Energy storage system responsive to carbon power generation parameters

Abstract

The present disclosure relates to systems and methods for using carbon power generation parameters with battery energy storage to achieve a desired optimization of energy storage operation of a battery. The at least one parameter implementing a desired optimization for managing energy storage includes a carbon power generation parameter of the energy being used in battery operation.

Description

[0001] Related Applications and Cross-References

[0002] This application claims the benefit of priority to a provisional patent application filed with the U.S. Patent Office on June 3, 2022 and assigned serial number 63 / 348,539. The entire contents of the aforementioned provisional application are incorporated herein by reference. Technical Field

[0003] The present disclosure relates to systems and methods for facilitating management of energy storage operations based at least in part on one or more characteristics of energy to be accessed / used when storing energy in an energy storage system. In an exemplary embodiment, the disclosed systems / methods support selective access to one or more energy sources that meet one or more specific carbon generation-related characteristics. The disclosed systems / methods provide, among other things, a further tool for facilitating and supporting energy generation operations that minimize carbon generation, thereby advancing ongoing efforts to address the challenges of climate change. Background Art

[0004] Energy storage systems based on rechargeable batteries are receiving increasing attention for the purpose of modernizing the power grid, for example, battery systems that are connected to the power grid and store and / or deliver energy based at least in part on a battery energy storage system (BESS). Many features, such as demand charge reduction (also known as peak shaving), time-of-use adjustment, and participation in scheduled demand response programs, can improve energy efficiency and help manage and respond to energy demand fluctuations. In many cases, the energy sources used for energy storage are relatively CO2-free. Common examples include solar, wind, hydro, and other sources of power generation that do not rely on fossil fuels. Even fossil fuel sources can be better utilized in energy storage systems, for example, energy storage systems that are based entirely or partially on battery storage.

[0005] Battery energy storage systems can take various forms, such as rechargeable batteries or mechanically rechargeable batteries, such as flow batteries, pumped storage, and compressed air energy storage. Rechargeable batteries include lithium-ion batteries, which can be liquid and polymer types or solid-state battery types. In addition to lithium-ion, there are many other types of rechargeable batteries. The disclosed systems and methods are applicable to this full range of rechargeable / mechanically rechargeable batteries. Although lithium-ion batteries are used in the examples described herein, the present disclosure is not limited to any particular type of energy storage function. The term "battery" as used herein refers to all devices that are capable of delivering electrical energy for energy storage functions.

[0006] The battery functionality of a conventional BESS includes various modes of operation that can be delivered to and from the power grid quickly or over longer periods of time. These modes of operation always affect the battery, causing it to discharge or charge as the BESS responds to monitoring functions or remote commands. The BESS is installed behind the meter (BTM) or in front of the meter (FTM). Sometimes, through software control delivered by a combination of software on the battery and software residing in a server connected through the Internet cloud, these batteries can be manipulated simultaneously in a virtual power plant (VPP) to achieve desired effects on the grid or commercial and industrial (C&I) or residential sites.

[0007] A VPP consists of multiple batteries installed behind a meter, or multiple batteries installed in front of the meter that together deliver a desired combined effect on the grid in a geographic area. When multiple batteries are deployed, the utility provider typically contracts with an aggregator that delivers so-called demand response signals to the installed batteries based on signals from the utility. Multiple batteries may also be controlled directly by the utility or the battery owner. Many publications from the National Renewable Energy Laboratory (NREL) and other organizations describe these battery systems and how they are typically deployed.

[0008] Typical functions seen in BESS include demand charge reduction (also known as peak shaving), demand response functions, time of use functions, frequency regulation, and backup power. For BTM batteries, energy management software is used to control the battery charging and discharging patterns to allow economical and efficient use of low-cost energy. Through the software, the BESS can react to external signals from an aggregator that requires a demand response function that delivers reduced power to a grid point during a specific time frame. Alternatively, still through software, the BESS can react to the power meter, enabling the battery to reduce the apparent power monitored by the smart meter, which is an operational tool that can reduce demand charges. Another approach is to store energy when the local solar energy system (connected behind the meter) or the grid itself has a lower energy cost than other high-cost periods, and dispatch this power at another "time of use". This reduces the cost of energy and allows more efficient use of solar energy, or shifts the use of solar energy from low-cost periods to high-cost periods. Similarly, the BESS can be charged by the grid during low-cost periods and discharged during high-cost periods.

[0009] A typical BESS consists of a DC battery with multiple cells connected in series and parallel. This DC battery is then connected to an inverter system that converts the DC current into AC current suitable for the grid, allowing the battery to discharge. Similarly, the inverter can take AC power from the grid and convert it into DC power capable of charging the battery. The inverter is usually designed to detect irregularities in the grid or the absence of the grid, which allows backup functions to be enabled. The inverter can also match the power characteristics, voltage, and frequency of the grid so that the BESS can be connected without causing any interruptions to the grid and without inrush current that harms the BESS.

[0010] Current transformers (CTs) or other power monitoring devices can communicate power levels to the BESS to monitor incoming power, and this information is often used when demand charge reduction is desired based on the detected power. Peak reduction can also be implemented as a function of time when the load is known. For sites where DC loads are relatively abundant, such as telecommunications sites, it is possible to peak shave the DC load and obtain a demand charge reduction response similar to when using inverters through rectifiers.

[0011] Unless the BESS is part of a stand-alone microgrid, a non-utility-owned BESS is typically installed behind the utility meter (see Figure 1A ). The BESS monitors the power behind the meter and optimizes the charge / discharge function based on economic metrics. The cost of electricity from the grid can be monitored against these metrics to reduce demand charges or to respond to demand response periods by reallocating energy to reduce electricity costs or generate revenue.

[0012] When a utility customer allows demand response actions to deliver electricity to a grid point, the net reduction in electricity allows the utility to indirectly manage the overall load on the grid during certain time periods. This in turn can optimize the utilization of the electrical energy generation system so that sufficient amounts of energy can be delivered without the risk of overload that could cause a grid failure. Similar approaches are used to optimize the use of renewable energy sources to efficiently deploy electrical energy to the grid. When a customer's renewable energy source responds to demand from the utility, the customer receives a financial incentive.

[0013] Monitoring capabilities that interact with the battery system can detect the power level generated by the load behind the meter and deploy batteries, thereby reducing the maximum power to the site during the electricity price period. This demand reduces the cost of customers who charge electricity prices based on the maximum power used by the site. In other scenarios, batteries can be used to change the time of energy used so that during high-usage periods or high-generation periods (such as wind bursts or strong solar periods), the energy stored in the battery can be discharged or charged at a later time. This type of action (i.e., optimizing the use of lower-cost periods in electricity prices) is called time of use and allows for financial rewards for customers with electricity prices that vary based on the time of day. These functions are in addition to the value of any backup power source. Some battery systems are designed to respond when the grid is unstable or outages. All of these functions are referred to as the value stream of the battery.

[0014] In the United States and other countries, the energy in the power grid is generated by many wholesale generators, which can be obtained from a combination of fossil fuels and renewable energy sources. In the United States, the public power grid is partially managed by an independent system operator (ISO) or a regional transmission organization that manages the transmission of electric energy in the region. Other countries also have similar structures. The ISO system was created by the Federal Energy Regulatory Commission as a transmission operator for a larger area and provides non-discriminatory access to the power grid. The ISO area has multiple transmission line owners and multiple energy generators that feed electric power to the public grid. Transmission lines are usually owned by local electric utilities, and they sometimes own one or more power generation facilities. These facilities can run on fossil fuels or can generate electricity from renewable energy resources.

[0015] Depending on the energy mix at any point in time, the grid can have a relatively high amount of renewable energy compared to fossil fuel-based energy, or vice versa. Over time, this mix can change significantly, depending on the conditions of the sun and wind. As the number of solar and wind farms increases, this change will continue and may become more unbalanced. Although fossil fuels generate most of the available electricity today, as global warming is addressed, renewable energy sources are becoming more and more common as a global source of electricity. In order to track the energy mix received by customers and allow for its transparency, ISO organizations and electricity providers use telemetry signals that describe certain characteristics of grid power to provide customers with real-time data. This telemetry is usually transmitted in real time, showing data such as total power levels, the amount of fossil and renewable energy, energy prices at any given time, and other important metrics such as "carbon intensity". Telemetry metrics, financial metrics, and other metrics of the grid power mix can be used to trade energy in real time.

[0016] In the case of modern power grids, providers can purchase energy only from renewable energy or any desired combination that provides optimized costs, as needed. These energy trading algorithms can be computerized / automated or managed by individuals. Customers can sign contracts with electrical energy providers to purchase energy at different price levels, using standardized monthly electricity price structures or rates that vary with natural price changes on the power grid. Pricing varies greatly and depends on factors such as the type of energy source and natural supply and demand changes. Low electricity consumption periods may have extremely low costs, while high electricity consumption periods may have temporary price spikes that are several times the baseline cost of the standard electricity price from the utility.

[0017] Many energy providers and ISOs also provide telemetry of delivered energy, which provides transparency into the type of energy and the price the customer is paying for the energy. Typically, C&I and residential customers choose to purchase their energy from a provider with a stable electricity rate, which often includes time-of-use or demand charge variations. However, for years, financial markets and battery bank owners have been trading in so-called retail markets, allowing batteries to charge during low-cost periods and sell the energy stored in the batteries during high-cost periods, generating net income for the battery bank owner.

[0018] With the above options, commercial and industrial (C&I) or residential customers can purchase their electricity from the utility that manages the generation and transmission of electricity. Alternatively, customers can choose to purchase energy generated by a provider with a higher renewable content than the utility, while still using the same utility transmission lines.

[0019] Another type of customer is one who benefits from energy storage associated with a battery, either by owning and controlling the battery or by being a beneficiary of the battery's actions, such as through a service agreement with a third party. In deregulated areas, customers can purchase their energy directly from any available electric energy provider and contract for the transmission of this energy from the utility that is local to the customer and owns the transmission lines. The energy provider, in turn, can source energy from one or more energy generators and sell this energy combination to the customer. Although these scenarios use the same transmission lines, customers can choose to purchase energy from a provider with a high renewable content or simply purchase the standard combination provided by the utility. These customer decisions are primarily driven by the cost of the energy supplied. Summary of the invention

[0020] The methods and systems of the present disclosure advantageously provide customers with the ability to acquire their energy using an algorithm that optimizes their purchases based on criteria that can include not only traditional financial metrics related to electricity prices or spot prices, but also based on the carbon intensity of the grid at a relevant point in time. Financial metrics are typically measured in cost per kilowatt or cost per kilowatt-hour. Carbon intensity is a metric related to the mass of CO2 per kilowatt-hour (g / kWh) of energy at any particular point in time. Carbon intensity can also be reported in grams per kilowatt at a given point in time. The carbon intensity metric is used to determine the carbon state in the battery. The carbon state is a separate metric that is different from the typical state of charge measured in the battery.

[0021] In one embodiment, a battery utilizing the disclosed systems and methods may store energy (by charging the battery) when carbon intensity is low (based on a relative benchmark) and release energy to a site or grid by discharging the battery when carbon intensity is high (based on a relative benchmark). In another embodiment, a battery utilizing the disclosed systems and methods may optimize energy operations of the battery based on carbon intensity and financial metrics. In another embodiment, a battery utilizing the disclosed systems and methods customizes energy management in the battery by using two or more metrics, at least one of which is carbon intensity.

[0022] The present methods and systems are optimally tied to wholesale energy sources, allowing for easy control and transaction measures, including the use of blockchain technology. While the systems and methods disclosed herein can also be tied to a utility, the consumer may receive a less favorable combination of electricity that minimizes carbon generation, which is less favorable than being tied to a wholesale energy market. The systems and methods disclosed herein (whether tied to a wholesale energy provider, a utility provider, or other energy source) provide a unique and unexpected system and method for managing energy storage operations.

[0023] The disclosed systems and methods operate in a counterintuitive manner relative to traditional battery usage in that carbon intensity metrics do not always follow the desired financial optimization. However, by using the innovative systems and methods, customers can decide whether to optimize their energy purchases based on optimizing carbon intensity, rather than optimizing based solely on financial metrics, and can implement optimization schemes that prioritize optimization of carbon intensity in the customer's energy usage patterns.

[0024] Thus, the disclosed systems and methods allow and facilitate optimization of battery usage behavior based on carbon intensity, financial, and other metrics, which delivers a net reduced carbon intensity of the energy used while reducing the cost of the site. By tracking the energy stored in the battery and its carbon intensity through a carbon indicator meter (described further below), various operating modes can be deployed, allowing customers to customize their carbon footprint with respect to financial metrics. Examples include operating modes such as providing demand charge reductions using low carbon intensity energy from the battery compared to the average grid; optimizing cost and carbon intensity simultaneously using artificial intelligence and / or traditional algorithms; and charging the battery through a meter that optimizes charging based on a carbon intensity metric and discharging to the site load after a second meter.

[0025] In an exemplary embodiment of the present disclosure, a battery system is provided, comprising a processor programmed with an algorithm that utilizes carbon intensity metrics received from one or more energy sources to make a decision to charge or discharge, wherein the algorithmic decision to charge or discharge is based at least in part on the carbon intensity metrics. The disclosed algorithm may include or operate based on both financial metrics and carbon intensity metrics. The algorithm allows a user to set a ratio between the financial metric and the carbon intensity metric.

[0026] In an exemplary embodiment, the carbon intensity metric may be recorded in a ledger as a carbon credit.

[0027] In an exemplary embodiment, the decision to charge or discharge is based on the average battery life sum of the carbon intensity metric. The decision to charge or discharge can also be based on the average value of the carbon intensity metric over a regular time period.

[0028] The disclosed algorithm may optimize based on a lowest cost metric with a lowest carbon intensity metric.The algorithm may also include or operate based on a battery life metric.

[0029] The disclosed battery system may include a first meter and a second meter, wherein the battery system may charge and discharge from the first meter and affect the second meter through post-meter charging and discharging. The energy provider of the first meter may be different from the energy provider of the second meter.

[0030] The disclosed battery system may include an indicator that displays a carbon intensity metric. The indicator may display a comparison of the carbon intensity metric of one or more batteries in the system with the carbon intensity metric of the grid. The indicator may display the weight of carbon in the battery system.

[0031] The disclosed algorithm can use artificial intelligence to optimize carbon intensity metrics.

[0032] Carbon credits can be recorded using blockchain technology.

[0033] The present disclosure also provides a method for operating a battery system, the method comprising:

[0034] (i) Providing a battery system capable of charging and discharging energy;

[0035] (ii) providing at least one energy source connected to the battery system, wherein the at least one energy source has a carbon intensity metric;

[0036] (iii) providing an algorithm for analyzing carbon intensity metrics of energy sources; and

[0037] (iv) Using the algorithm to make a decision on whether to charge or discharge the battery system.

[0038] The algorithm used in the disclosed method may include and / or operate based on both a financial metric and a carbon intensity metric. The algorithm may allow a user to set a ratio between the financial metric and the carbon intensity metric.

[0039] The disclosed method may also include recording the carbon intensity metric as a carbon credit in a ledger. The carbon credit may be recorded using blockchain technology.

[0040] The disclosed algorithm may calculate an average of the carbon intensity metric over the life of the battery system. The disclosed algorithm may calculate an average of the carbon intensity metric over a regular time period. The disclosed algorithm may include and / or operate based on the battery life metric. The algorithm may use artificial intelligence to optimize the carbon intensity metric. The algorithm may analyze the carbon intensity metric in real time.

[0041] The present disclosure also provides a battery system, wherein the battery system includes at least one lithium-ion battery and a carbon state meter.

[0042] Additional features, functionality, and benefits of the disclosed systems and methods will be apparent from the following detailed description, particularly when read in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to help those skilled in the art to practice the subject matter of the present application, reference is made to the accompanying drawings, in which:

[0044] Figure 1A is a schematic diagram showing an exemplary architecture of a conventional BESS installed behind the meter.

[0045] Figure 1B is a schematic diagram showing an exemplary architecture of a BESS, wherein the BESS is installed behind one utility meter M2 for charging a battery while providing the ability to discharge energy behind a second utility meter M1, wherein E1 is energy provider 1 and E2 is energy provider 2.

[0046] Figure 2 is a flow chart illustrating an exemplary decision-making matrix according to the present disclosure.

[0047] Figure 3 is a second flow chart illustrating another exemplary decision-making matrix according to the present disclosure. DETAILED DESCRIPTION

[0048] The disclosed system and method uses carbon generation parameters with battery energy storage to achieve a desired optimization in battery energy storage operation. At least one parameter that achieves the desired optimization for managing energy storage includes a carbon generation parameter of energy being used in battery operation.

[0049] Key to the disclosed systems / methods is the utilization and response to carbon intensity, with or without other telemetry, such as financial metrics. Notably, the operating mode of the disclosed systems and methods is different from traditional renewable energy supply during non-solar or non-wind periods, and also different from batteries charged by local renewable energy plants installed next to the batteries. By using one or more algorithms that simultaneously monitor the carbon intensity of the grid (or relevant parts of the energy source environment), and taking into account financial metrics from electricity prices or retail markets, batteries utilizing the innovative systems and methods can not only optimize the traditional financial value streams available to the battery, but can also have a positive impact on the customer's carbon footprint.

[0050] The systems and methods according to the present disclosure advantageously allow charging and discharging decisions for the battery based on grid telemetry for: (a) carbon intensity, (b) utility electricity prices and / or retail market changes, and (c) other battery metrics. The energy management system records telemetry from the local solar or wind power grid (or any other renewable energy source), as well as telemetry of the grid at a specific point in time with financial metrics and carbon intensity metrics. The energy management software, through specific algorithms or decision making implemented through artificial intelligence, then decides to charge or discharge the BESS depending on the carbon intensity metric (optionally in combination with the financial metric).

[0051] One potential operating parameter and benefit according to the present disclosure relates to optimizing energy usage based solely on a carbon intensity metric. This can be contrasted with conventional uses of BESSs with conventional functionality that allow energy usage to be optimized based solely on a financial metric. Various optimization priorities can be set using the disclosed systems and methods, such as always optimizing the carbon intensity metric first and as a second priority, optimizing the financial metric only when the carbon intensity metric allows. Another algorithm can have a threshold financial metric that must be reached first, and within that metric, the carbon intensity is optimized by the algorithm. Thus, the disclosed algorithm can be used to optimize carbon intensity and financial metrics within a billing cycle or within another period of the carbon intensity metric. Notably, the billing cycle can be different from the carbon intensity period. For example, the period can be the life of the battery or any period that is different from the standard billing cycle.

[0052] In one aspect, the disclosed systems and methods provide customers with the lowest cost of energy and combine that metric with functionality that allows customers to further optimize their purchases based on a carbon intensity target or any other metric that substantially indicates the amount of CO2 that predominates in the energy mix purchased from one or more electrical energy providers.

[0053] A commonality of the functionality associated with the present disclosure and implemented thereby is the use of a BESS that temporarily stores a desired energy combination and then uses that energy combination to provide energy to a behind-the-meter (BTM) usage site, thereby simultaneously optimizing energy costs and reducing carbon intensity. The battery can be used to effectively reduce the carbon intensity of the site. This allows the customer to achieve a higher level of control over the financial and carbon intensity combination than is provided by the energy provider, or, if desired, optimize carbon intensity while sacrificing some economic benefits.

[0054] In order for the system to decide whether to charge or discharge the BESS that optimizes the net effect of both the financial metric and the carbon intensity metric, a carbon meter or indicator tool is typically provided. The carbon meter is a measure of the CO2 content of the energy available in the battery. If the battery is fully discharged, the meter is "0" or "0%" of the total energy. The meter displays the carbon status, which is summarized as follows.

[0055] If the battery is charged only by renewable energy, the carbon status can be characterized and reported as a percentage of the total carbon that can be stored, or a measure of the carbon weight (or its CO2 equivalent) of the available stored battery energy (battery nameplate capacity) in kWh. The carbon status can be created in a variety of ways, such as in a way that is meaningful to the customer and allows for a determination based on telemetry of the input energy whether to charge or discharge the battery, as it relates to at least the carbon intensity metric. Financial metrics can also be used in conjunction with the carbon intensity metric to determine whether to charge or discharge the battery.

[0056] For example, the disclosed carbon status can be operated such that if the battery is 100% full with non-renewable energy, the carbon meter can be at one extreme, such as 100%. If the battery is filled only by renewable energy (such as solar energy), the meter will be at another extreme, such as 0%. 0% is universal for any amount of renewable energy present in the battery. However, for fossil fuel energy sources, a 100% reading is only applicable when the battery is fully charged by fossil fuel, while a 50% full battery has a 50% measurement. If such a battery is filled to 50% with fossil fuel and then filled to 100% with renewable energy, the carbon meter will remain at 50%. However, for the former case, if a system at a nominal energy (such as 100kWh) is charged 50% by 100% fossil fuel, the carbon meter for 100kWh will be 50%. For a 100% charged battery, the carbon meter remains at 50% because it is only 50% filled with fossil fuel. The carbon meter therefore associates a certain amount of CO2 or a relative mixture thereof based on the total energy in the cell and the relative carbon intensity of the electricity used when the cell was refueled.

[0057] In another example, when the battery is charged or discharged, the carbon weight (g or kg) represented by the stored energy is evaluated in a meter or indicator. This carbon weight can be compared to a favorable or unfavorable metric based on the general carbon intensity of the utility grid.

[0058] The carbon intensity metric can be used to track customers' improvements to their carbon footprint to achieve compliance with corporate carbon targets and / or to reduce carbon consumption in accordance with relevant laws, such as the recently implemented Local Law 97 in New York State (USA). In this case, fines can be avoided due to laws and regulations that penalize site owners for excessive use of CO2 or failure to reduce their carbon footprint within the required time frame.

[0059] The Carbon State Meter allows the average carbon from the grid to be compared to the discrete carbon levels stored in the battery at any point in time, and is therefore an important metric that can be used to make decisions about whether to charge or discharge the battery depending on the carbon intensity metric of the grid and the operating parameters set by the user.

[0060] The carbon status meter associated with the present disclosure will be accurate and immutable, and therefore auditable by various accounting methods, including but not limited to blockchain technology. The carbon status or carbon intensity metric is recorded in a ledger or accounting system as a carbon credit. Carbon credits can be traded or used for other purposes after being recorded. This allows mitigation technologies to address the climate crisis, create carbon offsets, and create new market opportunities in carbon trading.

[0061] New systems that provide innovative features using carbon intensity metrics can be used in traditional BTMs ( Figure 1A ) Install or use a second meter ( Figure 1B ) installation, where the BESS is charged by an optimized charging function and discharged to a load behind a second meter. In this case, the second meter has a different energy mix than the first meter. For systems with local renewable energy sources in the microgrid, further mixing the purchase function for charging the BESS may be beneficial. The first energy meter may have a different energy provider than the second energy meter.

[0062] First reference Figure 1A , the grid 100 is fed with two different categories of energy: E1 energy (102) based on one or more fossil fuels (e.g., oil, natural gas, coal), and E2 energy (104) based on one or more renewable energy sources (e.g., solar, wind, hydro). The energy mix on the grid can change over time. M1 meters (108) meter energy from the grid 100 to charge batteries 110 / battery packs 112. Batteries / batteries can perform various functions, such as backup power, demand response, peak shaving, and CO2 savings. The energy released from the batteries 110 / battery packs 112 can be used for various site loads 114 (e.g., telecommunication towers, buildings, traffic control systems, data centers, etc.).

[0063] refer to Figure 1B ,and Figure 1A As in the case of , the grid 100 is fed with two different categories of energy: E1 energy (102) is based on one or more fossil fuels (e.g., oil, natural gas, coal), and E2 energy (104) is based on one or more renewable energy sources (e.g., solar, wind, hydro). The energy mix on the grid can change over time. The M2 meter (106) meters the renewable energy used to charge the battery 110 / battery pack 112, while the M1 meter (108) meters the fossil fuel energy used to charge the battery 110 / battery pack 112. The battery / battery pack can perform various functions, such as backup power, demand response, peak shaving, and CO2 conservation. The energy discharged from the battery 110 / battery pack 112 can be used for various site loads 114 (e.g., telecommunication towers, buildings, flow control systems, data centers, etc.).

[0064] refer to Figure 2, flowchart 200 illustrates an exemplary decision making matrix according to the present disclosure. Various data elements may be fed into one or more charge / discharge algorithms 202 operating on a processor, including PV meter data 204, grid meter data 206, grid price 208, and CO2 intensity 210. Additional data elements that may be fed into the charge / discharge algorithm 202 include supply forecast data 212 and load forecast data 214, as well as battery startup data 216, load data 218, and AI forecast inputs 220, for example, from the cloud. Based on the data inputs, the charge / discharge algorithm evaluates the relative benefits of charging and / or discharging based on applicable criteria.

[0065] The decision-making matrix associated with the flowchart 200 may assume a resting / inactive / idle state 215 between charging / discharging actions. Based on the determination of the charging / discharging algorithm 202, an action 217 may be prompted / initiated, such as a charging action 219 or a discharging action 222. The system / method records "charging" (224) and "discharging" (226), and these actions may be entered into a ledger 228, such as a carbon dioxide equivalent ledger that may be immutably recorded in a blockchain platform.

[0066] The disclosed system / method generally produces beneficial carbon-related performance based on the charge / discharge decision making driven by the charge / discharge algorithm 202. The carbon performance can be used to calculate CO2 credits (e.g., credits from a government / regulatory body) at step 230, validate the CO2 credits at step 232, and facilitate trading of CO2 credits at step 234. At step 236, the system can also display CO2 performance data, such as instantaneous performance and / or trend-related performance.

[0067] Go to Figure 3 , flowchart 300 illustrates an alternative exemplary decision making matrix according to the present disclosure. As with flowchart 200, various data elements may be fed into one or more charge / discharge algorithms 302 operating on a processor, including PV meter data 304, grid meter data 306, grid price 308, and CO2 intensity 310. Additional data elements that may be fed into the charge / discharge algorithm 302 include supply forecast data 312, load forecast data 314, and CO2 emissions data 315 (e.g., from an ISO), as well as battery startup data 316, load data 318, and peak shaving forecast input 320 (e.g., from a Cloud AI program). Notably, the peak shaving forecast input 320 may receive and incorporate real-time data 321 fed through a BESS real-time data analysis function 323. Based on the data inputs, the charge / discharge algorithm evaluates the relative benefits of charging and / or discharging based on applicable criteria.

[0068] The decision-making matrix associated with the flowchart 300 may assume a rest / inactive / idle state 315 between charge / discharge actions. Based on the determination of the charge / discharge algorithm 302, an action 317 may be prompted / initiated, such as a charge action 319 or a discharge action 322. The system / method records "charge" (324) and "discharge" (326), and these actions may be entered into a ledger 328, such as a carbon dioxide equivalent ledger that may be immutably recorded in a blockchain platform, such as in a general format shown in box 329.

[0069] The disclosed system / method generally produces beneficial carbon-related performance based on the charge / discharge decision making driven by the charge / discharge algorithm 302. The carbon performance can be used to calculate CO2 credits (e.g., credits from a government / regulatory body) at step 330, verify the CO2 credits at step 332, and facilitate the trading of CO2 credits at step 334. At step 336, the system can also display CO2 performance data, such as instantaneous performance and / or trend-related performance.

[0070] According to an embodiment of the present disclosure, a battery energy management system and method are provided, which includes a meter or indicator suitable for running an algorithm that controls the charging and discharging behavior of one or more associated batteries, so that the carbon intensity metric and carbon state in the battery system can be optimized to achieve the desired goal. In addition, the energy cost can be optimized by considering the carbon intensity.

[0071] The disclosed systems and methods may be operable to maintain a real-time reading of a carbon state meter associated with one or more batteries controlled by an energy management system. While measuring the carbon state, the system may maintain a real-time reading of the amount of energy stored on the one or more batteries controlled by the energy management system. Additionally, while measuring the carbon state, the system may maintain a real-time reading of the amount of additional energy (i.e., unused storage capacity) stored on the one or more batteries controlled by the energy management system.

[0072] In an embodiment, the battery system may access real-time (or available) carbon intensity metrics for energy available for purchase / download from one or more power grids, from which the energy management system may purchase / download energy to its one or more batteries. The battery system may additionally access real-time (or available) pricing metrics for energy available for purchase / download from one or more energy grids, from which the energy management system may choose to purchase / download energy to its one or more batteries.

[0073] In an embodiment, while measuring the carbon status, the battery system may calculate applicable pricing metrics based on unique customer contracts or arrangements that affect pricing metrics unique to a particular energy management system.

[0074] Based on criteria established by the energy management system in an algorithm for energy decision making, energy may be downloaded from the grid to one or more batteries when the download criteria are met. The criteria include a carbon intensity metric of the energy available for download from the grid at a relevant point in time, and a potential financial metric associated with the same energy available for download from the grid at a relevant point in time. The weight of the carbon intensity metric relative to the financial metric (compared to a reference standard for each metric) and potentially other criteria may be considered in the disclosed energy management system's algorithm for making energy download determinations.

[0075] Based on criteria established by the energy management system in an energy decision making algorithm, energy may be uploaded to the grid (or otherwise used) from one or more batteries when these upload (or use) criteria are met. These criteria include a carbon intensity metric of the energy available on the grid at a relevant point in time, and a potential financial metric associated with the same energy available on the grid at a relevant point in time. The weight of the carbon intensity metric relative to the financial metric (compared to a reference standard for each metric) and potentially other criteria may be considered in the disclosed energy management system's algorithm for making energy upload determinations.

[0076] In one embodiment, carbon intensity may be used and / or measured in conjunction with thermal storage in addition to battery storage via the disclosed systems and methods.

[0077] In another embodiment, artificial intelligence (AI) is used to improve decision making for uploading and / or downloading energy to and / or from one or more batteries associated with an energy management system. The algorithm used to determine whether to charge or discharge one or more batteries within the system may include user input, artificial intelligence, or a combination thereof.

[0078] In another embodiment, the system provides reports based on the operation of the energy management system, including reports reflecting carbon intensity metrics of energy downloaded to and / or uploaded by one or more batteries, reports of financial metrics of energy downloaded to and / or uploaded by one or more batteries, and / or reports comparing carbon intensity and / or financial metrics associated with energy utilization independent of the energy management system (i.e., compared to control conditions).

[0079] The disclosed energy management system and method may be adapted to allow a user to prioritize the financial metric and the carbon metric with a configurable priority ratio between the two metrics. The above ratio may be varied by the user from time to time or in response to measured conditions.

[0080] The disclosed energy system may be adapted to optimize energy-related actions based on various criteria, such as based on carbon metrics over the entire battery life and / or on a periodic basis (eg, monthly), achieving low costs with the lowest carbon.

[0081] Among other things, measurement and reporting functions associated with the disclosed energy management systems and methods may include the ability to capture / record carbon intensity metrics as carbon credits.

[0082] The disclosed systems and methods can use artificial intelligence to enhance algorithmic functionality to further optimize battery charging and discharging. Examples of using AI in optimizing carbon intensity, whether or not financial optimization is performed, include machine learning based on geography, season, time of day, historical and evolving practices of energy generators and distributors, battery capacity, battery chemistry, etc. According to the present disclosure, a range of AI-related methods can be implemented.

[0083] Although the present disclosure has been provided with reference to exemplary embodiments and / or implementations, the present disclosure is not limited to these exemplary embodiments / implementations. On the contrary, modifications, improvements and enhancements can be made without departing from the spirit or scope of the present disclosure, as will be apparent to those skilled in the art based on the disclosure provided herein.

Claims

1. A battery system comprising a processor programmed with an algorithm that utilizes carbon intensity metrics received from one or more energy sources to make charging or discharging decisions, wherein: The algorithmic decision to charge or discharge is based at least in part on a carbon intensity metric.

2. The battery system according to claim 1, wherein: The algorithm includes both financial metrics and the carbon intensity metric.

3. The battery system according to claim 2, wherein: The algorithm allows a user to set a ratio between the financial metric and the carbon intensity metric.

4. The battery system according to claim 1, wherein: The carbon intensity metric is recorded in the ledger as a carbon credit.

5. The battery system according to claim 1, wherein: The decision to charge or discharge is based on the average battery life sum of the carbon intensity metrics.

6. The battery system according to claim 1, wherein: The decision to charge or discharge is based on an average value of the carbon intensity metric over a regular time period.

7. The battery system according to claim 1, wherein: The algorithm is optimized based on the lowest cost metric with the lowest carbon intensity metric.

8. The battery system according to claim 1, wherein: The algorithm also includes a battery life metric.

9. The battery system according to claim 1, wherein: The battery system includes a first meter and a second meter, wherein the battery system is capable of charging and discharging from the first meter and of affecting the second meter through post-meter charging and discharging.

10. The battery system according to claim 9, wherein: The energy provider of the first meter is different from the energy provider of the second meter.

11. The battery system according to claim 1, wherein: The battery system includes an indicator that displays the carbon intensity metric.

12. The battery system according to claim 11, wherein: The indicator displays a carbon intensity metric of one or more batteries in the system compared to a carbon intensity metric of an electrical grid.

13. The battery system according to claim 11, wherein: The indicator displays the weight of carbon in the battery system.

14. The battery system according to claim 1, wherein: The algorithm uses artificial intelligence to optimize the carbon intensity metric.

15. The battery system according to claim 4, wherein: The carbon credits are recorded using blockchain technology.

16. The battery system according to claim 1, wherein: The decision to charge or discharge is recorded.

17. The battery system according to claim 16, wherein: The records are based on blockchain technology.

18. A method of operating a battery system, the method comprising: Providing a battery system capable of charging and discharging energy; providing at least one energy source connected to the battery system, wherein the at least one energy source has a carbon intensity metric; providing an algorithm for analyzing a carbon intensity metric of said energy source; and The algorithm is utilized to make a decision to charge or discharge the battery system.

19. The method according to claim 18, wherein: The algorithm includes both financial metrics and the carbon intensity metric.

20. The method according to claim 18, wherein: The algorithm allows a user to set a ratio between the financial metric and the carbon intensity metric.

21. The method of claim 18, further comprising recording the carbon intensity metric as a carbon credit in a ledger.

22. The method according to claim 21, wherein: The carbon credits are recorded using blockchain technology.

23. The method according to claim 18, wherein: The algorithm calculates an average value of the carbon intensity metric over the lifetime of the battery system.

24. The method of claim 18, wherein: The algorithm calculates an average of the carbon intensity metric over a regular time period.

25. The method of claim 18, wherein: The algorithm includes a battery life metric.

26. The method of claim 18, wherein: The algorithm uses artificial intelligence to optimize the carbon intensity metric.

27. The method according to claim 18, wherein: The algorithm analyzes the carbon intensity metric in real time.

28. The method of claim 18, further comprising recording a decision to charge or discharge the battery system.

29. The method according to claim 28, wherein: The records are based on blockchain technology.

30. A battery system, wherein: The battery system includes at least one lithium-ion battery and a carbon state meter.

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