Energy storage system responsive to carbon generation parameters

Abstract

The present disclosure relates to systems and methods for achieving a desired optimization in the energy storage operation of a battery using carbon generation parameters associated with battery energy storage. At least one parameter in achieving the desired optimization for managing energy storage includes the carbon generation parameter of the energy used in battery operation.

Description

Technical Field

[0001] This application claims the benefit of priority of a provisional patent application filed with the United States Patent and Trademark Office on June 3, 2022, and assigned application number 63 / 348,539. The entire content of the foregoing provisional application is incorporated herein by reference.

[0002] The present disclosure is directed to systems and methods that facilitate managing energy storage operations based at least in part on one or more characteristics of energy accessed / used when storing energy in an energy storage system. In an exemplary embodiment, the disclosed system / method supports selective access to an energy source that meets one or more specified carbon generation-related characteristics. The disclosed system / method provides, among other things, additional tools for promoting and supporting energy generation operations that minimize carbon generation, thereby advancing ongoing efforts to address the challenges of climate change.

Background Art

[0003] Energy storage systems based on rechargeable batteries, such as battery systems connected to an electrical grid and storing and / or transmitting energy based at least in part on a battery energy storage system (BESS), are receiving even more attention for the purpose of modernizing the electrical grid. Several functions, such as reducing demand charges (also called peak shaving), adjusting the time of use, and participating in a scheduled demand response program, help increase energy efficiency, manage, and respond to fluctuations in energy demand. In many cases, the energy source for energy storage is relatively CO2-free. Common examples include solar, wind, hydro, and other power generation sources that do not rely on fossil fuels. Even fossil fuel sources can be better utilized in energy storage systems, such as those based in whole or in part on battery storage.

[0004] Battery energy storage systems can take various forms, such as rechargeable batteries or mechanically charged batteries like flow batteries, pumped hydro, and compressed air energy storage. Rechargeable batteries include Li-ion batteries, which can be of liquid and polymer types or solid battery types. In addition to Li-ion, there are many other types of rechargeable batteries. The disclosed systems and methods are applicable to this entire range of rechargeable / mechanically charged batteries. Although Li-ion batteries are used in the examples described herein, the present disclosure is not limited to any particular type of energy storage function. As used herein, the term "battery" refers to all devices for an energy storage function that enables the transmission of electrical energy.

[0005] The battery functions of conventional BESSs include a wide range of operating modes that can be transmitted to and from the electrical grid quickly or over a longer period. These operating modes always affect the battery such that the battery is discharged or charged when the BESS responds to a monitoring function or a remote command. The BESS is installed either behind-the-meter (BTM) or in-front-of-the-meter (FTM). Sometimes, through software control provided by a combination of the software in the battery and the software present in a server cloud-connected through the Internet, these batteries can be operated simultaneously in a virtual power plant (VPP) to obtain the desired effects on the power grid or on commercial and industrial (C&I) sites or residential sites.

[0006] The VPP consists of multiple batteries installed behind a single meter or is configured as several batteries installed in front of a meter that together provide a desired combined effect on the grid within a geographic area. When multiple batteries are deployed, it is common for the utility provider to contract with an aggregator that transmits so-called demand response signals to the installed batteries based on signals from the utility. The utility or the owner of the batteries can also directly control the multiple batteries. Several publications from the National Renewable Energy Laboratory (NREL) and other organizations describe these battery systems and how they are typically deployed.

[0007] Typical functions seen in BESS include reduction of demand charge (also called peak shaving), demand response function, time-of-use function, frequency regulation, and backup power. For BTM batteries, energy management software that controls the battery charging and discharging modes is used to enable cost-effective utilization of low-cost energy. Through the software, the BESS can respond to external signals from an aggregator that requires a demand response function to achieve power reduction at a grid point during a certain time frame. Alternatively, also through the software, the BESS can respond to a power meter, enabling the battery to reduce the apparent power monitored through a smart meter, which is an operating tool that results in a reduced demand charge. Yet another method is for a local solar system (connected behind the meter) or the grid itself to store energy when it has lower-cost energy than other high-cost periods and send this power out at another "time of use". This reduces the cost of energy, enables more effective use of solar energy, or enables shifting 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.

[0008] A typical BESS consists of a DC battery having several cells connected in series and parallel. And this DC battery is connected to an inverter system that converts DC current into AC current suitable for the grid, enabling the battery to discharge. Similarly, the inverter can obtain AC power from the grid and convert it into DC power that can charge the battery. The inverter is generally designed to detect grid irregularities or the absence of the grid, thereby enabling the backup function to be executed. The inverter can also match the power characteristics, voltage, and frequency of the grid so that the BESS can be connected without any disconnection to the grid or inrush current that damages the BESS.

[0009] A current transformer (CT) or other power monitoring device can communicate the power level to the BESS to monitor the input power, and this information is typically used when demand charge reduction is desired as a function of the detected power. Peak reduction can also be implemented by a time function when the load is known. For sites such as electric communication sites where there is a relatively large amount of DC load, the DC load may be peak-shaved, and a demand charge reduction response similar to when using an inverter can be obtained through the rectifier.

[0010] Unless the BESS is part of a microgrid that has been disconnected, a BESS owned by a non-utility owner is typically installed behind the utility meter (see Figure 1A). The BESS monitors the power behind this one meter and optimizes the charge / discharge function based on economic metrics. By targeting these metrics, it is possible to monitor the cost of power from the grid, thereby redistributing energy to respond to demand response periods or reducing demand charges, thereby reducing electricity costs or generating revenue.

[0011] When a utility customer enables a demand response action to transmit power to a grid point, the net reduction in power enables the utility to indirectly manage the overall load on the grid during a period. And this can optimize the utilization of the electrical energy generation system so that a sufficient amount of energy can be transmitted without the risk of overloading that could cause the electrical grid to fail. A similar method is used to optimize the usage of renewable energy sources for the effective deployment of electrical energy to the grid. When a customer's renewable energy source responds to a demand called by the utility, an economic incentive is obtained by the customer.

[0012] A monitoring function that interacts with the battery system can detect the power levels generated by the load behind the meter and deploy the battery so that the maximum power to the site is reduced over the billing period. Such a reduction in demand reduces the cost to the customer charged in the rate schedule based on the maximum power used by the site. In yet another scenario, the battery can be used to shift the timing of the energy used, such that high usage periods or high generation periods, such as during a wind burst or a period of strong sun, can later use the energy stored in the battery for discharging or charging. This type of action, i.e., optimizing the use of lower cost periods in the rate schedule, is called time of use and enables an economic reward for customers with electricity rates that vary based on time. These functions are added to any backup power value. Some battery systems are designed to respond when the grid is unstable or down. All of these functions are called a value stream for the battery.

[0013] In the United States and other countries, the energy in the electric grid is generated from several large-scale generators that can obtain energy from a combination of fossil fuels and renewable energy sources. In the United States, the common electric grid is partially managed by an Independent System Operator (ISO) or a regional transmission organization that manages the transmission of electric energy within the region. Other countries have similar structures. The ISO system is created by the Federal Energy Regulatory Commission as a larger regional transmission operator and provides non-discriminatory access to the electric grid. The ISO region has multiple transmission line owners and multiple energy generators that supply power to the common grid. Transmission lines are typically owned by local electric utility companies, and sometimes local electric utility companies own one or more power generation facilities. These facilities can operate on fossil fuels or generate electrical energy from renewable energy resources.

[0014] Depending on the energy mix at any given time, the grid can have a relatively high amount of renewable energy compared to fossil fuel-based energy, or vice versa. This mix has significant variability over time, depending on solar and wind conditions. As the number of solar farms and wind farms increases, this variability continues and potentially becomes even more uneven. Fossil fuels currently generate most of the available electrical energy, but as global warming is addressed, renewable energy sources are becoming increasingly widespread as a global power source. To track the energy mix that customers receive and provide transparency about that energy mix, ISO organizations and electricity providers offer customers real-time data using telemetry signals that describe certain characteristics of the grid power. This telemetry is often delivered in real time and shows data such as the total power level, the amount of fossil versus renewable energy, the price of energy at any given time, and other important metrics such as "carbon intensity". Financial and other telemetry metrics about the grid's power mix can be used to trade energy in real time.

[0015] In the case of modern grids, energy can be purchased by providers from only renewable sources as needed, or from any desired mix that offers an optimized cost. These energy trading algorithms can be computerized / automated or managed by individuals. Customers can contract with an electricity provider and buy energy at different price levels using either a standardized monthly rate structure or a rate that varies with the natural price fluctuations of the electricity grid. Prices vary widely and depend on factors such as the type of energy source and natural supply and demand variations. Periods of low electricity usage can be extremely low cost, while periods of high usage can have temporary price spikes that are many times the baseline cost of the utility's standard rate.

[0016] Many energy providers and ISOs provide telemetry on the energy being transmitted, which enables transparency for customers regarding what type of energy or at what price they are paying for electricity. Typically, C&I and residential customers choose to buy energy from a provider with a stable rate, which often includes variations in time-of-use or demand charges. However, over the years, financial markets and owners of battery banks have been trading in the so-called retail market, charging the battery during low-cost periods and enabling the energy stored in the battery to be sold during high-cost periods, resulting in a net profit for the battery bank owner.

[0017] Regarding the options described above, commercial and industrial (C&I) customers or residential customers can purchase their electricity from a utility that manages both generation and transmission. Alternatively, customers have the option to buy energy generated by a provider with a higher renewable content than the utility, while still using the same utility transmission lines.

[0018] 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 contract with a third party. In deregulated areas, customers can buy their energy directly from any available electricity provider and contract for the transmission of this energy from a utility that is local to the customer and owns the transmission lines. The energy provider can procure the energy from one or more energy generators and sell this energy mix to the customer. These scenarios use the same transmission lines, but customers have the option to buy energy from a provider with a high renewable content or simply buy the standard mix provided by the utility. These customer decisions are mainly determined by the cost of the energy supplied. SUMMARY OF THE INVENTION

Means for Solving the Problem

[0019] The methods and systems of the present disclosure advantageously provide customers with the ability to procure their energy using an algorithm that optimizes customer purchases based not only on criteria that may include traditional financial metrics related to rate tables or spot prices, but also on the carbon intensity of the grid at the relevant time points. The financial metrics are typically measured as cost per kW or cost per kWh. The carbon intensity is a metric (g / kWh) related to the mass of CO2 per kWh of energy at any given point in time. The carbon intensity can also be reported as grams per kilowatt at a given point in time. The carbon intensity metric is used to determine the State of Carbon within the battery. The State of Carbon is a metric that is separate and distinct from the typical state of charge measured within the battery.

[0020] In one embodiment, a battery utilizing the disclosed systems and methods can store energy (by charging the battery) when the (relative) carbon intensity is low and release energy to the site or grid (by discharging the battery) when the (relative) carbon intensity is high. In another embodiment, a battery utilizing the disclosed systems and methods can optimize the energy operation of the battery based on carbon intensity and financial metrics. In yet another embodiment, a battery utilizing the disclosed systems and methods can customize the energy management in the battery by using two or more metrics, at least one of the metrics being the carbon intensity.

[0021] The present method and system are preferably coupled to a wholesale energy source and enable easy control and trading means including the use of blockchain technology. The systems and methods disclosed herein can also be connected to a utility, but consumers may receive a less favorable mix of electrons that makes carbon generation less preferable than if coupled to a wholesale energy market. The systems and methods disclosed herein may be coupled to a wholesale energy supplier, a utility provider, or other energy source, but provide a unique and unexpected system and method for managing energy storage operations.

[0022] The disclosed systems and methods operate in a counterintuitive manner with respect to the use of traditional batteries because the carbon intensity metric does not always follow the desired financial optimization. However, through the use of this innovative system and method, customers can optimize or determine their energy purchases based on optimizing carbon intensity, rather than just optimizing based on financial metrics alone, and can implement an optimization regimen that prioritizes optimizing carbon intensity in the pattern of the customer's energy usage.

[0023] Accordingly, the disclosed systems and methods enable and facilitate optimizing battery usage actions based on carbon intensity metrics, financial metrics, and other metrics, which provide a net reduced carbon intensity of the energy used while reducing costs to the site. Various operating modes can be deployed that enable customers to customize their carbon footprint with respect to financial metrics by tracking the energy stored in the battery through a carbon indicator gauge (further described below) to its carbon intensity. Examples include reducing demand charges by using lower carbon intensity energy from the battery compared to the average grid; optimizing cost and carbon intensity simultaneously using artificial intelligence and / or traditional algorithms; charging the battery through a meter that optimizes charging based on carbon intensity metrics and optimizes discharging to the site load behind a second meter.

[0024] In an exemplary implementation of the present disclosure, a battery system including a processor programmed with an algorithm that uses a carbon intensity metric received from one or more energy sources to make a charging or discharging decision, wherein the algorithmic decision to charge or discharge is at least partially based on the carbon intensity metric, is provided. The disclosed algorithm may include financial metrics and carbon intensity metrics, or may operate based on both financial metrics and carbon intensity metrics. The algorithm enables a user to set a ratio between financial metrics and carbon intensity metrics.

[0025] In an exemplary embodiment, the carbon intensity metric may be recorded as a carbon credit in a ledger.

[0026] In an exemplary embodiment, the determination of whether to charge or discharge is based on the total average battery life of the carbon intensity metric. The determination of whether to charge or discharge can also be based on the average of the carbon intensity metric over a certain time period.

[0027] The disclosed algorithm may be optimized based on the lowest cost metric along with the lowest carbon intensity metric. The algorithm may further include a battery life metric or may operate based on a battery life metric.

[0028] The disclosed battery system may include a first meter and a second meter, and the battery system can be charged and discharged from the first meter and affect the second meter by behind-the-meter charging and discharging. The energy provider of the first meter may be different from the energy provider of the second meter.

[0029] 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 electrical grid. The indicator may display the weight of carbon in the battery system.

[0030] The disclosed algorithm may use artificial intelligence to optimize the carbon intensity metric.

[0031] Carbon credits may be recorded using blockchain technology.

[0032] The present disclosure provides a method for operating a battery system, comprising: (i) providing a battery system capable of charging and discharging energy; (ii) providing at least one energy source connected to the battery system, wherein the at least one energy source has a carbon intensity metric; (iii) providing an algorithm for analyzing the carbon intensity metric of the energy source; and (iv) using the algorithm to determine whether to charge or discharge the battery system.

[0033] The algorithm used in the disclosed method may include both a financial metric and a carbon intensity metric and / or may operate based on both the financial metric and the carbon intensity metric. The algorithm may enable a user to set a ratio between the financial metric and the carbon intensity metric.

[0034] The disclosed method may further include recording the carbon intensity metric as a carbon credit in a ledger. The carbon credit may be recorded using blockchain technology.

[0035] The disclosed algorithm can calculate the average of the carbon intensity metric over the total life of the battery system. The disclosed algorithm may calculate the average of the carbon intensity metric over a certain time period. The disclosed algorithm may include a battery life metric and / or may 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.

[0036] The present disclosure further provides a battery system, which includes at least one lithium-ion battery and a state-of-carbon gauge.

[0037] Further features, functions, and advantages of the disclosed systems and methods will become apparent from the following detailed description, especially when read in conjunction with the accompanying drawings.

[0038] To assist those skilled in the art in practicing the subject matter of this application, reference is made to the following accompanying drawings.

Brief Description of the Drawings

[0039]

Figure 1A

Figure 1B

Figure 2

Figure 3

Modes for Carrying Out the Invention

[0040] The disclosed systems and methods achieve a desired optimization in the energy storage operation of the battery using carbon generation parameters associated with battery energy storage. At least one parameter in achieving the desired optimization for managing energy storage includes the carbon generation parameter of the energy used in the battery operation.

[0041] The key to the disclosed system / method is the utilization of carbon intensity and the response to carbon intensity, with or without other telemetry such as financial metrics. Notably, the mode of operation of the disclosed system and method is different from the traditional supply of renewable energy during the off-solar period or off-wind period from a battery charged by a local renewable energy plant installed next to the battery. By using an algorithm that simultaneously monitors the carbon intensity on the grid (or the relevant segment of the energy procurement environment) and taking into account the account financial metric from the rate table or the retail market, the battery utilizing this innovative system and method can not only optimize the traditional financial value stream available to the battery but also have a positive impact on the customer's carbon footprint.

[0042] The systems and methods according to the present disclosure advantageously enable the determination of battery charging and discharging based on (a) carbon intensity, (b) utility rates and / or fluctuations in the retail market, and (c) grid telemetry for other battery metrics. Telemetry from a local solar or wind grid (or any other renewable energy source) at a particular point in time, as well as grid telemetry with financial and carbon intensity metrics, is recorded by the energy management system. The energy management software then makes a decision to charge or discharge the BESS, optionally in combination with the financial metric, in response to the carbon intensity metric, by means of a decision implemented by a specific algorithm or by artificial intelligence.

[0043] One possible operating parameter and benefit according to the present disclosure includes optimizing energy usage based solely on a carbon intensity metric. This can be in contrast to the traditional use of a BESS using conventional functions that enable optimizing energy usage based solely on financial metrics. Various optimization priorities can be set using the disclosed systems and methods, such as optimizing the carbon intensity metric first and, as a second priority, optimizing the financial metric only when enabled by the carbon intensity metric. Yet another algorithm may have a threshold financial metric that must first be reached, within which the carbon intensity is optimized by the algorithm. Accordingly, the disclosed algorithms can be used to optimize the carbon intensity and financial metrics within a billing cycle or within another period for the carbon intensity metric. Notably, the billing cycle may be different from the carbon intensity period. For example, the period may be the life of the battery or any period different from a standard billing cycle.

[0044] In one aspect, the disclosed systems and methods provide the customer with the lowest energy cost, combined with the ability to further optimize their purchases based on this metric, based on a carbon intensity goal or any other metric that essentially indicates the amount of CO2 dominant in the energy mix purchased from one or more electricity providers.

[0045] Associated with and implemented by the present disclosure, what is common to the functions is the use of a BESS to temporarily store a desired energy mix and then use this energy mix to supply energy to a site using behind-the-meter (BTM), thereby simultaneously optimizing the reduction of energy cost and carbon intensity. The battery can be used to effectively reduce the carbon intensity of the site. This enables the customer to achieve a higher level of control over the mix of finance and carbon intensity compared to that provided by the energy provider, or, if necessary, to optimize the carbon intensity while sacrificing some of the economic benefits.

[0046] A carbon gauge or indicator tool is generally provided to determine whether to charge or discharge a BESS that optimizes the net effect of both financial metrics and carbon intensity metrics. The carbon gauge is a measure of how much CO2 is present in the energy available within the battery. When the battery is fully discharged, the gauge is "0" or "0%" of the total energy. This gauge indicates the state of charge, and the state of charge is as outlined below.

[0047] The state of charge can be characterized and reported as the percentage of total carbon that can be stored when the battery is charged only with renewable energy, or as a measure of the weight of carbon (or its CO2 equivalent) relative to the stored battery energy available in kWh, which is the nameplate capacity of the battery. The state of charge is created in various ways, such as in a way that is meaningful to the customer, and based on the telemetry of the input energy, it enables the determination of whether to charge or discharge the battery when it is related to at least the carbon intensity metric. In the determination of whether to charge or discharge the battery, the financial metric can also be used together with the carbon intensity metric.

[0048] For example, the disclosed carbon state can operate such that when the battery is filled to 100% with non-renewable energy, this carbon gauge can be at one extreme, for example, 100%. When the battery is filled only with renewable power such as solar, the gauge can be at the other extreme, for example, 0%. 0% is broad across any amount of renewable energy present in the battery. However, in the case of a fossil fuel energy source, a 100% reading only applies when the battery is fully charged by fossil fuel, while a 50% filled battery will have a 50% scale. If such a battery is filled to 100% with renewable energy after being 50% filled with fossil fuel, the carbon gauge remains at 50%. However, for the former case, when a system at a nominal energy, for example 100 kWh, is charged 50% from 100% fossil fuel, the carbon gauge is 50% for the 100 kWh. For a 100% charged battery, the carbon gauge remains at 50% since the battery was filled only 50% from fossil fuel. Thus, the carbon gauge relates an amount of CO2 or its relative mixture based on the total energy in the battery to the relative carbon intensity for the electrical energy used when the battery was fueled.

[0049] In another example, the weight of carbon represented by the stored energy (g or kg) is evaluated in a gauge or indicator when the battery is charged or discharged. This weight of carbon can be compared to a preferred or non-preferred scale based on the common grid's typical carbon intensity.

[0050] Carbon intensity metrics can be used to track customer improvements to their carbon footprint in order to enable companies to meet their carbon goals and / or to reduce carbon consumption under relevant laws such as the recently implemented Local Law 97 in the State of New York (USA). In such cases, fines imposed by laws and regulations that penalize site owners can be avoided for excessive use of CO2 or for failure to reduce their carbon footprint within the required time frame.

[0051] The carbon state gauge enables the average carbon sourced from the grid to be compared to the average carbon of the individual levels of carbon stored in the battery at any given time, and is thus an essential metric that enables the determination of whether to charge or discharge the battery depending on the grid's carbon intensity metric and the operating parameters set by the user.

[0052] The carbon state gauge related to the present disclosure is accurate and immutable and is thus auditable through various accounting methods including, but not limited to, blockchain technology. The carbon state or carbon intensity metric is recorded in a ledger or accounting system as a carbon credit. The carbon credit may be traded or used for other purposes after being recorded. This enables mitigation techniques to address the climate crisis, create carbon offsets, and create new market opportunities in carbon trading.

[0053] A novel system that provides innovative features using carbon intensity metrics can be installed with a traditional BTM (Figure 1A) or using a second meter (Figure 1B). The BESS is charged through an optimized charging function and discharged to the load behind the second meter. In this case, the second meter has an energy mix different from that of the first meter. For systems with local renewable energy sources within the microgrid, it may be beneficial to further combine a purchasing function for charging the BESS. The first energy meter may have an energy provider different from that of the second energy meter.

[0054] Referring first to Figure 1A, the grid 100 is supplied with two different classes of energy; E1 energy (102) is based on fossil fuels (e.g., oil, natural gas, coal), and E2 energy (104) is based on renewable energy sources (e.g., solar, wind, hydro). The energy mix on the grid can vary over time. The M1 meter (108) measures the energy from the grid 100 for charging the battery 110 / battery bank 112. The battery / battery bank can provide various functions, such as backup power, demand response, peak shaving, and CO2 reduction. The energy discharged from the battery 110 / battery bank 112 can be used for various site loads 114 (e.g., communication towers, buildings, traffic control systems, data centers, etc.).

[0055] Referring to FIG. 1B, as was the case with FIG. 1A, the grid 100 is supplied with two different classes of energy: E1 energy (102) is based on fossil fuels (e.g., oil, natural gas, coal), and E2 energy (104) is based on renewable energy sources (e.g., solar, wind, hydro). The mix of energy on the grid can vary over time. The M2 meter (106) measures the renewable energy used to charge the battery 110 / battery bank 112, while the M1 meter (108) measures the fossil fuel energy used to charge the battery 110 / battery bank 112. The battery / battery bank can provide various functions, such as backup power, demand response, peak shaving, and CO2 reduction. The energy discharged from the battery 110 / battery bank 112 can be used for various site loads 114 (e.g., communication towers, buildings, traffic control systems, data centers, etc.).

[0056] Referring to FIG. 2, flowchart 200 shows an exemplary decision matrix according to the present disclosure. Various data elements, including PV meter data 204, grid meter data 206, grid price 208, and CO2 intensity 210, can be input into a charge / discharge algorithm 202 operating on a processor. Additional data elements that can be input into the charge / discharge algorithm 202 include supply forecast data 212, and load forecast data 214, as well as battery start data 216, load data 218, and AI prediction inputs 220 from, for example, the cloud. Based on the data inputs, the charge / discharge algorithm evaluates the relative benefits of charging and / or discharging based on applicable criteria.

[0057] The decision matrix associated with flowchart 200 may assume a stop / inactive / idle state 215 between charge / discharge actions. Based on the determination by the charge / discharge algorithm 202, an action 217, e.g., a charge action 219 or a discharge action 222, may be facilitated / initiated. The system / method records "charge" (224) and "discharge" (226), and such actions may be input into a ledger 228, e.g., a carbon dioxide equivalent ledger that is immutably recorded on a blockchain platform.

[0058] Based on the charge / discharge decision made by the charge / discharge algorithm 202, the disclosed system / method generally results in beneficial carbon-related performance. The carbon performance can be used to calculate CO2 credits (e.g., credits from a government / regulatory authority) in step 230, verify the CO2 credits in step 232, and facilitate the trading of CO2 credits in step 234. In step 236, the system can also display CO2 performance data, e.g., instantaneous performance and / or trend-related performance.

[0059] Referring to FIG. 3, flowchart 300 shows an alternative exemplary decision matrix according to the present disclosure. Similar to flowchart 200, various data elements including PV meter data 304, grid meter data 306, grid price 308, and CO2 intensity 310 can be input into a charge / discharge algorithm 302 operating on a processor. Additional data elements that can be input into charge / discharge algorithm 302 include supply forecast data 312, load forecast data 314, CO2 emission data 315 (e.g., from an ISO), as well as battery start data 316, load data 318, and peak shaving prediction inputs 320 from, for example, a cloud AI program. Notably, peak shaving prediction input 320 can receive and incorporate real-time data 321 input through a BESS real-time data analysis function 323. Based on the data input, the charge / discharge algorithm evaluates the relative benefits of charging and / or discharging based on applicable criteria.

[0060] The decision matrix associated with flowchart 300 may assume a stop / inactive / idle state 315 between charge / discharge actions. Based on the determination by charge / discharge algorithm 302, an action 317, for example, a charge action 319 or a discharge action 322, can be facilitated / initiated. The system / method records "charge" (324) and "discharge" (326), and such actions can be input into a ledger 328, for example, a carbon dioxide equivalent ledger that is immutably recorded on a blockchain platform, in a general format shown in box 329.

[0061] Based on the charge / discharge decision made by the charge / discharge algorithm 302, the disclosed system / method generally provides beneficial carbon-related performance. The carbon performance can be used to calculate CO2 credits (e.g., credits from the government / regulatory authorities) at step 330, verify the CO2 credits at step 332, and facilitate the trading of CO2 credits at step 334. The system can also display CO2 performance data, such as instantaneous performance and / or trend-related performance, at step 336.

[0062] According to one embodiment of the present disclosure, there is provided a battery energy management system and method including a gauge or indicator adapted to execute an algorithm that controls the charging and discharging behavior of the associated battery (or batteries) such that the carbon intensity metric and carbon state within the battery system can be optimized to achieve a desired goal. Further, the energy cost can be optimized considering the carbon intensity.

[0063] The disclosed system and method can operate to maintain real-time readings of a carbon state gauge associated with a battery (or batteries) under the control of an energy management system. During the measurement of the carbon state, the system can maintain real-time readings of the amount of energy stored in the battery (or batteries) under the control of the energy management system. Further, during the measurement of the carbon state, the system can maintain real-time readings of the amount of additional energy (i.e., unused storage capacity) that can be stored in the battery (or batteries) under the control of the energy management system.

[0064] In one embodiment, the battery system can access in real time (or when available) a carbon intensity metric for the energy available for purchase / download from an energy grid (or multiple grids) from which an energy management system can purchase / download energy to its battery (or batteries). The battery system can further access in real time (or when available) a price metric for the energy available for purchase / download from an energy grid (or multiple grids) from which the energy management system can choose to purchase / download energy to its battery (or batteries).

[0065] In one embodiment, during measuring the carbon state, the battery system may calculate an applicable price setting metric based on a specific customer contract or agreement that affects the price metric specific to a particular energy management system.

[0066] Based on criteria established in an algorithm for energy decision-making by the energy management system, when such criteria for the download are met, energy can be downloaded from the grid to the battery (or batteries). These criteria include a carbon intensity metric for the energy available for download from the grid at the relevant point in time, and potentially a financial metric associated with the same energy available for download from the grid at the relevant point in time. The weighting of the carbon intensity metric relative to the financial metric (compared to each reference criterion), and potentially other criteria, can be considered in the algorithm used by the disclosed energy management system to make an energy download determination.

[0067] Based on criteria established in an algorithm for energy decision-making by an energy management system, energy can be uploaded (or otherwise utilized) from a battery (or batteries) to the grid when such criteria for upload (or utilization) are met. These criteria include a carbon intensity metric for the energy available on the grid at the relevant time point, and potentially a financial metric associated with the same energy available on the grid at the relevant time point. The weighting of the carbon intensity metric relative to the financial metric (compared to each reference criterion), and potentially other criteria, can be considered in the algorithm used by the disclosed energy management system to make an energy upload determination.

[0068] In one embodiment, carbon intensity can be used and / or measured by the disclosed systems and methods, along with thermal storage, in addition to battery storage.

[0069] In another embodiment, artificial intelligence (AI) is used to refine the decision-making regarding the upload and / or download of energy to / from a battery (or batteries) associated with an energy management system. The algorithm for determining whether to charge or discharge one or more batteries within the system may include user input, artificial intelligence, or a combination thereof.

[0070] In yet another embodiment, the system provides a report based on the operation of the energy management system, including a carbon intensity metric of the energy downloaded to and / or uploaded by the battery (or batteries), a financial metric of the energy downloaded to and / or uploaded by the battery (or batteries), and / or a comparison of the carbon intensity and / or financial metrics for the use of energy independent of the energy management system (i.e., as compared to control conditions).

[0071] The disclosed energy management systems and methods can be adapted to enable a user to prioritize financial and carbon metrics using a configurable ratio of priorities between the two metrics. The aforementioned ratio may be changed from time to time by the user or may be changed according to measured conditions.

[0072] The disclosed energy systems may be adapted to optimize energy-related actions based on various criteria, such as based on carbon metrics over the total battery life and / or over a fixed period, such as a month, to achieve the lowest cost reduced by carbon.

[0073] The measurement and reporting functions associated with the disclosed energy management systems and methods may include, among other things, the ability to capture / record carbon intensity metrics as carbon credits.

[0074] Artificial intelligence may be used by the disclosed systems and methods to further optimize battery charging and discharging by enhancing algorithmic functionality. Examples of using AI with or without financial optimization when optimizing carbon intensity include machine learning based on geographical, seasonal, time-of-day, historical, and ongoing practices such as energy generators and distributors, battery capacity, battery chemistry, etc. Various AI-related methods may be implemented in accordance with the present disclosure.

[0075] The present disclosure has been provided with reference to exemplary embodiments and / or implementations, but the present disclosure is not limited to, or by, such exemplary embodiments / implementations. Rather, modifications, improvements, and enhancements as would be apparent to one of ordinary skill in the art based on the disclosure provided herein may be made without departing from the spirit and scope of the present disclosure.

Claims

1. A battery system comprising a processor programmed with an algorithm that makes a determination to charge or discharge using a carbon intensity metric received from one or more energy sources, wherein the algorithmic determination to charge or discharge is based at least in part on the carbon intensity metric, the battery system.

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

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

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

5. The determination to charge or discharge is based on the total average battery life of the carbon intensity metric, the battery system according to claim 1.

6. The determination to charge or discharge is based on the average of the carbon intensity metric over a certain time period, the battery system according to claim 1.

7. The algorithm is optimized based on a minimum carbon intensity metric and a minimum cost metric, the battery system according to claim 1.

8. The algorithm further includes a battery life metric, the battery system according to claim 1.

9. Comprising a first meter and a second meter, charged and discharged from the first meter, and capable of affecting the second meter by behind-the-meter charging and discharging, the battery system according to claim 1.

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

11. The battery system according to claim 1, comprising an indicator for displaying a carbon intensity metric.

12. The indicator according to claim 11, which displays a comparison of the carbon intensity metric of one or more batteries in the system with the carbon intensity metric of the electrical grid.

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

14. The algorithm according to claim 1, which uses artificial intelligence to optimize the carbon intensity metric.

15. The carbon credit is recorded using blockchain technology, and the battery system according to claim 4.

16. The determination of whether to charge or discharge is recorded, and the battery system according to claim 1.

17. The record is based on blockchain technology, and the battery system according to claim 16.

18. A method for operating a battery system, 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 the carbon intensity metric of the energy source; Using the algorithm to determine whether to charge or discharge the battery system; and Including the method.

19. The algorithm is the method according to claim 18, including both a financial metric and a carbon intensity metric.

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

21. The method according to claim 18, further including recording the carbon intensity metric as a carbon credit in a ledger.

22. The method according to claim 21, wherein the carbon credit is recorded using blockchain technology.

23. The algorithm is the method according to claim 18, calculating an average of the carbon intensity metric over the total life of the battery system.

24. The algorithm is the method according to claim 18, calculating an average of the carbon intensity metric over a certain time period.

25. The algorithm is the method according to claim 18, including a battery life metric.

26. The algorithm is the method according to claim 18, using artificial intelligence to optimize the carbon intensity metric.

27. The algorithm is the method according to claim 18, analyzing the carbon intensity metric in real time.

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

29. The recording is based on blockchain technology, according to the method of claim 28.

30. A battery system comprising at least one lithium-ion battery and a state-of-carbon gauge.

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