Energy storage management system management for participation in multiple energy markets, ancillary service markets, and voluntary electricity market programs

Abstract

Examples described herein provide a computer-implemented method for battery system management. The method includes receiving a trigger event. The method further includes determining a target battery level for a battery system based on the trigger event, the target battery level defining an amount of charge of the battery system that is desired at the beginning of a specified time interval. The method further includes determining a baseline net power for the battery system to satisfy the target battery level. The method further includes controlling the battery system to achieve the baseline net power for the battery system while the battery system is participating in at least one of at least one energy market and at least one ancillary service market.

Description

ENERGY STORAGE MANAGEMENT SYSTEM MANAGEMENT FORPARTICIPATION IN MULTIPLE ENERGY MARKETS, ANCILLARY SERVICE MARKETS, AND VOLUNTARY ELECTRICITY MARKET PROGRAMSBACKGROUND

[0001] Embodiments described herein generally relate to energy management, and more specifically, to an energy storage management system management for participation in multiple energy and ancillary service markets as well as voluntary electricity market programs.

[0002] An energy storage management system can manage charging and discharging of a battery system, which can provide electrical power to various loads. A battery system can participate in one or more energy markets, one or more ancillary service markets, and / or one or more voluntary electricity market programs. A voluntary electricity market program is an energy program that a customer can voluntarily participate in, such as peak demand reduction. Battery systems can receive electrical power from and / or commit electrical power to one or more energy markets and / or one or more ancillary service markets.

[0003] Energy markets are systems or environments where energy (e.g., electricity, natural gas, etc.) is bought and sold. Energy markets are intermediaries between energy producers (e.g., a power plant, a natural gas supplier) and energy consumers. Energy markets sell energy for different prices based on a number of factors, such as location, type of energy, time of year, time of day, and / or the like, including combinations and / or multiples thereof.

[0004] Ancillary service markets are markets where services that support the operation of the electrical grid are bought and sold. Ancillary service markets assist with maintaining a balance between electrical power supply and demand, support grid stability, and manage contingencies, such as fluctuations in demand or the unexpected loss of energy production.

[0005] According to an embodiment, a computer-implemented method for battery system management is provided. The method includes receiving a trigger event. The methodfurther includes determining a target battery level for a battery system based on the trigger event, the target battery level defining an amount of charge of the battery system that is desired at the beginning of a specified time interval. The method further includes determining a baseline net power for the battery system to satisfy the target battery level. The method further includes controlling the battery system to achieve the baseline net power for the battery system while the battery system is participating in at least one of at least one energy market and at least one ancillary service market.

[0006] In addition to one or more of the features described herein, or as an alternative, further embodiments of the method may include that the specified time interval is one of a plurality of time intervals occurring within a time period.

[0007] In addition to one or more of the features described herein, or as an alternative, further embodiments of the method may include that the each of the plurality of time intervals is substantially one hour, and wherein the time period is substantially one day.

[0008] In addition to one or more of the features described herein, or as an alternative, further embodiments of the method may include that controlling the battery system includes determining a charging powerfor the battery system based at least in part on a minimum state of charge threshold £s, a minimum state of charge at one time step before an event associated with the trigger eventand a charging efficiency of the battery system.

[0009] In addition to one or more of the features described herein, or as an alternative, further embodiments of the method may include that the charging power for the battery system is determined using the following equation:

[0010] In addition to one or more of the features described herein, or as an alternative, further embodiments of the method may include that controlling the battery system includes maintaining a state of charge of the battery system between the minimum state of charge threshold and a maximum state of charge threshold.

[0011] In addition to one or more of the features described herein, or as an alternative, further embodiments of the method may include receiving a price forecast, the price forecast being a projected energy price of electrical power received from one or more sources.

[0012] In addition to one or more of the features described herein, or as an alternative, further embodiments of the method may include that controlling the battery system includes charging the battery system based on the price forecast and the trigger event prior to occurrence of an event associated with the trigger event.

[0013] In addition to one or more of the features described herein, or as an alternative, further embodiments of the method may include that determining the baseline net power includes solving a robust optimization problem to satisfy an equality constraint.

[0014] In addition to one or more of the features described herein, or as an alternative, further embodiments of the method may include that the equality constraint requires a minimum state of charge of the battery system at one time step before occurrence of an event associated with the trigger event to be sufficient to charge the battery system to a desired level at the occurrence of the event.

[0015] In addition to one or more of the features described herein, or as an alternative, further embodiments of the method may include that the desired level is fully charged.

[0016] In addition to one or more of the features described herein, or as an alternative, further embodiments of the method may include that wherein controlling the battery system is based on a regulation signal and a reserve call received from an operation associated with the at least one of the at least one energy market and the at least one ancillary service market.

[0017] According to another embodiment, embodiment a system is provided. The system includes a memory having computer readable instructions and a processing device for executing the computer readable instructions. The computer readable instructions control the processing device to perform operations. The operations include receiving a trigger event.The operations further include determining a target battery level for a battery system based on the trigger event, the target battery level defining an amount of charge of the battery system that is desired at the beginning of a specified time interval. The operations further include determining a baseline net power for the battery system to satisfy the target battery level. The operations further include controlling the battery system to achieve the baseline net power for the battery system while the battery system is participating in at least one of at least one energy market and at least one ancillary service market.

[0018] According to another embodiment, embodiment a system is provided. The system includes that the specified time interval is one of a plurality of time intervals occurring within a time period.

[0019] According to another embodiment, embodiment a system is provided. The system includes that controlling the battery system includes determining a charging powerfor the battery system based at least in part on a minimum state of charge threshold £s, a minimum state of charge at one time step before an event associated with the trigger event EtF_^ and a charging efficiency of the battery system, wherein the charging power for the battery system is determined using the following equation:

[0020] According to another embodiment, embodiment a system is provided. The system includes that controlling the battery system includes maintaining a state of charge of the battery system between the minimum state of charge threshold and a maximum state of charge threshold.

[0021] According to another embodiment, embodiment a system is provided. The system includes that the operations further include receiving a price forecast, the price forecast being a projected energy price of electrical power received from one or more sources, wherein controlling the battery system includes charging the battery system based on the price forecast and the trigger event prior to occurrence of an event associated with the trigger event.

[0022] According to another embodiment, embodiment a system is provided. The system includes that determining the baseline net power includes solving a robust optimization problem to satisfy an equality constraint.

[0023] According to another embodiment, embodiment a system is provided. The system includes that the equality constraint requires a minimum state of charge of the battery system at one time step before occurrence of an event associated with the trigger event to be sufficient to charge the battery system to a desired level at the occurrence of the event.

[0024] According to another embodiment, embodiment a system is provided. The system includes that the desired level is fully charged.

[0025] The above features and advantages, and other features and advantages, of the disclosure are readily apparent from the following detailed description when taken in connection with the accompanying drawings.BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The specifics of the exclusive rights described herein are particularly pointed out and distinctly claimed in the claims at the conclusion of the specification. The foregoing and other features and advantages of one or more embodiments described herein are apparent from the following detailed description taken in conjunction with the accompanying drawings in which:

[0027] FIG. 1 shows an environment for battery system management for participation in various energy and ancillary service markets according to one or more embodiments;

[0028] FIG. 2 shows a schematic illustration of a battery system management system according to one or more embodiments;

[0029] FIG. 3 shows a flow diagram of a method for battery system management according to one or more embodiments; and

[0030] FIG. 4 shows a processing system for performing battery system management according to one or more embodiments.DETAILED DESCRIPTION

[0031] The figures discussed below, and the various embodiments used to describe the principles of the present disclosure in this patent document are by way of illustration only and should not be construed in any way to limit the scope of the disclosure. Those skilled in the art will understand that the principles of the present disclosure may be implemented in any suitably arranged device. The numerous innovative teachings of the present application will be described with reference to exemplary non-limiting embodiments.

[0032] One or more embodiments described herein provide for achieving robust optimal energy markets, ancillary service markets, and / or voluntary electricity market programs participation for an energy storage management system, which may be referred to simply as a “battery system” for brevity but is not so limited. The battery system can includeone or more batteries for storing electrical power. The battery system can be installed in a residential or commercial building, a microgrid, a utility grid, and / or the like, including combinations and / or multiples thereof. The battery system enables more efficient procurement and use of electrical power by obtaining electrical power during optimal times, storing the electrical power, and providing the electrical power on-demand or according to scheduled usage. For example, it may be desirable to charge the battery system during times when electrical power is more readily or cheaply available. Similarly, it may be desirable to discharge the battery system to supply electrical power to help balance the grid. In some situations, the full capacity of a battery system is committed for certain durations (e.g., the battery has been bid into long-term capacity markets, anticipating its full capacity during high-demand periods).

[0033] Managing battery systems that participate in energy markets and ancillary service markets is complex. For example, such battery systems can obtain electrical power at different times and at different costs and can provide electrical power at different times and at different costs. Modeling these different scenarios is challenging and time consuming. Moreover, using a battery system inefficiently can cause the battery system to degrade, thereby decreasing the lifespan of the battery system and thus functionality of the battery system. For example, deep discharges (e.g., discharging the battery to near zero percent charge) can shorten the lifespan of the battery system.

[0034] One or more embodiments described herein provide for efficiently and effectively managing battery systems while providing an amount of charge of the battery system that is desired at a specified time interval. One or more embodiments utilize day- ahead planning techniques and real-time battery system control. In some situations, the full capacity of a battery system is committed for certain durations (e.g., the battery has been bid into long-term capacity markets, anticipating its full capacity during high-demand periods), and embodiments described herein provide for managing the battery system in such situations. For example, if an event is scheduled or anticipated to occur during a particular time on a particular day, the battery system can be managed to provide a desired level of charge (e.g., a full charge) to support the event at the particular time / day demanded.

[0035] One or more embodiments described herein provide for entities, such as utility companies with a large battery system, to make optimal and robust decisions on battery system commitment to multiple energy markets and / or ancillary service markets in order toincrease battery system efficiency, improve the health and longevity of battery systems, maximize revenues / profits, and / or minimize costs.

[0036] FIG. 1 shows an environment 100 for battery system management for participation in various energy and ancillary service markets according to one or more embodiments. The environment 100 includes a processing system 110 that performs battery management for a battery system 120 for participation in one or more energy markets 122a, 122b, 122c (collectively referred to as energy markets 122), and / or one or more ancillary service markets 124a, 124b (collectively referred to as ancillary service markets 124). Although three energy markets 122 and two ancillary service markets 124 are shown in FIG. 1, it should be appreciated that any suitable number of energy markets 122 and ancillary service markets 124 other than what is shown is possible and is within the scope of the present disclosure unless explicitly stated otherwise.

[0037] The processing system 110 manages the battery system 120 by managing charging and discharging of the battery system 120, which includes one or more batteries for storing electrical power. The battery system 120 receives electrical power from and / or provides electrical power to one or more of the energy markets 122 and / or ancillary service markets 124. Other combinations are also possible as can be appreciated by a person having ordinary skill in the art.

[0038] It should be appreciated that the processing system 110 can be any device suitable for managing the battery system 120. For example, the processing system 110 can be a device implemented in or otherwise associated with the battery system 120. As another example, the processing system 110 can be a field-programmable gate array (FPGA), microcontroller, smartphone, tablet computer, laptop computer, desktop computer, cloudbased application, and / or the like, including combinations and / or multiples thereof. In the example of FIG. 1, the processing system 110 includes a processing device 112, a memory 114, a day-ahead planning engine 116, and a battery control engine 118.

[0039] The processing device 112 is any suitable processing circuitry for processing data and / or instructions. The processing device 112 is an example of one or more of the processing devices 421 of FIG. 4, as described in more detail herein.

[0040] The memory 114 is any suitable device for storing data and / or instructions.The memory 114 is an example of one or more of the system memory 422, the random access memory 423, and / or the read-only memory 424 of FIG. 4, as described in more detail herein.

[0041] As described herein, managing battery systems (e.g., the battery system 120) that participate in energy markets 122 and ancillary service markets 124 is complex.According to one or more embodiments, battery system management can be expressed in terms of a mathematical model, as follows:Other battery scheduling constraints and / or electricity market constraints can be considered in other embodiments. In one or more embodiments, the battery system 120 can be independently operated or co-located with other assets, such as solar and / or load.The decision variables of this model are as follows:Pt is net power consumption (also referred to as “baseline net power”) of the battery system 120; p is electrical power imported (bought) from the grid (e.g., from one or more of the energy markets 122); and p™ is reserve committed to the ancillary market m (e.g., from one or more of the ancillary service markets 124).

[0042] The parameterrepresents the forecasted energy price. The parameter N denotes the set of energy markets. The parameter M denotes the set of the ancillary markets of interest. The parameter ^represents the forecasted unit power prices for the ancillary market m. The parameter nDCis utilized to penalize the maximum power consumption during certain periods (e.g., during the day), to reduce the monthly demand charge. Theparameter E denotes the state of charge (SoC) of the battery system 120, with the — B parameters E_ and E representing the minimum SoC and the maximum SoC, respectively, of the battery system 120. The constraints (3) pertain to limiting the SoC of the battery — B system 120 between E_ and E for possible uncertainties in the system (e.g., frequency regulation signal, also referred to as a “regulation signal”; a reserve utilization rate, also referred to as a “reserve call”). The parameter tFrepresents the specific time step of a start time of an event for which the battery system 120 is to be at a desired charge (e.g., fully charged). The parameter TFdenotes the set of the time steps of the event (e.g., the duration of the event). The charging / discharging power of the battery system 120 is fixed to a predefined value Pf during the event.

[0043] The processing system 110 that manages the battery system 120 faces various uncertainties, including the frequency regulation signal, reserve utilization rate, renewable generation, and load demand. Prior approaches to battery system management employed rulebased control techniques. However, such approaches typically involve charging the battery system 120 during the periods of relatively low demand and discharging the battery system 120 when demand is relatively high, thus effectively reducing the cost of electrical power to whomever is using the electrical power provided by the battery system 120. Prior approaches for managing battery systems to participate in various markets are distinguishable from the one or more embodiments described herein in that the prior approaches are based on the specific services that they consider and how they handle uncertainties within the battery system. .

[0044] To address the uncertainties, one or more of the following approaches can be used: propose a robust optimization problem that is feasible to any uncertainty in defined uncertainty set; impose chance-constrained constraint and reformulate it in tractable forms; or gather scenarios for the uncertainties and find a feasible solution for all the scenarios. When it comes to handling uncertainties within frequency regulation signals, these studies have relied on scenario-based approaches or chance-constrained constraints. However, these approaches do not provide robustness concerning the frequency regulation signal. More particularly, some of these prior approaches may provide robustness in meeting inequality constraints but are not equally effective in addressing equality constraints. Accordingly, none of these prior approaches address the situation where the battery system 120 is to be charged to a desired level (e.g., fully charged) at a certain time due to an event, such as to satisfy the constraints(4) outlined herein. Some prior approaches have introduced multi-stage scheduling algorithms aimed at simultaneous participating in multiple markets with different operation timeframes. For instance, a multi-level scheduling algorithm encompassing day-ahead energy market scheduling and hour-ahead frequency regulation market offer scheduling has been proposed. However, none of these prior approaches focus on devising robust multi-stage algorithms against the uncertainties within the service requests and signals from an operator (e.g., operator 202 of FIG. 2).

[0045] To address these and other shortcomings, one or more embodiments described herein provide an algorithm that addresses uncertainties by identifying an optimal solution to managing the battery system 120 to provide for efficiently and effectively managing battery systems while providing an amount of charge to the battery system that is desired during a specified time interval. More particularly, the processing system 110 implements the day- ahead planning engine 116 and the battery control engine 118. Further aspects and features of the day-ahead planning engine 116 and the battery control engine 118 are described herein with respect to FIGS. 2 and 3.

[0046] The various components, modules, engines, etc. described regarding FIG. 2 (e.g., the day-ahead planning engine 116 and the battery control engine 118) can be implemented as instructions stored on a computer-readable storage medium, as hardware modules, as special-purpose hardware (e.g., application specific hardware, application specific integrated circuits (ASICs), application specific special processors (ASSPs), field programmable gate arrays (FPGAs), as embedded controllers, hardwired circuitry, etc.), as a cloud-based implementation, or as some combination or combinations of these. According to aspects of the present disclosure, the engine(s) described herein can be a combination of hardware and programming. The programming can be processor executable instructions stored on a tangible memory, and the hardware can include the processing device 112 for executing those instructions. Thus, a system memory (e.g., memory 114) can store program instructions that when executed by the processing device 112 implement the engines described herein. Other engines can also be utilized to include other features and functionality described in other examples herein.

[0047] FIG. 2 shows a schematic illustration of a battery management system 200 according to one or more embodiments. The battery management system 200 is an example of the processing system 110 of FIG. 1 and includes the day-ahead planning engine 116 andthe battery control engine 118. The battery management system 200 manages the battery system 120 for use with an operator 202. The operator 202 can include one or more of an independent system operator, a regional transmission operator (RTO), and / or a utility. The operator 202 is responsible for operating and managing one or more of the energy markets 122 and / or one or more of the ancillary service markets 124.

[0048] The battery management system 200 receives a price forecast Ti , which is the projected energy price of electrical power received from one or more sources (e.g., one or more of the energy markets 122), and determines a target battery level for the battery system 120 for an event that is to occur in the future (e.g., the next day) at a particular time (e.g., between noon and 1 pm). The particular time may be one or more time steps (e.g., time intervals) during a time period (e.g., the day). The target battery level defines a target state- of-change of the battery system 120 that is desired at the start of a specified time interval (or intervals). According to one or more embodiments, the target battery level is a range defined by a lower battery level and an upper battery level.

[0049] The day-ahead planning engine 116 solves a robust optimization problem using the price forecast and the trigger event (e.g., an event forecast or notification) to obtain a baseline net power p for the battery system 120 based on an event that is to occur in the future (e.g., the next day). The day-ahead planning engine 116 also bids to the ancillary service markets 124 p™ for one or more periods of the next day (e.g., each hour of the next day (e.g., t = 1, ... ,24)). It should be appreciated that a day can be segmented into any suitable number of periods as desired (e.g., four six-hour periods, six three-hour periods, twelve two- hour periods; 24 one-hour periods, 48 thirty -minute periods, 96 fifteen-minute periods, and / or the like, including combinations and / or multiples thereof).

[0050] As described herein, due to the uncertainties, obtaining a robust solution to the optimization problem using the price forecast and the trigger event to satisfy the equality — B constraint EtF= E (e.g., the constraint (4) described herein) is challenging. To address this issue, the equality constraint is alternated to a constraint that requires the minimum SoC of the battery system 120 at one-step before the event of the trigger event (i.e., min Etp-) to be sufficiently large to allow the battery system 120 to be fully charged (or charged to a desired level) at tp. Thus, the charging power to change the battery system 120 to full=(E — EtF-1) / p, where p is the charging efficiency of the battery system 120, must be lower than the maximum charging power of the battery system 120.

[0051] Additionally, the constraints (3) concerning the minimum SoC and the maximum SoC are reformulated into a set of tractable constraints based on how the uncertainty set is defined. For example, a reformulation can be expressed as follows in an example in which the uncertainty set is a q-norm bounded set W = {w =(w1;... , w24) | 11 w| |q< <5} or a set with uncertainty budget £, T (i.e., bounds on the sum of the uncertainties):

[0052] According to one or more embodiments, where the battery system 120 is colocated with another asset (e.g., solar or load), the uncertainty budget can be similarly defined.

[0053] In case of a set with uncertainty budget, the problem can be reformulated by leveraging the dual problem of the optimization problem of the SoC of the battery system 120.

[0054] The battery control engine 118 command net charging power of the battery system 120 as the baseline p if the battery system 120 is not participating in any of the ancillary service markets 124. The battery control engine 118 can command the net charging power hourly or based on any other suitable or desired time interval (e.g., thirty minutes, two hours, etc.). If the battery system 120 is participating in any of the ancillary service markets 124, the battery management system 200 determines the power required by the operator 202 from the battery system 120 when the battery is engaged in one or more of the ancillary service markets 124.

[0055] For example, at t = tF— 1, the battery control engine 118 determines the — B charging power for the battery system 120 as p = E — EtF-1) / p, where p is the charging efficiency of the battery system 120, which is lower than the maximum power of the battery system 120 as described herein. This ensures the battery system 120 is fully charged at t =tF. By using the battery control engine 118 in this way, the SoC of the battery system 120 is — B maintained between E_ and E .

[0056] One or more embodiments described herein can be extended to include other markets that are operated in different timescales (e.g., hourly) by introducing an intermediary layer between the day-ahead planning of the day-ahead planning engine 116 and real-time control of the battery system 120 by the battery control engine 118. From the proposed day- ahead scheduling optimization problem, additional decision variables, robust constraints, and predicted revenues from those markets can be incorporated into the day-ahead scheduling optimization problem solved using the day-ahead planning engine 116. Then, the revised scheduling algorithm can be solved to determine optimal offers to those additional markets. For example, participating in real-time energy markets and demand response can be considered. Responsive to receiving a demand response request from the operator 202, the optimization problem can be solved that additionally incorporates the decision variables, predicted revenue from demand response, and the robust constraints that limits p below the requested values against possible uncertainties within solar photovoltaic generation and uncontrollable loads into the day-ahead scheduling optimization problem with fixed reserve to the ancillary service markets 124.

[0057] FIG. 3 shows a flow diagram of a method 300 for battery system management according to one or more embodiments. The method can be performed by any suitable device or system. For example, the method 300 can be implemented using the processing system 110 of FIG. 1, the battery management system 200 of FIG. 2, and / or the processing system 400 of FIG. 4. The method 300 is now described with reference to the battery management system 200 of FIG. 2 but is not so limited.

[0058] At block 302, the battery management system 200 receives a trigger event, which could be an event forecast or notification. At block 304, a target battery level for the battery system 120 is determined based on the trigger event for an event that is to occur in the future (e.g., the next day) at a particular time (e.g., between noon and 1 pm). The target battery level defines an amount of charge of the battery system 120 that is desired during a specified time interval (e.g., between noon and 1 pm) on the following day. According to one or more embodiments, the specified time interval is one of a plurality of time intervals occurring within a time period. For example, the specified time intervals can be one-hourintervals occurring within a day (e.g., the time period). It should be appreciated that intervals and time periods of other durations are also possible. For example, the specified time intervals can be two-hour intervals occurring with a twelve-hour time period.

[0059] According to one or more embodiments, the battery management system 200 receives a price forecast n for all n in N and TT”1for all m in M at block 302 in addition to the event forecast. The price forecast is the projected energy price of electrical power received from one or more sources (e.g., one or more of the energy markets 122). It should be appreciated that the price forecast can vary depending on the time, day, source, etc., from which the electrical power is received.

[0060] At block 306, the battery management system 200 (e.g., using the day-ahead planning engine 116) determines a baseline net power p for the battery system 120 to satisfy the target battery level. The baseline net power is determined, as described herein, by solving the robust optimization problem to satisfy the equality constraint. The equality constraint requires the minimum SoC of the battery system 120 at one-step before the event of the event forecast to be sufficiently large to allow the battery system 120 to be fully charged (or charged to a desired level) at the time of the event.

[0061] At block 308, the battery management system 200 (e.g., using the battery control engine 118), controls the battery system to achieve the baseline net power for the battery system while the battery system is participating in at least one of at least one energy market (e.g., one or more of the energy markets 122) and at least one ancillary service market (e.g., one or more of the ancillary service markets 124). According to one or more embodiments, the battery system 120 can participate, alternatively or additionally, in one or more voluntary electricity market program. According to one or more embodiments, controlling the battery system is based on a regulation signal and a reserve call received from the operator 202 associated with one or more ancillary service markets 124. As a result, the battery system 120 is able to satisfy electrical power demands associated with the event while participating in one or more ancillary service markets 124. According to one or more embodiments, the same outcome (e.g., controls the battery system to achieve the baseline net power for the battery system while the battery system is participating in at least one of at least one energy market (e.g., one or more of the energy markets 122) and at least one ancillary service market (e.g., one or more of the ancillary service markets 124)) by managing flexible load for some use cases. That is, in some cases, the load can be adjusted to achieve theoutcome (e.g., by reducing consumption) rather than and / or in addition to discharging the battery system 120.

[0062] According to one or more embodiments, controlling the battery system includes determining a charging powerfor the battery system based at least in part on a minimum state of charge threshold Es, a minimum state of charge at one time step before an event associated with the event forecast £^-1, and a charging efficiency of the battery system. In such cases, the charging power for the battery system can be determined using the following equation:— BPtF-i = ( - EtF-i) / P-

[0063] According to one or more embodiments, controlling the battery system 120 includes maintaining a state of charge of the battery system between the minimum state of charge threshold and a maximum state of charge threshold.

[0064] In an embodiment in which the battery management system 200 receive a price forecast as described herein, controlling the battery system 120 can include charging the battery system 120 based on the price forecast and the event forecast prior to occurrence of an event associated with the event forecast. This enables the battery system 120 to be adequately charged to meet the target battery level specified in the event forecast while reducing costs and / or maximizing profits associated with charging the battery system 120 based on the price forecast.

[0065] Additional processes also may be included, and it should be understood that the processes depicted in FIG. 3 represent illustrations, and that other processes may be added or existing processes may be removed, modified, or rearranged without departing from the scope of the present disclosure. It should also be understood that the processes depicted in FIG. 3 may be implemented as programmatic instructions stored on a non-transitory computer-readable storage medium that, when executed by a processor (e.g., the processing device 112) of a computing system (e.g., the processing system 110), cause the processor to perform the processes described herein.

[0066] It should be appreciated that one or more embodiments described herein optimize a battery system participating in multiple energy markets and / or ancillary service markets, while providing for the battery system to be fully charged at some point due to anevent. While some approaches have offered strategies for energy arbitrage and multiple ancillary service markets, existing approaches fail to consider the scenario where the full capacity of the battery system has been committed at certain time step. One or more embodiments described herein provide the ability to robustly satisfy an equality constraint to fully charge the battery system at certain time step, in contrast to previous approaches that only consider inequality constraints.

[0067] It should be further appreciated that one or more embodiments described herein provides a robust algorithm for uncertainties associated with the regulation signal and reserve call. While some approaches have adopted scenario-based approaches or chance- constrained constraints to tackle these, such approaches do not and cannot guarantee satisfaction of the constraints. One or more embodiments described herein, on the other hand, defines an uncertainty set of the frequency regulation signal and reserve call and identifies a solution that consistently satisfies the constraints for elements in the uncertainty set.

[0068] It should be further appreciated that one or more embodiments described herein leverages the concept of uncertainty budgets to describe the uncertainty within the daily regulation signal and reserve call. This not only reduces the size of the uncertainty set but also depicts the frequency regulation signal and reserve call in a more accurate way. Thus, while the solution from the proposed algorithm is feasible for the possible regulation and reserve scenarios, the one or more embodiments described herein is less conservative than prior approaches.

[0069] One or more embodiments described herein can impose chance-constrained constraints instead of the constraints (3). To do this, the probability distribution of the frequency regulation signal and reserve call are estimated. Then, the chance-constrained constraints are reformulated into tractable constraints based on the estimation.

[0070] It is understood that one or more embodiments described herein is capable of being implemented in conjunction with any other type of computing environment now known or later developed. For example, FIG. 4 depicts a block diagram of a processing system 400 for performing battery system management according to one or more embodiments. In accordance with one or more embodiments described herein, the processing system 400 is an example of a cloud computing node of a cloud computing environment. In examples, processing system 400 has one or more central processing units (referred to also as“processors” or “processing resources” or “processing devices”) 421a, 421b, 421c, etc. (collectively or generically referred to as processor(s) 421 and / or as processing device(s)). In aspects of the present disclosure, each processor 421 can include a reduced instruction set computer (RISC) microprocessor. Processors 421 are coupled to a system memory 422 and / or various other components via a system bus 433. The system memory 422 can include one or more temporary and / or persistent memory devices, such as a random access memory (RAM) 423, a read-only memory (ROM) 424, and / or the like, including combinations and / or multiples thereof. The system bus 433 may include a basic input / output system (BIOS), which controls certain basic functions of processing system 400.

[0071] Further depicted are an input / output (I / O) adapter 427 and a network adapter 426 coupled to system bus 433. I / O adapter 427 may be a small computer system interface (SCSI) adapter that communicates with a hard disk 435 and / or a storage device 436 or any other similar component. I / O adapter 427, hard disk 435, and storage device 436 are collectively referred to herein as mass storage 434. Operating system 440 for execution on processing system 400 may be stored in mass storage 434. The network adapter 426 interconnects system bus 433 with an outside network 438 enabling processing system 400 to communicate with other such systems.

[0072] A display (e.g., a display monitor) 439 is connected to system bus 433 by display adapter 432, which may include a graphics adapter to improve the performance of graphics intensive applications and a video controller. In one aspect of the present disclosure, adapters 426, 427, and / or 432 may be connected to one or more I / O buses that are connected to system bus 433 via an intermediate bus bridge (not shown). Suitable I / O buses for connecting peripheral devices such as hard disk controllers, network adapters, and graphics adapters typically include common protocols, such as the Peripheral Component Interconnect (PCI). Additional input / output devices are shown as connected to system bus 433 via user interface adapter 428 and display adapter 432. A keyboard 429, mouse 430, and speaker 431 may be interconnected to system bus 433 via user interface adapter 428, which may include, for example, a Super I / O chip integrating multiple device adapters into a single integrated circuit.

[0073] In some aspects of the present disclosure, processing system 400 includes a graphics processing unit (GPU) 437. Graphics processing unit 437 is a specialized electronic circuit designed to manipulate and alter memory to accelerate the creation of images in aframe buffer intended for output to a display. In general, graphics processing unit 437 is very efficient at manipulating computer graphics and image processing, and has a highly parallel structure that makes it more effective than general-purpose CPUs for algorithms where processing of large blocks of data is done in parallel.

[0074] Thus, as configured herein, processing system 400 includes processing capability in the form of processors 421, storage capability including the system memory 422 and mass storage 434, input means such as keyboard 429 and mouse 430, and output capability including speaker 431 and display 439. In some aspects of the present disclosure, a portion of system memory 422 and mass storage 434 collectively store the operating system 440 to coordinate the functions of the various components shown in processing system 400.

[0075] Those skilled in the art will recognize that, for simplicity and clarity, the full structure and operation of all systems suitable for use with the present disclosure is not being depicted or described herein. Instead, only so much of the physical systems as is unique to the present disclosure or necessary for an understanding of the present disclosure is depicted and described. The remainder of the construction and operation of the systems disclosed herein may conform to any of the various current implementations and practices known in the art.

[0076] It is important to note that while the disclosure includes a description in the context of a fully functional system, those skilled in the art will appreciate that at least portions of the mechanism of the present disclosure are capable of being distributed in the form of a instructions contained within a machine-usable, computer-usable, or computer- readable medium in any of a variety of forms, and that the present disclosure applies equally regardless of the particular type of instruction or signal bearing medium or storage medium utilized to actually carry out the distribution. Examples of machine usable / readable or computer usable / readable mediums include: nonvolatile, hard-coded type mediums such as read only memories (ROMs) or erasable, electrically programmable read only memories (EEPROMs), and user-recordable type mediums such as floppy disks, hard disk drives and compact disk read only memories (CD-ROMs) or digital versatile disks (DVDs). In particular, computer readable mediums can include transitory and non- transitory mediums, unless otherwise limited in the claims appended hereto.

[0077] Although an exemplary embodiment of the present disclosure has been described in detail, those skilled in the art will understand that various changes, substitutions,variations, and improvements disclosed herein may be made without departing from the spirit and scope of the disclosure in its broadest form. In particular, the features and operations of various examples described herein and in the incorporated applications can be combined in any number of implementations.

[0078] None of the description in the present application should be read as implying that any particular element, step, or function is an essential element which must be included in the claim scope: the scope of patented subject matter is defined only by the allowed claims. Moreover, none of these claims are intended to invoke 35 USC §112(f) unless the exact words “means for” are followed by a participle.

[0079] As used herein the terms “include” and “comprise,” as well as derivatives thereof, mean inclusion without limitation; the term “or” is inclusive, meaning and / or; the phrases “associated with” and “associated therewith,” as well as derivatives thereof, may mean to include, be included within, interconnect with, contain, be contained within, connect to or with, couple to or with, be communicable with, cooperate with, interleave, juxtapose, be proximate to, be bound to or with, have, have a property of, or the like; and the term “controller” means any device, system or part thereof that controls at least one operation, whether such a device is implemented in hardware, firmware, software or some combination of at least two of the same. It should be noted that the functionality associated with any particular controller may be centralized or distributed, whether locally or remotely.Definitions for certain words and phrases are provided throughout this patent document, and those of ordinary skill in the art will understand that such definitions apply in many, if not most, instances to prior as well as future uses of such defined words and phrases. While some terms may include a wide variety of embodiments, the appended claims may expressly limit these terms to specific embodiments.

Claims

CLAIMSWhat is claimed is:

1. A computer-implemented method for battery system management, the method comprising: receiving a trigger event; determining a target battery level for a battery system based on the trigger event, the target battery level defining an amount of charge of the battery system that is desired at the beginning of a specified time interval; determining a baseline net power for the battery system to satisfy the target battery level; and controlling the battery system to achieve the baseline net power for the battery system while the battery system is participating in at least one of at least one energy market and at least one ancillary service market.

2. The computer-implemented method of claim 1, wherein the specified time interval is one of a plurality of time intervals occurring within a time period.

3. The computer-implemented method of claim 2, wherein the each of the plurality of time intervals is substantially one hour, and wherein the time period is substantially one day.

4. The computer-implemented method of claims 1 or 2, wherein controlling the battery system comprises determining a charging powerfor the battery system based at least in part on a minimum state of charge threshold Es, a minimum state of charge at one time step before an event associated with the trigger eventand a charging efficiency of the battery system.

5. The computer-implemented method of claim 4, wherein the charging power for the battery system is determined using the following equation:

6. The computer-implemented method of claim 4, wherein controlling the battery systemcomprises maintaining a state of charge of the battery system between the minimum state of charge threshold and a maximum state of charge threshold.

7. The computer-implemented method of claims 1 to 6, further comprising receiving a price forecast, the price forecast being a projected energy price of electrical power received from one or more sources.

8. The computer-implemented method of claim 7, wherein controlling the battery system comprises charging the battery system based on the price forecast and the trigger event prior to occurrence of an event associated with the trigger event.

9. The computer-implemented method of claims 1 to 8, wherein determining the baseline net power comprises solving a robust optimization problem to satisfy an equality constraint.

10. The computer-implemented method of claim 9, wherein the equality constraint requires a minimum state of charge of the battery system at one time step before occurrence of an event associated with the trigger event to be sufficient to charge the battery system to a desired level at the occurrence of the event.

11. The computer-implemented method of claim 10, wherein the desired level is fully charged.

12. The computer-implemented method of claims 1 to 11, wherein controlling the battery system is based on a regulation signal and a reserve call received from an operation associated with the at least one of the at least one energy market and the at least one ancillary service market.

13. A system comprising: a memory comprising computer readable instructions; and a processing device for executing the computer readable instructions, the computer readable instructions controlling the processing device to perform operations comprising: receiving a trigger event; determining a target battery level for a battery system based on the triggerevent, the target battery level defining an amount of charge of the battery system that is desired at the beginning of a specified time interval; determining a baseline net power for the battery system to satisfy the target battery level; and controlling the battery system to achieve the baseline net power for the battery system while the battery system is participating in at least one of at least one energy market and at least one ancillary service market.

14. The system of claim 13, wherein the specified time interval is one of a plurality of time intervals occurring within a time period.

15. The system of claims 13 or 14, wherein controlling the battery system comprises determining a charging powerfor the battery system based at least in part on a minimum state of charge threshold Es, a minimum state of charge at one time step before an event associated with the trigger event £^-1, and a charging efficiency of the battery system, wherein the charging power for the battery system is determined using the following equation:

16. The system of claim 15, wherein controlling the battery system comprises maintaining a state of charge of the battery system between the minimum state of charge threshold and a maximum state of charge threshold.

17. The system of claims 13 to 16, wherein the operations further comprise receiving a price forecast, the price forecast being a projected energy price of electrical power received from one or more sources, wherein controlling the battery system comprises charging the battery system based on the price forecast and the trigger event prior to occurrence of an event associated with the trigger event.

18. The system of claims 13 to 17, wherein determining the baseline net power comprises solving a robust optimization problem to satisfy an equality constraint.

19. The system of claim 18, wherein the equality constraint requires a minimum state of charge of the battery system at one time step before occurrence of an event associated withthe trigger event to be sufficient to charge the battery system to a desired level at the occurrence of the event.

20. The system of claim 19, wherein the desired level is fully charged.

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