Energy storage system capacity configuration method and device, computer equipment and storage medium

Abstract

The invention relates to the technical field of energy storage configuration, and discloses an energy storage system capacity configuration method and device, computer equipment and a storage medium. Firstly, capacity division is carried out based on the capacity of the energy storage system to obtain a plurality of candidate capacity values; and then, linear programming is performed based on resource parameters, constraint functions, decision variables and energy value data corresponding to each decision interval of the energy storage system, so that the complexity is simplified, the solving efficiency is improved, expected income data corresponding to each candidate capacity value is determined, and the decision intervals are obtained by performing discretization processing on reference time. And finally, under the condition that the change condition of the expected revenue data meets a preset condition, configuring the candidate capacity value corresponding to the expected revenue data as the target capacity of the energy storage system, thereby effectively assisting the user to perform optimal configuration calculation of the energy storage side capacity, providing support for the user to make a decision, and improving the user experience. Therefore, the optimal utilization of energy storage resources and the maximization of economic benefits are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage configuration, and in particular to a method, device, computer equipment and storage medium for configuring the capacity of an energy storage system. Background Art

[0002] With the rapid development of renewable energy and the widespread adoption of smart grids, energy storage systems are playing an increasingly prominent role in power systems. Energy storage systems not only balance electricity supply and demand and improve system flexibility, but also play a vital role in peak load shifting and valley filling, enhancing power supply reliability. By rationally allocating energy storage capacity, users can maximize economic benefits in the electricity market, for example, by exploiting the difference between peak and valley electricity prices for arbitrage and reducing basic electricity bills. However, over-allocation of energy storage capacity can lead to wasted investment; under-allocation of capacity can underutilize price differences and fail to maximize energy storage benefits. Therefore, a capacity allocation method for energy storage systems is needed to achieve optimal allocation of energy storage resources and maximize economic benefits. Summary of the Invention

[0003] The embodiments of this specification aim to solve at least one of the technical problems in the related art to a certain extent. To this end, the embodiments of this specification propose a method, apparatus, computer device and storage medium for energy storage system capacity configuration.

[0004] The present disclosure provides a method for configuring energy storage system capacity, the method comprising:

[0005] Performing capacity division based on the energy storage system capacity to obtain multiple candidate capacity values;

[0006] Determining expected revenue data corresponding to each candidate capacity value by performing linear programming based on resource parameters, constraint functions, decision variables, and energy value data corresponding to each decision interval of the energy storage system, wherein the decision interval is obtained by discretizing the reference time;

[0007] When the change in the expected profit data satisfies a preset condition, the candidate capacity value corresponding to the expected profit data is configured as the target capacity of the energy storage system.

[0008] In one embodiment, performing linear programming based on resource parameters, constraint functions, decision variables, and energy value data corresponding to each decision interval of the energy storage system to determine expected revenue data corresponding to each candidate capacity value includes:

[0009] Constructing an objective function based on resource parameters, decision variables, and energy value data corresponding to each decision interval of the energy storage system;

[0010] For any candidate capacity value, under the constraints of the candidate capacity value and the constraint function, the minimum value of the objective function is determined to be the expected revenue data corresponding to the candidate capacity value.

[0011] In one embodiment, the resource parameters corresponding to each decision interval of the energy storage system include: energy storage conversion coefficient, energy release conversion coefficient, energy storage power, energy release power and at least one of the candidate capacity values.

[0012] In one embodiment, the decision variables corresponding to each decision interval of the energy storage system include: energy storage duration and energy release duration.

[0013] In one embodiment, the constraint function includes at least one of the following:

[0014] The sum of the energy storage powers corresponding to multiple decision intervals is less than or equal to the preset total power supplied by the grid;

[0015] The sum of the energy release powers corresponding to the multiple decision intervals is less than or equal to the total power of the preset load demand.

[0016] In one embodiment, the constraint function includes:

[0017] The sum of the remaining energy storage capacity and the initial energy storage capacity corresponding to the multiple decision intervals is greater than or equal to zero and less than or equal to the candidate capacity value, wherein the initial energy storage capacity is the remaining energy storage capacity of the energy storage system before the reference time.

[0018] In one embodiment, the constraint function includes at least one of the following:

[0019] The energy storage duration corresponding to each decision interval is greater than or equal to zero;

[0020] The energy release time corresponding to each decision interval is greater than or equal to zero;

[0021] The sum of the energy storage duration and energy release duration corresponding to each decision interval is less than or equal to the duration of each decision interval.

[0022] The embodiments of this specification provide a device for configuring energy storage system capacity, the device comprising:

[0023] An energy storage system capacity division module, configured to perform capacity division based on the energy storage system capacity to obtain a plurality of candidate capacity values;

[0024] an expected revenue data determination module, configured to perform linear programming based on resource parameters, constraint functions, decision variables, and energy value data corresponding to each decision interval of the energy storage system to determine expected revenue data corresponding to each candidate capacity value, wherein the decision interval is obtained by discretizing a reference time;

[0025] The target capacity configuration module is configured to configure the candidate capacity value corresponding to the expected profit data as the target capacity of the energy storage system when the change in the expected profit data meets a preset condition.

[0026] An embodiment of this specification provides a computer device, which includes: a memory, and one or more processors communicatively connected to the memory; the memory stores instructions that can be executed by the one or more processors, and the instructions are executed by the one or more processors to enable the one or more processors to implement the steps of the method described in any of the above embodiments.

[0027] The embodiments of this specification provide a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the method described in any one of the above embodiments are implemented.

[0028] An embodiment of this specification provides a computer program product, which includes instructions. When the instructions are executed by a processor of a computer device, the computer device can perform the steps of the method described in any of the above embodiments.

[0029] In the implementation method of the above specification, first, the capacity is divided based on the capacity of the energy storage system to obtain multiple candidate capacity values. Then, linear programming is performed based on the resource parameters, constraint functions, decision variables and energy value data corresponding to each decision interval of the energy storage system, thereby simplifying the complexity and improving the solution efficiency to determine the expected profit data corresponding to each candidate capacity value, wherein the decision interval is obtained by discretizing the reference time. Finally, when the change in the expected profit data meets the preset conditions, the candidate capacity value corresponding to the expected profit data is configured as the target capacity of the energy storage system, which can effectively assist users in performing optimal configuration calculations on the energy storage side capacity, provide support for users to make decisions, and thus achieve optimal utilization of energy storage resources and maximize economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 A flow chart of a method for configuring energy storage system capacity provided in an embodiment of this specification;

[0031] Figure 2 A schematic diagram of a process for determining expected revenue data provided for an embodiment of this specification;

[0032] Figure 3a A schematic diagram of a charge and discharge operation sequence manually formulated based on experience provided in the embodiments of this specification;

[0033] Figure 3bA schematic diagram of a charging and discharging operation sequence formulated for the energy storage system scheduling method provided in the embodiments of this specification;

[0034] Figure 3c A scatter plot of expected revenue data and capacity for embodiments of this specification;

[0035] Figure 4 A schematic diagram of a device for configuring energy storage system capacity provided in an embodiment of this specification;

[0036] Figure 5 This is a diagram of the internal structure of a computer device provided in accordance with an embodiment of this specification. DETAILED DESCRIPTION

[0037] The following describes embodiments of the present invention in detail, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and are not to be construed as limiting the present invention.

[0038] With the rapid development of renewable energy and the widespread adoption of smart grids, energy storage systems are playing an increasingly prominent role in power systems. Energy storage systems not only balance electricity supply and demand and improve system flexibility, but also play a vital role in peak load shifting and valley filling, enhancing power supply reliability. By properly allocating energy storage capacity, users can maximize economic benefits in the electricity market, for example, by exploiting the price differential between peak and valley electricity prices and reducing basic electricity bills. However, over-allocation of energy storage capacity can lead to wasted investment; under-allocation of capacity can underutilize price differentials and fail to maximize energy storage benefits.

[0039] In related technologies, a user-side energy storage optimization configuration model is constructed by combining multiple revenue models such as peak-valley arbitrage, demand management, and demand response to maximize the net profit of the energy storage system throughout its life cycle. Alternatively, the net profit of the user-side energy storage throughout its life cycle is taken as the goal, and an objective function is constructed to comprehensively consider the impact of multiple revenue models. Alternatively, the kernel function method is used to optimize the ISODATA load clustering algorithm, and cluster analysis is performed on the daily electricity load curves of power users to ensure that the energy storage configuration corresponding to each type of load cluster has high adaptability. However, related technologies still have the following shortcomings:

[0040] 1. High model complexity and difficulty understanding: Some optimization models involve multiple parameters and constraints, making them inherently complex and challenging for non-expert users to understand and apply. Furthermore, the objective function and constraints are often complex, requiring specialized knowledge for in-depth analysis and solution.

[0041] 2. Insufficient solution accuracy: A large number of models usually use simplified electricity price models to estimate the return on investment over the entire life cycle, but do not consider the specific daily charging and discharging strategies and related micro data, which may lead to large deviations in the profit estimation results.

[0042] Based on the above analysis, the embodiment of this specification provides a method for configuring the capacity of an energy storage system. First, capacity division is performed based on the capacity of the energy storage system to obtain a plurality of candidate capacity values. Then, linear programming is performed based on the resource parameters, constraint functions, decision variables and energy value data corresponding to each decision interval of the energy storage system, thereby simplifying the complexity and improving the solution efficiency to determine the expected profit data corresponding to each candidate capacity value, wherein the decision interval is obtained by discretizing the reference time. Finally, when the change in the expected profit data meets the preset conditions, the candidate capacity value corresponding to the expected profit data is configured as the target capacity of the energy storage system, which can effectively assist users in performing optimal configuration calculations of the energy storage side capacity and provide support for users to make decisions, thereby achieving optimal utilization of energy storage resources and maximizing economic benefits.

[0043] This specification provides a method for configuring the capacity of an energy storage system. Figure 1 , the energy storage system capacity configuration method may include the following steps:

[0044] S110 , performing capacity division based on the energy storage system capacity to obtain multiple candidate capacity values.

[0045] Among them, energy storage systems can be divided into multiple types such as electricity storage, cold storage and heat storage.

[0046] Specifically, based on the actual operating environment and technical requirements of the energy storage system, it is first necessary to reasonably set the capacity of the energy storage system and determine a traversal range for the energy storage system capacity. The setting of this range should be based on the load demand of the energy storage system, the characteristics of the energy storage equipment, the economic analysis, and the constraints of the system scheduling. This range should cover all possible energy storage system capacity value ranges, including the system's minimum feasible energy storage capacity and maximum energy storage capacity limits, to ensure that the optimal energy storage system capacity can be found within the entire range. In order to further optimize the selection of energy storage system capacity, the energy storage system capacity needs to be subdivided. In this process, the changes in the system's energy storage demand under different operating modes should be considered, and scientific capacity division should be carried out in combination with factors such as electricity market price fluctuations, energy supply conditions, load demand curves, and the charging and discharging efficiency of energy storage equipment to obtain multiple candidate capacity values.

[0047] S120 , performing linear programming based on resource parameters, constraint functions, decision variables, and energy value data corresponding to each decision interval of the energy storage system to determine expected revenue data corresponding to each candidate capacity value.

[0048] The decision interval is obtained by discretizing the reference time.

[0049] Specifically, when discretizing the reference time to obtain multiple decision intervals, this division method offers flexibility, allowing for more detailed or coarse segmentation strategies to be selected based on actual circumstances. During the specific interval division process, the energy value data and storage and discharge power parameters within each interval remain constant. For example, storage power (e.g., charging storage and cooling storage) and discharge power (e.g., discharging and cooling) should maintain fixed values within their respective intervals. Furthermore, both storage and discharge power can exist simultaneously within the same interval, and their values can vary. Setting power to a fixed value is primarily because this stability facilitates subsequent linear modeling. However, if the storage and discharge power vary significantly over time, smaller intervals may be necessary to more accurately approximate the charging and discharging characteristics. During the operation of an energy storage system, each decision interval involves multiple resource parameters, which reflect the operating status and performance characteristics of the energy storage system at that moment. Decision variables are control variables that can be adjusted within the decision interval, while energy value data encompasses information on energy-related economic indicators. The constraint function is a mathematical expression describing the various restrictive conditions that must be followed during the operation of the energy storage system. By using a linear programming solver, a corresponding linear programming model is constructed based on the resource parameters, constraint functions, decision variables, and energy value data corresponding to each decision interval of the energy storage system. Next, one of the multiple candidate capacity values is selected as the basis, and in the process of solving the linear programming model based on this candidate capacity value, the feasible solution space range clearly defined by the constraint function is used. Within this given range, the optimal value of the decision variable corresponding to each decision interval under the candidate capacity value is accurately determined to determine the minimum data corresponding to the candidate capacity value, and this minimum data is used as the expected benefit data corresponding to the candidate capacity value. Then, without changing other parameters, the above process is repeated for each candidate capacity value among the multiple candidate capacity values to finally determine the expected benefit data corresponding to each candidate capacity value.

[0050] For example, the duration of the decision interval is T i , i=1…N. In each decision interval, there is only one energy value data, which is recorded as U i ,i=1…N. Where i is the i-th decision interval and N is the total number of decision intervals.

[0051] In some embodiments, the reference time can be discretized at equal intervals to obtain multiple decision intervals. Specifically, since the divisions within the reference time are equal intervals, the equal interval discretization of the reference time intervals can help accurately control the scheduling process in the time dimension. Through this discretization method, the reference time is divided into equal intervals to obtain multiple decision intervals. The energy storage and release scheduling in each decision interval can be adjusted according to changes in real-time demand and energy supply, thereby achieving flexible energy management. It should be noted that within each divided decision interval, the energy storage power remains consistent and the energy release power remains consistent.

[0052] For example, a day of 24 hours is used as a reference time, and a day is divided into 96 equally spaced decision intervals at intervals of 15 minutes.

[0053] In other implementations, the reference time can be discretized based on the gradient intervals of the energy value data to obtain multiple decision intervals. Specifically, because energy value data fluctuates over time, the energy value data within different time intervals is different. Therefore, the reference time can be discretized based on the gradient intervals of the energy value data to obtain multiple decision intervals, which can facilitate subsequent system scheduling. It should be noted that within each decision interval, the energy storage power and energy release power remain consistent.

[0054] For example, a 24-hour day is divided into multiple decision intervals based on the gradient of electricity prices. For example, the decision interval is 6:00-10:00: the electricity price is the flat price; the decision interval is 10:00-12:00: the electricity price is the peak price; the decision interval is 12:00-14:00: the electricity price is the peak price; the decision interval is 14:00-16:00: the electricity price is the peak price; the decision interval is 16:00-18:00: the electricity price is the flat price; and the decision interval is 18:00-6:00: the electricity price is the valley price.

[0055] S130: When the change in the expected profit data satisfies a preset condition, configure the candidate capacity value corresponding to the expected profit data as the target capacity of the energy storage system.

[0056] Specifically, the expected revenue data corresponding to each candidate capacity value must first be analyzed for trends in order to accurately determine its changes. Next, the determined changes in expected revenue data are compared with pre-set conditions. When the expected revenue data corresponding to a candidate capacity value meets the pre-set conditions, that candidate capacity value is configured as the target capacity of the energy storage system. The target capacity is the energy storage system capacity that, under current conditions, maximizes the energy storage system's revenue.

[0057] For example, the preset condition may be that the expected revenue data no longer increases. When observing changes in the expected revenue data, if the expected revenue data no longer increases, the candidate capacity value corresponding to the expected revenue data is configured as the target capacity of the energy storage system.

[0058] It's important to note that, based on the target capacity, targeted transformation processing is performed on the values of the decision variables in each decision interval corresponding to the expected revenue data of the target capacity, thereby accurately determining the energy storage and discharge sequence corresponding to each decision interval. Ultimately, based on these energy storage and discharge sequences corresponding to each decision interval, the energy storage system implements scientific and reasonable energy storage and discharge scheduling operations, thereby efficiently achieving the optimization goals of the scheduling strategy, ensuring that the energy storage system can maximize economic benefits while satisfying various constraints.

[0059] In the above implementation, first, the capacity is divided based on the capacity of the energy storage system to obtain multiple candidate capacity values. Then, linear programming is performed based on the resource parameters, constraint functions, decision variables and energy value data corresponding to each decision interval of the energy storage system, thereby simplifying the complexity and improving the solution efficiency to determine the expected profit data corresponding to each candidate capacity value, wherein the decision interval is obtained by discretizing the reference time. Finally, when the change in the expected profit data meets the preset conditions, the candidate capacity value corresponding to the expected profit data is configured as the target capacity of the energy storage system, which can effectively assist users in performing optimal configuration calculations on the energy storage side capacity and provide support for users to make decisions, thereby achieving optimal utilization of energy storage resources and maximizing economic benefits.

[0060] In some embodiments, see Figure 2 , performing linear programming based on resource parameters, constraint functions, decision variables, and energy value data corresponding to each decision interval of the energy storage system to determine the expected benefit data corresponding to each candidate capacity value may include the following steps:

[0061] S210 , constructing an objective function according to resource parameters, decision variables, and energy value data corresponding to each decision interval of the energy storage system.

[0062] S220 . For any candidate capacity value, under the constraints of the candidate capacity value and the constraint function, determine that the minimum value of the objective function is the expected revenue data corresponding to the candidate capacity value.

[0063] Specifically, during the operation of an energy storage system, each decision interval involves various resource parameters, which reflect the operating status and performance characteristics of the energy storage system at that moment. Decision variables are control quantities that can be adjusted within the decision interval, while energy value data encompasses energy-related economic indicators. By comprehensively considering the interrelationships among resource parameters, decision variables, and energy value data, an objective function is constructed that quantitatively measures the operational benefits of the energy storage system. Constraint functions mathematically describe the various restrictive conditions that must be observed during the operation of the energy storage system.

[0064] One of multiple candidate capacity values is selected as the basis for optimizing the objective function within the feasible solution space defined by the constraint function. Specifically, the optimization algorithm searches for the value of the decision variable within each decision interval that minimizes the objective function, while satisfying the constraints. The minimum value of the objective function is then used as the expected return for that candidate capacity value.

[0065] In the above embodiment, an objective function is constructed based on the resource parameters, decision variables, and energy value data corresponding to each decision interval of the energy storage system. For any candidate capacity value, under the constraints of the candidate capacity value and the constraint function, the minimum value of the objective function is determined to be the expected benefit data corresponding to the candidate capacity value, providing a data basis for the subsequent determination of the target capacity.

[0066] In some embodiments, the resource parameters corresponding to each decision interval of the energy storage system may include: at least one of: energy storage conversion coefficient, energy release conversion coefficient, energy storage power, energy release power, and candidate capacity values.

[0067] Specifically, energy storage systems can be uniformly described using two processes: energy storage and energy discharge. The energy conversion during the storage and discharge processes is not completely efficient, so there are corresponding energy storage conversion coefficients and energy discharge conversion coefficients. The energy storage conversion coefficient refers to the ratio between the actual stored energy and the consumed electrical energy during the energy storage process. The energy discharge conversion coefficient refers to the ratio between the effective energy output and the stored energy during the energy discharge process. The candidate capacity value is a capacity value that can be determined based on the requirements and application scenarios of the energy storage system. The energy storage power is the power consumed during energy storage. The energy discharge power is the power reduced from the power drawn from the grid at that moment when the energy storage system is output. It should be noted that the actual stored energy power needs to be obtained by multiplying the energy storage power by the energy storage conversion coefficient.

[0068] In some embodiments, assuming that there are M energy storage systems, the energy storage conversion coefficient of each energy storage system can be expressed as c j , j=1…M represents that the energy conversion coefficient of each energy storage system can be expressed as d j , j=1…M means that the energy storage power of each energy storage system can be expressed as Pij , i=1…N, j=1…M, the energy release power of each energy storage system can be expressed as Q ij , i=1…N, j=1…M, the candidate capacity value of each energy storage system can be expressed as C ij , i = 1…N, j = 1…M, where j is the jth energy storage system, i is the i-th decision interval, and M is the total number of energy storage systems.

[0069] For example, the energy storage system can be a power storage system. The energy storage process is the charging process, and the energy release process is the discharging process. During discharge, the consumption of electricity directly from the grid can be reduced. For example, if 100 kWh of energy is consumed and the power storage system only stores 90 kWh, the energy storage conversion factor is 0.9. If the power storage system stores 90 kWh and can actually release 81 kWh, the energy release conversion factor is 0.9.

[0070] Energy storage systems can be cold storage systems. The energy storage process involves using a chiller or other refrigeration system to cool water or make ice. The energy release process involves using cold water or ice to provide cooling, reducing the need for direct grid electricity consumption. However, there will be corresponding losses due to mechanical and heat transfer losses.

[0071] Energy storage systems can be thermal storage systems. Similar to cold storage systems, the energy storage process utilizes a heat source (such as an electric water heater or thermal storage heater) to store hot water or thermal energy. The energy release process utilizes this stored hot water or thermal energy for heating or hot water supply, reducing the amount of electricity drawn directly from the grid.

[0072] A candidate capacity value of 100KWh is selected for the power storage system, which is the capacity that the power storage system can achieve.

[0073] For a cold storage system, assuming cooling is provided during a cooling period, the chiller's operation is reduced during this period, and the total cold storage capacity is released from 100% to 0. If the energy consumption required to operate the chiller during this period is 100 kWh, the capacity of the cold storage system can be defined as 100 kWh. The same method can be used to define capacity for thermal storage systems.

[0074] If a storage system consumes 100 kW of energy when charging, its storage power is defined as 100 kW. When discharging, it outputs 90 kW of energy, reducing the power drawn from the grid by 90 kW, so its discharge power is 90 kW.

[0075] If a cold storage system is used for cooling, assuming that the same cooling load is achieved, the refrigeration machine needs to be turned on for cooling, and the power consumption is 100KW, then the energy release power of the cold storage system is defined as 100KW.

[0076] In the above embodiment, the resource parameters corresponding to each decision interval of the energy storage system may include at least one of the energy storage conversion coefficient, the energy release conversion coefficient, the energy storage power, the energy release power, and the candidate capacity value, providing a data basis for the subsequent determination of the energy storage and release scheduling strategy.

[0077] In some embodiments, the decision variables corresponding to each decision interval of the energy storage system may include: energy storage duration and energy release duration.

[0078] Specifically, because the energy value data corresponding to each decision interval is different, it is necessary to dynamically adjust the energy storage and release durations for each decision interval based on the energy value data of different decision intervals to ensure maximum benefits. Therefore, the decision variables corresponding to each decision interval of the energy storage system can include energy storage duration and energy release duration.

[0079] For example, taking N decision intervals and M energy storage systems as an example, the decision variables corresponding to each decision interval of the energy storage system may include: energy storage time S ij (i=1…N,j=1…M) and energy release time R ij (i=1…N,j=1…M). Where i is the i-th decision interval, and j is the j-th energy storage system.

[0080] In the above embodiment, the decision variables corresponding to each decision interval of the energy storage system may include energy storage duration and energy release duration, providing a basis for subsequent determination of energy storage and release scheduling of the energy storage system.

[0081] In some embodiments, the constraint function includes at least one of the following:

[0082] The sum of the energy storage powers corresponding to the multiple decision intervals is less than or equal to the preset total power supplied by the power grid.

[0083] The sum of the energy release powers corresponding to the multiple decision intervals is less than or equal to the total power of the preset load demand.

[0084] Specifically, at any moment in the operation of the energy storage system, the sum of the energy storage powers of all power devices involved in energy storage must be less than or equal to the total power that the grid can provide at that moment. Because the total power of the grid is limited, it not only has to meet the electricity demand of the existing load, but may also need to reserve a certain amount of capacity to deal with emergencies or as a backup. Therefore, the energy storage system must reasonably allocate power when storing energy to avoid excessive occupation of grid resources, thereby affecting the normal operation and stability of the grid. Therefore, the constraint function can include the sum of the energy storage powers corresponding to multiple decision intervals being less than or equal to the preset total power supply of the grid to ensure that the energy storage system does not place an excessive burden on the grid during the entire operation cycle.

[0085] When the energy storage system discharges energy, the total power demanded by the load is the minimum requirement to meet user electricity needs. If the discharge power is too high, resulting in an oversupply of electricity, this can cause problems such as increased grid frequency and voltage instability, impacting power quality and even causing power outages. Therefore, the energy storage system's discharge power must match the load demand to ensure safe and stable grid operation. Therefore, within multiple decision intervals, the total discharge power of the energy storage system must be less than or equal to the total power demanded by the preset loads to avoid damage to the energy storage equipment due to excessive discharge or the inability to respond promptly to subsequent load changes.

[0086] For example, taking multiple energy storage systems as an example, the constraint functions of energy storage power and energy release power corresponding to each energy storage system are as follows:

[0087]

[0088] Among them, P ij is the energy storage power, Q ij is the energy release power, i is the i-th decision interval, j is the j-th energy storage system, N is the total number of decision intervals, M is the total number of energy storage systems, ES i Total power supplied to the preset grid, ER i is the total power required by the preset load.

[0089] In the above embodiment, the constraint functions of the total energy storage power and the total energy release power corresponding to multiple decision intervals are used to comprehensively consider various constraints of the energy storage system in the subsequent process, optimize the energy storage and release processes, and improve energy utilization.

[0090] In some embodiments, the constraint function may include: the sum of the remaining energy storage capacity and the initial energy storage capacity corresponding to the multiple decision intervals is greater than or equal to zero and less than or equal to the candidate capacity value.

[0091] Among them, the initial energy storage capacity is the remaining energy storage capacity of the energy storage system before the reference time.

[0092] Specifically, starting from the first decision interval, the sum of the remaining energy storage capacity and the initial energy storage capacity corresponding to that interval is calculated and constrained to be greater than or equal to zero and less than or equal to the candidate capacity value. This constraint ensures that the total capacity of the energy storage system will not reach a negative value or exceed the design capacity under any circumstances, thereby ensuring the safe operation of the energy storage system.

[0093] Based on the constraints imposed on the first decision interval, the remaining energy storage capacity corresponding to the first and second decision intervals combined with the initial energy storage capacity are accumulated, and then the same constraints are applied. This ensures that the accumulated value is greater than or equal to zero and less than or equal to the candidate capacity value, ensuring that the total capacity of the energy storage system remains within a safe range when multiple decision intervals are continuously operated.

[0094] Repeat the above process for each additional decision interval. The remaining energy storage capacity accumulated in that decision interval and the previous decision interval is added to the initial energy storage capacity, and the constraint is applied. This ensures that within any decision interval, the total capacity of the energy storage system meets the requirement of being greater than or equal to zero and less than or equal to the candidate capacity value.

[0095] For example, taking multiple energy storage systems as an example, the constraint function corresponding to each energy storage system is as follows:

[0096]

[0097] Among them, C 0j is the initial energy storage capacity of the jth energy storage system, C ij is the candidate capacity value of the j-th energy storage system, S ij is the energy storage duration, R ij is the energy release time, P ij is the energy storage power, Q ij is the energy release power, c j is the energy storage conversion coefficient, d j is the energy conversion coefficient, i is the i-th decision interval, j is the j-th energy storage system, N is the total number of decision intervals, M is the total number of energy storage systems, and k is the cumulative number of decision intervals up to the k-th decision interval.

[0098] In the above embodiment, the sum of the remaining energy storage capacity and the initial energy storage capacity corresponding to the multiple decision intervals is greater than or equal to zero and less than or equal to the candidate capacity value, so that various constraints of the energy storage system can be comprehensively considered in the subsequent process to optimize the energy storage and release processes and improve energy utilization.

[0099] In some embodiments, the constraint function includes at least one of the following:

[0100] The energy storage duration corresponding to each decision interval is greater than or equal to zero.

[0101] The energy release time corresponding to each decision interval is greater than or equal to zero.

[0102] The sum of the energy storage duration and energy release duration corresponding to each decision interval is less than or equal to the duration of each decision interval.

[0103] Specifically, the energy storage process is the process of converting external electrical energy into chemical energy or other forms of potential energy and storing it in the energy storage system. This process must occupy a non-negative duration in time, so the energy storage duration corresponding to each decision interval is greater than or equal to zero.

[0104] Energy release is the process of converting chemical energy or other forms of potential energy stored in the energy storage system into usable energy such as electrical energy. Its duration is also non-negative, so the energy release duration corresponding to each decision interval is greater than or equal to zero.

[0105] Within each decision interval, by constraining the energy storage time and energy release time, limiting the sum of the two to be less than or equal to the duration of the decision interval, the purpose is to avoid the situation where the energy storage time and energy release time in the same decision interval are too long and interfere with each other or cannot be reasonably completed. This helps to ensure that the energy storage system can efficiently complete the corresponding charging and discharging tasks within each decision interval.

[0106] For example, taking multiple energy storage systems as an example, the constraint function of each decision interval corresponding to each energy storage system is as follows:

[0107] S ij ≥0, i=1…N, j=1…M

[0108] R ij ≥0, i=1…N, j=1…M

[0109] S ij +R ij ≤T ij , i=1…N,j=1…M

[0110] Among them, S ij is the energy storage duration, R ij is the energy release time, T ij is the duration of the decision interval, i is the i-th decision interval, j is the j-th energy storage system, N is the total number of decision intervals, and M is the total number of energy storage systems.

[0111] In the above implementation, constraints are imposed based on the constraint functions of the energy storage duration and energy release duration corresponding to each decision interval, so that various constraints of the energy storage system can be comprehensively considered in the subsequent process to optimize the energy storage and release processes and improve energy utilization.

[0112] In some embodiments, the objective function is as follows:

[0113]

[0114] Among them, J is the value of the objective function, U i is the energy value data, S ij is the energy storage duration, R ijis the energy release time, P ij is the energy storage power, Q ij is the energy release power, c j is the energy storage conversion coefficient, d j is the energy conversion coefficient, i is the i-th decision interval, j is the j-th energy storage system, N is the total number of decision intervals, and M is the total number of energy storage systems.

[0115] Taking the power storage system as an example, suppose a factory is equipped with a power storage system named HA1. The candidate capacity value of the power storage system HA1 can be initially configured as 2362kWh. The power storage system HA1 has two energy storage power options: slow charging power of 474KW and fast charging power of 1542KW; the energy discharge power is 1200KW. The overall energy loss of the power storage and discharge cycle of the power storage system HA1 is approximately 15%. Based on the gradient division interval of the electricity price, the day is discretized to obtain multiple decision intervals. Please refer to Table 1 for the time period and electricity price corresponding to each decision interval:

[0116] Decision interval Electricity price (yuan / KWh) 1. Valley 0:00-8:00 0.358 2. Flat 8:00-10:00 0.7668 3. Peak 10:00-11:00 1.2073 4. Spike 11:00-12:00 1.4857 5. Ping 12:00-14:00 0.7668 6. Peak 14:00-15:00 1.2073 7. Spike 15:00-17:00 1.4857 8. Peak 17:00-19:00 1.2073 9. Ping 19:00-24:00 0.7668

[0117] Table 1

[0118] In related technologies, operators have developed energy storage and release scheduling strategies based on experience, forming the following operation sequence. Figure 3a ,The specific steps of this scheduling strategy are as follows:

[0119] 1. Energy storage is performed in the decision-making period of 0:00-8:00, using slow charging power, and a full charge is achieved in about 6 hours.

[0120] 2. No energy storage and release operations will be performed during the decision period of 8:00-10:00.

[0121] 3. Perform energy release operations in the decision-making interval 10:00-11:00 and the decision-making interval 11:00-12:00.

[0122] 4. Energy storage is performed during the decision period of 12:00-14:00, using fast charging power, and energy storage is performed until 14:00.

[0123] 5. Perform energy release operation in the decision-making period of 14:00-15:00.

[0124] 6. In the subsequent decision-making interval, the energy discharge operation is performed until the remaining power is discharged.

[0125] According to the above scheduling strategy, the expected daily profit data is approximately 2,766.61 yuan.

[0126] The embodiments of this specification provide an embodiment of a method for configuring energy storage system capacity that can optimize the energy storage and discharge scheduling strategy based on operator experience and determine the target capacity. Specifically, since there is only one energy storage system, the value of j is 1. The resource parameters corresponding to each decision interval of the power storage system HA1 are shown in Table 2:

[0127]

[0128] Table 2

[0129] Based on the decision variables, the resource parameters in Table 2, and the electricity price, the objective function is constructed. Under the constraints of the above constraint function, the values of the decision variables corresponding to each decision interval corresponding to the minimum value of the objective function, namely the energy storage duration and energy release duration, are determined. The values of the decision variables corresponding to each decision interval are shown in Table 3:

[0130]

[0131] Table 3

[0132] From the results in Table 3, we can see that the optimized scheduling strategy for the storage system HA1 with a candidate capacity of 2362 kWh is as follows: energy storage is performed during the decision interval 0:00-8:00, using slow charging power to fully charge the battery in about 6 hours; no energy storage or discharge is performed during the decision interval 8:00-10:00; energy is discharged for about 0.968 hours during the decision interval 10:00-11:00, retaining some power; energy is discharged for 1 hour during the decision interval 11:00-12:00, completely releasing the stored energy; then, energy storage is performed during the decision interval 12:00-14:00, using fast charging power to fully charge the battery; no energy storage or discharge is performed during the decision interval 14:00-15:00; and energy discharge is performed for about 1.968 hours during the decision interval 15:00-17:00, releasing the remaining energy. Figure 3b Compared with the scheduling strategy formulated by manual experience, the expected profit data of the optimized scheduling strategy is increased to 3269.32 yuan.

[0133] Then, under the condition that all parameters and constraint functions remain unchanged, only the candidate capacity value is changed, and the above operation is repeated to determine the expected profit data corresponding to each candidate capacity value. Refer to Table 4, it can be seen that after the candidate capacity value increases from 4200kWh to 4300kWh, the expected profit data no longer changes. It can be determined that 4300kWh is the optimal configuration capacity, that is, the target capacity, of the power storage system HA1. Figure 3c ,You can also visualize the relationship between expected revenue data and capacity to obtain a scatter plot, thereby determining the target capacity.

[0134] Capacity (KWh) Expected earnings data (yuan) 2362 3269.32 2600 3478.59 2800 3635.82 3000 3793.04 3200 3950.2 3400 4022.46 3600 4083.5 3800 4144.54 4000 4205.57 4100 4236.09 4200 4266.6 4300 4281.99 4600 4281.99 4800 4281.99 5000 4281.99 6000 4281.99

[0135] Table 4

[0136] In addition, it should be noted that if a more precise target capacity is required, a more detailed capacity division of the search range between 4200kWh and 4300kWh is required to determine a more precise target capacity of the energy storage system.

[0137] This specification provides a device 400 for configuring the capacity of an energy storage system. Figure 4 The energy storage system capacity configuration device 400 includes: an energy storage system capacity division module 410 , an expected revenue data determination module 420 , and a target capacity configuration module 430 .

[0138] An energy storage system capacity division module 410 is configured to perform capacity division based on the energy storage system capacity to obtain a plurality of candidate capacity values;

[0139] an expected revenue data determination module 420 for performing linear programming based on resource parameters, constraint functions, decision variables, and energy value data corresponding to each decision interval of the energy storage system to determine expected revenue data corresponding to each candidate capacity value, wherein the decision interval is obtained by discretizing a reference time;

[0140] The target capacity configuration module 430 is configured to configure the candidate capacity value corresponding to the expected revenue data as the target capacity of the energy storage system if the change in the expected revenue data satisfies a preset condition.

[0141] For a detailed description of the energy storage system capacity configuration device, please refer to the description of the energy storage system capacity configuration method above, which will not be repeated here.

[0142] In some embodiments, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 5As shown. The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a method for configuring the capacity of an energy storage system is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad provided on the computer device housing, or an external keyboard, touchpad or mouse.

[0143] Those skilled in the art will understand that Figure 5 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution disclosed in this specification, and does not constitute a limitation on the computer device to which the solution disclosed in this specification is applied. Specifically, the computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0144] In some embodiments, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the method steps in the above embodiments when executing the computer program.

[0145] An embodiment of this specification provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the method in any of the above embodiments are implemented.

[0146] One embodiment of the present specification provides a computer program product, which includes instructions. When the instructions are executed by a processor of a computer device, the computer device is enabled to perform the steps of the method of any of the above embodiments.

[0147] It should be noted that the logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic device), a portable computer disk cartridge (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and portable compact disc read-only memory (CDROM). Furthermore, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or otherwise processing it in a suitable manner if necessary, and then storing it in a computer memory.

Claims

1. A method for configuring energy storage system capacity, characterized in that: The method comprises: Performing capacity division based on the energy storage system capacity to obtain multiple candidate capacity values; Determining expected revenue data corresponding to each candidate capacity value by performing linear programming based on resource parameters, constraint functions, decision variables, and energy value data corresponding to each decision interval of the energy storage system, wherein the decision interval is obtained by discretizing the reference time; When the change in the expected profit data satisfies a preset condition, the candidate capacity value corresponding to the expected profit data is configured as the target capacity of the energy storage system.

2. The method according to claim 1, characterized in that The performing of linear programming based on resource parameters, constraint functions, decision variables, and energy value data corresponding to each decision interval of the energy storage system to determine expected revenue data corresponding to each candidate capacity value includes: Constructing an objective function based on resource parameters, decision variables, and energy value data corresponding to each decision interval of the energy storage system; For any candidate capacity value, under the constraints of the candidate capacity value and the constraint function, the minimum value of the objective function is determined to be the expected revenue data corresponding to the candidate capacity value.

3. The method according to claim 1, characterized in that The resource parameters corresponding to each decision interval of the energy storage system include: an energy storage conversion coefficient, an energy release conversion coefficient, energy storage power, energy release power and at least one of the candidate capacity values.

4. The method according to claim 1, wherein The decision variables corresponding to each decision interval of the energy storage system include: energy storage time and energy release time.

5. The method according to claim 3, characterized in that The constraint function includes at least one of the following: The sum of the energy storage powers corresponding to multiple decision intervals is less than or equal to the preset total power supplied by the grid; The sum of the energy release powers corresponding to the multiple decision intervals is less than or equal to the total power of the preset load demand.

6. The method according to claim 3, characterized in that The constraint function includes: The sum of the remaining energy storage capacity and the initial energy storage capacity corresponding to the multiple decision intervals is greater than or equal to zero and less than or equal to the candidate capacity value, wherein the initial energy storage capacity is the remaining energy storage capacity of the energy storage system before the reference time.

7. The method according to claim 4, characterized in that The constraint function includes at least one of the following: The energy storage duration corresponding to each decision interval is greater than or equal to zero; The energy release time corresponding to each decision interval is greater than or equal to zero; The sum of the energy storage duration and energy release duration corresponding to each decision interval is less than or equal to the duration of each decision interval.

8. A device for configuring energy storage system capacity, characterized in that: The device comprises: An energy storage system capacity division module, configured to perform capacity division based on the energy storage system capacity to obtain a plurality of candidate capacity values; an expected revenue data determination module, configured to perform linear programming based on resource parameters, constraint functions, decision variables, and energy value data corresponding to each decision interval of the energy storage system to determine expected revenue data corresponding to each candidate capacity value, wherein the decision interval is obtained by discretizing a reference time; The target capacity configuration module is configured to configure the candidate capacity value corresponding to the expected profit data as the target capacity of the energy storage system when the change in the expected profit data meets a preset condition.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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