Control method and device of energy storage power station and nonvolatile storage medium

Abstract

The invention discloses a control method and device of an energy storage power station and a nonvolatile storage medium. The method comprises the following steps: acquiring an energy storage power station parameter, a retail electricity price, a photovoltaic generating capacity and an electrical load in a power distribution network; determining a plurality of charge states of the energy storage power station based on the parameters of the energy storage power station; discretizing charging and discharging operations of the energy storage power station to obtain a plurality of operations; based on the plurality of charge states and the plurality of operations of the energy storage power station, obtaining a decision set of the plurality of charge states; traversing the decision set based on the retail electricity price, the photovoltaic generating capacity and the electrical load to obtain a total power cost value corresponding to the decision; selecting a decision of which the total power cost value meets a preset condition as a target charge state and a target decision; and controlling the energy storage power station based on the target charge state and the target decision. According to the invention, the technical problems that the charging and discharging control strategy of the energy storage power station is not optimized enough and the economic benefit cannot be maximized because other factors are not fully considered in the regulation and control of the current energy storage power station are solved.

Description

Technical Field

[0001] This invention relates to the field of power distribution network technology, and more specifically, to a control method, device, and non-volatile storage medium for an energy storage power station. Background Technology

[0002] Currently, county-level power distribution networks face an increasingly complex energy supply and demand environment. Most energy storage power station control strategies rely on static electricity prices and estimated photovoltaic output, often neglecting the impact of real-time electricity price fluctuations on the economics of energy storage systems. For example, when photovoltaic power generation exceeds the electricity load, existing technologies may not be able to fully utilize this excess energy, leading to low economic efficiency. Furthermore, the charging and discharging power adjustment mechanisms of energy storage systems are typically relatively simple, making it difficult to dynamically optimize based on real-time changes in building electricity load. Especially under conditions of frequent peak and off-peak electricity consumption, optimal matching of charging and discharging power is often impossible, further reducing the operating efficiency and economic benefits of the energy storage system.

[0003] There is currently no effective solution to the above problems. Summary of the Invention

[0004] This invention provides a control method, device, and non-volatile storage medium for an energy storage power station, which at least solves the technical problem that current energy storage power station regulation does not fully consider other factors, resulting in an insufficiently optimized charging and discharging control strategy and an inability to maximize economic benefits.

[0005] According to one aspect of the present invention, a control method for an energy storage power station is provided, comprising: acquiring parameters of the energy storage power station, retail electricity price, photovoltaic power generation, and electricity load in a county-level distribution network; determining multiple states of charge (SOCs) of the energy storage power station based on the SOC parameters; discretizing the charging and discharging operations of the energy storage power station to obtain multiple operations corresponding to the energy storage power station; obtaining decision sets corresponding to each of the multiple SOCs based on the multiple SOCs and the multiple operations corresponding to the energy storage power station; traversing the decision sets corresponding to each of the multiple SOCs based on the retail electricity price, photovoltaic power generation, and electricity load to obtain the total electricity cost value corresponding to the decisions in the decision sets corresponding to each of the multiple SOCs; selecting the decisions in the decision sets corresponding to the SOCs whose total electricity cost value satisfies a preset condition as the target SOC and target decision; and controlling the energy storage power station based on the target SOC and target decision.

[0006] Optionally, based on the parameters of the energy storage power station, multiple states of charge of the energy storage power station are determined, including: based on the parameters of the energy storage power station, determining the maximum charge and discharge power and the depth of discharge of the energy storage power station; based on the maximum charge and discharge power and the depth of discharge, determining the state of charge boundary of the energy storage power station; and based on the state of charge boundary, discretizing the state of charge of the energy storage power station to determine multiple states of charge.

[0007] Optionally, the charging and discharging operations of the energy storage power station are discretized to obtain multiple operations corresponding to the energy storage power station, including: obtaining the charging and discharging power step size; determining the maximum charging and discharging power corresponding to the energy storage power station based on the parameters of the energy storage power station; and discretizing the charging and discharging operations of the energy storage power station based on the maximum charging and discharging power and the charging and discharging power step size to obtain multiple operations corresponding to the energy storage power station.

[0008] Optionally, based on multiple states of charge of the energy storage power station and multiple operations corresponding to the energy storage power station, a decision set corresponding to each of the multiple states of charge is obtained, including: applying the multiple operations corresponding to the energy storage power station to the multiple states of charge to obtain the target states of charge corresponding to each of the multiple operations corresponding to the multiple states of charge; obtaining the maximum state of charge and the minimum state of charge corresponding to the energy storage power station; filtering out the target states of charge that are outside the intervals corresponding to the maximum state of charge and the minimum state of charge among the target states of charge corresponding to the multiple operations corresponding to the multiple states of charge, and determining the final state of charge corresponding to each of the multiple states of charge; and determining the decision set corresponding to each of the multiple states of charge based on the operations corresponding to the final state of charge corresponding to the multiple states of charge.

[0009] Optionally, based on retail electricity price, photovoltaic power generation, and electricity load, the decision sets corresponding to multiple states of charge are traversed to obtain the total electricity cost value corresponding to the decisions in the decision sets corresponding to the multiple states of charge. Determining the total electricity cost value corresponding to any target decision in the decision sets corresponding to the multiple states of charge includes: when the target decision is to control the charging of the energy storage station, determining the charging amount corresponding to the energy storage station; determining the first additional purchase amount based on electricity load and photovoltaic power generation; determining the total electricity cost corresponding to the target decision based on retail electricity price, the charging amount corresponding to the energy storage station, and the additional purchase amount; and / or, when the target decision is to control the discharging of the energy storage station, determining the discharging amount of the energy storage station; determining the second additional purchase amount based on electricity load, photovoltaic power generation, and the discharging amount of the energy storage station; determining the total electricity cost corresponding to the target decision based on retail electricity price, the discharging amount of the energy storage station, and the second additional purchase amount; and / or, when the target decision is not to operate the energy storage station, determining the purchase amount based on electricity load and photovoltaic power generation; and determining the total electricity cost value corresponding to the target decision based on the purchase amount and retail electricity price.

[0010] Optionally, when the objective decision is to control the discharge of the energy storage power station, the discharge amount of the energy storage power station is determined based on the following formula:

[0011] ,

[0012] in, This refers to the discharge capacity of the energy storage power station. To determine the state of charge of the energy storage power station before executing the target decision, To determine the state of charge of the energy storage power station after implementing the target decision, The total capacity of the energy storage power station This refers to the discharge efficiency of the energy storage power station.

[0013] According to another aspect of the present invention, a control device for an energy storage power station is also provided, comprising: an acquisition module for acquiring parameters of the energy storage power station, retail electricity price, photovoltaic power generation, and electricity load in a county-level power distribution network; a first determination module for determining multiple states of charge (SOCs) of the energy storage power station based on the SOC parameters; a discretization module for discretizing the charging and discharging operations of the energy storage power station to obtain multiple operations corresponding to the energy storage power station; a second determination module for obtaining decision sets corresponding to each of the multiple SOCs based on the multiple SOCs and the multiple operations corresponding to the energy storage power station; a traversal module for traversing the decision sets corresponding to each of the multiple SOCs based on the retail electricity price, photovoltaic power generation, and electricity load to obtain the total electricity cost value corresponding to the decisions in the decision sets corresponding to each of the multiple SOCs; a selection module for selecting the decisions in the decision sets corresponding to the SOCs whose total electricity cost value satisfies preset conditions, as the target SOC and target decision; and a control module for controlling the energy storage power station based on the target SOC and target decision.

[0014] According to another aspect of the present invention, a non-volatile storage medium is also provided, the non-volatile storage medium including a stored program, wherein, when the program is running, the device where the non-volatile storage medium is located is controlled to execute any of the above-described control methods for an energy storage power station.

[0015] According to another aspect of the present invention, a computer device is also provided, the computer device including a processor, the processor being configured to run a program, wherein the program executes any of the above-described control methods for an energy storage power station.

[0016] According to another aspect of the present invention, a computer program product is also provided, including a computer program that, when executed by a processor, implements any of the above-described control methods for an energy storage power station.

[0017] In this embodiment of the invention, a control method for an energy storage power station is adopted. This method involves acquiring parameters of the energy storage power station, retail electricity price, photovoltaic power generation, and electricity load in a county-level power distribution network. Based on the energy storage power station parameters, multiple states of charge (SOCs) of the energy storage power station are determined. The charging and discharging operations of the energy storage power station are discretized to obtain multiple corresponding operations. Based on the multiple SOCs and corresponding operations, decision sets corresponding to each SOC are obtained. Based on the retail electricity price, photovoltaic power generation, and electricity load, the decision sets corresponding to each SOC are traversed to obtain the total electricity cost value corresponding to the decisions in each SOC. The decision set corresponding to the SOC that satisfies the preset conditions is selected as the target SOC and target decision. Based on the target SOC and target decision, the energy storage power station is controlled. This achieves the goal of comprehensively considering the operating states of multiple devices to regulate the energy storage power station, thereby improving the technical effect of energy utilization and economic benefits. Furthermore, it solves the technical problem that current energy storage power station regulation does not fully consider other factors, resulting in suboptimal charging and discharging control strategies and an inability to maximize economic benefits. Attached Figure Description

[0018] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0019] Figure 1 A hardware block diagram of a computer terminal for implementing a control method for an energy storage power station is shown.

[0020] Figure 2 This is a flowchart illustrating the control method for an energy storage power station provided according to an embodiment of the present invention;

[0021] Figure 3 This is a structural block diagram of the control device for an energy storage power station provided according to an embodiment of the present invention. Detailed Implementation

[0022] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0023] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0024] According to an embodiment of the present invention, a method embodiment of a control method for an energy storage power station is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0025] The method embodiment provided in Embodiment 1 of this application can be executed on a mobile terminal, computer terminal, or similar computing device. Figure 1 A hardware block diagram of a computer terminal for implementing a control method for an energy storage power station is shown. Figure 1 As shown, the computer terminal 10 may include one or more processors (shown as 102a, 102b, ..., 102n in the figure) (the processor may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of a BUS bus), a network interface, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, computer terminal 10 may also include... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0026] It should be noted that the aforementioned one or more processors and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be embodied, in whole or in part, in software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be integrated, in whole or in part, into any other element within the computer terminal 10. As involved in the embodiments of this application, the data processing circuits serve as a processor control mechanism (e.g., selection of a variable resistor termination path connected to an interface).

[0027] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the control method of the energy storage power station in this embodiment of the invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory 104, thereby realizing the control method of the energy storage power station described above. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor, and these remote memories can be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0028] The display can be, for example, a touchscreen liquid crystal display (LCD) that allows the user to interact with the user interface of the computer terminal 10.

[0029] Figure 2 This is a flowchart illustrating the control method for an energy storage power station provided according to an embodiment of the present invention, as shown below. Figure 2 As shown, the method includes the following steps:

[0030] Step S202: Obtain the parameters of the energy storage power station, retail electricity price, photovoltaic power generation and electricity load in the county distribution network.

[0031] In this step, the parameters of the energy storage power station include, but are not limited to, battery type, capacity, charge / discharge efficiency, charge / discharge rate (power), state of charge (SOC) range, and battery cycle life. This information can generally be obtained from the energy storage system manufacturer or operator. Obtaining real-time and forecasted retail electricity price information typically requires access to the electricity market or grid company's public data platform, or subscription to relevant electricity price information services. Photovoltaic power generation can be obtained through photovoltaic power station monitoring systems installed within a county, or estimated through weather forecasts and solar irradiance prediction models. For example, sensors installed on photovoltaic arrays can measure real-time solar irradiance and power generation. Electricity load data reflects the electricity consumption in the region at different times and is typically collected from smart meters installed at the user end.

[0032] Step S204: Based on the parameters of the energy storage power station, determine multiple states of charge of the energy storage power station.

[0033] In this step, the state of charge (SOC) represents the ratio of the remaining electrical energy in the energy storage device to its total capacity, usually expressed as a percentage. Discretizing the continuous SOC in the optimal control of energy storage systems simplifies dynamic programming problems, making calculations more efficient and feasible. The SOC of an energy storage power station is typically between 0% and 100%, but considering battery health and safety, a smaller range, such as 20% to 80%, may be set in practice. This step ensures that the SOC remains within a safe and effective range. The SOC discretization interval can then be set to determine the degree of discretization. For example, if the SOC discretization interval is set to 5%, the SOC will be discretized into 13 states (including the start and end states) from 20% to 80%. A series of discrete SOC values ​​are then generated based on the SOC discretization interval.

[0034] Through the above steps, a series of discrete states of charge can be determined based on the parameters of the energy storage power station. These states will be used to store and analyze the state transitions and cost-effectiveness of the energy storage system under different operating conditions, and then used for optimization control in dynamic programming algorithms.

[0035] Step S206: Discretize the charging and discharging operations of the energy storage power station to obtain multiple operations corresponding to the energy storage power station.

[0036] In this step, the boundaries of the energy storage power station's charging and discharging power, namely the maximum and minimum charging and discharging power, can be determined first. Then, a power discretization interval is set. The choice of the power discretization interval depends on the range of charging and discharging power and the required optimization accuracy. For example, if the maximum charging and discharging power is 100kW, a power discretization interval of 10kW can be chosen, meaning the charging and discharging power can be {0, 10, 20, ..., 100}kW. Based on the charging and discharging power range and the power discretization interval, a series of possible operating options are constructed. The operating set should include all possible charging and discharging power values, as well as a "no operation" (i.e., the case where the power is 0).

[0037] The above steps transform the originally continuous charging and discharging operation into a series of discrete decisions. This not only simplifies the optimization control problem but also allows for the use of advanced optimization algorithms to find the global optimum. In practical applications, this may require continuous iteration and adjustment to adapt to changes in grid conditions, energy storage system characteristics, and the external environment.

[0038] Step S208: Based on the multiple states of charge of the energy storage power station and the multiple operations corresponding to the energy storage power station, obtain the decision sets corresponding to each of the multiple states of charge.

[0039] In this step, the decision set contains all feasible operations and their results starting from the current state of charge (SOC). For each discrete SOC, a corresponding decision set is constructed, including all possible operations and their resulting new SOC. This involves calculating the new SOC after charging and the new SOC after discharging. Based on the energy storage station's charging and discharging efficiency, SOC range limitations (such as minimum and maximum SoC), battery health, and safety constraints, all feasible operations are selected from the decision set for each SOC. For example, it is necessary to ensure that the SoC after charging does not exceed the maximum SoC limit, and the SoC after discharging does not fall below the minimum SoC limit. After selection, decision sets corresponding to multiple SOCs are obtained.

[0040] Step S210: Based on retail electricity price, photovoltaic power generation and electricity load, traverse the decision sets corresponding to each of the multiple states of charge to obtain the total electricity cost value corresponding to the decisions in the decision sets corresponding to each of the multiple states of charge.

[0041] Step S212: Select the decision from the decision set corresponding to the state of charge where the total power cost meets the preset conditions, and use it as the target state of charge and the target decision.

[0042] In the above steps, based on retail electricity prices, photovoltaic power generation, and electricity load, the decision sets corresponding to multiple states of charge (SoCs) are traversed, and the total electricity cost for each decision is calculated. This aims to determine the economic cost of the energy storage system under various possible operations (charging and discharging), thereby assisting decision-makers in selecting the most economical operating mode. For each decision, the electricity cost or benefit is calculated. This typically involves calculating the charging cost; if the decision is to charge, the cost equals the charging power multiplied by the charging time, then multiplied by the retail electricity price. The discharging benefit, if the decision is to discharge and the power generation is insufficient to meet load demand, equals the cost of avoiding purchased electricity minus the operational losses from discharging (such as charging / discharging efficiency losses). The no-operation cost / benefit, if the decision is no operation, may be zero, but opportunity cost must be considered—the cost if charging or discharging at this time would provide better economics.

[0043] For each decision, store its corresponding total power cost and the new state of charge (SOC). This data can be stored using a two-dimensional array or a database, where the first dimension represents the current SOC and the second dimension represents the decision. The array values ​​represent the power cost or benefit. After iterating through all decisions, select the decision that minimizes the total power cost for each SOC. This selection may be dynamic, as the optimal decision may also depend on predictions of future SOCs. For the next time step, the above steps can be repeated, but with the updated SOC using the optimal decision selected in the previous step. The goal is to minimize the total power cost over the entire time period.

[0044] After optimization across all time steps, the optimal decision sequence for the entire optimization cycle can be output. These decision sequences will guide the charging and discharging strategies of the energy storage system in real-world operating environments.

[0045] Through these steps, a model based on dynamic programming or other optimization methods can be constructed for the economic operation optimization of energy storage systems. This model takes into account fluctuations in photovoltaic power generation, retail electricity prices, and electricity load, as well as the characteristics and limitations of the energy storage system itself. The ultimate goal is to meet electricity demand while minimizing economic costs.

[0046] Step S214: Control the energy storage power station based on the target state of charge and target decision.

[0047] In this step, the current state of charge (SOC) of the energy storage power station can be monitored in real time. Real-time data such as retail electricity prices, predicted photovoltaic power generation, and electricity load are collected. The currently monitored SOC is compared with the previously optimized target SOC list to find the closest or matching target SOC. Once the target SOC matching the current state is determined, its target decision set is examined, and the decision most relevant to the current state is applied. For example, if the target decision is to charge under the current SOC, then the charging process begins according to the charging power and time specified in the decision. Because actual conditions (such as electricity prices, solar irradiance, and electricity demand) may change over time, it is necessary to periodically reassess whether the current state still matches the initially determined target SOC, and make dynamic adjustments as necessary. Through the energy storage power station's control system, the decisions are translated into specific control commands, such as adjusting the inverter's charging and discharging power, and starting or stopping the charging / discharging process.

[0048] Through the above steps, the charging and discharging strategy can be automatically or semi-automatically adjusted according to the preset target state of charge and target decision to adapt to constantly changing external conditions, while ensuring the economy and reliability of the system.

[0049] This embodiment employs the core idea of ​​dynamic programming, constructing an optimized control model by discretizing the state of charge and operations, effectively solving the optimization scheduling problem of energy storage systems in complex environments. The technology in this embodiment can respond in real time to electricity price fluctuations, changes in photovoltaic output, and electricity load demand. By intelligently regulating the charging and discharging behavior of the energy storage power station, it significantly improves the system's economy and energy utilization rate. In other embodiments, machine learning algorithms, such as neural networks or reinforcement learning, can be introduced to further improve prediction accuracy and control strategy flexibility, addressing more complex electricity market environments and technical problems.

[0050] Through the above steps, the goal of regulating the energy storage power station can be achieved by comprehensively considering the operating status of multiple devices, thereby improving the technical effect of energy utilization and economic benefits. This solves the technical problem that the current regulation of energy storage power stations does not fully consider other factors, resulting in an insufficiently optimized charging and discharging control strategy and an inability to maximize economic benefits.

[0051] As an optional embodiment, multiple states of charge of the energy storage power station are determined based on the parameters of the energy storage power station, including: determining the maximum charge and discharge power and depth of discharge of the energy storage power station based on the parameters of the energy storage power station; determining the state of charge boundary of the energy storage power station based on the maximum charge and discharge power and depth of discharge; and discretizing the state of charge of the energy storage power station based on the state of charge boundary to determine multiple states of charge.

[0052] Optionally, the total capacity of the energy storage power station can be determined, i.e., the maximum amount of electricity the energy storage system can store; the maximum charge / discharge power, i.e., the maximum charging power the system can accept and the maximum discharging power it can release in a short period of time; and the depth of discharge, i.e., the minimum permissible state of charge (SOC). Typically, to protect the battery, the SOC is not set to 0%. Based on these parameters, the SOC boundary of the energy storage power station is determined. To discretize the SOC, a reasonable discretization interval is set. This interval depends on the application scenario of the energy storage system and the accuracy requirements of the optimization objectives. Based on the minimum and maximum values ​​of the SOC and the discretization interval, the SOC is discretized, creating a series of possible SOC values.

[0053] By setting reasonable state-of-charge boundaries, equipment damage caused by overcharging and discharging is avoided, while ensuring the efficient operation of the energy storage system, unnecessary energy loss is prevented, and the overall performance and economic benefits of the system are improved. Furthermore, discretizing the state of charge significantly saves time in subsequent dynamic programming, improving the control efficiency of the energy storage power station.

[0054] As an optional embodiment, the charging and discharging operation of the energy storage power station is discretized to obtain multiple operations corresponding to the energy storage power station, including: obtaining the charging and discharging power step size; determining the maximum charging and discharging power corresponding to the energy storage power station based on the parameters of the energy storage power station; and discretizing the charging and discharging operation of the energy storage power station based on the maximum charging and discharging power and the charging and discharging power step size to obtain multiple operations corresponding to the energy storage power station.

[0055] Optionally, by setting the charging and discharging power step size, fine-grained control of the charging and discharging operation is achieved, ensuring a smooth transition in the charging and discharging process and efficient energy utilization. Transforming continuous charging and discharging operations into a series of discrete state transitions facilitates algorithm solution and optimization, improves the computational efficiency of the control strategy, avoids abrupt changes in charging and discharging operations, reduces the impact on the power grid, and also reduces wear and tear on the energy storage system, extending its service life. In other embodiments, an adaptive step size adjustment mechanism can be introduced to dynamically adjust the charging and discharging power step size according to actual operating conditions, further improving the system's response speed and adaptability, and addressing the uncertainties caused by power grid fluctuations and load changes.

[0056] First, the discretization step size of the charging and discharging power can be determined, which dictates the fineness of the operation set. This step size can be set based on battery characteristics, the fluctuation range of electricity prices, or the system's energy demand; for example, 1% or 5% of the total charging and discharging power can be chosen. In practical applications, for safety reasons, a threshold slightly lower than the maximum power might be set as the upper limit of operation to avoid battery overheating or overcharging / discharging. Based on the determined maximum charging and discharging power and the selected power step size S, the charging and discharging operations of the energy storage power station are discretized into multiple levels:

[0057] Discretization of charging operation: A series of charging power levels are generated from 0 to the maximum charging power, based on the charging and discharging power step size.

[0058] Discharge operation discretization: From 0 to maximum discharge power, a series of discharge power levels are generated based on the charge and discharge power step size.

[0059] Operation option set: This includes charging, discharging, and no operation (i.e., power is 0), creating a set of all possible operation options.

[0060] For energy storage power stations, based on the aforementioned discretized charging and discharging operations, a set containing all possible operations is constructed. For example, if the maximum charging power is 100kW, the maximum discharging power is also 100kW, and the power step size (PDS) is 10kW, then the operation set may include all charging powers from 0kW to 100kW (intervals of 10kW), and all discharging powers from 0kW to -100kW (intervals of 10kW as well).

[0061] Through the above steps, the continuous charge and discharge operation space can be transformed into a series of discrete operation options, providing a clear and algorithmically easy-to-process set of decisions for the optimized control of energy storage power stations. This helps to achieve efficient energy utilization while ensuring system safety and the battery's long-term operating capability.

[0062] As an optional embodiment, based on multiple states of charge of the energy storage power station and multiple operations corresponding to the energy storage power station, a decision set corresponding to each of the multiple states of charge is obtained, including: applying the multiple operations corresponding to the energy storage power station to the multiple states of charge to obtain the target states of charge corresponding to each of the multiple operations corresponding to the multiple states of charge; obtaining the maximum state of charge and the minimum state of charge corresponding to the energy storage power station; filtering out the target states of charge that are outside the intervals corresponding to the maximum state of charge and the minimum state of charge among the target states of charge corresponding to the multiple operations corresponding to the multiple states of charge, and determining the final state of charge corresponding to each of the multiple states of charge; and determining the decision set corresponding to each of the multiple states of charge based on the operations corresponding to the final state of charge corresponding to the multiple states of charge.

[0063] Optionally, for each discrete state of charge (SOC), multiple operations (charging / discharging / no operation) of the energy storage power station are applied. The new SOC after each operation is predicted, taking into account the battery's charge / discharge efficiency and the charge change at the current time step. It is checked whether the predicted SOC after each applied operation lies between the minimum and maximum SOC. All operations that would cause the target SOC to exceed these two boundaries are excluded. Operations that can transform the current SOC into the final SOC (i.e., the target SOC within the boundaries) are retained. For each SOC, a decision set is constructed based on the filtered effective operations.

[0064] By screening the target state of charge (SOC), the SOC of the energy storage power station is ensured to remain within a safe range, avoiding the risk of overcharging and discharging. This effectively constrains the decision space and simplifies the solution process for the optimization control problem. The above method ensures the stable operation of the energy storage power station, avoids energy loss and equipment failure caused by abnormal SOC, and improves the reliability and safety of the system. In other embodiments, the concept of a virtual SOC can be introduced to pre-simulate SOC changes under various extreme conditions, further enhancing the robustness of the system and addressing technical challenges under sudden events and extreme weather conditions.

[0065] As an optional embodiment, based on retail electricity price, photovoltaic power generation, and electricity load, multiple decision sets corresponding to each state of charge are traversed to obtain the total electricity cost value corresponding to the decisions in the decision sets corresponding to each state of charge. Determining the total electricity cost value corresponding to any target decision in the decision sets corresponding to each state of charge includes: when the target decision is to control the charging of the energy storage station, determining the charging amount corresponding to the energy storage station; determining a first additional purchase amount based on electricity load and photovoltaic power generation; determining the total electricity cost corresponding to the target decision based on retail electricity price, the charging amount corresponding to the energy storage station, and the additional purchase amount; and / or, when the target decision is to control the discharging of the energy storage station, determining the discharging amount of the energy storage station; determining a second additional purchase amount based on electricity load, photovoltaic power generation, and the discharging amount of the energy storage station; determining the total electricity cost corresponding to the target decision based on retail electricity price, the discharging amount of the energy storage station, and the second additional purchase amount; and / or, when the target decision is not to operate the energy storage station, determining the purchase amount based on electricity load and photovoltaic power generation; and determining the total electricity cost value corresponding to the target decision based on the purchase amount and retail electricity price.

[0066] Optionally, by comprehensively considering retail electricity prices, photovoltaic power generation, and electricity load, dynamic optimization of charging and discharging decisions for energy storage power stations is achieved, ensuring optimal decisions are made under different operating conditions. By calculating the total electricity cost under different decisions, the economic benefits of various decisions can be intuitively evaluated, providing a quantitative basis for the intelligent control of energy storage power stations.

[0067] Based on retail electricity prices, photovoltaic (PV) power generation, and electricity load, the decision sets corresponding to multiple states of charge (SoCs) are traversed, and the total electricity cost under each decision is calculated. When the target decision is to control the charging of the energy storage station, the required charging amount can be determined, i.e., based on the current SoC and the ideal target SoC (usually a higher SoC), the required charging amount is calculated. The electricity load is compared with PV power generation; if the load exceeds PV power generation, additional electricity needs to be purchased from the grid, i.e., the first additional electricity purchase. In cases where charging is required, in addition to directly meeting the electricity load, additional electricity needs to be purchased for charging. The total cost of charging is calculated based on the retail electricity price. When the target decision is to discharge the energy storage station, the discharge amount is determined based on the current SoC and the ideal SoC for the next time step. If PV power generation is insufficient to meet the electricity load, electricity may still need to be purchased from the grid even after the energy storage station discharges. If electricity still needs to be purchased after discharge, this is called the second additional electricity purchase. Based on the above, the total electricity cost under the discharge scenario can be determined. If the objective decision is not to operate the energy storage power station, the purchased electricity is obtained by subtracting the current photovoltaic power generation from the electricity required to meet the current electricity load purchased from the grid. The total cost is then calculated based on the retail electricity price.

[0068] By iterating through all decision sets, for each SoC state and its corresponding decision, the total power cost is calculated. Then, based on these cost values, the optimal strategy—that is, the operation plan with the lowest cost or the highest benefit—can be selected, thereby achieving economic optimization of the energy storage system operation.

[0069] These steps consider not only the immediate costs of the current time step, but also the impact of charging and discharging operations on the state of charge, and potential modifications to future electricity purchase costs, ensuring the comprehensiveness and forward-looking nature of the strategy. In practical applications, these strategies also need to be dynamically adjusted to cope with changes in real-time market conditions, such as fluctuations in retail electricity prices, uncertainties in photovoltaic output, and changes in electricity load.

[0070] The methods described above can significantly reduce electricity costs, improve energy efficiency, and enhance the system's flexibility and adaptability. In other embodiments, the decision-making process can be further refined by incorporating real-time market electricity prices and weather forecast data, addressing the uncertainties caused by electricity price fluctuations and weather changes, and improving the system's economic efficiency and energy management capabilities.

[0071] As an optional embodiment, when the objective decision is to control the discharge of the energy storage power station, the discharge amount of the energy storage power station is determined based on the following formula:

[0072] ,

[0073] in, This refers to the discharge capacity of the energy storage power station. To determine the state of charge of the energy storage power station before executing the target decision, To determine the state of charge of the energy storage power station after implementing the target decision, The total capacity of the energy storage power station This refers to the discharge efficiency of the energy storage power station.

[0074] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0075] Through the above description of the embodiments, those skilled in the art can clearly understand that the control method of the energy storage power station according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platform. Of course, it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0076] According to an embodiment of the present invention, a control device for an energy storage power station for implementing the above-described control method for an energy storage power station is also provided. Figure 3 This is a structural block diagram of the control device for an energy storage power station provided according to an embodiment of the present invention, such as... Figure 3 As shown, the control device of the energy storage power station includes: an acquisition module 302, a first determination module 304, a discrete module 306, a second determination module 308, a traversal module 310, a selection module 312, and a control module 314. The control device of the energy storage power station will be described below.

[0077] The acquisition module 302 is used to acquire parameters of energy storage power stations, retail electricity prices, photovoltaic power generation and electricity load in the county distribution network.

[0078] The first determining module 304, connected to the acquiring module 302, is used to determine multiple states of charge of the energy storage power station based on the parameters of the energy storage power station.

[0079] Discrete module 306, connected to first determining module 304, is used to discretize the charging and discharging operations of the energy storage power station to obtain multiple operations corresponding to the energy storage power station.

[0080] The second determining module 308, connected to the discrete module 306, is used to obtain decision sets corresponding to each of the multiple states of charge of the energy storage power station and the multiple operations corresponding to the energy storage power station.

[0081] The traversal module 310, connected to the second determination module 308, is used to traverse the decision sets corresponding to multiple states of charge based on retail electricity prices, photovoltaic power generation, and electricity load, and obtain the total electricity cost value corresponding to the decisions in the decision sets corresponding to the multiple states of charge.

[0082] The selection module 312, connected to the traversal module 310, is used to select the decision from the decision set corresponding to the state of charge where the total power cost value meets the preset conditions, as the target state of charge and the target decision.

[0083] The control module 314, connected to the selection module 312, is used to control the energy storage power station based on the target state of charge and target decision.

[0084] It should be noted that the aforementioned acquisition module 302, first determination module 304, discrete module 306, second determination module 308, traversal module 310, selection module 312, and control module 314 correspond to steps S202 to S214 in the embodiments. Multiple modules and their corresponding steps implement the same instances and application scenarios, but are not limited to the content disclosed in the above embodiments. It should also be noted that the aforementioned modules, as part of the device, can run on the computer terminal 10 provided in the embodiments.

[0085] Embodiments of the present invention may provide a computer device. Optionally, in this embodiment, the computer device may be located in at least one of a plurality of network devices in a computer network. The computer device includes a memory and a processor.

[0086] The memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the control method and device of the energy storage power station in this embodiment of the invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, thereby realizing the aforementioned control method of the energy storage power station. The memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to a computer terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0087] The processor can access information and applications stored in the memory via a transmission device to execute the following steps: acquire parameters of the energy storage power station, retail electricity price, photovoltaic power generation, and electricity load in the county distribution network; determine multiple states of charge (SOCs) of the energy storage power station based on the SOC parameters; discretize the charging and discharging operations of the energy storage power station to obtain multiple operations corresponding to the energy storage power station; obtain decision sets corresponding to each SOC based on the multiple SOCs and the multiple operations corresponding to the energy storage power station; traverse the decision sets corresponding to each SOC based on the retail electricity price, photovoltaic power generation, and electricity load to obtain the total electricity cost value corresponding to the decisions in the decision sets corresponding to each SOC; select the decision set corresponding to the SOC that satisfies the preset conditions as the target SOC and target decision; and control the energy storage power station based on the target SOC and target decision.

[0088] Optionally, the processor may also execute program code that performs the following steps: determining multiple states of charge of the energy storage power station based on the parameters of the energy storage power station, including: determining the maximum charge and discharge power and depth of discharge of the energy storage power station based on the parameters of the energy storage power station; determining the state of charge boundary of the energy storage power station based on the maximum charge and discharge power and depth of discharge; and discretizing the state of charge of the energy storage power station based on the state of charge boundary to determine multiple states of charge.

[0089] Optionally, the processor may also execute program code that performs the following steps: discretizes the charging and discharging operations of the energy storage power station to obtain multiple operations corresponding to the energy storage power station, including: obtaining the charging and discharging power step size; determining the maximum charging and discharging power corresponding to the energy storage power station based on the parameters of the energy storage power station; and discretizing the charging and discharging operations of the energy storage power station based on the maximum charging and discharging power and the charging and discharging power step size to obtain multiple operations corresponding to the energy storage power station.

[0090] Optionally, the processor may also execute program code for the following steps: based on multiple states of charge of the energy storage power station and multiple operations corresponding to the energy storage power station, obtain decision sets corresponding to each of the multiple states of charge, including: applying the multiple operations corresponding to the energy storage power station to the multiple states of charge to obtain the target states of charge corresponding to each of the multiple operations corresponding to each of the multiple states of charge; obtaining the maximum state of charge and the minimum state of charge corresponding to the energy storage power station; filtering out the target states of charge that are outside the intervals corresponding to the maximum state of charge and the minimum state of charge among the target states of charge corresponding to each of the multiple operations corresponding to each of the multiple states of charge, and determining the final state of charge corresponding to each of the multiple states of charge; and determining the decision sets corresponding to each of the multiple states of charge based on the operations corresponding to the final state of charge corresponding to each of the multiple states of charge.

[0091] Optionally, the processor may also execute program code for the following steps: based on retail electricity price, photovoltaic power generation, and electricity load, traverse the decision sets corresponding to multiple states of charge to obtain the total electricity cost value corresponding to the decisions in the decision sets corresponding to the multiple states of charge. Determining the total electricity cost value corresponding to any target decision in the decision sets corresponding to the multiple states of charge includes: when the target decision is to control the charging of the energy storage station, determining the charging amount corresponding to the energy storage station; determining the first additional purchase amount based on the electricity load and photovoltaic power generation; and determining the charging amount corresponding to the energy storage station based on the retail electricity price and the charging amount corresponding to the energy storage station. The total electricity cost corresponding to the target decision is determined based on the quantity and additional purchased electricity; and / or, if the target decision is to control the discharge of the energy storage station, the discharge quantity of the energy storage station is determined; a second additional purchased electricity quantity is determined based on the electricity load, photovoltaic power generation, and the discharge quantity of the energy storage station; the total electricity cost corresponding to the target decision is determined based on the retail electricity price, the discharge quantity of the energy storage station, and the second additional purchased electricity quantity; and / or, if the target decision is not to operate the energy storage station, the purchased electricity quantity is determined based on the electricity load and photovoltaic power generation; the total electricity cost corresponding to the target decision is determined based on the purchased electricity quantity and the retail electricity price.

[0092] Optionally, the processor may also execute program code that performs the following steps: When the objective decision is to control the discharge of the energy storage power station, determine the discharge amount of the energy storage power station based on the following formula:

[0093] ,

[0094] in, This refers to the discharge capacity of the energy storage power station. To determine the state of charge of the energy storage power station before executing the target decision, To determine the state of charge of the energy storage power station after implementing the target decision, The total capacity of the energy storage power station This refers to the discharge efficiency of the energy storage power station.

[0095] This invention provides a control method for an energy storage power station. The method involves acquiring parameters of the energy storage power station, retail electricity price, photovoltaic power generation, and electricity load in a county-level power distribution network. Based on the parameters, multiple states of charge (SOCs) of the energy storage power station are determined. The charging and discharging operations of the energy storage power station are discretized to obtain multiple corresponding operations. Based on the multiple SOCs and corresponding operations, decision sets are obtained for each SOC. Based on the retail electricity price, photovoltaic power generation, and electricity load, the decision sets for each SOC are traversed to obtain the total electricity cost value corresponding to each decision set. The decision set for the SOC that satisfies a preset condition is selected as the target SOC and target decision. Based on the target SOC and target decision, the energy storage power station is controlled. This achieves the goal of comprehensively considering the operating states of multiple devices to regulate the energy storage power station, thereby improving energy utilization and economic efficiency. It also solves the technical problem that current energy storage power station regulation does not fully consider other factors, resulting in suboptimal charging and discharging control strategies and an inability to maximize economic benefits.

[0096] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing the hardware related to the terminal device. The program can be stored in a non-volatile storage medium, which may include: flash drive, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0097] Embodiments of the present invention also provide a non-volatile storage medium. Optionally, in this embodiment, the aforementioned non-volatile storage medium can be used to store the program code executed by the control method of the energy storage power station provided in the above embodiments.

[0098] Optionally, in this embodiment, the non-volatile storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals.

[0099] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: obtaining parameters of the energy storage power station, retail electricity price, photovoltaic power generation, and electricity load in the county distribution network; determining multiple states of charge of the energy storage power station based on the energy storage power station parameters; discretizing the charging and discharging operations of the energy storage power station to obtain multiple operations corresponding to the energy storage power station; obtaining decision sets corresponding to each of the multiple states of charge of the energy storage power station based on the multiple states of charge of the energy storage power station and the multiple operations corresponding to the energy storage power station; traversing the decision sets corresponding to each of the multiple states of charge based on the retail electricity price, photovoltaic power generation, and electricity load to obtain the total power cost value corresponding to the decisions in the decision sets corresponding to each of the multiple states of charge; selecting the decisions in the decision sets corresponding to the states of charge whose total power cost value meets preset conditions as the target states of charge and target decisions; and controlling the energy storage power station based on the target states of charge and target decisions.

[0100] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: determining multiple states of charge of the energy storage power station based on the parameters of the energy storage power station, including: determining the maximum charge / discharge power and depth of discharge of the energy storage power station based on the parameters of the energy storage power station; determining the state of charge boundary of the energy storage power station based on the maximum charge / discharge power and depth of discharge; and discretizing the state of charge of the energy storage power station based on the state of charge boundary to determine multiple states of charge.

[0101] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: discretizing the charging and discharging operations of the energy storage power station to obtain multiple operations corresponding to the energy storage power station, including: obtaining the charging and discharging power step size; determining the maximum charging and discharging power corresponding to the energy storage power station based on the parameters of the energy storage power station; and discretizing the charging and discharging operations of the energy storage power station based on the maximum charging and discharging power and the charging and discharging power step size to obtain multiple operations corresponding to the energy storage power station.

[0102] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: obtaining decision sets corresponding to each of the multiple states of charge of the energy storage power station and the multiple operations corresponding to the energy storage power station, including: applying the multiple operations corresponding to the energy storage power station to the multiple states of charge to obtain the target states of charge corresponding to the multiple operations corresponding to the multiple states of charge; obtaining the maximum state of charge and the minimum state of charge corresponding to the energy storage power station; filtering out the target states of charge that are outside the intervals corresponding to the maximum state of charge and the minimum state of charge among the target states of charge corresponding to the multiple operations corresponding to the multiple states of charge, and determining the final state of charge corresponding to each of the multiple states of charge; and determining the decision sets corresponding to each of the multiple states of charge based on the operations corresponding to the final state of charge corresponding to the multiple states of charge.

[0103] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: based on retail electricity price, photovoltaic power generation, and electricity load, traversing multiple decision sets corresponding to each state of charge to obtain the total electricity cost value corresponding to the decisions in the decision sets corresponding to each of the multiple states of charge, wherein determining the total electricity cost value corresponding to any target decision in the decision sets corresponding to each of the multiple states of charge includes: when the target decision is to control the charging of the energy storage station, determining the charging amount corresponding to the energy storage station; based on electricity load and photovoltaic power generation, determining the first additional purchase amount; based on retail electricity price, photovoltaic power generation, and electricity load, determining the total electricity cost value corresponding to any target decision ... The total electricity cost corresponding to the target decision is determined by the charging amount and additional purchased electricity amount corresponding to the energy storage power station; and / or, if the target decision is to control the discharge of the energy storage power station, the discharge amount of the energy storage power station is determined; a second additional purchased electricity amount is determined based on the electricity load, photovoltaic power generation, and the discharge amount of the energy storage power station; the total electricity cost corresponding to the target decision is determined based on the retail electricity price, the discharge amount of the energy storage power station, and the second additional purchased electricity amount; and / or, if the target decision is not to operate the energy storage power station, the purchased electricity amount is determined based on the electricity load and photovoltaic power generation; the total electricity cost corresponding to the target decision is determined based on the purchased electricity amount and the retail electricity price.

[0104] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: when the target decision is to control the discharge of the energy storage power station, the discharge amount of the energy storage power station is determined based on the following formula:

[0105] ,

[0106] in, This refers to the discharge capacity of the energy storage power station. To determine the state of charge of the energy storage power station before executing the target decision, To determine the state of charge of the energy storage power station after implementing the target decision, The total capacity of the energy storage power station This refers to the discharge efficiency of the energy storage power station.

[0107] Embodiments of the present invention also provide a computer program product, including a computer program. Optionally, in this embodiment, when the computer program is executed by a processor, it can: acquire parameters of an energy storage power station, retail electricity price, photovoltaic power generation, and electricity load in a county-level power distribution network; determine multiple states of charge (SOCs) of the energy storage power station based on the SOC parameters; discretize the charging and discharging operations of the energy storage power station to obtain multiple operations corresponding to the energy storage power station; obtain decision sets corresponding to each of the multiple SOCs based on the multiple SOCs and the multiple operations corresponding to the energy storage power station; traverse the decision sets corresponding to each of the multiple SOCs based on the retail electricity price, photovoltaic power generation, and electricity load to obtain the total electricity cost value corresponding to the decisions in the decision sets corresponding to each of the multiple SOCs; select the decision in the decision set corresponding to the SOC that satisfies the preset condition as the target SOC and target decision; and control the energy storage power station based on the target SOC and target decision.

[0108] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0109] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0110] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0111] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0112] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0113] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a non-volatile storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0114] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A control method for an energy storage power station, characterized in that, include: Obtain parameters of energy storage power stations, retail electricity prices, photovoltaic power generation, and electricity load in the county's power distribution network; Based on the parameters of the energy storage power station, multiple states of charge of the energy storage power station are determined; Discretize the charging and discharging operations of the energy storage power station to obtain multiple operations corresponding to the energy storage power station; Based on the multiple states of charge of the energy storage power station and the multiple operations corresponding to the energy storage power station, a decision set corresponding to each of the multiple states of charge is obtained; Based on the retail electricity price, the photovoltaic power generation and the electricity load, the decision sets corresponding to each of the multiple states of charge are traversed to obtain the total electricity cost value corresponding to the decision set corresponding to each of the multiple states of charge. Select the decision from the decision set corresponding to the state of charge where the total power cost meets the preset conditions, and use it as the target state of charge and the target decision. The energy storage power station is controlled based on the target state of charge and the target decision.

2. The method according to claim 1, characterized in that, The determination of multiple states of charge of the energy storage power station based on the parameters of the energy storage power station includes: Based on the parameters of the energy storage power station, determine the maximum charge / discharge power and depth of discharge of the energy storage power station; Based on the maximum charge / discharge power and the depth of discharge, the state of charge boundary of the energy storage power station is determined; Based on the state of charge boundary, the state of charge of the energy storage power station is discretized to determine the multiple states of charge.

3. The method according to claim 1, characterized in that, The discretization of the charging and discharging operations of the energy storage power station yields multiple operations corresponding to the energy storage power station, including: Obtain the charge / discharge power step size; Based on the parameters of the energy storage power station, determine the maximum charging and discharging power corresponding to the energy storage power station; Based on the maximum charge / discharge power and the charge / discharge power step size, the charge / discharge operation of the energy storage power station is discretized to obtain multiple operations corresponding to the energy storage power station.

4. The method according to claim 1, characterized in that, The process of obtaining decision sets corresponding to each of the multiple states of charge of the energy storage power station and the multiple operations corresponding to the energy storage power station includes: The multiple operations corresponding to the energy storage power station are applied to the multiple states of charge to obtain the target states of charge corresponding to the multiple operations corresponding to each of the multiple states of charge. Obtain the maximum and minimum state of charge corresponding to the energy storage power station; Filter out the target charge states that are outside the intervals corresponding to the maximum charge state and the minimum charge state from the target charge states corresponding to the multiple operations corresponding to the multiple charge states, and determine the final charge state corresponding to each of the multiple charge states. Based on the operations corresponding to the final charged state of each of the multiple charged states, a decision set corresponding to each of the multiple charged states is determined.

5. The method according to claim 1, characterized in that, The process involves iterating through the decision sets corresponding to each of the multiple states of charge based on the retail electricity price, the photovoltaic power generation, and the electricity load to obtain the total electricity cost value corresponding to the decisions in the decision sets corresponding to each of the multiple states of charge. Determining the total electricity cost value corresponding to any one target decision in the decision sets corresponding to each of the multiple states of charge includes: When the target decision is to control the charging of the energy storage power station, the corresponding charging amount of the energy storage power station is determined; Based on the electrical load and the photovoltaic power generation, the first additional electricity purchase amount is determined; Based on the retail electricity price, the charging amount corresponding to the energy storage station, and the additional electricity purchased, the total electricity cost corresponding to the target decision is determined; And / or, if the target decision is to control the discharge of the energy storage power station, determine the discharge amount of the energy storage power station; The second additional electricity purchase amount is determined based on the electricity load, the photovoltaic power generation, and the discharge amount of the energy storage station; Based on the retail electricity price, the discharge capacity of the energy storage power station, and the second additional electricity purchase, the total electricity cost corresponding to the target decision is determined; And / or, if the target decision is not to operate the energy storage power station, the amount of electricity to be purchased is determined based on the electricity load and the photovoltaic power generation; Based on the purchased electricity volume and the retail electricity price, the total electricity cost corresponding to the target decision is determined.

6. The method according to claim 5, characterized in that, When the objective decision is to control the discharge of the energy storage power station, the discharge amount of the energy storage power station is determined based on the following formula: ， in, This represents the discharge capacity of the energy storage power station. The state of charge of the energy storage power station before executing the target decision. The state of charge of the energy storage power station after executing the target decision, The total capacity of the energy storage power station is [missing information]. The discharge efficiency of the energy storage power station is denoted as .

7. A control device for an energy storage power station, characterized in that, include: The acquisition module is used to acquire parameters of energy storage power stations, retail electricity prices, photovoltaic power generation, and electricity load in the county-level power distribution network; The first determining module is used to determine multiple states of charge of the energy storage power station based on the parameters of the energy storage power station. The discrete module is used to discretize the charging and discharging operations of the energy storage power station to obtain multiple operations corresponding to the energy storage power station. The second determining module is used to obtain a decision set corresponding to each of the multiple states of charge of the energy storage power station and the multiple operations corresponding to the energy storage power station. The traversal module is used to traverse the decision sets corresponding to each of the multiple states of charge based on the retail electricity price, the photovoltaic power generation and the electricity load, and obtain the total electricity cost value corresponding to the decision set corresponding to each of the multiple states of charge. The selection module is used to select the decision from the decision set corresponding to the state of charge that meets the preset conditions for the total power expenditure value, and use it as the target state of charge and the target decision. The control module is used to control the energy storage power station based on the target state of charge and the target decision.

8. A non-volatile storage medium, characterized in that, The non-volatile storage medium includes a stored program, wherein, when the program is executed, it controls the device containing the non-volatile storage medium to perform the control method of the energy storage power station according to any one of claims 1 to 6.

9. A computer device, characterized in that, include: Memory and processor The memory stores computer programs; The processor is configured to execute a computer program stored in the memory, wherein when the computer program is executed, the processor performs the control method of the energy storage power station according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the control method of the energy storage power station according to any one of claims 1 to 6.

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