeds the storage capacity associated with the ESS and the power demand associated with the controllable load, or (B) there are grid conditions associated with the power grid. Method 700 further includes, at 706, causing the controllable load to do one of the following when grid conditions exist and the power generated by at least one RES does not exceed the local storage capacity and local power demand: reduce or increase the power demand associated with the controllable load. Alternatively or additionally, in some implementations (not shown), method 700 may include increasing the power demand at at least one controllable load in response to detecting / determining that the ESS has reached its storage limit and / or in response to a prediction that the ESS will reach its storage limit at a future time (e.g., the time when it is predicted that the ESS will next reach its storage limit without supplying energy to at least one controllable load), such that the excess energy can be utilized by at least one controllable load (e.g., to perform pre-cooling for the data center).

[0123] In some embodiments of method 700, the controllable load includes a data center. Alternatively or additionally, the controllable load may include one or more of the following: an artificial intelligence (AI) training center, a cryptocurrency mining machine, an electric vehicle (EV) charging station, a vertical farm, a hydrogen production facility, a smelter, a water treatment plant (including seawater desalination and purification), an industrial process heater, or a thermal battery.

[0124] In some embodiments of method 700, during periods when the power grid is above a high threshold of net load or below a low threshold of net load, the net load curve of at least one controllable load is substantially uncorrelated with the net load curve of the power grid.

[0125] In some embodiments of method 700, during periods when the power grid is above a high threshold of net load or below a low threshold of net load, the net load curve of at least one controllable load is substantially inversely correlated with the net load curve of the power grid.

[0126] In some embodiments of method 700, the peaks of the load curve of at least one controllable load are not correlated, inconsistent, or overlap with the peaks of the load curve of at least one additional load electrically coupled to the power grid.

[0127] In some embodiments of method 700, the instruction to cause at least one of the RES or ESS to supply power to the controllable load includes an instruction to supply power to the controllable load concurrently with the instruction to cause at least one of the RES or ESS to supply power to the grid.

[0128] In some implementations, the non-transitory processor-readable medium also stores instructions that, when executed by the processor, cause the processor to deliver power from the grid to a controllable load.

[0129] In some implementations, the non-transitory processor-readable medium also stores instructions that, when executed by the processor, cause the processor to balance the energy distribution associated with multiple controllable loads, including the controllable load.

[0130] In some implementations, the non-transient processor-readable medium also stores instructions that, when executed by the processor, cause the processor to switch between or concurrently operate in the following modes: (1) a first mode in which at least one of the RES or ESS operates as a peaking power plant or baseload of the power grid, and (2) at least one additional mode in which at least one of the RES or ESS operates as a baseload or peaking power plant of a controllable load.

[0131] FIG. 8 This is a flowchart illustrating a fourth method for controlling a power generation, storage, and distribution system according to some embodiments. In some such embodiments, the system includes: at least one renewable energy source (RES) configured to be electrically coupled to a grid interconnection point; at least one energy storage system (ESS) electrically coupled to the grid interconnection point and at least one RES; at least one non-renewable energy source (NRES); and a controller communicatively coupled to at least one controllable load, at least one ESS, at least one NRES, and at least one RES. The controller is configured to perform... FIG. 8 Method 800, the method comprising, at 802, providing a first instruction to at least one of at least one RES, at least one NRES, or at least one ESS to provide a first portion of power to at least one controllable load until aggregate power demand is reached. FIG. 8 The method 800 further includes, at 804, providing a second instruction to at least one of at least one RES, at least one NRES, or at least one ESS to provide a second portion of the power to the grid. FIG. 8The method 800 also includes, at 806 and in response to determining that grid conditions exist, providing a third instruction to at least one controllable load to change (e.g., increase or decrease) the power demand at the at least one controllable load.

[0132] In some embodiments of this disclosure, the correlation between the net load curve of at least one controllable load and the net load curve of the power grid is less than about 0.1, or between about 0.1 and 0.2, or between about 0.05 and about 0.5, or between about 0.2 and about 0.5, or between about 0.2 and about 0.3, or between about 0.3 and about 0.5. The aforementioned correlation values ​​may be associated with, for example, a predefined time period or interval. This predefined time period or interval may be approximately (e.g., having such a time scale) minutes (e.g., one minute, five minutes, 20 minutes, etc.), hours (e.g., one hour, two hours, between about two hours and about ten hours, between about four hours and about six hours, etc.), days (e.g., between one day, two days, between about three days and about five days, etc.), weeks, months, seasons (e.g., summer, winter, autumn, spring), or years.

[0133] FIG. 9A This is a first example set of graphs according to some embodiments, used to compare the net load curve (net load relative to time) of the controllable load (CL) with that of the power grid (e.g., for a common time period). FIG. 9A The diagrams in the diagrams are not drawn "to scale" (for example, in practice, the net load of the CL will often be much smaller than the net load of the grid), but are instead scaled for readability. FIG. 9A The graph can represent, for example, the net load curve for CL (Clean Air Delivery Rate) systems used in vertical farms, desalination plants, or AI training facilities. (As shown in...) FIG. 9A As can be observed in the figure (i) on the left, the peak net load value of the power grid (e.g., when the demand for electricity from the grid is high and therefore the associated costs of receiving electricity from the grid reach a local or global maximum, such as 5 p.m. Pacific Time) is temporally aligned with the relatively high electricity demand levels of vertical farms, desalination plants, or AI training facilities. FIG. 9A Figure (ii) on the right shows the net load curves for CL and grid repair / optimization, for example, as exemplified by... FIG. 1Systems such as System 100 and / or implemented using one or more methods described herein, wherein the peak net load value of the power grid during the observed time period now substantially overlaps with or coincides with the minimum net load value of CL. Furthermore, the peak net load value of CL has shifted towards the lower portion of the power grid's net load curve (e.g., where demand for electricity from the grid is low and therefore the associated cost of receiving electricity from the grid tends to a local or global minimum, such as at midnight). In other words, the correlation between the power grid's net load curve and the CL's net load curve has been modified such that there is a substantially inverse correlation between them. Where the power grid's net load is relatively high, the CL's net load is relatively low, and where the power grid's net load is relatively low, the CL's net load is relatively high. Similarly, where the power grid's net load increases, the CL's net load decreases, and where the power grid's net load decreases, the CL's net load increases.

[0134] FIG. 9B This is a second example set of graphs according to some embodiments, used to compare the net load curve (net load relative to time) of CL with that of the power grid (e.g., for a common time period). FIG. 9B The diagrams in the diagrams are not drawn "to scale" (for example, in practice, the net load of the CL will often be much smaller than the net load of the grid), but are instead scaled for readability. FIG. 9B A graph can represent the net load curve of a data center, for example, including data centers (e.g., AI training facilities and / or cryptocurrency mining rigs). As in... FIG. 9B As can be observed in Figure (i) on the left, the peak net load value of the power grid (e.g., when the demand for electricity from the power grid is high and therefore the associated cost of receiving electricity from the power grid reaches a local or global maximum, such as at 5 p.m. Pacific Time) is substantially aligned with the relatively high operating level of the data center in time, and the AUC of the CL net load curve, which overlaps with the area under the curve (AUC) of the power grid net load curve, has a first value. FIG. 9B Figure (ii) on the right shows the net load curves for CL and grid repair / improvement, for example, as exemplified by... FIG. 1 Systems such as System 100 and / or implemented using one or more methods described herein, wherein the peak net load value of the grid's net load curve is now substantially aligned in time with the trough / low value of the CL net load curve, and the AUC of the CL net load curve overlapping with the AUC of the grid's net load curve has a second value less than the first value. Furthermore, FIG. 9BThe diagram on the right (ii) can represent the pre-charging and / or pre-cooling of one or more batteries of the CL during a more favorable period (e.g., from the perspective of price and / or grid demand for energy), and then during subsequent periods when it is less favorable to operate the CL using the grid or to power the CL, the operation of the CL gradually ramps down and / or idles.

[0135] FIG. 9C This is a third example set of diagrams based on some embodiments, used to compare the net load curves of a controllable load with those of the power grid. FIG. 9C The diagrams in the diagram are not drawn "to scale" (for example, in practice, the net load of the CL will often be much smaller than the net load of the grid), but are instead scaled for readability. (See diagram in...) FIG. 9C As can be observed in Figure (i) on the left, the peak net load of the grid (e.g., when the demand for electricity from the grid is high and therefore the associated costs of receiving electricity from the grid reach a local or global maximum, such as 5 p.m. Pacific Time) is basically aligned with the relatively high operating level of CL in time. FIG. 9C Figure (ii) on the right shows the net load curves for CL and grid repair / improvement, for example, as exemplified by... FIG. 1 Systems such as System 100 and / or implemented using one or more methods described herein, wherein the peak net load value of the grid's net load curve remains substantially aligned with the local high operating level of the CL, but the upper limit of the net load value of the CL has been significantly reduced to mitigate the impact of power consumption from the grid during peak net load / peak pricing periods.

[0136] In some embodiments, the power grid may have a very low net load during a given period / time interval. In some grid markets, this can result in very low or negative energy prices because the grid is approaching a dangerous mechanism of overgeneration that could lead to overvoltage conditions and carries the risk of potential grid failure. In such cases, the net load curve of the controllable load can be controlled to be negatively correlated (e.g., inversely correlated) with the net load curve of the grid, thereby guiding / instructing the controllable load (CL) to increase its load as much as possible to help stabilize the grid (and, optionally, take advantage of low or negative prices).

[0137] Networked energy generation, storage, and distribution

[0138] Some aspects of this disclosure include processing, including: receiving power data from a power data source; using predictive algorithms for energy generation, storage, and distribution and based on the power data to generate anticipated power supply, energy storage status of power, and demand curves; determining, based on the anticipated power supply, energy storage status of power, and demand curves, whether there are conditions for issuing control commands to one or more controllable power components; and in response to determining that the existence of such conditions, providing control commands associated with such conditions to one or more controllable power components.

[0139] Some aspects of this disclosure include a tangible, non-transitory machine-readable medium storing instructions that, when executed by a data processing apparatus, cause the data processing apparatus to perform operations including the aforementioned processing.

[0140] Some aspects of this disclosure include a traffic information exchange service platform, comprising: one or more processors; and a memory storing instructions that, when executed by the processor, cause the processor to perform the operations described above.

[0141] As discussed above, due to the nature of renewable energy generation, renewable energy power plants (REPPs) typically have inconsistent or intermittent power output. Solar power plants receive variable amounts of sunlight based on the time of day, seasonal cycles, and weather patterns. Wind power plants receive variable amounts of wind based on weather patterns and various other factors. However, intermittent power delivery is incompatible with loads or grid systems that require real-time balancing of loads and production.

[0142] To help alleviate some inefficiencies and load balancing issues in power grid systems, some embodiments of this technology can be used in conjunction with the technology described in U.S. Patent No. 11,611,217, filed May 12, 2022, entitled "Networked Power Plants," the entire contents of which are incorporated herein by reference. Embodiments of this disclosure allow operators of networked REPPs to deliver power with greater reliability. Combining the outputs of REPPs whose outputs are not perfectly correlated results in a combined output with lower variability and intermittency than the outputs of individual REPPs. This means that power can be delivered more stably through networked REPPs than through individual REPPs. Furthermore, some load operators may only want to use renewable energy but may require or expect consistent power delivery. These load operators may want to receive power from the grid and use only renewable energy. These load operators can correlate their power usage with renewable power production to use only renewable energy. These load operators can send power delivery curves to renewable energy sources representing requests for the amount of renewable power production at different times. If the load's power delivery curve is met, then the load operator can claim to use only renewable energy for the load. The REPP output allocated to the load can be considered as an addition to the rest of the electricity delivered to the grid, since it is assumed to be produced at the REPP and delivered to the load, ignoring the inevitable mixing of electricity from different sources on the grid. Although electricity from different sources on the grid inevitably mixes, this output is actually produced at the REPP and delivered to the load.

[0143] To consistently meet power delivery curves, consistent power delivery is required or expected. Individual REPPs may struggle to provide consistent power delivery. This means some loads may have to draw power from non-renewable sources, or a REPP may have to have power capacity far exceeding the load's power delivery curve to consistently meet the load's power delivery curve even with power generation fluctuations. Grid-connected REPPs can be able to provide more stable power entirely from renewable sources. Additionally and / or alternatively, each REPP may have less power capacity than a single REPP would need or use to provide consistent power. This less power capacity per REPP than a single ungrid-connected REPP would require to provide consistent power results in increased efficiency and lower costs in building REPPs, as each REPP requires less excess capacity, which is typically not fully utilized. Grid-connected REPPs can also generate more power than various loads need or use. This excess power can be considered a virtual REPP, or a virtual power plant that can deliver power to additional loads.

[0144] The output of networked REPPs and virtual power plants can be transmitted through the grid and distributed to various loads. This combined output of networked REPPs can be considered an addition to the rest of the electricity transmitted through the grid, as it is actually produced at the networked REPP and transmitted to the various loads to which it is distributed, ignoring the inevitable mixing of electricity from different sources on the grid. This addition can be viewed as a green grid, utilizing the existing grid infrastructure but transmitting renewable electricity from REPPs to various loads. A green grid can operate similarly to the grid it operates on, where the market for renewable energy differs from the market for non-green electricity. A green grid can be owned and operated by a single entity, or it can include REPPs owned and operated by various entities.

[0145] Some embodiments of this technology can be used in conjunction with the technology described in U.S. Patent No. 11,611,217 to further employ those technologies and incorporate controllable power components including the introduction of controllable loads (e.g., unrelated or related loads). Loads downstream of the meter (e.g., directly connected to REPP without being connected to the grid system) can be introduced into the system, or loads located on the grid system but controllable by a grid-connected power plant can be introduced. In some embodiments, these loads on the grid system may be unrelated to the typical energy consumption curve experienced by the grid on a given date or other time period (e.g., vertical farming operations, training AI models (and other computationally insensitive workloads), aluminum smelting, direct carbon capture from the air, hydrogen production via electrolyzers through water electrolysis, or other loads that will be apparent to those skilled in the art possessing this disclosure). Controllable loads may also include related or known loads, in which certain contractual arrangements may be satisfied. For example, an office building experiencing peak electricity demand during a hot summer day when air conditioning is running to cool the office space can be an example of a related but controllable load. Even if these loads are generally related to other parts of the grid's energy use, they can still be controllable. Therefore, these loads can be controlled to operate at times of the day that differ from peak times. For example, office buildings can use HVAC systems to pre-cool or pre-heat the building in anticipation of reduced power consumption during peak energy demand periods.

[0146] In various embodiments, downstream loads and other controllable loads on the grid system can communicate with networked energy generation, storage, and distribution controllers. Downstream controllable loads allow energy producers to scale and enhance the performance of their REPPs. For example, a REPP can be configured to generate more capacity than it can supply to the grid. This provides economies of scale in terms of cost and performance compared to having no controllable loads. When generation is not at its peak (e.g., cloudy, early morning, evening, or low wind speeds) or when the energy storage system included in the REPP is full, excess energy is absorbed by the downstream loads. Furthermore, when REPP generation is low, the over-configured system can deliver more power to more critical or valuable loads on the grid, realizing the bandwidth the REPP can provide to the grid or providing more load to the energy storage system. Therefore, REPPs can be designed for better performance and lower cost, resulting in better overall system performance and a more consistent energy supply to the grid.

[0147] A networked energy generation, storage, and distribution controller for a grid-connected power plant may include predictive algorithms for balancing energy allocation to controllable loads. For example, the networked energy generation and distribution controller may ingest data from various data sources, such as weather forecasts, event schedules, calendars, historical energy usage data, sensor data, or other data sources that will be obvious to those skilled in the art possessing this disclosure. In other embodiments, the data source may include state-of-power data or analytics of other REPPs not on the network, such that predictions of how much energy storage another REPP not on the network may have are used to anticipate how much energy will be available to the grid. The networked energy generation and distribution controller, using machine learning algorithms trained on similar data, can then anticipate the energy demand of uncontrollable loads on the grid and the energy supply of the grid-connected power plant. Based on the anticipated energy demand and supply, the networked energy generation and distribution controller can determine whether one or more energy balance conditions associated with a corresponding controllable load are met to increase or decrease power allocation to that controllable load. For example, in exchange for a better energy price rate or some other energy allocation factor desired by the controllable load, a controllable load may allow networked energy generation and distribution controllers to reduce energy consumption at that controllable load, thereby reallocating energy supply from networked power plants to uncontrollable loads that may pay a higher premium and have higher priority based on various factors (e.g., more necessary / high-priority infrastructure such as hospitals, water plants, etc.). Similarly, a controllable load may include an energy storage system in which networked energy generation, storage, and distribution controllers can increase or decrease the power allocation to energy storage devices. Furthermore, more optimized decisions can be made regarding which energy storage device in the energy storage system should store energy. For example, zinc-air batteries can be charged when cheap electricity is available, while lithium-ion batteries can be charged when more expensive electricity is available. Therefore, the type of storage device and other factors associated with the energy storage device can be used to determine when a particular energy storage device should be charged and how much electricity a particular energy storage device should receive.

[0148] In other embodiments, the networked energy generation and distribution controller can also use anticipated energy demand and supply to balance the storage of energy generated by REPP on associated batteries. For example, the networked energy generation and distribution controller can determine the amount of energy stored on each battery and how those batteries in the networked power plant will distribute energy in an optimized manner. For example, to maintain the expected lifespan of a battery, under normal conditions, the battery cannot be fully charged or fully discharged, as doing so would reduce the battery's expected lifespan. However, if anticipated energy supply and demand indicate that fully charging or discharging the battery is more beneficial than considering the battery's expected lifespan, then the networked energy generation and distribution controller can fully charge the battery in preparation for future events. For example, if there is a anticipated event requiring or involving high energy demand, then the networked energy generation and distribution controller can fully charge the battery. In other embodiments, the networked energy generation and distribution controller can classify the batteries such that a first battery is allocated energy based on a first condition, a second battery is allocated energy based on a second condition, and a third battery is allocated energy based on a third condition. These conditions can be prioritized based on different levels. For example, a third battery can only be allocated energy if the price of energy is above a certain threshold.

[0149] In other embodiments of this disclosure, the networked energy generation and distribution controller can determine when to provide energy storage to power plants not included in the networked power plant network (such as power plants on the grid). The networked energy generation and distribution controller can determine conditions under which off-grid power plants can store energy on batteries or other energy storage systems at networked power plants. Using predictive energy demand and energy storage decisions made by machine learning algorithms of the networked energy generation and distribution controller, the networked energy generation and distribution controller can determine when to purchase electricity from power plants on the grid or provide storage to contracted off-grid power plants. The networked energy generation and distribution controller can communicate with applications located at off-grid power plants, similar to applications provided at controllable loads and storage locations within networked power plants. Therefore, the systems and methods of this disclosure provide more optimized and consistent energy generation, storage, and distribution of energy generated by REPP.

[0150] FIG. 10The illustration depicts an example networked energy generation, storage, and distribution system 1000 according to one or more embodiments. The networked energy generation, storage, and distribution system 1000 may include a networked energy generation, storage, and distribution controller 1002; a network 1004; a networked power plant system 1006, including power plants 1006a and 1006b, energy storage devices 1007a and 1007b, loads 1008a and 1008b; a power grid 1010; one or more data sources 1011; a power plant 1012; loads 1014a and 1014b; and a controllable load 1014c. Although described as networked, in some embodiments, the power plant system 1006 may contain only one power plant. In some such embodiments, the networked energy generation, storage, and distribution system 1000 may not be networked, and instead may be an energy generation, storage, and distribution system. Similarly, in some such embodiments, the networked power plant system 1006 may not be networked, and instead may be a power plant system. Loads 1014a, 1014b, and controllable load 1014c may be electrically coupled to the power grid 1010. Loads 1014a, 1014b, and controllable load 1014c may be geographically separated and have individual power requirements. Load 1014a may have a first power delivery curve detailing its power requirements at different times. Load 1014b may have a second power delivery curve detailing its power requirements at different times. Controllable load 1014c may have a third power delivery curve detailing its power requirements at different times. In some embodiments, the power grid 1010 may be a public power grid owned and operated by a single utility or system operator. In other embodiments, the power grid 1010 may be a plurality of electrical connections, thereby allowing power to be transmitted from power plants 1006a, 1006b and 1012 to loads 1014a, 1014b and controllable load 1014c.

[0151] Power plant 1006a may be a first renewable energy power plant (REPP). Power plant 1006b may be a second REPP, and power plant 1012 may be a third REPP or other power plants. Examples of REPPs include, but are not limited to, solar power plants, wind power plants, geothermal power plants, and biomass power plants. A REPP may include an energy storage system (ESS) 1007a or 1007b. An example of an ESS is a battery. A battery-based ESS may be called a battery ESS or a BESS. Power plant 1006a may have a first power output that varies over time. Power plant 1006b may have a second power output that varies over time. Power plant 1012 may have a second power output that varies over time. The first power output and the second power output may vary differently, making them not closely related. For example, power plant 1006a may be geographically distant from power plant 1006b, causing weather patterns at power plant 1006a to differ from those at power plant 1006b. Therefore, the variation in the first power output will not be closely related to the variation in the second power output. The lower the correlation between the outputs of power plant 1006a and power plant 1006b, the greater the effect of grid interconnection. A lower correlation between the outputs of power plants 1006a and 1006b results in smaller variations in their combined output. Smaller variations in the combined output make the power transmission curves for loads 1014a and 1014b more reliable. Smaller variations in the combined output also lower the capacity requirements for power plants 1006a and 1006b.

[0152] In some embodiments, power plant 1006a may be directly connected to load 1008a or other directly connected loads, such that load 1008a is located after the meter or otherwise not connected to the power grid 1010. Similarly, power plant 1006b may be directly connected to load 1008b. Load 1008a or load 1008b may be a controllable load and, in some cases, may be an unrelated load. In some embodiments, power plant 1012 may be connected to the networked power plant system 1006 via the power grid 1008 and may supply energy to the ESS of power plant 1006a or 1006b.

[0153] Power plants 1006a and 1006b can communicate with the networked energy generation, storage, and distribution controller 1002 via network 1004. Similarly, controllable loads 1008a, 1008b, and 1014, as well as power plant 1012, can communicate with the networked energy generation, storage, and distribution controller 1002 via network 1004. Furthermore, the networked energy generation, storage, and distribution controller 1002 can communicate with a data source 1011 via network 1004. The data source may include sensor data, weather data, local timetables, or any other system data or third-party information that is obvious to those skilled in the art possessing this disclosure. Network 1004 can be any local area network (LAN) or wide area network (WAN). In some embodiments, the network is the Internet. In other embodiments, the network is a private communication network. The networked energy generation, storage, and distribution controller 1002 may include a processor and memory.

[0154] A networked energy generation, storage, and distribution controller 1002 can control power plants 1006a and 1006b. The networked energy generation, storage, and distribution controller 1002 can coordinate a first power output from power plant 1006a and a second power output from power plant 1006b to deliver power to loads 1014a, 1014b, and controllable loads 1008a, 1008b, and 1014c. The networked energy generation, storage, and distribution controller 1002 can receive a first power delivery curve for load 1014a, a second power delivery curve for load 1014b, and corresponding power delivery curves for controllable loads 1008a, 1008b, and 1014c. In some embodiments, the networked energy generation, storage, and distribution controller 1002 receives the first power delivery curve for load 1014a, the second power delivery curve for load 1014b, and the corresponding power delivery curves for controllable loads 1008a, 1008b, and 1014c via a network 1004. In other embodiments, the networked energy generation, storage, and distribution controller 1002 receives from another source a first power delivery curve for load 1014a, a second power delivery curve for load 1014b, and corresponding power delivery curves for controllable loads 1008a, 1008b, and 1014c. The networked energy generation, storage, and distribution controller 1002 can direct power plant 1006a to direct power to any one of loads 1014a, 1014b, or controllable loads 1008a, 1008b, or 1014c. Similarly, the networked energy generation, storage, and distribution controller 1002 can direct power plant 1006b to direct power to any one of loads 1014a, 1014b, or controllable loads 1008a, 1008b, or 1014c. A networked energy generation, storage, and distribution controller 1002 can direct power plant 1006a to direct a first portion of its power output to load 1014a, a second portion of its power output to load 1014b, or other portions of its power output to any one of controllable loads 1008a, 1008b, or 1014c. Similarly, the networked energy generation, storage, and distribution controller 1002 can direct power plant 1006b to direct a first portion of its power output to load 1014a, a second portion of its power output to load 1014b, or other portions of its power output to any one of controllable loads 1008a, 1008b, or 1014c. In some embodiments, directing power from the power plant to the load is achieved by sending power from the power plant to the grid and informing the load how much power has been sent to the grid. The load draws power from the grid equal to the amount of power the power plant has sent to the grid. The load can match its energy consumption within a time window with the energy the power plant sends to the grid within that time window.This time window can be a year, a month, a day, an hour, a minute, or any other unit of time. When power is directed from multiple power plants to a load, the load can match its energy consumption within the time window to the total power transmitted by the multiple power plants within that time window. When the energy required by the load exceeds the total energy transmitted by the multiple power plants within that time window, the load operator can draw energy from other sources (which may not be renewable) and keep separate records of the energy consumed from the multiple power plants and from the other sources, as input to an algorithm that will adjust its future energy requests from the multiple power plants.

[0155] In some embodiments, FIG. 10 One or more functions of system 1000 can be combined with FIG. 1 System 100 FIG. 4 System 400 FIGS. 13-17 System 1300 and / or FIG. 20 The system 2000 may use one or more of its functions in combination or as a substitute. Alternatively or additionally, FIG. 10 One or more functions of the controller 1002 can be used FIG. 2 Controller 200 FIG. 11 Controller 1102 FIG. 18 Controller 1802 and / or FIG. 21 Implemented by one or more functions / features of any one of the controllers 2102. Alternatively or additionally, FIG. 10 System 1000 can be configured to execute FIG. 3 Method 300 FIG. 5 Method 500 FIG. 6 Method 600 FIG. 7 Method 700 FIG. 8 Method 800 FIG. 12 Method 1200 FIG. 19 Method 1900 or FIG. 22 One or more of methods 2200.

[0156] In other embodiments, this disclosure provides systems and methods for servicing multiple electrical loads with renewable electricity. The multiple electrical loads may be unrelated. The multiple electrical loads may be unrelated or partially related. For example, the value directing electricity to at least one of the electrical loads may vary over time, and this variation may be at least partially independent of the value directing electricity to another electrical load. Unrelated or partially related loads may also be controllable or uncontrollable loads, wherein networked energy generation, storage, and distribution controllers (e.g., FIG. 10 The controller 1002 in the middle can control the energy consumption at the load.

[0157] The methods and algorithms described herein can be used to determine or optimize the allocation of energy generated by an energy storage system (ESS). In such cases, an energy storage system (ESS) can be considered as an electrical load along with other electrical loads, and the energy generated by the ESS can be allocated between the electrical loads and the ESS. Alternatively, the methods described herein can be used to allocate power generated by an ESS-ESS power plant. In this case, the ESS is part of the ESS-ESS, and the capacity is allocated among electrical loads excluding the ESS. EMS can implement methods or algorithms for determining power delivery between multiple unrelated or partially related electrical loads. In some embodiments, the methods and algorithms can be flexibly adjusted to: a) the amount of power sent to / drawn from the ESS; and b) the amount of power sent to each electrical load over time, thereby allowing for economically valuable opportunities. This can advantageously allow for improved power allocation among multiple unrelated (fully) related loads and optimization of the total value of delivering power to multiple loads (e.g., the power grid, BESS, green hydrogen, crypto mining, etc.).

[0158] This disclosure provides systems, system architectures, and methods that allow REPP-ESS power plants to serve one or more unrelated or partially related loads. The methods and systems described herein can be readily extended and applied to any number of unrelated or partially related loads, or to any power plant configuration. In some cases, unrelated or partially related loads may include one or more grid loads, and / or one or more loads directly connected to the RES-ESS without grid-based energy dissipation (i.e., off-grid loads). One or more grid loads may include, for example, a grid that is effectively used as a single load (e.g., a network serving multiple individual loads), one or more loads connected to the grid, etc. In some cases, one or more grid loads may include one or more additional, remotely located RES connected to the RES-ESS-load system via the grid.

[0159] U.S. Patent No. 12,119,646 (“Systems and Methods for Renewable PowerplantServing Multiple Loads”) FIG. 1 The present disclosure is illustrated schematically. FIG. 10 The system is an example system of system 1000, and its entire contents are incorporated herein by reference for all purposes.

[0160] FIG. 11 An embodiment of a networked energy generation, storage, and distribution controller 1100 is illustrated, which may be as described above. FIG. 10The networked energy generation, storage, and distribution controller 1002 is discussed. Although described as a standalone system, those skilled in the art will recognize that the networked energy generation, storage, and distribution controller 1100 can be distributed across numerous computing devices, such as in a cloud environment. In the illustrated embodiment, the networked energy generation, storage, and distribution controller 1100 includes a chassis 1102 housing the components of the networked energy generation, storage, and distribution controller 1100. FIG. 11 Only some of the components are illustrated. For example, chassis 1102 may house a processing system (not shown) and a non-transitory memory system (not shown) that includes instructions, when executed by the processing system, to cause the processing system to provide a networked energy generation, storage, and distribution engine 1104, which is configured to perform the functions of a networked energy generation and distribution engine or a networked energy generation, storage, and distribution controller discussed below. FIG. 11 In the specific examples shown, the networked energy generation, storage, and distribution engine 1104 may include an energy generation, storage, and distribution predictive algorithm 1105 configured to perform the functions of the energy generation, storage, and distribution predictive algorithms discussed herein. In various embodiments, the energy generation, storage, and distribution predictive algorithm 1105 may ingest data provided by a data source and predict energy demand and supply, or any other functions discussed herein. In various embodiments, the energy generation, storage, and distribution predictive algorithm 1105 may include a network simulator to model behavior, which, due to a lack of historical data, can predict components to be integrated into the power grid by running simulations. In other examples, the energy generation, storage, and distribution predictive algorithm 1105 may include model predictive control or other predictive algorithms / machine learning algorithms that are obvious to those skilled in the art with this disclosure.

[0161] The chassis 1102 may also house a communication system 1106 coupled to the networked energy generation, storage, and distribution engine 1104 (e.g., via coupling between the communication system 1106 and the processing system) and configured to provide communication via a communication network 1004, as described below. The chassis 1102 may also house a storage system 1108 coupled to the networked energy generation, storage, and distribution engine 1104 via the processing system and configured to store rules or other data used by the networked energy generation, storage, and distribution engine 1104 to provide the functions discussed below. While the networked energy generation, storage, and distribution controller 1100 has been illustrated, those skilled in the art will recognize that other networked energy generation and distribution controllers (or other devices operating in a manner similar to that described below for the networked energy generation, storage, and distribution controller 1100) may include various components and / or component configurations to provide known computing device functions and the functions discussed below, while still remaining within the scope of this disclosure.

[0162] FIG. 12 Embodiments of a networked energy generation and distribution method 1200 with controllable load are described, in some embodiments of which the method may utilize the methods discussed above. FIG. 10 and FIG. 11 At least some of the components are used to implement this. As discussed below, some embodiments make technical improvements to REPP. Method 1200 is described as being performed by a networked energy generation, storage, and distribution engine 1104 included on a networked energy generation, storage, and distribution controller 1002 / 1100. Furthermore, other computer systems considered in the networked energy generation, storage, and distribution system 1000 may include some or all of the functionality of the networked energy generation, storage, and distribution engine 1104. Therefore, some or all of the steps of method 1200 may be performed by other participants in the networked energy generation, storage, and distribution system 1000, and still fall within the scope of this disclosure. Furthermore, and as mentioned above, the networked energy generation, storage, and distribution controller 1002 / 1100 may include one or more processors or one or more servers, and therefore method 1200 may be distributed across those one or more processors or one or more servers.

[0163] Method 1200 begins at operation 1202, wherein power data is received from a power data source. In an embodiment, at operation 1202, a networked energy generation, storage, and distribution engine 1104 may receive messages including various power data from the power data source via a communication system 1106. For example, power data may include sensor data, weather data, activity or calendar data for a given area, historical power data, power curves from each load or power plant in the system, ESS health status or years of service, or other data obvious to those skilled in the art possessing this disclosure.

[0164] Method 1200 can proceed to run 1204, where predicted power supply and demand curves are determined. In an embodiment, at run 1204, an energy generation, storage, and distribution predictive algorithm 1105 can determine predicted power supply and demand curves for loads 1008a, 1008b, 1014a, 1014b, or 1014c and power plant 1006a or 1006b. The predicted power supply and demand curves may include forecasts of power supply for power plant 1006a or 1006b, and may also include forecasts of ESS included in each of power plants 1006a or 1006b. The predicted power supply and demand curves may include forecasts of power demand for controllable loads 1008a, 1008b, and 1014c and loads 1014a and 1014b.

[0165] Method 1200 may then proceed to decision operation 1206, where it is determined whether conditions exist for issuing control commands to controllable components of the networked energy generation, storage, and distribution system 1000. In an embodiment, at operation 1206, the networked energy generation, storage, and distribution engine 1104 may determine whether conditions exist for issuing control commands to controllable components based on anticipated energy supply and demand curves. For example, the networked energy generation, storage, and distribution engine 1104 may determine that conditions exist such that power plant 1012, not included in the networked power plant system 1006, can store energy via grid 1010 to an ESS included in power plant 1006a or 1006b. In other examples, the networked energy generation, storage, and distribution engine 1104 may determine that conditions exist such that control commands should be sent to one or more of controllable loads 1008a, 1008b, or 1014c to reduce power consumption or allow increased power consumption, and the timing of such increases and decreases. In other embodiments, the networked energy generation, storage, and distribution engine 1104 can determine that conditions exist that require sending control commands to one or more of power plants 1006a or 1006b to control power storage and distribution on the included ESS. In other embodiments, conditions may include contractual or regulatory constraints that the networked energy generation, storage, and distribution engine 1104 also checks. If no conditions exist or no new conditions arise, the networked energy generation, storage, and distribution engine 1104 can continue to monitor data and generate predictive energy supply and demand curves.

[0166] If the conditions are met, method 1200 can proceed to run 1208, where control commands are sent to the controllable power components. In an embodiment, at run 1208, the networked energy generation, storage, and distribution engine 1104 can send control commands to the controlled power components via network 1004. For example, control commands can be sent via network to applications at controllable loads 1008a, 1008b, or 1014c to reduce power consumption, causing supply to be redirected to load 1014a or 1014b. In other embodiments, control commands can be sent to power plant 1006a or 1006b, causing the ESS to store power in a specific battery or distribute power from a specific battery to a specific load 1014a or 1014b or controllable load 1008a, 1008b, or 1014c. In still other embodiments, control commands can be sent to power plant 1012, causing power generated by the load to be "routed" via the grid to the ESS at power plant 1006a or 1006b. While specific examples of control commands have been discussed, those skilled in the art with this disclosure will consider that other control commands for controllable electrical components may be taken into account for various purposes and conditions in a system.

[0167] In some embodiments, a method includes a controller receiving power data from a power data source. The controller uses predictive algorithms for energy generation, storage, and distribution and generates anticipated power supply and demand curves based on the power data, and determines whether conditions exist for issuing control commands to one or more controllable power components. In response to the existence of conditions, the controller issues control commands to one or more controllable power components.

[0168] In some embodiments, a non-transitory processor-readable medium stores instructions that, when executed by a processor, cause the processor to set the power output of the first REPP and the power output of the second REPP based on the power delivery curve of a first load, the power delivery curve of a second load, the power output capacity of a first renewable energy power plant (REPP), and the power output capacity of the second REPP. The non-transitory processor-readable medium also stores instructions to cause the processor to allocate a combined power output from the first REPP and the second REPP to the first load and the second load for a predefined time window, the allocation being at least partially based on anticipated power supply and demand curves generated using predictive algorithms for energy generation, storage, and allocation. The non-transitory processor-readable medium also stores instructions to cause the processor to transmit a first signal representing a first portion of the combined power output for the predefined time window and the first load, and a second signal representing a second portion of the combined power output for the predefined time window and the second load. The non-transitory processor-readable medium also stores instructions to cause the processor to deliver the allocated combined power output to the power grid, with the first load receiving a different amount of power from the grid during the predefined time window than indicated by the first signal. The non-transient processor-readable medium also stores instructions to cause the processor to make the first portion of the stored combined output the difference between the total electrical power received from the grid during a predefined time window.

[0169] In some embodiments, the non-transient processor-readable medium also stores instructions to cause the processor to determine, based on anticipated power supply and demand curves, whether conditions exist for issuing control commands to one or more controllable power components, and to provide control commands associated with the conditions to the one or more controllable power components in response to determining that the conditions exist. In some such embodiments, the conditions may be associated with at least one of the following: the price of electricity associated with the grid, the price of ancillary services associated with the grid, grid-related reductions, grid-related congestion prices, or grid-related congestion mitigation values. Alternatively or additionally, the system may have an associated capacity factor of at least approximately 60%, and the condition may be associated with at least one of the following: the price of electricity associated with the grid, the price of ancillary services associated with the grid, grid-related reductions, grid-related congestion prices, or grid-related congestion mitigation values.

[0170] In some implementations, the anticipated power supply and demand curves include data associated with at least one uncontrollable load.

[0171] In some implementations, at least one of the first load or the second load includes a controllable load. The at least one controllable load may include at least one of the following: a data center, an artificial intelligence (AI) training center, a cryptocurrency mining machine, an electric vehicle (EV) charging station, a vertical farm, a hydrogen production facility, a water treatment plant, an industrial process heater, or a thermal battery.

[0172] In some embodiments, a method includes: causing a signal representing a predicted power curve to be transmitted via a processor at a first time, the predicted power curve being generated using a predictive algorithm for energy generation, storage, and distribution. The method further includes: receiving power from the power grid at a system including a load and a processor, at a second time after the first time. The method further includes: comparing (1) the amount of power received from the power grid with (2) the predicted power curve via a processor at a third time after the second time, and, in response to determining that the amount of power drawn from the power grid matches or exceeds the predicted power curve, causing an indication that renewable power has been used to satisfy the load.

[0173] In some embodiments, a method includes receiving power from a power grid at a system including a load and a processor. The method also includes comparing (1) the amount of power received from the power grid with (2) a predicted power curve generated using predictive algorithms for energy generation, storage, and distribution via the processor, and in response to determining that the amount of power drawn from the power grid matches or exceeds the predicted power curve, such that an indication is transmitted to the load that renewable power has been used.

[0174] Intelligent seasonal electric power resource allocation with controllable loads

[0175] Some aspects of this disclosure include a process comprising: setting a first charge / discharge of a first REPP power storage system (ESS) and a second charge / discharge of a second REPP ESS by a controller of a renewable energy power plant (REPP), such that when the REPP renewable energy (RES) of the REPP generates electricity, the REPP delivers electricity to a first load for a first period of time longer than a first production period, wherein the first ESS is electrically coupled to the RES and a first meter, and wherein the second ESS is electrically coupled to the RES and the first meter via a switch; in response to a first triggering condition being met, actuating the switch to electrically couple the second ESS to a controllable load; setting a third charge / discharge of the first ESS such that when the RES generates electricity, the REPP delivers electricity to the first load for a third period of time longer than a second production period; setting a fourth charge / discharge of the second ESS such that the second ESS retains a portion of its charge for a third load; in response to a second triggering condition being met, actuating the switch to electrically couple the second ESS to a second meter; and setting a fifth charge / discharge of the second ESS such that the second ESS retains a portion of its charge for a second meter, wherein the RES is regulated to meet the power delivery requirements of the first load and to retain a portion of the charge of the second ESS.

[0176] Some aspects of this disclosure include a tangible, non-transitory machine-readable medium storing instructions that, when executed by a data processing apparatus, cause the data processing apparatus to perform operations including the aforementioned processing.

[0177] Some aspects of this disclosure include a system comprising: one or more processors; and a memory storing instructions that, when executed by the processor, cause the processor to perform the aforementioned processing.

[0178] The embodiments of this disclosure address the technical problem of allocating a specific energy storage resource (ESS) to a specific load or purpose. To help mitigate some of the inefficiencies associated with allocating a specific ESS to a specific load or purpose, some embodiments of this technology can be used in conjunction with the technology described in U.S. Patent No. 11,621,566, filed October 5, 2022, entitled “Seasonal Electrical Resource Allocation,” the entire contents of which are incorporated herein by reference. Embodiments discussed herein include using a switch to change the connection between the ESS and a first meter, such that the ESS is connected to a second meter instead of the first. This allows the use of the ESS to be directly tied to the meter, thereby enabling the segmentation and cycling of the ESS resource. Cycling the ESS resource allows for the use of different ESSes at different times for different purposes, enabling the management or leveling of ESS use and degradation among multiple ESSes. Managing and / or leveling ESS degradation allows for accurate prediction of ESS lifetime and performance.

[0179] Furthermore, more optimized decisions can be made regarding which energy storage device in an ESS (Energy Storage System) should be used to store energy. For example, a lower-cost ESS with lower round-trip efficiency (e.g., zinc-air batteries) may be selected, as it can be charged when cheap electricity (even negative-priced electricity) is available, anticipating longer storage cycles and lower parasitic loads, making it more efficient overall. Conversely, a more efficient but more expensive ESS (e.g., lithium-ion batteries) may be charged when more expensive electricity is available, anticipating faster response times, higher energy storage efficiency, or when other beneficial conditions are apparent to those skilled in the art possessing this disclosure. Thus, the type of energy storage device or other factors associated with the energy storage device can be used to determine when to charge a particular energy storage device or how much electricity a particular energy storage device receives.

[0180] Furthermore, the embodiments discussed herein address the technical problem of underutilized ESS (Energy Utilization Limits). By changing the connection and usage of the ESS, ESS storage capacity can be shifted from its underutilized use to a more fully utilized one. For example, if renewable energy (RES) output drops below a threshold, an ESS connected to a load to time-shift RES output may be underutilized. Actuating a switch to change the ESS's usage provides the technical advantage of using unused ESS capacity for unique, measurable use cases. The use of ESS to time-shift RES output is discussed in U.S. Patent No. 11,451,060 (“Consistent Power Delivery via Power Delivery Limits”), which is incorporated herein by reference in its entirety for all purposes.

[0181] FIG. 13This is a block diagram of an example renewable energy power plant (REPP) 1300 according to one or more embodiments. REPP 1300 may include a renewable energy source (RES) 1335, an RES inverter 1340, a first energy storage system (ESS) 1310, a first ESS inverter 1315, a first meter 1320, a second ESS 1365, a second ESS inverter 1360, a switch 1345, a second meter 1350, and an energy management system (EMS) 1305. RES 1335 may be a solar power source, a wind power source, a geothermal power source, or any other source of non-renewable or renewable energy generated from non-renewable resources that is obvious to those skilled in the art possessing this disclosure. RES may be electrically connected to RES inverter 1340. RES inverter 1340 may convert DC power from RES 1335 into AC power. RES inverter 1340 may be connected to the first meter 1320. A first energy storage system (ESS) inverter 1315 can be connected to a RES inverter 1340 and a first meter 1320. A first ESS 1310 can be connected to the first ESS inverter 1315. The first ESS 1310 can be configured to receive power from the RES 1335 and supply power to the first meter 1320. The first ESS 1310 can be charged from the RES 1335 and can discharge to supply power to the first meter 1320. The first ESS inverter 1315 can be a bidirectional inverter. The first ESS inverter 1315 can convert AC power from the RES inverter 1340 into DC power to charge the first ESS 1310, and convert DC power from the first ESS 1310 into AC power to supply power to the first meter 1320. A REPP 1300 can be connected to a first load 1330, a second load 1355, or a controllable load 1375 via the grid 1325. Although the example illustration shows three loads, those skilled in the art will recognize that hundreds, thousands, tens of thousands, or any number of loads (controllable or uncontrollable) can be coupled to the power grid 1325. In some embodiments, the power grid 1325 may be a public power grid. A first meter 1320 may be associated with a first load 1330. The first meter 1320 may measure the amount of electricity delivered by REPP 1300 through the power grid 1325 to the first load 1330 or other loads.

[0182] RES inverter 1340 can be connected to switch 1345. A second ESS inverter 1360 can be connected to both RES inverter 1340 and switch 1345. A second ESS 1365 can be connected to a second ESS inverter 1360. The second ESS 1365 can be configured to receive power from RES 1335 and supply power to switch 1345. The second ESS 1365 can be charged from RES 1335 and can discharge to supply power to switch 1345. The second ESS inverter 1315 can be a bidirectional inverter. The second ESS inverter 1315 can convert AC power from RES inverter 1340 into DC power to charge the second ESS 1365, and convert DC power from the second ESS 1365 into AC power to supply power to switch 1345. A second meter 1350 can be associated with a second load 1355. The second meter 1350 can measure the amount of power supplied by REPP 1300 to the second load 1355 or other loads. Switch 1345 can be configured to connect the second ESS inverter 1360 to the second meter 1350. Switch 1345 can also be configured to connect the second ESS inverter 1360 to a fourth load 1370. The fourth load 1370 can be a downstream load that receives power directly from REPP 1300 instead of through the grid, and can be a controllable load. Switch 1345 can also be configured to connect the second ESS inverter 1360 to the first meter 1320.

[0183] REPP 1300 may include controllable power components, which include controllable loads (e.g., loads 1370 and 1375) that may be unrelated or related loads, such that those loads are related or unrelated to other loads, and they generally define the energy consumption curve of grid 1325. Loads after the meter (e.g., directly connected to REPP 1300 and not connected to grid 1325) or loads located on grid 1325 but controllable by EMS controller 1305 may be introduced into the system. In some embodiments, loads on grid 1325 or after the meter may be unrelated to the typical energy consumption curve experienced by the grid on a given date or other time period (e.g., vertical farming operations, training AI models (and other latency-insensitive computational workloads), data centers, aluminum smelting, direct carbon capture from the air, hydrogen production using an electrolyzer via water electrolysis, or other loads obvious to those skilled in the art possessing this disclosure). Related loads may be loads that provide a typical energy consumption curve. Controllable loads may also include related or known loads, where certain contractual arrangements can be satisfied by virtually integrating them into the network. For example, an office building experiencing peak power demand during hot summer days when air conditioning is running to cool the office space can be an example of a relevant but controllable load. Even though these loads are generally related to the rest of the grid's energy use, they can still be controllable. Therefore, these loads can be controlled to operate at times of the day different from peak times. For example, an office building could use an HVAC system to pre-cool or pre-heat the building in anticipation of reducing power consumption during peak energy demand periods.

[0184] In various embodiments, downstream loads (e.g., load 1370) and other controllable loads on grid 1325 can communicate with EMS controller 1305. Downstream controllable loads allow energy producers to increase the scale and performance of REPP 1300. For example, REPP 1300 can be configured to generate more capacity than REPP 1300 can supply to the grid. This provides economies of scale in terms of cost and performance compared to systems without downstream controllable loads. When generation is not at its peak (e.g., cloudy, early morning, late afternoon, or low wind speeds) or when ESS 1310 or 1365 included in REPP 1300 are full, excess energy is absorbed by downstream load 1370. Furthermore, when REPP generation is low, an over-configured system can deliver more power to more critical or valuable loads on the grid and meet the bandwidth that REPP 1300 can supply to grid 1325 or provide more power to ESS 1310 and 1365. Therefore, the REPP 1300 can be designed for better performance and lower cost, i.e., better overall system performance, enabling a more consistent energy supply, capacity or other ancillary services to the grid.

[0185] EMS 1305 can be configured to collect data via network 104 from first meter 1320, first ESS 1310, first ESS inverter 1315, RES inverter 1340, second ESS inverter 1360, second ESS 1365, switch 1345, second meter 1350, controllable load 1375, and load 1370. EMS 1305 can be configured to control the first ESS inverter 1315, RES inverter 1340, and second ESS inverter 1360 by adjusting inverter setpoints. EMS 1305 can control various components via network 1304. EMS 1305 can control RES inverter 1340 to adjust RES output. EMS 1305 can control the first ESS inverter 1315 to control the charging / discharging of the first ESS 1310 and allow energy to flow directly from RES inverter 1340 to the first meter 1320. EMS 1305 can control the second ESS inverter 1360 to control the charging / discharging of the second ESS 1365 and allow energy to flow directly from the RES inverter 1340 to any load connected via switch 1345. EMS 1305 can be configured to control switch 1345 to selectively connect the second ESS inverter 1360 to a first meter 1320, a second meter 1350, or a third load 1370. EMS 1305 can control the power usage of the third load 1370 or the controllable load 1375 by increasing or decreasing the load. Furthermore, EMS controller 1305 can communicate with data source 1311 via network 1304. The data source may include sensor data, weather data, local timetables, or any other system data or third-party information that is obvious to those skilled in the art possessing this disclosure. Network 1304 can be any local area network (LAN) or wide area network (WAN). In some embodiments, the network is the Internet. In other embodiments, the network is a private communication network.

[0186] EMS controller 1305 may include predictive algorithms for balancing energy distribution to controllable loads 1370 or 1375. For example, EMS controller 1305 may ingest data from various data sources 1311 (e.g., weather forecasts, event schedules, calendars, historical energy usage data, sensor data, or other data sources obvious to those skilled in the art possessing this disclosure) or data collected from networked components. In other embodiments, the data source may include state-of-power data or analytical values ​​from other REPPs and their energy storage systems. These other REPPs may include energy storage systems not on the network and may be competing energy storage systems. Therefore, predicting how much energy another REPP has stored can be beneficial in anticipating how much energy will be available on the grid at a given time, enabling the management of control over networked energy storage systems.

[0187] EMS controller 1305, using predictive algorithms trained on historical or simulator data, can then anticipate the energy demand of uncontrollable loads on the grid (e.g., loads 1330 or 1355) and the energy supply on REPP 1300. Based on the anticipated energy demand and supply, EMS controller 1305 can determine whether one or more energy balance conditions associated with the corresponding controllable load are met to increase or decrease power allocation to controllable loads 1370 or 1375. For example, to obtain a better rate for exchanger energy prices or some other energy allocation factor desired by the controllable load, controllable load 1370 or 1375 may allow EMS controller 1305 to reduce energy consumption at that controllable load to reallocate energy supply from grid-connected power plants to uncontrollable loads that may pay a higher premium and have higher priority based on various factors (e.g., more necessary / high-priority infrastructure, such as hospitals, water plants, critical communication infrastructure, etc.).

[0188] Similarly, controllable loads 1370 or 1375 may include an ESS, wherein the EMS controller 1305 can increase or decrease the power distribution to the ESS. Furthermore, a more optimized decision can be made regarding which energy storage device among the ESSs 1310 or 1365 should be used to store energy. For example, an ESS with lower cost and lower round-trip efficiency (e.g., a zinc-air battery) may be selected, which can be charged when cheap electricity (even negative-priced electricity) is available, anticipating longer storage cycles and lower parasitic loads that make it more efficient overall. Conversely, an efficient but more expensive ESS (e.g., a lithium-ion battery) may be charged when more expensive electricity is available, anticipating faster response times, higher energy storage efficiency, or when other beneficial conditions are apparent to those skilled in the art possessing this disclosure. Therefore, the type of energy storage device or other factors associated with the energy storage device can be used to determine when to charge a particular energy storage device or how much electricity a particular energy storage device receives.

[0189] In other embodiments, the EMS controller 1305 may also use anticipated energy demands and supplies to balance the storage of energy generated by REPP on associated batteries. For example, the EMS controller 1305 may determine the amount of energy stored on each battery and how those batteries in a networked power plant will allocate energy in an optimized manner. For example, to maintain the expected lifespan of a battery, under normal conditions, the battery cannot be fully charged or fully discharged, as doing so would reduce the battery's expected lifespan. However, if anticipated energy supplies and demands indicate that fully charging or discharging the battery is more beneficial than considering the battery's expected lifespan, then the EMS controller 1305 may fully charge the battery in preparation for future events. For example, if there is a anticipated event requiring or involving high energy demand, then the EMS controller 1305 may fully charge the battery. In other embodiments, the EMS controller 1305 may classify the batteries of the ESS 1310 or 1365 such that a first battery allocates energy based on a first condition, a second battery allocates energy based on a second condition, and a third battery allocates energy based on a third condition. These conditions may be prioritized based on different levels. For example, a third battery can allocate energy only when the price of energy is above a certain threshold.

[0190] In other embodiments of this disclosure, the EMS controller 1305 can determine when to provide energy storage to power plants not included in the REPP 1300 (such as power plants on the grid 1325). The EMS controller 1305 can determine conditions under which a power plant can store energy on batteries or other ESSs at a grid-connected power plant. Using anticipated energy demand and energy storage determinations made by machine learning algorithms of the EMS controller 1305, the EMS controller 1305 can determine when to purchase electricity from a power plant on the grid or when to provide storage to a contracted power plant or transfer energy between storage devices. The EMS controller 1305 can communicate with applications located at the power plant, similar to applications provided at controllable loads and storage locations at grid-connected power plants. Therefore, the systems and methods of this disclosure provide more optimized and consistent energy generation, storage, and distribution of energy generated by the REPP, which may be inherently seasonal.

[0191] In various embodiments, EMS 1305, based on decisions made using predictive algorithms and collected data satisfying trigger conditions, can actuate switch 1345 to selectively connect a second ESS inverter 1360 to a first meter 1320, a second meter 1350, or a third load 1370, based on trigger conditions. In some embodiments, the trigger condition may be the end of a time period. The time period may be a season. For example, EMS 1305 may actuate switch 1345 based on the end of summer and the beginning of autumn. In other embodiments, the trigger condition may be that the RES output (such as the daily average RES output) drops below a predefined threshold. In some embodiments, the predefined threshold may be based on the power demand of the first load 1330. For example, the RES output may drop below a threshold such that RES 1335 cannot generate enough daily energy to meet the power demand of the first load 1330 as well as the power demands of the second load 1355 and the third load 1370. While the actuation of switch 1345 can be based on a trigger condition that reacts to the condition being met at the current time, the trigger condition can also be based on predictive conditions that may occur on REPP 1300, grid 1325, load 1330, 1355, 1370 or 1375 or other components of REPP 1300.

[0192] RES 1335 can supply power directly to the first load via the power grid 1325. RES 1335 can supply power to the first ESS 1310 to charge the first ESS 1310. EMS 1305 can determine how much power RES 1335 supplies to the first load 1330, how much power RES 1335 supplies to the first ESS 1310, and how much power the first ESS 1310 supplies to the first load 1330. EMS 1305 can determine how much power RES 1335 generates. The first ESS 1310 can be charged by RES 1335 and then discharged to supply power to the first load 1330. In some embodiments, the first ESS 1310 can be simultaneously charged and discharged by RES 1335 to supply power to the first load 1330. A first meter 1320 measures the amount of energy supplied to the first load 1330. The amount of energy delivered to the first load 1330 can be the sum of the energy delivered to the first load 1330 by the RES 1335 and the energy delivered to the first load 1330 by the first ESS 1310. The first ESS 1310 can deliver power to the first load 1330 when the RES 1335 is not delivering power to the first load 1335, or when the RES 1335 is delivering power to the first load 1335. The EMS 1305 can determine the charging / discharging status and state of charge (SOC) of the first ESS 1310.

[0193] In some embodiments, RES 1335 and the first ESS 1310 are sized such that REPP 1300 can deliver power to the first load 1330 with a capacity factor greater than or equal to approximately 60-80%. In some embodiments, the capacity factor is 80-100%. In some embodiments, REPP is over-configured to have a capacity factor exceeding 100%, wherein the capacity factor is defined by dividing the total output of REPP 1300 by the connected output to grid 1325. This can be achieved by increasing the maximum power output of RES 1335 and its ESS 1310 and 1365 to generate more power or provide more power than can be injected into grid 1325. The excess power can be consumed by ESS 1310, 1360, or the downstream load 1370. Controllable loads on grid 1325 can also be used to reduce or increase power consumption based on the total output required by other loads 1330 or 1355 on the grid, so that the capacity factor remains stable and close to 100%. By utilizing a controllable load, the EMS controller can now shut down the controllable load during winter or other seasons, splitting REPP 1300 into two REPPs with controllable loads and partitioned batteries. One REPP 1300 can be partitioned into two power plants, one as a post-meter power plant and the other as a power plant on the grid 1325. This ensures an emergency backup power plant, such as when grid 1325 needs power due to power outages or cold waves in winter, power can be quickly distributed from load 1370 and supplied on the grid. In various embodiments, the base load portion of REPP 1300 can be larger in summer and smaller in winter. However, in some scenarios, it may be expected that the base load portion of REPP 1300 is larger in winter, larger in other seasons, or temporarily larger in winter.

[0194] In some embodiments, the capacity factor varies (e.g., seasonally). Although there is inherent variation in the output of many RES, RES 1335 can be sized to generate sufficient energy to meet the power demand of the first load 1330. RES 1335 may have a peak output higher than the power demand of the first load 1330. The first ESS 1310 may be sized to store an amount of energy from RES 1335 sufficient to time-shift the RES output to meet the power demand of the first load 1330. EMS 1305 may control RES 1335 or the first ESS 1310 to deliver power to the first load 1330. RES 1335 may generate a first amount of energy per day sufficient to meet the power demand of the first load 1330. RES 1335 may generate the first amount of energy at a certain reliability level (i.e., RES 1335 generates the first amount of energy on a specific percentage of days in a year). A reliability level may be specified for the first load 1330. The first ESS 1310 can store a portion of the energy of the first quantity, allowing the power supplied from REPP 1300 to the first load 1330 to be distributed throughout the day. REPP 1300 can supply power to the first load 1330 for a longer period than RES 135 generates power. In one example, RES 1335 is a solar power source that generates power until 7:00 PM for certain times of the year, and the first ESS 1310 stores a portion of the energy of the first quantity and releases that portion so that REPP 1300 can supply power to the first load 1330 until midnight. In another example, REPP 1300 is a solar power source that generates power until 7:00 PM for certain times of the year, and the first ESS 1310 stores a portion of the energy of the first quantity, allowing REPP 1300 to continuously supply power to the first load 1330.

[0195] FIG. 14 yes FIG. 13A block diagram of the REPP is shown, in which switch 1345 connects the second energy storage system (ESS) 1365 to the third load 1370, but does not connect the second ESS 1365 to the first meter 1320 or the second meter 1350. In this configuration, the ESS 1335 and the second ESS 1365 can supply power to the third load 1370. In this configuration, the REPP 1300 supplies power to the first load 1330 via the grid 1325 and directly to the third load 1370 (e.g., after the meter). For discussion purposes, this configuration is referred to herein as the "summer mode." However, this configuration is not limited to use in the summer. In fact, as discussed above, this configuration can be used in winter, where REPP 1300 is partitioned into two or more power plants, with load 1370 being controllable. This allows a portion of RES 1335, as well as ESS 1310 and 1365, to be partitioned for use with load 1370, and another portion of RES 1335, ESS 1310, and 1365 to be dedicated to grid 1325. Because grid 1325 can limit the required power due to lower load demand in winter, some of the power generated and stored by REPP can be dedicated to the controllable load 1370. However, the balancing mechanism may require load and generation stability, thus requiring emergency power in winter due to unexpected power demand. To provide this emergency power, power can be reduced to load 1370, allowing emergency power to be quickly redistributed to grid 1325 without creating excessive load on grid 1325. Typically, "dirty" standby generators or idling fossil fuel power plants provide emergency power to the grid, and this configuration allows RES 1335 to continue operating to provide backup power in the event of a system emergency requiring instantaneous emergency power. Therefore, the systems and methods of this disclosure reduce or eliminate the need for fossil fuel power plants to idle, burn expensive fuels, and cause environmental damage.

[0196] Regarding the summer mode, it can be used when the RES 1335 generates an amount of excess energy exceeding the power demand of the first load 1330. For example, a solar array may generate more power in the summer than in the winter, resulting in excess power production during the summer. In some embodiments, the REPP 1300 may feed some or all of the excess energy to the grid 1325. However, in areas where solar energy is abundant, power delivered during the time when the solar source generates power (such as actual delivered solar power) generally has low value compared to power delivered during times when the solar source does not generate power. In other embodiments, the REPP 1300 may deliver power to a third load 1370. The REPP 1300 may deliver power to the third load 1370 because the ability to time-shift energy to a higher-value time of day using the ESS may be more advantageous operationally and economically than delivering power. REPP 1300 can use a second ESS 1360 to time-shift the RES output, so as to supply power to the third load 1370 for a longer period than RES 1335 generates power, or during periods when power supply is below peak solar production time. The second ESS 1360 can have a sufficiently large storage capacity to store excess energy generated by RES 1335. The second ESS 1360 can also have a sufficiently large charge / discharge capacity to charge from excess power generated by RES 1335 and supply power to the third load 1370 when needed.

[0197] EMS1305 can control the charging / discharging of the first ESS1310 and the second EMS1360 to time-shift the RES output, thereby meeting the power demand of the first load 1330 and supplying power to the third load 1370. In some embodiments, EMS1305 can control the RES output of RES1335 to meet the power demand of the first load 1330 and supply power to the third load. EMS1305 can adjust the inverter setpoints of the first ESS inverter 1315 and the second ESS inverter 1360 to control the charging / discharging of the first ESS1310 and the second EMS1360.

[0198] When the RES output exceeds the power demand of the first load 1330, the EMS 1305 can direct the first load portion of the RES output to the first load 1330 until the power limit of the first load 1330 is reached. If the current time is for supplying power to the third load 1370, then based on the power demand of the third load 1370 and the current energy price, the EMS 1305 can direct the third load portion of the RES output to the third load 1370 until the power limit of the third load 1370 is reached. The EMS 1305 can supply the portion of the RES output exceeding that directed to the first load 1330 and the third load 1370 to the first ESS 1310 until the charging power limit of the first ESS 1310 is reached and the first ESS 1310 is fully charged. EMS 1305 can supply RES outputs exceeding those directed to the first load 1330, the third load 1370, and the first ESS 1310 to the second ESS 1365 until the charging power limit of the second ESS 1365 is reached and the second ESS 1365 is fully charged. If the first ESS 1310 and the second ESS 1365 are fully charged, EMS 1305 can use the inverter setpoint of RES inverter 1340 to reduce RES outputs exceeding the combined power limit of the first load 1330 and the second load.

[0199] If the RES output is less than the power limit of the first load 1330 and energy is stored in the first ESS 1310, then the EMS 1305 can set the discharge of the first ESS 1310 such that the REPP 1300 delivers power to the first load 1330 equal to the power limit of the first load 1330, limited by the rated discharge rate of the first ESS 1310 and the energy stored in the first ESS 1310.

[0200] If the RES output is greater than the power limit of the first load 1330 but less than the combined power limit of the first load and the third load 1370, and the current time is the time to deliver power to the third load 1370 based on the power demand of the third load 1370 and the current energy price, then the EMS 1305 can set the discharge of the second ESS 1365 such that the REPP 1300 delivers power to the third load 1370 equal to the power limit of the third load 1370, limited by the rated discharge rate of the second ESS 1365 and the energy stored in the second ESS 1365.

[0201] FIG. 15 yes FIG. 13A block diagram of the REPP is provided, in which switch 1345 connects the second ESS 1365 to the second meter 1350, but not to the first meter 1320 or the third load 1370. In this configuration, RES 1335 and the second ESS 1365 can supply power to the second load 1355. In this configuration, REPP 1300 supplies power to the first load 1330 and the second load 1355 via the grid 1325. For discussion purposes, this configuration is referred to herein as the "winter mode." However, this configuration is not limited to use in winter. As discussed above, this mode can also be implemented in summer. Energy delivered through the grid inevitably mixes on the grid. However, the energy flowing through the first meter 1320 (operating, economically, and contractually) is considered to have been delivered to the first load 1330. Similarly, the energy flowing through the second meter 1350 (operating, economically, and contractually) can be considered to have been delivered to the second load 1355.

[0202] Winter mode can be used when RES1335 is unable to generate enough power to fully cycle the daily energy of the second ESS1365 to exceed the power demand of the first load 1330. For example, a solar array may generate more power in summer than in winter, resulting in less power generation in winter than in summer. When the second ESS1365 is set to time-shift the RES output delivered to the third load 1370, the lower winter RES output is insufficient to meet the power demand of the third load 1370. However, when RES1335, along with ESS1310 and 1365, are over-built, REPP1300 may generate too much power in winter and require offloading some of the excess power by creating more loads on the grid, while still maintaining emergency power for load 1370 or controllable load 1375, as mentioned above. FIG. 14 The EMS controller 1305 with predictive algorithms can better manage when the switch 1345 should be actuated between the load 1370 and the meter 1350 during each season.

[0203] In winter mode, the second ESS1365 can provide power capacity for the second load 1355 or the controllable load 1375. The second ESS1365 can store energy for use by the second load 1355 when needed. In some embodiments, the second load 1355 is used only occasionally. The EMS1305 can direct power to the second ESS1365 to fully charge it. In some embodiments, the EMS1305 can charge the second ESS1365 with an output exceeding the power demand of the first load 1330. The EMS1305 can offset the self-discharge of the second ESS1365 (i.e., the tendency of the second ESS1365 to lose stored energy over time even when not discharging) by directing power from the RES1335 to the second ESS1365. Charging the second ESS1365 with an output exceeding the power demand of the first load 1330 typically requires the RES1335 to be set large enough to have excess output even when output is reduced (e.g., in winter, when using solar resources). RES 1335 can be configured large enough to generate an RES output sufficient to meet the power demand of the first load 1330, compensate for round-trip energy losses in the first ESS 1310, charge the second ESS 1365 for an acceptable period of time (as discussed below), and maintain the charge of the second ESS 1365. In other embodiments, the second ESS 1365 can be charged by temporarily reducing the power supplied to the first load 1330. The second ESS 1365, when fully charged to a ready-to-charge state, can act as a short-term power source for emergency or contingency use of the second load 1355. The emergency capacity provided by the second ESS 1365 can allow the operator of grid 1325 to avoid keeping fossil fuel power plants online as fast-response spinning reserves. Alternatively, the grid operator can use the second ESS 1365 as a spinning reserve and utilize the energy stored in the second ESS 1365 for fast response. Depending on the length of the emergency or contingency, grid operators may have time to bring fossil fuel power plants online, or they may be able to avoid the need to use fossil fuel power plants altogether.

[0204] In emergency or contingency situations, once the second ESS 1365 is discharged, the EMS 1305 can direct power from the RES 1335 to the second ESS 1365 to fully charge it. Depending on how much the RES output exceeds the power demand of the first load 1330 and whether the EMS 1305 reduces the power supplied to the first load 1330, the time required or used to fully charge the second ESS 1365 can be one hour, one day, one week, or any amount of time. The target amount of time for fully charging the second ESS 1365 can be used to determine the size of the RES 1335. The RES 1335 can be sized to produce sufficient winter RES output to fully charge the second ESS 1365 within the target amount of time. In some embodiments, as discussed above, the EMS 1305 can reduce the power supplied to the first load 1330 to charge the second ESS 1365 more quickly.

[0205] In some embodiments, the second ESS1365 can charge and discharge the second ESS in small increments as needed to provide grid services to the grid 1325, such as voltage and frequency support. In some embodiments, the second ESS1365 can provide grid service capacity to the second load 1355 to offset the impact of the second load 1355 on the grid. In some embodiments, the second load 1355 can communicate with the EMS1305 to coordinate the charging / discharging of the second ESS1365 with the power consumption fluctuations of the second load 1355.

[0206] In another example, grid 1325 may include two or more connected grids regulated by different balancing agencies. A first grid may supply power to load 1330, and a second grid may supply power to load 1355. The geographical location of load 1330 may differ from that of load 1355. For example, load 1330 may be located in Southern California, where it is associated with a specific load curve that is lower in winter and higher in summer due to air conditioning use. In contrast, load 1355 may be located in Northern California, where the load is associated with the energy consumption of large data centers and contracts for serving these data centers with renewable energy. Therefore, the load curve for load 1355 fluctuates less significantly between summer and winter. However, the clean solar energy generated in the region during the winter months may not be sufficient to meet the needs of load 1355 when power production is lower. Therefore, excess power generated by RES 1335 during the winter months that is not needed by load 1330 can be redirected to load 1355 to meet that load's demand for both electricity and renewable energy. Therefore, loads 1330 and 1355 can be described as mutually related, mutually unrelated, and complementary, as their load and power production curves at different times of the year provide room for efficiency and optimization in sharing power across the grid. Thus, EMS 1305 can actuate switch 1345 to connect to meter 1350 during winter, allowing inverter 1360 to power both meter 1350 and load 1355, while during summer months, switch 1345 is not connected to meter 1350.

[0207] FIG. 16 yes FIG. 13 A block diagram of REPP 1300 is provided, in which switch 1345 connects the second ESS 1365 to the first meter 1320, but not to the second meter 1350 or the third load 1370. In this configuration, RES 1335, the first ESS 1310, and the second ESS 1365 can supply power to the first load 1330 via the grid 1325. For discussion purposes, this configuration will be referred to herein as "focused mode." However, this terminology is not restrictive.

[0208] In some embodiments, a focus mode can be used when the storage capacity required (or used) by the first load 1330 is greater than the capacity provided by the first ESS 1310. For example, the RES output may be large enough or timed such that the first ESS 1310 cannot time-shift the RES output to a level sufficient to meet the power requirements of the first load 1330. A second ESS 1365 can assist the first ESS 1310 in time-shifting the RES output to meet the power requirements of the first load 1330. In some embodiments, a focus mode can be used in summer when the RES output is greater than those that the first ESS 1310 can time-shift, and a summer mode can be used in autumn and spring when the RES output can be time-shifted solely by the first ESS 1310.

[0209] In some embodiments, a focus mode can be used when the first load 1330 requests (or uses) energy storage capacity. The second ESS 1365 can be charged to a ready state as in winter mode and can supply power to the first load 1330 as needed. The focus mode, in which the second ESS 1365 supplies capacity to the first load 1330, can be used in any season. Furthermore, although summer mode and winter mode are described as supplying power to the third load 1370 and providing capacity to the second load 1355, respectively, the EMS 1305 can control the REPP 1300 according to summer mode to supply power to the second load 1355 and provide capacity to the third load 1370, respectively.

[0210] EMS 1305 can actuate switch 1345 to modify the configuration of REPP 1300 to a summer mode, winter mode, or focus mode. EMS 1305 can actuate switch 1345 to electrically decouple the second ESS 1360 from any connected devices, such as the third load 1370, the second meter 1350, or the first meter 1320. EMS 1305 can actuate switch 1345 based on trigger conditions, as discussed herein. In some embodiments, the trigger condition can be the end of a time period. This time period can be a season. For example, EMS 1305 can actuate switch 1345 based on the end of summer and the beginning of autumn, or based on the end of spring and the beginning of summer. In other embodiments, the trigger condition can be that the RES output (e.g., daily average RES output) drops below or rises above a predefined threshold. In some embodiments, the predefined threshold can be based on the power demand of the first load 1330. In one example, based on the RES output falling below a threshold, EMS 1305 can actuate switch 1345 such that RES 1335 does not generate sufficient daily energy to meet the power demands of the first load 1330, as well as the power demands of the second load 1355 and the third load 1370. In another example, based on the RES output rising above a threshold, EMS 1305 can actuate switch 1345 such that RES 1335 generates sufficient daily energy to meet the power demands of the first load 1330, as well as the power demands of the second load 1355 and the third load 1370. In other embodiments, as discussed above, EMS controller 1305 can actuate switch 1345 based on data collected from data source 1311 or from the REPP component coupled to network 1304 and input that data into a predictive algorithm, such as, for example, Model Predictive Control (MPC) or Model-Based Reinforcement Learning (MBRL). Adaptive Model predictive control (AMPC) or other predictive / machine learning algorithms. MPC can be implemented using long short-term memory (LSTM), state-space models, or transformer architectures. Some implementations may use multimodal time series forecasting models (e.g., considering weather, wind power, solar power, grid demand, and post-meter load output values), examples of which include: autoregressive moving average (ARMA) models (e.g., seasonal ARIMA); autoregressive integral moving average (ARIMA) models; generalized autoregressive conditional heteroscedasticity (GARCH) models; vector autoregressive models; Holt-Winters exponential smoothing; state-space models; and Kalman filters.

[0211] FIG. 17 yes FIG. 13The diagram shows a REPP (Resistant Power Supply) circuit where a second switch 1346 connects a first ESS 1310 to a second meter 1350. An EMS 1305 can connect the first ESS 1310 to the second meter 1350 and a second ESS 1365 to the first meter 1320 to switch the use of the first ESS 1310 and the second ESS 1365. The first ESS 1310 can be used to provide capacity for a second load 1355, while the second ESS 1365 can be used to time-shift the RES output to meet the power demand of the first load 1330. Similarly, the EMS 1305 can switch the use of the first ESS 1310 and the second ESS 1365 in summer and winter modes: in summer mode, the first ESS 1310 is connected to a third load 1370 and the second ESS 1365 is connected to the first meter 1320, while in winter mode, the first ESS is connected to the second load 1355 and the second ESS 1365 is connected to the first load 1330. EMS 1305 can change the connection of the first ESS 1310 and the second ESS 1365 by actuating switch 1345 and / or the second switch. Alternating use of the first ESS 1310 and the second ESS 1360 can be used to balance the degradation of the first ESS 1310 and the second ESS 1360. ESS degradation can include a reduction in total storage capacity, a reduction in maximum charge / discharge rate, and / or an increase in self-discharge rate. Balancing the degradation of the first ESS 1310 and the second ESS 1365 can include monitoring the degradation of the first ESS and the second ESS, and changing the usage of the first ESS and the second ESS such that the degradation of the first ESS equals the degradation of the second ESS. Different uses of the first ESS 1310 and the second ESS 1365 can result in different levels of degradation. In some embodiments, balancing the degradation of the first ESS 1310 and the second ESS 1365 can include balancing a first amount of the first ESS 1310 and the second ESS 1365 for a first purpose and a second amount of the first ESS 1310 and the second ESS 1365 for a second purpose. For example, if an ESS is used on a daily cycle, its degradation can be faster than when it's used to provide capacity. In this example, if the first ESS 1310 is run daily to time-shift the RES output while the second ESS 1365 is used to provide capacity, the first ESS 1310 will degrade faster. In this example, the first ESS 1310 and the second ESS 1365 can be balanced by running the second ESS 1365 daily to time-shift the RES output while the first ESS 1310 is used to provide capacity, making the degradation of the first ESS equal to the degradation of the second ESS.Actuating switch 1345 and switch 1346 to alternate between first ESS 1310 and second ESS 1365 can reduce the degradation rate and / or degradation difference between first ESS 1310 and second ESS 1365. Switch 1345 and switch 1346 can be actuated periodically to alternate between first and second ESS 1310, 1365, such as by season or by year.

[0212] In some embodiments, FIGS. 13-17 One or more functions of System 1300 can be combined with FIG. 1 System 100 FIG. 4 System 400 FIG. 10 System 1000 and / or FIG. 20 The system 2000 may combine or replace one or more functions of any of its components. Alternatively or additionally, FIG. 13 One or more functions of the EMS 1305 can be used FIG. 2 Controller 200 FIG. 11 Controller 1102 FIG. 18 Controller 1802 and / or FIG. 21 Implemented by one or more functions / features of any one of the controllers 2102. Alternatively or additionally, FIG. 1 System 100 can be configured to execute FIG. 3 Method 300 FIG. 5 Method 500 FIG. 6 Method 600 FIG. 7 Method 700 FIG. 8 Method 800 FIG. 12 Method 1200 FIG. 19 Method 1900 or FIG. 22 One or more of methods 2200.

[0213] FIG. 18 An embodiment of the EMS controller 1800 is illustrated, which can be the one described in the above reference. FIG. 13 The EMS controller 1305 is discussed. Although described as a standalone system, those skilled in the art will recognize that the EMS controller 1800 can be distributed across many computing devices, such as in a cloud environment. In the illustrated embodiment, the EMS controller 1800 includes a chassis 1802 that houses the components of the EMS controller 1800. FIG. 18 Only some of these components are shown. For example, chassis 1802 may house a processing system (not shown) and a non-transitory memory system (not shown) containing instructions that, when executed by the processing system, cause the processing system to provide an EMS engine 1804, configured to perform the functions of an EMS engine or EMS controller, as discussed below. FIG. 18In the specific examples shown, EMS engine 1804 may include EMS predictive algorithm 1805, which is configured to perform the functions of the energy generation, storage, and distribution predictive algorithms discussed herein. In various embodiments, energy generation, storage, and distribution predictive algorithm 1805 may ingest data provided by a data source and predict energy demand and supply, or any other functions discussed herein. In various embodiments, energy generation, storage, and distribution predictive algorithm 1805 may include a network simulator to model behavior, which, due to a lack of historical data, can predict components to be integrated into the power grid by running simulations. In other examples, EMS predictive algorithm 1805 may include model predictive control or other predictive algorithms / machine learning algorithms that are obvious to those skilled in the art with this disclosure.

[0214] The chassis 1802 may also house a communication system 1806 coupled to the EMS engine 1804 (e.g., coupling between the communication system 1806 and the processing system) and configured to provide communication via a communication network 1304, as detailed below. The chassis 1802 may also house a storage system 1808 coupled to the EMS engine 1804 via the processing system and configured to store rules or other data (e.g., trained models, training data, etc.) used by the EMS engine 1804 to provide the functions discussed below. While the EMS controller 1800 has been illustrated, those skilled in the art with this disclosure will recognize that other EMS controllers (or other devices operating in a manner similar to that described below for the EMS controller 1800, in accordance with the teachings of this disclosure) may include various components and / or component configurations for providing known computing device functions and the functions discussed below, while still remaining within the scope of this disclosure.

[0215] The description in U.S. Patent No. 11,621,566, filed October 5, 2022, entitled "Seasonal Electrical Resource Allocation" FIGS. 6-17 Various example power distributions, example power delivery, and example ESS state of charge (SOC) of REPP are described herein, the entire contents of which are incorporated herein by reference and used in conjunction with embodiments thereof.

[0216] In some embodiments, a renewable energy power plant (REPP) includes a renewable energy source (RES), a first meter associated with a first load, a second meter associated with a second load, a first energy storage device (ESS) electrically coupled to the RES and the first meter, a second ESS electrically coupled to the RES and the first meter via a switch, a controllable load coupled to the RES via a switch, and a controller configured to set a first charge / discharge of the first ESS and a second charge / discharge of the second ESS such that the REPP delivers power to the first load for a longer period than the RES generates power, actuates a switch in response to a trigger condition such that the second ESS is electrically coupled to the controllable load, and sets a fourth charge / discharge of the second ESS such that the second ESS retains a portion of its charge for the controllable load. The trigger condition and the trigger switch are determined by a predictive algorithm using machine learning based on data collected from the REPP or third-party sources.

[0217] In some embodiments, a non-transitory processor-readable medium stores instructions that, when executed by a processor, cause the processor to: (1) set a first charge / discharge of a first energy storage system (ESS) of a renewable energy power plant (REPP) and (2) set a second charge / discharge of a second ESS of the REPP, such that the REPP supplies power to a first load during a first time period, the first time period being longer than a first production time period during which the REPP's renewable energy (RES) generates power. The first ESS is electrically coupled to the RES and a first meter, and the second ESS is electrically coupled to the RES and the first meter via a switch. The non-transitory processor-readable medium also stores instructions to cause the processor to brake the switch in response to a first trigger condition, such that the second ESS is electrically coupled to a controllable load. The non-transitory processor-readable medium also stores instructions to cause the processor to set a third charge / discharge for the first ESS, such that the REPP supplies power to the first load during a third time period, the third time being longer than the second production time period during which the RES generates power. The non-transitory processor-readable medium also stores instructions to cause the processor to set a fourth charge / discharge for the second ESS, such that the second ESS retains a portion of its charge for the second load. The non-transitory processor-readable medium also stores instructions to cause the processor to actuate a switch in response to a second trigger condition, such that the second ESS is electrically coupled to a second meter different from the first meter. The non-transitory processor-readable medium also stores instructions to cause the processor to set a fifth charge / discharge for the second ESS, such that the second ESS maintains a portion of its charge reserved by the second meter, and the RES is adjusted to meet the power delivery parameters of the first load and maintain a portion of the charge of the second ESS.

[0218] In some implementations, the non-transitory processor-readable medium also stores instructions to detect whether at least one of a first triggering condition or a second triggering condition is satisfied based on a prediction made by a predictive algorithm. The predictive algorithm may include at least one of model predictive control (MPC), model-based reinforcement learning (MBRL), or adaptive model predictive control (AMPC).

[0219] In some implementations, the controllable load is located after at least one of the first or second meters.

[0220] In some embodiments, the non-transitory processor-readable medium also stores instructions to cause the processor to perform at least one of the following: determining a first charge / discharge of the first ESS based at least in part on the type of energy storage device associated with the first ESS, or determining a second charge / discharge of the second ESS based at least in part on the type of energy storage device associated with the second ESS.

[0221] In some embodiments, a non-transitory processor-readable medium stores instructions that, when executed by a processor, cause the processor to set a first charge / discharge of a first energy storage system (ESS) of a renewable energy power plant (REPP) and a second charge / discharge of a second ESS of the REPP, such that the REPP delivers power to a first load for a first time, longer than a first production period during which the REPP's renewable energy (RES) generates power, wherein the first ESS is electrically coupled to the RES and a first meter, and the second ESS is electrically coupled to the RES and the first meter via a switch. The non-transitory processor-readable medium also stores instructions to cause the processor to determine, based on a prediction generated using a first predictive algorithm, that a first trigger condition is met. The non-transitory processor-readable medium further stores instructions to cause the processor to brake a switch in response to determining that the first trigger condition is met, such that the second ESS is electrically coupled to a controllable load. The non-transitory processor-readable medium further stores instructions to cause the processor to set a third charge / discharge of the first ESS, such that the REPP delivers power to the first load for a third time, longer than the second production period during which the RES generates power. The non-transitory processor-readable medium also stores instructions to cause the processor to set a fourth charge / discharge time for the second ESS, such that the second ESS retains a portion of its charge for the second load. The non-transitory processor-readable medium also stores instructions to cause the processor to determine, based on a prediction generated using a first predictive algorithm, that a second trigger condition is satisfied. The non-transitory processor-readable medium also stores instructions to cause the processor to actuate a switch in response to determining that the second trigger condition is satisfied, such that the second ESS is electrically coupled to the second meter. The non-transitory processor-readable medium also stores instructions to cause the processor to set a fifth charge / discharge time for the second ESS, such that the second ESS retains a portion of its charge for the second meter.

[0222] In some implementations, the controllable load includes at least one of the following: an artificial intelligence (AI) training center, a cryptocurrency mining machine, an electric vehicle (EV) charging station, a vertical farm, a hydrogen production facility, a smelter, a water treatment plant, an industrial process heater, or a thermal battery.

[0223] In some implementations, the non-transient processor-readable medium also stores instructions to cause the processor to operate at least one of the RES, the first ESS, or the second ESS as at least one of the peaking power plant of the power grid or a provider of ancillary services for the power grid.

[0224] In some embodiments, the non-transient processor-readable medium also stores instructions for a controller to operate REPP in a first mode as at least one of a baseload, half-baseload, half-peaking plant, or peaking plant of a controllable load; and (2) concurrently with the controller operating REPP in the first mode, a controller to operate REPP in a second mode as at least one of a peaking plant, half-peaking plant, half-baseload, baseload, or ancillary service provider of the power grid.

[0225] In some implementations, at least one of the first triggering conditions or the second triggering condition includes an output of at least one of the first RES or the second RES exceeding a predefined threshold, such that at least one of the first RES or the second RES generates sufficient daily energy to meet the power demands of the first load and the second load.

[0226] In some implementations, determining that a first trigger condition is met or determining that a second trigger condition is met is based on a model predictive control (MPC) algorithm implemented using one of long short-term memory (LSTM), a state-space model, or a transformer architecture.

[0227] System and method for intelligent renewable energy power plant serving multiple controllable and uncontrollable loads

[0228] Some aspects of this disclosure include a process comprising: a) determining one or more metrics for different time periods of a forecast time range, wherein the one or more metrics relate to sending energy generated by a first renewable energy system (RES) to: (1) an energy storage system (ESS), (2) a power grid including one or more loads, and (3) one or more meter-based loads; b) prioritizing: (1) the ESS, (2) the power grid, and (3) one or more meter-based loads, wherein the prioritization is based on one or more of: (1) one or more metrics determined in (b), (2) the state of charge of the ESS during the forecast time range, (3) one or more constraints related to the energy demand of the power grid during the forecast time range, or (4) one or more constraints related to the energy demand of one or more meter-based loads during the forecast time range; and c) providing instructions based on the prioritization to deliver electricity generated by the first RES to at least one of: (1) the ESS, (2) the power grid, or (3) one or more meter-based loads. The one or more meter-based loads include controllable loads, and the process includes providing instructions to the controllable loads based on the prioritization to increase or decrease energy demand. Alternatively or additionally, one or more loads included on the power grid include grid-controlled loads, and the process includes providing instructions to the grid-controlled loads based on prioritization to increase or decrease energy demand. Some embodiments include machine learning algorithms for determining prioritization or generating instructions.

[0229] Some aspects of this disclosure include a tangible, non-transitory machine-readable medium storing instructions that, when executed by a data processing apparatus, cause the data processing apparatus to perform operations including the aforementioned processing.

[0230] Some aspects of this disclosure include a system comprising: one or more processors; and a memory storing instructions that, when executed by the processor, cause the processor to perform the aforementioned processing.

[0231] As discussed above, renewable energy can be produced in a variety of ways, such as solar power plants, wind turbines, geothermal power plants, hydroelectric power plants, and many others. The electricity output of renewable energy sources (RES) can vary in a predictable or random manner. For example, solar power generation can have seasonal and daily cycles depending on the season and the sun's path across the sky, as well as certain random patterns influenced by the passage of clouds between the solar array and the sun. In the example of wind power, it can have different seasonal and daily patterns, as well as a random component influenced by weather patterns. Sometimes renewable energy sources (RES) are producing an excess of energy relative to the demand for electricity, while at other times RES cannot meet the demand.

[0232] One type of technology currently in use is the installation of an energy storage system (ESS), which can absorb energy when the production of the electrical load exceeds the demand of the electrical load, and then supply the energy to the grid when the production of the electrical load is below the demand of the electrical load. In some cases, when the value of supplying electricity to the electrical load is relatively low (e.g., overproduction), the ESS can be charged through the electrical load, while when the value of supplying electricity to the electrical load is relatively high, the ESS can be discharged to supplement any output of the electrical load, while remaining within the various power limits of the connection to the electrical load and the power limits of the equipment in the electrical load and the ESS.

[0233] In addition to charging ESS or supplying power to the grid, renewable energy sources (RES) can also be used to serve other types of electrical loads or energy-consuming processes, which may or may not be off-grid. For example, RES can directly supply energy to loads such as industrial processes (e.g., producing “green” hydrogen, producing ammonia, metal smelting, cryptocurrency mining, “vertical” agriculture, powering server farms, water purification, and glass production) without going through the grid (e.g., off-grid loads, or also referred to as post-meter loads in this document).

[0234] However, the various types of electrical loads or industrial processes connected to the RES may be unrelated, where the value of directing power to one or more of these loads can vary over time, and such variations may be independent of each other. Therefore, there is a need to manage and improve methods for distributing the amount of energy and power among multiple unrelated loads.

[0235] To help alleviate some inefficiencies and energy and power distribution in power grid systems, some embodiments of this technology can be used in conjunction with the technology described in U.S. Patent No. 12,119,646, issued October 15, 2024, entitled "Systems and Method for Renewable Powerplants Serving Multiple Loads," the entire contents of which are incorporated herein by reference for all purposes.

[0236] Embodiments of this disclosure describe the use of renewable electricity to serve multiple electrical loads. Specifically, energy can be distributed among multiple electrical loads that may be unrelated. In some cases, the multiple electrical loads may be unrelated or partially related. Unrelated or partially related electrical loads or energy consumption processes generally mean that the value of directing electricity to an electrical load or the use of energy is not entirely related to each other. For example, the value of directing electricity to at least one of the electrical loads may vary over time, and this variation may be at least partially independent of the value of directing electricity to another electrical load. In some embodiments of this disclosure, unrelated or related loads may be controllable or uncontrollable loads. A load that is a controllable load may include a load whose demand can be changed by a controller as described herein by increasing or decreasing the electricity demand at that load. Therefore, this disclosure contemplates adjusting both the energy distribution from the RES and the energy demand from one or more of these controllable loads (which may be on the grid or after the meter).

[0237] In various embodiments of this disclosure, the controller may include predictive algorithms, such as, for example, model predictive control (MPC) or model-based reinforcement learning (MBRL). Adaptive Model predictive control (AMPC) or other predictive / machine learning algorithms. MPC can be implemented using Long Short-Term Memory (LSTM), state-space models, or transformer architectures. Some implementations can use multimodal time series forecasting models (e.g., considering weather, wind power, solar power, grid demand, and the value of post-metered load output), examples of which include: Autoregressive Moving Average (ARMA) models (e.g., seasonal ARIMA); Autoregressive Integral Moving Average (ARIMA) models; Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models; Vector Autoregressive models; Holt-Winters exponential smoothing; state-space models; and Kalman filters. Predictive algorithms can predict priorities within future time intervals, and based on priority and predicted total energy storage and generation, predictive algorithms can determine any demand adjustments to controllable loads and allocate energy and power to various loads or ESSs based on priority.

[0238] In some embodiments, priority sequencing is performed multiple times over the entire forecast time period. In some embodiments, if it is impossible to deliver all available energy from the ESS to the highest priority of the following due to reaching maximum energy or power limits: (1) the power grid and (2) one or more industrial processes, then the controller is configured or programmed to perform an operation that delivers excess energy to the second highest priority of the following: (1) the power grid and (2) one or more meter-down loads, and repeats this operation until no excess energy remains. In some cases, one or more meter-down loads include one or more of the following: hydrogen generation by electrolysis, ammonia production, metal smelting, cryptocurrency mining, data center operations, vertical agriculture, food production, atmospheric water generation, AI training systems, water purification, direct carbon capture / direct air capture, or other processes that will be apparent to those skilled in the art with this disclosure.

[0239] Therefore, some embodiments of this technology can be used in conjunction with the technology described in U.S. Patent No. 12,119,646 to further employ those technologies and incorporate controllable power components, including controllable loads (e.g., unrelated or related loads). Loads can be introduced into the system after the meter (e.g., directly connected to the RES without being connected to the grid system) or loads located on the grid system but controllable by a controller. In some embodiments, these loads located on the grid system or after the meter may be unrelated to the typical energy consumption curve experienced by the grid on a given date or other time period (e.g., vertical farming operations, training AI models (and other computationally insensitive workloads), aluminum smelting, direct carbon capture from the air, hydrogen production via water electrolysis using an electrolyzer, or other loads that will be apparent to those skilled in the art possessing this disclosure). Controllable loads may also include related or known loads, where certain contractual arrangements can be met by virtually incorporating these loads into the network. For example, an office building experiencing peak power demand during hot summer days when air conditioning is running to cool office spaces can be an example of a related but controllable load. Even though these loads are generally related to the rest of the grid's energy use, they can be controlled. Therefore, these loads can be controlled to operate at times of the day different from peak times. For example, office buildings can use HVAC systems to pre-cool or pre-heat the building in anticipation of reduced power consumption during peak energy demand periods.

[0240] In various embodiments, downstream loads and other controllable loads on the grid system can communicate with the energy generation, storage, and distribution controller. Downstream controllable loads allow energy producers to increase the scale and performance of the energy generation, storage, and distribution (RES). For example, the RES can be configured to generate more capacity than it can supply to the grid. This provides economies of scale in terms of cost and performance compared to systems without downstream controllable loads. When generation is not at its peak (e.g., cloudy, early morning, evening, or low wind speeds) or when the energy storage system included in the RES is full, excess energy is absorbed by the downstream loads. Furthermore, when RES generation is low, the over-provisioned system can deliver more power to more critical or valuable loads on the grid and meet the bandwidth the RES can provide to the grid or supply more load or power to the energy storage system. Therefore, REPPs can be designed for better performance and lower cost, i.e., better overall system performance, resulting in a more consistent energy supply, capacity, or other ancillary services to the grid.

[0241] Energy generation, storage, and distribution controllers for grid-connected power plants may include predictive algorithms for balancing energy distribution to manageable loads. For example, the energy generation and distribution controller may ingest data from various data sources, such as weather forecasts, event schedules, calendars, historical energy usage data, sensor data, or other data sources that will be obvious to those skilled in the art with this disclosure. In other embodiments, the data source may include state-of-energy data or analytical values ​​from other RES and their energy storage systems. These other RES may include energy storage systems not on the network and may be competing energy storage systems. Therefore, predicting how much energy another RES has stored can be beneficial in anticipating how much energy is available on the grid at a given point in time, thereby enabling the management of the ESS.

[0242] Then, the energy generation and distribution controller, using predictive algorithms trained on historical or simulator data, can anticipate the energy demand of uncontrollable loads on the grid and the energy supply from power plants. Based on the predicted energy demand and supply, the energy generation and distribution controller can determine whether one or more energy balance conditions associated with the corresponding controllable load are met, to increase or decrease power allocation to that controllable load. For example, in exchange for a better energy price rate or some other energy allocation factor desired by the controllable load, the controllable load may allow the energy generation, storage, and distribution controller to reduce energy consumption at that controllable load to reallocate energy supply from grid-connected power plants to uncontrollable loads that may pay a higher premium and have higher priority based on various factors (e.g., more necessary / high-priority infrastructure such as hospitals, water plants, critical communication infrastructure, etc.). Furthermore, the controllable load itself can adjust to reduce or increase energy consumption.

[0243] Similarly, controllable loads can include energy storage systems, where energy generation, storage, and distribution controllers can increase or decrease the power allocation to energy storage devices. Furthermore, more optimized decisions can be made regarding which energy storage device in the energy storage system is used to store energy. For example, zinc-air batteries can be charged when inexpensive electricity is available, while lithium-ion batteries can be charged when more expensive electricity is available, a faster response time is anticipated, or other beneficial conditions are apparent to those skilled in the art possessing this disclosure. Therefore, the type of energy storage device or other factors associated with the energy storage device can be used to determine when to charge a particular energy storage device or how much electricity a particular energy storage device should receive.

[0244] In other embodiments, the energy generation and distribution controller may also use anticipated energy demand and supply to balance the storage of energy generated by the RES on associated batteries. For example, the energy generation and distribution controller may determine the amount of energy stored on each battery and how those batteries in the power plant will distribute energy in an optimized manner. For example, to maintain the expected lifespan of the batteries, under normal conditions, batteries cannot be fully charged or fully discharged, as doing so would reduce their expected lifespan. However, if anticipated energy supply and demand indicate that fully charging or discharging the batteries is more beneficial than considering their expected lifespan, then the networked energy generation and distribution controller may fully charge the batteries in preparation for future events. For example, if there is a anticipated event requiring high energy demand, the energy generation and distribution controller may fully charge the batteries. In other embodiments, the networked energy generation and distribution controller may classify the batteries such that a first battery is allocated energy based on a first condition, a second battery is allocated energy based on a second condition, and a third battery is allocated energy based on a third condition. These conditions may be prioritized based on different levels. For example, a third battery may only be allocated energy if the price of energy is above a certain threshold.

[0245] In other embodiments of this disclosure, the energy generation and distribution controller can determine when to provide energy storage to power plants not included in the RES (Restoration Energy Platform), such as those on the grid. The energy generation and distribution controller can determine conditions under which off-grid power plants can store energy on batteries or other ESS (Energy Storage Facilities) within the RES. Using predictive energy demand and energy storage decisions made by machine learning algorithms of the networked energy generation and distribution controller, the controller can determine when to purchase electricity from grid-connected power plants or provide storage to contracted off-grid power plants. The energy generation and distribution controller can communicate with applications located at off-grid power plants, similar to applications provided at controllable loads and storage locations within networked power plants. Therefore, the systems and methods of this disclosure provide more optimized and consistent energy generation, storage, and distribution of energy generated by the REPP (Restoration Energy Platform).

[0246] FIG. 19 Embodiments of a method 1900 for generating, storing, and distributing renewable energy are described, in some embodiments of which the method may utilize the methods discussed above. FIG. 10 and FIG. 11 At least some of the components in the REPP are used for implementation. As discussed below, some embodiments make technical improvements to REPP. Method 1900 is described as being performed by the energy generation, storage, and distribution engine 1104 included on the networked energy generation, storage, and distribution controller 1002 / 1100. Furthermore, considering that other computer systems in the networked energy generation, storage, and distribution system 1000 may include some or all of the functions of the networked energy generation, storage, and distribution engine 1104, some or all of the steps of method 1900 may be performed by other participants in the energy generation, storage, and distribution system 1000, and still fall within the scope of this disclosure. Furthermore, as mentioned above, the networked energy generation, storage, and distribution controller 1002 / 1100 may include one or more processors or one or more servers, therefore method 1900 may be distributed across one or more processors or one or more servers.

[0247] Method 1900 may begin at block 1902, wherein one or more metrics for different time periods of the forecast time range are determined. In an embodiment, at block 1902, the energy generation, storage, and distribution controller 1104 may determine one or more metrics for different time periods of the forecast time range. These one or more metrics may relate to sending energy generated by the first renewable energy system (RES) to: (1) an energy storage system (ESS), (2) a power grid including one or more loads, and (3) one or more post-meter loads. In some embodiments, these one or more metrics may include opportunity costs / prices associated with one or more components. However, in other embodiments, these one or more metrics may include other information that is obvious to those skilled in the art possessing this disclosure. FIG. 11 And U.S. Patent Application No. 17 / 668,258 FIG. 2 The corresponding description schematically illustrates the opportunity cost / price approach, which is an example of method 1900, and is incorporated herein by reference in its entirety according to some embodiments thereof.

[0248] Method 1900 may proceed to block 1904, wherein (1) the ESS, (2) the power grid, and (3) one or more meter downstream loads are prioritized. In an embodiment, at block 304, the energy generation, storage, and distribution controller 1104 may prioritize the ESS (e.g., ESS 1007a or 1007b), the power grid 1010 (e.g., loads 1014a, 1014b, and 1014c), and one or more meter downstream loads (e.g., controllable loads 1008a and 1008b). Prioritization may be based on one or more of the following: (1) one or more determined metrics, (2) the energy status of the ESS during the forecast time period, (3) one or more constraints related to the energy demand of the power grid during the forecast time period, (4) one or more constraints related to the energy demand of one or more meter downstream loads during the forecast time period, (5) constraints on the interconnection between the power grid and the RES, or any other information obvious to those skilled in the art possessing this disclosure. For example, the priority order of multiple loads (e.g., POI, ESS, hydrogen production system, etc.) can be determined based on their respective priority prices / costs. Controller 1002 or computer can organize the priority order of different loads in descending order, such that the processing, ESS, or grid associated with the highest opportunity cost / price has the highest priority. In various embodiments, the priority order can be determined based on the determination of the predictive energy generation, storage, and distribution algorithm 1105. Method 1900 can proceed to block 1906, where instructions are generated and provided to deliver the electricity generated by the first RES to at least one of the following based on priority order: (1) ESS, (2) grid, or (3) one or more metered loads. In an embodiment, at block 1906, energy generation, storage, and distribution engine 1104 can generate and provide instructions to deliver the electricity generated by the first RES to at least one of the following based on priority order: (1) ESS, (2) grid, or (3) one or more metered loads. The instructions can be generated based on determining the total generated energy available in the next time interval or period. The next time interval can be an upcoming second, minute, hour, day, week, month, etc. In some cases, the available total generated energy can be the sum of the energy expected to be generated by the RES (e.g., power plant 1006a or 1006b) and the energy expected to be delivered to the grid 1010 by remote renewable energy sources (e.g., remote wind resources, such as power plant 1012). It may also include the amount of energy stored in ESS 1007a and 1007b. Any suitable method or technique can be used to estimate the total generated energy available for future time intervals. For example, the available total generated energy can be estimated using the model described above or daily and / or annual production forecasts. In some embodiments, the predictive algorithm 1105 for energy generation, storage, and distribution can perform the determination of instructions by executing artificial intelligence / machine learning algorithms.

[0249] Method 1900 can proceed to block 1908, where instructions are generated and provided for a controllable load to adjust energy requirements based on priority. In an embodiment, at block 1908, energy generation, storage, and distribution engine 1104 can generate and provide instructions to a controllable load on the grid or after the meter based on priority. The generated instructions for adjusting the power demand of the controllable load can be generated based on determining the total generated energy. In some embodiments, energy generation, storage, and distribution predictive algorithm 1105 can perform the determination of instructions.

[0250] In a specific example of method 1900, the current state of charge of ESS 1007a or 1007b can be high (e.g., 85%). For example, on a relatively hot and sunny day at 10:00 AM, the interconnection between grid 1010 and RES can be low. Therefore, for the solar RES, energy production will be high in the next few hours, and energy demand on the grid will also be high due to the high temperature. However, there may be a large amount of solar energy produced and supplied to the grid by other producers. Therefore, the grid may not need as much energy as the interconnection can provide, and power generation can be high. Because the state of charge of ESS is 85%, only a small amount of power can be supplied to ESS. Therefore, for the next forecast time range, the downstream controllable load can be assigned high priority, the ESS secondary priority, and the grid low priority. Instructions can be generated and provided to the controllable load to ramp up energy consumption, and to the RES to allocate energy accordingly to the ESS, controllable load, and grid. Once the ESS is fully charged, the priorities change, and further adjustments can be made to energy consumption and allocation to controllable load and grid.

[0251] In another forecast timeframe, such as shortly after sunset, when it is still hot but solar power generation is low and the amount of power supplied by third-party solar power plants (which constitutes a relatively high percentage of the power supplied to the grid) is also small, priorities can change. The grid has the highest priority due to the high cost of energy, downstream controllable loads have medium priority, and ESS (Electrical Storage and Power Supply) has low priority because the ESS must now provide power. Therefore, energy allocation and consumption can be adjusted to accommodate the new forecast timeframe. Thus, factors such as grid interconnection, ESS power status, and others influence prioritization.

[0252] In U.S. Patent No. 12,119,646 FIG. 19 The corresponding description illustrates examples of priority ordering methods, according to some embodiments, in U.S. Patent No. 12,119,646. FIG. 4The description and corresponding illustrations show examples of methods for hydrogen production, POI, and ESS in descending order of priority, and are found in U.S. Patent No. 12,119,646. FIG. 5 The corresponding descriptions show examples of method composition, each of which is incorporated as a whole by reference.

[0253] In some embodiments, a system includes one or more processors; and a memory storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations including: a) determining one or more metrics for different time periods of a forecast time range, wherein the one or more metrics relate to sending energy generated by a first renewable energy system (RES) to: (1) an energy storage system (ESS), (2) a power grid including one or more loads, and (3) one or more meter-based loads; b) prioritizing: (1) the ESS, (2) the power grid, and (3) one or more meter-based loads, wherein the prioritization is based on one or more of: (1) one or more metrics determined in (b), (2) the power status of the ESS during the forecast time range, (3) one or more constraints related to the energy demand of the power grid during the forecast time range, or (4) one or more constraints related to the energy demand of one or more meter-based loads during the forecast time range; and c) generating and providing instructions to deliver power generated by the first RES to at least one of: the ESS, the power grid, or one or more meter-based loads based on the prioritization.

[0254] In some embodiments, a system includes a controller configured to communicatively couple to a renewable energy system (RES), an energy storage system (ESS), and a power grid. The controller is configured to determine at least one metric for a forecast time range associated with sending energy generated by the RES to: (1) the ESS, (2) the power grid, and (3) at least one metered load. The controller is also configured to identify a priority for each of (1) the ESS, (2) the power grid, and (3) at least one metered load from a plurality of priorities based on at least one of: (1) at least one metric, (2) the energy status of the ESS during the forecast time range, (3) at least one constraint associated with energy parameters of the power grid during the forecast time range, (4) at least one constraint associated with energy parameters of the at least one metered load during the forecast time range, or (5) a cost value comparison of the ESS, the power grid, and the at least one metered load. The controller is further configured to cause electricity generated by the RES to be delivered to at least one of (1) the ESS, (2) the power grid, or (3) at least one metered load based on a plurality of priorities.

[0255] In some implementations, at least one downstream load includes a controllable load, and the controller is further configured to provide instructions to the controllable load based on multiple priorities to increase or decrease the energy parameters of the controllable load. The controllable load may include at least one of a data center, an artificial intelligence (AI) training center, a cryptocurrency mining machine, an electric vehicle (EV) charging station, a vertical farm, a hydrogen production facility, a water treatment plant, an industrial process heater, or a thermal battery.

[0256] In some implementations, the power grid includes grid-controlled loads, and the controller is further configured to provide instructions to the grid-controlled loads based on multiple priorities to increase or decrease the energy parameters of the grid-controlled loads.

[0257] In some implementations, the priority of each of (1) the ESS, (2) the power grid, and (3) at least one metered load is further based on a predictive algorithm implemented using machine learning. The predictive algorithm may include at least one of model predictive control (MPC), model-based reinforcement learning (MBRL), adaptive model predictive control (AMPC), or a multimodal time series predictive model.

[0258] In some implementations, the delivery of electricity generated by the RES to at least one of (1) the ESS, (2) the power grid, or (3) at least one metered load is also based on a predictive algorithm implemented using machine learning. The predictive algorithm may include at least one of model predictive control (MPC), model-based reinforcement learning (MBRL), adaptive model predictive control (AMPC), or a multimodal time series forecasting model.

[0259] System and method for renewable energy power resource with AC overbuild with storage and controllable loads Adaptive

[0260] Some aspects of this disclosure include a process comprising: converting RES direct current (DC) power into RES alternating current (AC) power via at least one first power inverter coupled between a renewable energy source (RES) and a grid interconnection point on the grid, wherein the total output capacity of the at least one first power inverter is set to exceed a grid interconnection point (POGI) limit; converting RESAC power into ESSDC power while charging the ESS with RESAC power via at least one second power inverter coupled between (i) an energy storage system (ESS) and the grid interconnection point and (ii) at least one first power inverter and the grid interconnection point; and converting the ESS AC power into ESSDC power via at least one second power inverter while charging the ESS with RESAC power. When AC power is discharged to the grid, ESSDC power is converted into ESSAC power; and while supplying a first portion of RESAC power to the grid, a second portion of RESAC power is transferred to at least one second power inverter and a third portion of RESAC power is transferred to a controllable load coupled between (i) at least one first power inverter and the grid interconnection point, and (ii) at least one second power inverter and the grid interconnection point, wherein the amounts of the second and third portions are sufficient to avoid supplying RESAC power to the grid beyond the POGI limit.

[0261] Some aspects of this disclosure include a tangible, non-transitory machine-readable medium storing instructions that, when executed by a data processing apparatus, cause the data processing apparatus to perform operations including the aforementioned processing.

[0262] Some aspects of this disclosure include a system having: one or more processors; and a memory storing instructions that, when executed by the processor, cause the processor to perform the aforementioned processing.

[0263] In recent years, the adoption of renewable energy generation resources has increased significantly, especially solar photovoltaic (PV) and wind turbines. However, the inherent variability of solar and wind power, influenced by natural and meteorological conditions, poses challenges to grid stability, including frequency and voltage deviations. As renewable energy generation resources begin to supply a larger share of the grid and replace known baseload units such as coal-fired and nuclear power plants, a range of technical challenges arise. These challenges include grid interconnection, power quality, reliability, stability, protection, and generation dispatch and control. The intermittent nature of solar and wind power, coupled with rapid fluctuations in output, makes the integration of energy storage devices, such as battery energy storage systems (BESS), an attractive option. This integration aims to enhance grid compatibility by smoothing fluctuations and improving the predictability of energy supply from renewable sources. Known renewable energy sources typically exhibit low capacity factors, generally ranging from 15% to 45%, depending on location and weather patterns. When these resources replace known fossil fuel baseload power plants, they often underutilize existing transmission infrastructure. This may require building new transmission infrastructure, a process fraught with challenges, including obtaining permits, which increases the cost per megawatt-hour of power generation and introduces delays and risks to integrating renewable energy generation into the existing grid.

[0264] Co-locating renewable energy generation and energy storage can reduce costs associated with site preparation, permitting, and installation. Furthermore, tax incentives can be obtained, especially if the storage unit is entirely charged from on-site renewable energy, minimizing transmission losses. Energy storage units also offer other benefits such as arbitrage (charging during periods of low prices and discharging during periods of peak demand) and load balancing to optimize the scheduling of generation resources. However, challenges such as battery degradation at full capacity and the need to provide ancillary services complicate its utilization.

[0265] Several factors can affect the efficient utilization of BESS (Brain Energy Storage and Energy Storage). Lithium-based batteries commonly used in such systems are prone to accelerated degradation when operating at or near full capacity. Grid operators regulating the deployment of integrated renewable energy generation and storage facilities can specify specific battery state of charge (SOC) requirements for certain times of day. SOC represents the percentage of a battery's full capacity that can be discharged. Once a battery reaches 100% SOC, it becomes inefficient at absorbing sudden increases in power output from associated renewable energy sources. This scenario may necessitate reducing excess power generation, typically through techniques such as power inverter limiting, to prevent undesirable impacts on the grid.

[0266] Additional factors influencing the effective utilization of BESS include its transmission capacity and its ability to be compensated for providing ancillary services. Ancillary services play a crucial role in maintaining grid reliability by ensuring that frequency, voltage, and electrical load remain within predefined thresholds. These services encompass a wide range of categories, including frequency maintenance (to meet the needs of spinning reserve, energy balancing, and offloadable loads), voltage compensation (to address power factor correction and reduce energy losses during transport), operations management (including grid monitoring, feed-in management, and rescheduling), and power restoration (facilitating rapid grid restart after outages).

[0267] The inherent variability and unpredictability of renewable energy sources such as wind and solar power amplify the demand for various ancillary services, thereby affecting the scheduling and pricing dynamics of these services. However, if the incentives for renewable energy producers are based solely on energy generation, they may lack the motivation to provide ancillary services, potentially compromising grid stability and reliability.

[0268] Power generation resources can be linked to grid transmission resources at a point of connection (POGI), which typically operates at the optimal voltage for long-distance power transmission with minimal transmission loss. To maintain reliability and protect transmission resources, POGI limits are established for each power generation resource, defining the maximum power that can be supplied to the transmission resource.

[0269] To enhance the revenue potential of photovoltaic energy generation resources and associated transmission resources with predetermined costs, a common practice is to over-provision the combined output of photovoltaic arrays or other renewable energy sources (RES) relative to the POGI limit. This strategic initiative is inspired by the occasional occurrence of peak photovoltaic power generation, which is attributed to various factors such as adverse weather conditions, illumination conditions, panel cleanliness, PV panel aging, and reduced PV panel output due to rising ambient temperatures.

[0270] While over-provisioning photovoltaic (PV) arrays boosts annual electricity sales, it also increases the need to reduce excess power during peak irradiance periods, typically achieved through inverter limiting. Regulations are crucial for shielding the grid from potential faults caused by circuit overloads, transmission line overloads, transformer strain, or the need for circuit breakers to disconnect over-generating facilities. To ensure compliance, inverters are typically installed between the PV arrays and the transmission system with a total output capacity equal to the POGI limit and slight allowances for power losses between the inverter and grid interconnection points.

[0271] Known renewable energy generation resources are typically limited by their capacity factors and load matching capabilities, factors closely tied to the availability of primary driving resources such as solar irradiance or wind power. Due to their low capacity factors and limited temporal availability, known renewable energy generation resources often fail to fully utilize transmission resources. This underutilization presents a significant challenge to utility companies, given the high costs and complexities of expanding transmission infrastructure.

[0272] Given these challenges, there is an urgent need to improve renewable energy generation resources and energy storage facilities. Furthermore, sophisticated control methods are required to effectively manage these facilities. Additionally, streamlined processing is needed to facilitate the transmission and trading of electricity generated by such facilities.

[0273] The systems and methods disclosed herein provide a renewable energy (“RES”) (e.g., solar, wind, etc.) and energy storage system (“ESS”) facility or plant, wherein this combination may herein be referred to as RES-ESS or a RES-ESS facility (a subset thereof is a photovoltaic plus storage or “PV+S” facility). In various embodiments, the RES-ESS may be directly coupled to a controlled load. Thus, a controlled load may be defined as being downstream of an electricity meter. The controlled load may be associated with or unassociated with a load on the grid. In some embodiments, the controlled load may be located on the grid. The RES-ESS facility can achieve a desired SOC by charging the ESS with the electricity generated by the RES. In some embodiments, the RES-ESS facility will achieve the desired SOC by preferentially charging when RES generation is high. For example, the ESS may charge more when more RES generation is available; and less (or not at all) when RES generation is limited. When RES generation is limited or unavailable, the ESS may be discharged. Furthermore, to achieve SOC and provide grid output at or near POGI, the RES may even be further overbuilt, and the controlled load may be used to consume the excess electricity generated by the RES.

[0274] As discussed in the previous paragraph, in addition to charging the ESS or supplying power to the grid, renewable energy sources (RES) can also be used to serve other types of electrical loads or energy-consuming processes, which may be off-grid or on-grid. For example, RES can directly supply energy to loads such as industrial processes (e.g., producing “green” hydrogen, producing ammonia, metal smelting, cryptocurrency mining, “vertical” agriculture, powering server farms, water purification, and glass production) without going through the grid (e.g., off-grid loads, or also referred to as post-meter loads in this paper). However, the various types of electrical loads or industrial processes connected to the RES may be unrelated, where the value of directing power to one or more of the loads can vary over time, and such variations may be unrelated to each other. Therefore, overbuilt RES-ESS facilities can be further overbuilt by utilizing the energy and power demand of multiple unrelated loads, which can also be managed by using those loads to stabilize the ESS’s SOC and power at the grid interconnection point.

[0275] In various embodiments, a load as a controllable load may include a load whose demand can be altered by the controller described herein by either increasing or decreasing the power demand at that load. Therefore, this disclosure considers both adjusting energy distribution from the RES and adjusting energy demand from one or more controllable loads (which may be located either on the grid or downstream of the meter). In various embodiments, downstream controllable loads allow energy producers to increase the scale and performance of the RES. For example, the RES may be constructed to generate a larger capacity than the RES can provide to the grid. This provides economies of scale in terms of cost and performance compared to systems without downstream controllable loads. When generation is not at its peak (e.g., cloudy, early morning, evening, or low wind speeds) or when the energy storage system included in the RES is full, excess energy is absorbed by both the ESS and the downstream loads. Moreover, when RES generation is low, over-configured systems can use the electricity stored on the ESS to deliver more power to more critical or valuable loads on the grid, and realize the bandwidth that the RES can provide to the grid, or provide more load or power to the energy storage system. Therefore, RES-ESS systems can be designed for better performance and lower cost, i.e., better overall system performance, enabling a more consistent supply of energy, capacity or other ancillary services to the grid.

[0276] In various embodiments of this disclosure, the controller may include predictive algorithms, such as, for example, model predictive control (MPC) or model-based reinforcement learning (MBRL). FIG. 20Model predictive control (AMPC) or other predictive / machine learning algorithms. MPC can be implemented using Long Short-Term Memory (LSTM), state-space models, or transformer architectures. Some implementations can use multimodal time series forecasting models (e.g., considering weather, wind power, solar power, grid demand, and post-meter load output values), examples of which include: Autoregressive Moving Average (ARMA) models (e.g., seasonal ARIMA); Autoregressive Integral Moving Average (ARIMA) models; Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models; Vector Autoregressive models; Holt-Winters exponential smoothing; state-space models; and Kalman filters. Predictive algorithms can predict priorities within future time intervals, and based on priorities and predicted total energy storage and generation, predictive algorithms can determine any demand adjustments to controllable loads and allocate energy and power to various loads (on-grid or off-grid) or ESS based on priority ranking.

[0277] Energy generation, storage, and distribution controllers may include predictive algorithms for balancing energy distribution to controllable loads. For example, the energy generation and distribution controller may ingest data from various data sources, such as weather forecasts, event schedules, calendars, historical energy usage data, sensor data, or other data sources that will be obvious to those skilled in the art possessing this disclosure. In other embodiments, the data source may include state-of-power data or analytical values ​​from other energy storage systems (RES) and their energy storage systems. These other RES may include energy storage systems not on the network and may be competing energy storage systems. Therefore, predicting how much energy another RES has stored can be beneficial in anticipating how much energy is available on the grid at a given point in time, thereby enabling the management of the energy storage system (ESS).

[0278] Then, the energy generation and distribution controller, using predictive algorithms trained on historical or simulator data, can anticipate the energy demand of uncontrollable loads on the grid and the energy supply from power plants. Based on the anticipated energy demand and supply, the energy generation and distribution controller can determine whether one or more energy balance conditions associated with the corresponding controllable load are met, to increase or decrease power allocation to that controllable load. For example, in exchange for a better energy price rate or some other energy allocation factor desired by the controllable load, the controllable load may allow the energy generation, storage, and distribution controller to reduce its energy consumption to reallocate the RES's energy supply to uncontrollable loads that may pay a higher premium and have higher priority based on various factors (e.g., more necessary / high-priority infrastructure such as hospitals, water plants, critical communication infrastructure, etc.). Furthermore, the controllable load itself can adjust to reduce or increase energy consumption.

[0279] Similarly, controllable loads can include energy storage systems, where energy generation, storage, and distribution controllers can increase or decrease the power allocation to energy storage devices. Furthermore, more optimized decisions can be made regarding which energy storage device in an ESS is used to store energy. For example, zinc-air batteries can be charged when inexpensive electricity is available, while lithium-ion batteries can be charged when more expensive electricity is available, a faster response time is anticipated, or other beneficial conditions are apparent to those skilled in the art possessing this disclosure. Therefore, the type of energy storage device or other factors associated with the energy storage device can be used to determine when to charge a particular energy storage device or how much electricity a particular energy storage device should receive.

[0280] In other embodiments, the energy generation and distribution controller may also use anticipated energy demand and supply to balance the storage of energy generated by the RES on associated batteries. For example, the energy generation and distribution controller may determine the amount of energy stored on each battery and how those batteries in the power plant will distribute energy in an optimized manner. For example, to maintain the expected lifespan of the batteries, under normal conditions, the batteries cannot be fully charged or fully discharged, as doing so would reduce the expected lifespan of the batteries. However, if anticipated energy supply and demand indicate that fully charging or discharging the batteries is more beneficial than considering the expected lifespan of the batteries, then the controller may fully charge the batteries in preparation for future events. For example, if there is a anticipated event requiring high energy demand, then the energy generation and distribution controller may fully charge the batteries. In other embodiments, the networked energy generation and distribution controller may classify the batteries such that a first battery is allocated energy based on a first condition, a second battery is allocated energy based on a second condition, and a third battery is allocated energy based on a third condition. These conditions may be prioritized based on different levels. For example, a third battery may only be allocated energy if the price of energy is above a certain threshold.

[0281] In other embodiments of this disclosure, the energy generation and distribution controller can determine when to provide energy storage to power plants not included in the RES (such as power plants on the grid). The energy generation and distribution controller can determine conditions under which off-grid power plants can store energy on the RES's batteries or other ESS (Energy Storage Facilities). Using predictive energy demand and energy storage decisions made by machine learning algorithms of the networked energy generation and distribution controller, the controller can determine when to purchase electricity from power plants on the grid or provide energy storage to contracted off-grid power plants. The energy generation and distribution controller can communicate with applications located at off-grid power plants, similar to applications provided at controlled loads and storage locations in networked power plants. Therefore, the systems and methods of this disclosure provide more optimized and consistent energy generation, storage, and distribution of energy generated by the RES.

[0282] FIG. 20 An example energy generation, storage, and distribution system 2000 according to one or more embodiments is illustrated. The energy generation, storage, and distribution system 2000 may include: an energy generation, storage, and distribution controller 2002; a network 2004; an integrated RES-ESS system 2006, including RES 2009, ESS 2007, a controllable load 2008, inverters 2016 and 2018; a power grid 2010; one or more data sources 2011; loads 2014a; loads 2014b; and a controllable load 2014c. Loads 2014a, 2014b, and the controllable load 2014c may be electrically coupled to the power grid 2010. Loads 2014a, 2014b, and the controllable load 2014c may be geographically separated from each other and have individual power requirements. Load 2014a may have a first power delivery curve detailing the power requirements of load 2014a at different times. Load 2014b may have a second power delivery curve detailing its power requirements at different times. Controllable load 2014c may have a third power delivery curve detailing its power requirements at different times. In some embodiments, grid 2010 may be a public grid owned and operated by a single utility or system operator. In other embodiments, grid 2010 may be multiple electrical connections, allowing power to be transferred from RES-ESS system 2006 to loads 2014a, 2014b, and controllable load 2014c.

[0283] RES 2009 may include a first renewable energy power plant (REPP). Examples of REPPs include, but are not limited to, solar power plants, wind power plants, geothermal power plants, and biomass power plants. RES-ESS may include an energy storage system (ESS) 2007. An example of an ESS is a battery. A battery-based ESS may be referred to as a battery ESS or BESS. RES 2009 may have a first electrical output that varies over time.

[0284] In some embodiments, the RES can be coupled to inverter 2016. Inverter 2016 can convert the DC power generated by the RES into AC power supplied to grid 2010 at a grid interconnection point. The grid interconnection point has a grid interconnection point (POGI) limit. Inverter 2016 has an AC power output limit greater than the POGI limit. The RES-ESS system 2006 may include inverter 2018, which can be coupled between ESS 2007 and grid 2010 and between inverter 2016 and grid 2010. Inverter 2018 can be bidirectional, such that the RES AC power output from inverter 2016 can be converted into DC power that can charge ESS 2007. Similarly, inverter 2018 can convert ESS DC power into AC power that can be output to grid 2010. In various embodiments, inverter 2018 may optionally be configured to have an AC power output greater than the POGI. Controllable loads can be coupled between inverter 2016 and grid 2010, and between inverter 2018 and grid 2010.

[0285] RES 2006 can communicate with the networked energy generation, storage, and distribution controller 2002 via network 2004. Similarly, controllable loads 2008 and 2014, RES 2009, and ESS 2007 can communicate with the networked energy generation, storage, and distribution controller 2002 via network 2004. Furthermore, the networked energy generation, storage, and distribution controller 2002 can communicate with the data source 2011 via network 2004. The data source may include sensor data, weather data, local timetables, or any other system data or third-party information that is obvious to those skilled in the art possessing this disclosure. Network 2004 can be any local area network (LAN) or wide area network (WAN). In some embodiments, the network is the Internet. In other embodiments, the network is a private communication network. The energy generation, storage, and distribution controller 2002 may include a processor and memory.

[0286] The energy generation, storage, and distribution controller 2002 can control the RES 2009 and direct power from the RES to the ESS, the controllable load 2008, and the grid 2010. The controller 2002 can also control when the ESS 2007 charges or discharges power received from the inverter 2018 from the RES 2009 or, in some embodiments, from the grid 2010. The controller 2002 can also control the power demand at the controllable loads 2008 and 2014c. While a specific system has been described, those skilled in the art, possessing this disclosure, will recognize that other variations, components, multiple RES, ESS, and controllable loads will be considered without departing from the scope of this disclosure.

[0287] In some embodiments,FIG. 1 One or more functions of System 2000 can be combined with FIG. 4 System 100 FIGS. 13-17 System 400 and / or FIG. 20 The system 1300 may combine or replace one or more functions of any of its components. Alternatively or additionally, FIG. 2 One or more functions of the controller 2002 can be used FIG. 11 Controller 200 FIG. 18 Controller 1102 FIG. 21 Controller 1802 and / or FIG. 20 Implemented by one or more functions / features of any one of the controllers 2102. Alternatively or additionally, FIG. 3 System 2000 can be configured to execute FIG. 5 Method 300 FIG. 6 Method 500 FIG. 7 Method 600 FIG. 8 Method 700 FIG. 12 Method 800 FIG. 19 Method 1200 FIG. 22 Method 1900 or FIG. 21 One or more of methods 2200.

[0288] This is publicly available. FIG. 20 An embodiment of an energy generation, storage, and distribution controller 2100 is illustrated, which may be as described above. FIG. 21 The energy generation, storage, and distribution controller 2100 is discussed. Although described as a standalone system, those skilled in the art will recognize that the energy generation, storage, and distribution controller 2100 can be distributed across many computing devices, such as in a cloud environment. In the illustrated embodiment, the networked energy generation, storage, and distribution controller 2100 includes a chassis 2102 housing the components of the energy generation, storage, and distribution controller 2100. FIG. 21 Only some of the components are illustrated. For example, chassis 2102 may house a processing system (not shown) and a non-transitory memory system (not shown) including instructions that, when executed by the processing system, cause the processing system to provide an energy generation, storage, and distribution engine 2104, which is configured to perform the functions of an energy generation and distribution engine or a networked energy generation, storage, and distribution controller discussed below. FIG. 22In the specific examples shown, the energy generation, storage, and distribution engine 2104 may include an energy generation, storage, and distribution predictive algorithm 2105 configured to perform the functions of the energy generation, storage, and distribution predictive algorithms discussed herein. In various embodiments, the energy generation, storage, and distribution predictive algorithm 2105 may ingest data provided by a data source and predict energy demand and supply, or any other functions discussed herein. In various embodiments, the energy generation, storage, and distribution predictive algorithm 2105 may include a network simulator to model behavior, which, due to a lack of historical data, can predict components to be integrated into the power grid by running simulations. In other examples, the energy generation, storage, and distribution predictive algorithm 2105 may include model predictive control or other predictive algorithms / machine learning algorithms that are obvious to those skilled in the art with this disclosure.

[0289] The chassis 2102 may also house a communication system 2106 coupled to the energy generation, storage, and distribution engine 2104 (e.g., via coupling with the processing system through the communication system 2106) and configured to provide communication via a communication network 2004, as detailed below. The chassis 2102 may also house a storage system 2108 coupled to the energy generation, storage, and distribution engine 2104 via the processing system and configured to store rules or other data used by the networked energy generation, storage, and distribution engine 2104 to provide the functions discussed below. While the energy generation, storage, and distribution controller 2100 has been illustrated, those skilled in the art will recognize that other networked energy generation and distribution controllers (or other devices operating in a manner similar to that described below for the energy generation, storage, and distribution controller 2100, in accordance with the teachings of this disclosure) may include various components and / or component configurations for providing known computing device functions and the functions discussed below, while still remaining within the scope of this disclosure.

[0290] FIG. 20 Embodiments of a method 2200 for generating, storing, and distributing renewable energy are described. In some embodiments, the method may utilize the methods discussed above. FIG. 21 and FIG. 1 At least some of the components in the system are used for implementation. As discussed below, some embodiments make technical improvements to the over-built RES-ESS system. Some or all of the steps of method 2200 may be performed by other participants in the energy generation, storage, and distribution system 2000 and still fall within the scope of this disclosure. Furthermore, and as mentioned above, the networked energy generation, storage, and distribution controller 2002 / 2100 may include one or more processors or one or more servers, so method 2200 may be distributed among these one or more processors or one or more servers.

[0291] Method 2200 may begin at block 2202, wherein RES direct current (DC) power is converted to RES alternating current (AC) power. In an embodiment, at block 2202, at least one first power inverter 2016 coupled between the renewable energy source (RES) 2009 and the grid interconnection point 2010 can convert the RES DC power to RES AC power. The combined output capacity of the at least one first power inverter 2016 is set to exceed the grid interconnection point (POGI) limit.

[0292] Method 2200 can proceed to step 2204, wherein the RESAC power is converted to ESSDC power when charging the ESS with RESAC power. In an embodiment, at block 2204, at least one second power inverter 2018 coupled between (i) the grid interconnection point on the energy storage system (ESS) 2007 and the grid 2010, and (ii) the grid interconnection point on the grid 2010 of at least one first power inverter 2016, can convert the RESAC power to ESSDC power when charging the ESS with RESAC power.

[0293] Method 2200 can proceed to block 2206, wherein ESSDC power is converted to ESSAC power when ESSAC power is discharged to the grid. In an embodiment, at block 2206, at least one second power inverter 2018 can convert ESSDC power to ESSAC power when ESSAC power is discharged to the grid 2010.

[0294] Method 2200 can proceed to block 2208, wherein while supplying a first portion of the RESAC power to the grid, a second portion of the RESAC power is transferred to at least one second power inverter and a third portion of the RESAC power is transferred to a controllable load. In an embodiment, at block 2208, controller 2002 can transfer a portion of the RESAC power from supplying the grid 2010 to inverter 2018 or controllable load 2008. As discussed above, the controllable load is coupled between (i) at least one first power inverter 2016 and a grid interconnection point on the grid 2010, and (ii) at least one second power inverter 2018 and a grid interconnection point. The amounts of the second and third portions are sufficient to prevent the supply of RESAC power to the grid 2010 exceeding the POGI limit.

[0295] In various embodiments of method 2200, the controller may provide instructions to controllable load 2008 or controllable load 2014c to adjust its load (e.g., increase or decrease the load). This will help balance and distribute the power generated by the over-built RES-ESS system 2006. For example, when the POGI is not yet met, but the power generation of RES 2009 is high and the SOC of ESS 2007 is high, controllable load 2014c may be instructed to increase its load. Conversely, when loads 2014a and 2014b have demand at or near the POGI limit, controllable load 2014c may be instructed to reduce its demand.

[0296] Regarding the controllable load 2008 located after the electricity meter, if the SOC of 2007 is high and the inverter 2016's power output exceeds the POGI limit due to the excessive power generated by the over-built RES2009, then the controller 2002 can instruct the controllable load 2008 to increase its demand. Conversely, if the SOC of ESS 2007 is low or RES 2009 generates very little or no power, and the grid demand is high, causing ESS 2007 to be exhausted or close to dropping below the expected SOC, then the controller 2002 can provide the controllable load 2008 with instructions to reduce its power demand.

[0297] Therefore, by adding controllable loads included on the grid or in the RES-ESS system, the RES-ESS system can be further over-configured, enabling greater economies of scale. Thus, in some embodiments of AC-over-configured RES-ESS facilities, the total output capacity of at least one first power inverter is set to exceed the POGI limit by at least 5%, at least 35%, at least 55%, at least 75%, at least 100%, at least 150%, at least 200%, or another threshold specified herein. In some embodiments, the aforementioned minimum threshold may optionally (where appropriate) be limited to values ​​(A) 120%, (B) 150%, (C) 200% or the sum of (i) the POGI limit, (ii) the ESS capacity, and (iii) the capacity of the controllable load. In some embodiments, the total output capacity of at least one first power inverter is set to be equal to the sum of (i) the POGI limit, (ii) the ESS capacity, and (iii) the capacity of the controllable load. The technical benefits of AC-overbuilt RES-ESS facilities include the ability to provide a higher capacity factor (e.g., 60-90% compared to the likely 25-45% range for known PV-BESS facilities). Such facilities can deliver more renewable energy using existing transmission resources, which are costly and time-consuming to build. Lower energy costs can be achieved because fixed development costs can be spread across more megawatt-hours of electricity produced annually.

[0298] As described above, AC-overbuilt RES-ESS facilities are also suitable for providing high levels of fixed reliable capacity (e.g., at least 70%, 80%, 90%, 95%, or 99% of POGI limits) over long periods (e.g., at least 6 hours, 8 hours, 12 hours, 16 hours, 20 hours, or 24 hours per day in some embodiments). In some embodiments, long-term weather data can be utilized when setting the size for the ESS and at least one first inverter to achieve the aforementioned capacity and duration thresholds with confidence windows of at least 90%, 95%, 98%, or 99% under all foreseeable weather conditions. In some embodiments, the confidence window corresponds to the number of days per month or year that the specified fixed reliable capacity and long-term duration are achieved. The ability to provide high levels of fixed reliable capacity enables AC-overbuilt RES-ESS facilities to replace known baseload assets (e.g., gas, coal, or nuclear power plants) and improve grid stability. Furthermore, predictive algorithms and machine learning can be used to allocate power and control the demand for manageable loads to better balance the system, thereby providing optimal power to the grid without overloading it or reducing power production.

[0299] In some embodiments, a method for controlling an integrated renewable energy storage system (RES-ESS) facility is disclosed, the facility being configured to supply power to the grid at a grid interconnection point. The RES-ESS facility includes a renewable energy source (RES) and an energy storage system (ESS), the energy storage system being chargeable by power generated by the RES. The RES-ESS facility also includes a controllable load directly connected to the RES-ESS, such that the controllable load is located between the RES and the grid interconnection point. The RES-ESS facility also has a grid interconnection point (POGI) limit. The RES has a power output exceeding the POGI limit, and provides excess power to the ESS or the controllable load when the RES generates more power than the POGI limit.

[0300] In some embodiments, a method includes providing at least one first power inverter between a renewable energy source (RES) and a grid interconnection point of the grid, and coupled to each of the RES and the grid interconnection point of the grid, the aggregate output capacity of the at least one first power inverter being set to exceed the limit of the grid interconnection point (POGI). The method further includes providing at least one second power inverter coupled between (i) an energy storage system (ESS) and the grid interconnection point, and (ii) between the at least one first power inverter and the grid interconnection point, the at least one second power inverter being configured to (a) convert AC power from the RES into DC power for the ESS when charging the ESS with AC power from the renewable energy source (RES), and (b) convert DC power from the ESS into AC power when discharging the ESS to the grid. The method further includes, while supplying a first portion of the AC power from the RES to the grid, transferring a second portion of the AC power from the RES to at least one second power inverter and a third portion of the AC power from the RES to a controllable load coupled between (i) at least one first power inverter and a grid interconnection point, and (ii) at least one second power inverter and a grid interconnection point, wherein the amounts of the second and third portions are sufficient to prevent the supply of AC power from the RES to the grid exceeding the POGI limit. Optionally, the method may also include generating a forecast signal comprising a time-related forecast of the energy generation of the RES, the forecast being based at least in part on at least one of: (a) data from a sky imaging sensor associated with the RES-ESS facility, (b) data from a satellite imaging sensor, or (c) meteorological data.

[0301] In some embodiments, the method further includes modifying the power distribution associated with the controllable load based on predictions generated using a predictive algorithm. The predictive algorithm may include at least one of model predictive control (MPC), model-based reinforcement learning (MBRL), adaptive model predictive control (AMPC), or a multimodal time series forecasting model.

[0302] In some embodiments, a non-transitory processor-readable medium stores instructions that, when executed by a processor, cause the processor to control the net load of at least one controllable load (CL). The non-transitory processor-readable medium also stores instructions to cause the processor to deliver a first portion of electricity from at least one of (1) at least one renewable energy source (RES) or (2) at least one energy storage system (ESS) to at least one CL. The non-transitory processor-readable medium also stores instructions to cause the processor to deliver a second portion of electricity from at least one of (1) at least one RES or (2) at least one ESS to the power grid. The non-transitory processor-readable medium also stores instructions to cause the processor, in response to determining that grid conditions exist and that the electricity generated by at least one RES does not exceed the aggregate power capacity of at least one ESS and the aggregate power demand of at least one CL, to perform one of the following: increase or decrease the power demand at at least one CL.

[0303] In some implementations, the amount of electricity generated by at least one RES exceeds the grid interconnection point (POGI) limit by a multiple between approximately 3 and approximately 6.

[0304] In some implementations, grid conditions are associated with at least one of the following: the price of electricity associated with the grid, the price of ancillary services associated with the grid, grid-related reductions, grid-related congestion prices, or grid-related congestion mitigation value.

[0305] In some implementations, the non-transient processor-readable medium also stores instructions to cause the processor to operate at least one RES or at least one ESS as at least one of the peaking power plant or ancillary service provider of the power grid, and the power grid conditions are associated with at least one of the following: the price of electricity associated with the power grid, the price of ancillary services associated with the power grid, a reduction associated with the power grid, a congestion price associated with the power grid, or a congestion mitigation value associated with the power grid.

[0306] In some implementations, at least one CL includes multiple CLs, and the non-transitory processor-readable medium also stores instructions to cause the processor to provide instructions to the multiple CLs to balance the energy allocation associated with the multiple CLs, and the grid conditions are associated with at least one of the following: the price of electricity associated with the grid, the price of ancillary services associated with the grid, grid-related reductions, grid-related congestion prices, or grid-related congestion mitigation values.

[0307] In some implementations, at least one CL includes at least one of a data center, an artificial intelligence (AI) training center, a cryptocurrency mining machine, an electric vehicle (EV) charging station, a vertical farm, a hydrogen production facility, a water treatment plant, an industrial process heater, or a thermal battery.

[0308] In some implementations, the non-transient processor-readable medium also stores instructions to cause the processor to modify the correlation between the load curve of at least one CL and the power grid.

[0309] In some embodiments, a method includes controlling the net load of at least one controllable load (CL) via a processor and delivering a first portion of electricity from at least one of (1) at least one renewable energy source (RES) or (2) at least one energy storage system (ESS) to at least one CL via the processor. The method also includes delivering a second portion of electricity from at least one of (1) at least one RES or (2) at least one ESS to the power grid via the processor. The method further includes, in response to determining that grid conditions exist and that the electricity generated by at least one RES does not exceed the aggregate power capacity of at least one ESS and the aggregate power demand of at least one CL, performing one of the following via the processor: increasing or decreasing the power demand at at least one CL.

[0310] In some implementations, the amount of electricity generated by at least one RES exceeds the grid interconnection point (POGI) limit by a multiple between approximately 3 and approximately 6.

[0311] In some implementations, grid conditions are associated with at least one of the following: the price of electricity associated with the grid, the price of ancillary services associated with the grid, grid-related reductions, grid-related congestion prices, or grid-related congestion mitigation value.

[0312] In some implementations, the ratio of electricity generated by at least one RES to the aggregate load of at least one CL is between approximately 3 and approximately 6, and the grid conditions are associated with at least one of the following: the price of electricity associated with the grid, the price of ancillary services associated with the grid, grid-related reductions, grid-related congestion prices, or grid-related congestion mitigation values.

[0313] In some implementations, the method further includes operating at least one RES or at least one ESS via a processor as at least one of a peaking power plant or a provider of ancillary services for the power grid. Grid conditions may be associated with at least one of the following: the price of electricity associated with the grid, the price of ancillary services associated with the grid, grid-related reductions, grid-related congestion prices, or grid-related congestion mitigation value.

[0314] In some implementations, at least one CL includes at least one of a data center, an artificial intelligence (AI) training center, a cryptocurrency mining machine, an electric vehicle (EV) charging station, a vertical farm, a hydrogen production facility, a water treatment plant, an industrial process heater, or a thermal battery.

[0315] In some embodiments, the method further includes at least one of the following: delivering power from the grid to at least one CL; modifying the correlation of the at least one CL with the load curve of the grid; or modifying (1) at least one peak of the net load curve associated with at least one CL and (2) the correlation of at least one peak of the net load curve associated with the grid.

[0316] In some embodiments, the method further includes: (1) operating the power plant in a first mode as at least one of a baseload, half-baseload, half-peak load, or peak load power plant of at least one CL; and (2) concurrently with operating the power plant in the first mode, operating the power plant in a second mode as at least one of a peak load power plant, half-peak load power plant, half-baseload, baseload, or ancillary service provider of the power grid.

[0317] In some implementations, a system includes: one or more processors; and a memory storing instructions that, when executed by the one or more processors, cause the one or more processors to perform actions including: receiving power data from a power data source; using predictive algorithms for energy generation, storage, and distribution and generating predicted power supply and demand curves based on the power data; determining, based on the predicted power supply and demand curves, whether conditions exist for issuing control commands to one or more controllable power components; and providing control commands associated with the conditions to the one or more controllable power components in response to determining that the conditions exist.

[0318] In some such implementations, one or more controllable power components include a controllable load.

[0319] In some embodiments, a r...

Claims

1. A system comprising: At least one renewable energy source (RES) is configured to be electrically coupled to a grid interconnection point of the grid, and the aggregated alternating current (AC) power output capacity of the at least one RES exceeds the grid interconnection point (POGI) limit of the grid interconnection point. At least one energy storage system (ESS), the at least one ESS being electrically coupled to a grid interconnection point and the at least one RES, and having a total power capacity less than the total AC power output capacity of the at least one RES; as well as A controller communicatively coupled to at least one controllable load (CL), at least one ESS, and at least one RES, the controller being configured to control the net load curve of the at least one CL such that the net load curve of the at least one CL includes at least one value between the maximum net load value and the minimum net load value of the at least one CL, the controller being further configured to: A first instruction is provided to at least one of the at least one RES or at least one of the at least one ESS to provide a first portion of the power generated by the at least one RES or stored by the at least one ESS to the at least one CL until the aggregate power demand is met; In response to (A) the power generated by the at least one RES exceeds the aggregated power capacity and aggregated power demand, or (B) the controller uses predictive algorithms and power data to determine that grid conditions exist in the power system forecast, a second instruction is provided to at least one of the at least one RES or at least one of the at least one ESS to provide a second portion of the power to the grid; as well as In response to determining that grid conditions exist and that the power generated by the at least one RES does not exceed the aggregated power capacity and aggregated power demand, a third instruction is provided to the at least one CL to perform one of the following: increase or decrease the power demand at the at least one CL.

2. The system of claim 1, wherein the total AC power output capacity of the at least one RES exceeds the POGI limit by a multiple between approximately 3 and approximately 6.

3. The system of claim 1, wherein the grid conditions are associated with at least one of the following: the price of electricity associated with the grid, the price of ancillary services associated with the grid, grid-related reductions, grid-related congestion prices, or grid-related congestion mitigation value.

4. The system of claim 1, wherein the system has an associated capacity factor of at least approximately 60%.

5. The system of claim 1, wherein the ratio of the power generated by the at least one RES to the total load of the at least one CL is between approximately 3 and approximately 6.

6. The system of claim 1, wherein the controller is further configured to operate the at least one RES or the at least one ESS as at least one of a peaking power plant or a provider of ancillary services for the power grid, and the power grid conditions are associated with at least one of the following: the price of electricity associated with the power grid, the price of ancillary services associated with the power grid, a reduction associated with the power grid, a congestion price associated with the power grid, or a congestion mitigation value associated with the power grid.

7. The system of claim 1, wherein the at least one CL comprises a plurality of CLs, and the controller is further configured to provide instructions to the plurality of CLs to balance the energy distribution associated with the plurality of CLs.

8. The system of claim 1, wherein the at least one CL includes a data center.

9. The system of claim 1, wherein the at least one CL includes an artificial intelligence (AI) training center.

10. The system of claim 1, wherein the at least one CL comprises a cryptocurrency miner.

11. The system of claim 1, wherein the at least one CL comprises an electric vehicle (EV) charging station.

12. The system of claim 1, wherein the at least one CL comprises a vertical farm.

13. The system of claim 1, wherein the at least one CL includes a hydrogen production facility.

14. The system of claim 1, wherein the at least one CL comprises a water treatment plant (including seawater desalination and purification).

15. The system of claim 1, wherein the at least one CL load comprises an industrial process heater.

16. The system of claim 1, wherein the at least one CL load comprises a thermal battery.

17. The system of claim 1, wherein the controller is further configured to deliver power from the grid to the at least one CL load.

18. The system of claim 1, wherein the controller is further configured to select a first instruction such that the correlation between the at least one CL and the power grid changes in response to the first instruction.

19. The system of claim 1, wherein the controller is further configured to select a first instruction such that the correlation of (1) at least one peak of the net load curve associated with the at least one CL and (2) at least one peak of the net load curve associated with the power grid changes in response to the first instruction.

20. The system of claim 1, wherein the first instruction is configured to cause a change in the correlation between the at least one CL and the power grid in response to the at least one first instruction.

21. The system of claim 1, wherein the system is configured to: (1) operate in a first mode as at least one of the baseload, half-baseload, half-peak load, or peak load of the at least one CL, and (2) operate concurrently with the operation in the first mode as at least one of the peak load, half-peak load, half-baseload, baseload, or ancillary service provider of the power grid in a second mode.

22. The system of claim 1, wherein the controller is further configured to provide a fourth instruction to at least one non-renewable energy source (NRES) to cause the at least one NRES to provide a third portion of the electricity generated by the at least one CL.

23. A method for providing power in a RES-ESS-CL system, the method comprising: Power is supplied to a point of grid interconnection (POGI) associated with the grid by at least one of the RES or ESS, the POGI being deployed between at least one CL and the grid at the first time; At a second time, power is supplied to the at least one CL by at least one of RES or ESS; In the third time, the power received from the grid at POGI will be supplied to ESS; In the fourth time, the power received from the grid at POGI is supplied to the at least one CL; as well as In the fifth time, instead of providing power via POGI, at least one of the following is performed: providing power from ESS to the at least one CL, providing power from RES to the at least one CL, or providing power from RES to ESS.

24. The method of claim 23, further comprising: At the fourth time, power is supplied from at least one of the RES or ESS to the at least one CL.

25. The method of claim 23, further comprising: At the fourth time, power is supplied from RES to ESS or at least one of the at least one CL.

26. The method of claim 23, wherein the provision at the fifth time comprises: (1) Power is supplied from the ESS to the at least one CL, and (2) one of the following: power is supplied from the RES to the at least one CL or power is supplied from the RES to the ESS.

27. The method of claim 23, wherein the provision at the fifth time comprises: (1) Power is supplied from RES to the at least one CL, and (2) one of the following: power is supplied from ESS to the at least one CL or power is supplied from RES to ESS.

28. The method of claim 23, wherein the provision at the fifth time comprises: (1) Power is supplied from RES to ESS, (2) Power is supplied from RES to the at least one CL, and (3) Power is supplied from ESS to the at least one CL.

29. The method of claim 23, further comprising: At the sixth time, electricity is supplied from at least one non-renewable energy source (NRES) to the at least one CL.

30. A system comprising: At least one renewable energy source (RES), said at least one RES being configured as a grid interconnection point electrically coupled to the power grid; At least one energy storage system (ESS), the at least one ESS being electrically coupled to a grid interconnection point and the at least one RES; At least one non-renewable energy source (NRES); as well as A controller, communicatively coupled to at least one CL, the at least one ESS, the at least one NRES, and the at least one RES, is configured to: A first instruction is provided to at least one of the at least one RES, the at least one NRES, or the at least one ESS to provide a first portion of power to the at least one CL until the aggregate power demand is met; Provide a second instruction to at least one of the at least one RES, the at least one NRES, or the at least one ESS to provide a second portion of the electricity to the power grid; as well as In response to determining that grid conditions exist, a third instruction is provided to the at least one CL to perform one of the following: increase or decrease the power demand at the at least one CL.

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