A method and device for configuring an energy storage system

Abstract

The present application discloses a configuration method and device for an energy storage system, wherein the method includes obtaining predicted power demand data of a grid object to be interacted with; evaluating the power demand data, and generating a number of capacity configuration schemes and a number of power configuration schemes for the energy storage system to be configured based on the evaluation results; inputting the capacity configuration schemes into the operation model of the grid object to be interacted with, and constructing a discharge power demand curve according to the output results of each model; inputting the power configuration schemes into the pre-constructed operation model of the grid object to be interacted with, and constructing a discharge capacity demand curve according to the output results of each model; processing the intersection results of the discharge power demand curve and the discharge capacity demand curve according to the optimization target to obtain the optimal set; and executing the configuration strategy constructed by the optimal set. The method provided by the present application provides a quantitative guidance range for the storage demand of the energy system.

Description

Technical Field

[0001] The present application relates to the field of energy configuration technology, and in particular to a configuration method and device for an energy storage system. Background Art

[0002] With the transformation of the global energy structure and the large-scale application of new energy, the volatility of renewable energy such as solar energy and wind energy has become an issue that cannot be ignored in the power system. Therefore, the application of energy storage systems in power systems has gradually received attention. Its core value lies in its ability to release energy during peak power demand and store energy during low power demand, thereby balancing the load of the power grid and improving the reliability of power supply.

[0003] However, the existing energy storage system configuration methods lack effective configuration strategies, resulting in the capacity and power configuration of the energy storage system not matching actual demand, causing resource waste or insufficient power supply.

[0004] It can be seen that how to optimize the configuration method of the energy storage system has become a technical problem that needs to be solved urgently by those skilled in the art. Summary of the invention

[0005] The present application provides an energy storage configuration and system for a data center to solve the technical problem of how to optimize the configuration of the energy storage system.

[0006] In order to solve the above technical problems, an embodiment of the present application provides a configuration method of an energy storage system, including:

[0007] Acquire predicted power demand data of the grid object to be interacted with;

[0008] Evaluate the power demand data, and generate a number of capacity configuration schemes and a number of power configuration schemes for the energy storage system to be configured based on the evaluation results, wherein the evaluation process is designed to evaluate the power demand data by time dimension and demand dimension respectively;

[0009] Input the capacity configuration schemes into the pre-built operation models of the grid objects to be interacted respectively, and construct the discharge power demand curve according to the output results of each model; Input the power configuration schemes into the pre-built operation models of the grid objects to be interacted respectively, and construct the discharge capacity demand curve according to the output results of each model;

[0010] Processing the intersection result of the discharge power demand curve and the discharge capacity demand curve according to a preset optimization target to obtain an optimal set;

[0011] Execute the configuration strategy of the energy storage system to be configured constructed by the optimal set.

[0012] As one of the preferred solutions, the grid object to be interacted includes a data center;

[0013] The step of obtaining predicted power demand data of the grid object to be interacted with includes:

[0014] Extracting equipment load information and new energy configuration information according to the resource configuration information of the data center;

[0015] Obtaining historical power consumption data of the data center to draw a load characteristic curve, and predicting load data of the data center in a future time period according to a change trend of the load characteristic curve and the equipment load information;

[0016] Predicting the new energy output data of the data center in a future time period according to the new energy configuration information and the acquired weather forecast information;

[0017] The power demand data of the data center in a future time period is obtained according to the load data and the new energy output data.

[0018] As one of the preferred solutions, predicting the new energy output data of the data center in a future time period according to the new energy configuration information and the acquired weather forecast information includes:

[0019] Determining weather features to be extracted according to the new energy configuration information;

[0020] Extracting features from the weather forecast information based on the weather features to be extracted to obtain weather features;

[0021] The weather characteristics are input into a pre-built new energy output prediction model to obtain new energy output data for the data center in a future time period.

[0022] As one of the preferred solutions, the power demand data is evaluated based on a time series algorithm, and a number of capacity configuration schemes and a number of power configuration schemes of the energy storage system to be configured are generated based on the evaluation results, including:

[0023] Analyze the power demand data using a time series algorithm to obtain peak and valley values ​​and instantaneous change rates of the power demand data;

[0024] Designing a number of capacity configuration schemes according to the peak and valley values;

[0025] According to the capacity configuration scheme and the instantaneous change rate, a number of power configuration schemes are designed.

[0026] As one of the preferred solutions, the capacity configuration schemes are respectively input into the pre-built operation models of the grid objects to be interacted, and the discharge power demand curve is constructed according to the output results of each model, including:

[0027] Constructing the operation model with the goal of minimizing the power shortage of the data center;

[0028] Inputting the capacity configuration schemes into the operation model respectively to obtain an initial discharge power data set of the energy storage system to be configured;

[0029] The initial discharge power data set is screened to obtain a target discharge power data set that meets a first preset criterion, and the discharge power demand curve is constructed according to the target discharge power data set.

[0030] As one of the preferred solutions, the power configuration schemes are respectively input into the pre-built operation models of the grid objects to be interacted, and the discharge capacity demand curve is constructed according to the output results of each model, including:

[0031] Constructing the operation model with the goal of minimizing the power shortage of the data center;

[0032] Inputting the power configuration schemes into the operation model respectively to obtain an initial capacity data set of the energy storage system to be configured;

[0033] The initial capacity data set is screened to obtain a target capacity data set that meets a second preset criterion, and the discharge capacity demand curve is constructed according to the target capacity data set.

[0034] As one of the preferred solutions, the operation model is expressed as:

[0035]

[0036] in, represents the absolute value of electricity surplus and deficit, , Respectively in The discharge aggregate power and charging aggregate power of the energy storage equipment to be configured in the time period, Indicates power shortage. Indicates a surplus of electricity.

[0037] As one of the preferred solutions, the constraints of the operating model are expressed as:

[0038]

[0039]

[0040] in, , Respectively in During the period The charging power and discharging power of the energy storage device to be configured. , Respectively The charging and discharging power limits of energy storage equipment are configured. is a Boolean variable.

[0041] As one of the preferred solutions, the intersection result of the discharge power demand curve and the discharge capacity demand curve is processed according to a preset optimization target to obtain an optimal set, including:

[0042] According to the intersection result and the optimization target, a candidate solution that meets the electricity demand data is screened out;

[0043] Optimizing the candidate solutions based on a multi-objective optimization method, and performing sensitivity analysis on the optimization results;

[0044] The optimal set is determined according to the optimization result and the result of the sensitivity analysis.

[0045] Another embodiment of the present application provides a configuration device for an energy storage system, including:

[0046] An acquisition module, used to acquire predicted power demand data of the grid object to be interacted with;

[0047] A generation module, used to evaluate the power demand data, and generate a number of capacity configuration schemes and a number of power configuration schemes for the energy storage system to be configured based on the evaluation results, wherein the evaluation process is designed to evaluate the power demand data by time dimension and demand dimension respectively;

[0048] A solution module, for respectively inputting the capacity configuration scheme into the pre-built operation model of the grid object to be interactive, and constructing a discharge power demand curve according to the output results of each model; respectively inputting the power configuration scheme into the pre-built operation model of the grid object to be interactive, and constructing a discharge capacity demand curve according to the output results of each model;

[0049] An optimization module, used for processing the intersection result of the discharge power demand curve and the discharge capacity demand curve according to a preset optimization target to obtain an optimal set;

[0050] A configuration module is used to execute the configuration strategy of the energy storage system to be configured constructed by the optimal set.

[0051] As one of the preferred solutions, the grid object to be interacted includes a data center;

[0052] The acquisition module is specifically used for:

[0053] Extracting equipment load information and new energy configuration information according to the resource configuration information of the data center;

[0054] Obtaining historical power consumption data of the data center to draw a load characteristic curve, and predicting load data of the data center in a future time period according to a change trend of the load characteristic curve and the equipment load information;

[0055] Predicting the new energy output data of the data center in a future time period according to the new energy configuration information and the acquired weather forecast information;

[0056] The power demand data of the data center in a future time period is obtained according to the load data and the new energy output data.

[0057] As one preferred solution, the acquisition module is further used for:

[0058] Determining weather features to be extracted according to the new energy configuration information;

[0059] Extracting features from the weather forecast information based on the weather features to be extracted to obtain weather features;

[0060] The weather characteristics are input into a pre-built new energy output prediction model to obtain new energy output data for the data center in a future time period.

[0061] As one of the preferred solutions, the generation module is specifically the same as:

[0062] Analyze the power demand data using a time series algorithm to obtain peak and valley values ​​and instantaneous change rates of the power demand data;

[0063] Designing a number of capacity configuration schemes according to the peak and valley values;

[0064] According to the capacity configuration scheme and the instantaneous change rate, a number of power configuration schemes are designed.

[0065] As one preferred solution, the solution module is specifically used for:

[0066] Constructing the operation model with the goal of minimizing the power shortage of the data center;

[0067] Inputting the capacity configuration schemes into the operation model respectively to obtain an initial discharge power data set of the energy storage system to be configured;

[0068] The initial discharge power data set is screened to obtain a target discharge power data set that meets a first preset criterion, and the discharge power demand curve is constructed according to the target discharge power data set.

[0069] As one preferred solution, the solution module is specifically used for:

[0070] Constructing the operation model with the goal of minimizing the power shortage of the data center;

[0071] Inputting the power configuration schemes into the operation model respectively to obtain an initial capacity data set of the energy storage system to be configured;

[0072] The initial capacity data set is screened to obtain a target capacity data set that meets a second preset criterion, and the discharge capacity demand curve is constructed according to the target capacity data set.

[0073] As one of the preferred solutions, the operation model is expressed as:

[0074]

[0075] in, represents the absolute value of electricity surplus and deficit, , Respectively in The discharge aggregate power and charging aggregate power of the energy storage equipment to be configured in the time period, Indicates power shortage. Indicates a surplus of electricity.

[0076] As one of the preferred solutions, the constraints of the operating model are expressed as:

[0077]

[0078]

[0079] in, , Respectively in During the period The charging power and discharging power of the energy storage device to be configured. , Respectively The charging and discharging power limits of energy storage equipment are configured. is a Boolean variable.

[0080] As one preferred solution, the optimization module is specifically used for:

[0081] According to the intersection result and the optimization target, a candidate solution that meets the electricity demand data is screened out;

[0082] Optimizing the candidate solutions based on a multi-objective optimization method, and performing sensitivity analysis on the optimization results;

[0083] The optimal set is determined according to the optimization result and the result of the sensitivity analysis.

[0084] Another embodiment of the present invention provides a configuration device for an energy storage system, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements the configuration method for the energy storage system as described above when executing the computer program.

[0085] Yet another embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, wherein when a device where the computer-readable storage medium is located executes the computer program, the method for configuring the energy storage system as described above is implemented.

[0086] Compared with the prior art, the beneficial effects of the embodiments of the present application are at least one of the following:

[0087] (1) By evaluating the electricity demand data in terms of time and quantity, this application can provide a more comprehensive understanding of the electricity consumption characteristics of the power grid, including peak hours, off-peak hours, and changes in demand in different time periods.

[0088] (2) This application uses the pre-built operation model of the grid object to be interacted with, inputs the capacity configuration plan and the power configuration plan into the model respectively, and obtains the model output result. The construction of the operation model makes the configuration of the energy storage system closer to the actual operation status of the grid, and improves the accuracy and practicality of the configuration.

[0089] (3) This application processes the intersection of the discharge power demand curve and the discharge capacity demand curve according to the pre-set optimization goal, aiming to find a storage system configuration that meets both power and capacity requirements. Through optimization processing, configuration solutions that do not meet the grid requirements can be eliminated, the preferred range can be narrowed, and the configuration efficiency can be improved.

[0090] (4) This application achieves the optimal configuration of the energy storage system through accurate prediction, efficient configuration, optimized processing and improvement of comprehensive benefits, which helps to balance the load of the power grid, improve the stability of power supply, reduce operating costs and promote the development of new energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0091] Figure 1is a flow chart of a method for configuring an energy storage system in one embodiment of the present application;

[0092] Figure 2 is a structural schematic diagram of a configuration device of an energy storage system in one embodiment of the present application;

[0093] Figure 3 It is a structural block diagram of a configuration device of an energy storage system in one embodiment of the present application. DETAILED DESCRIPTION

[0094] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. The purpose of providing these embodiments is to make the disclosure of the present application more thorough and comprehensive. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0095] In the description of this application, the terms "first", "second", "third", etc. are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first", "second", "third", etc. may explicitly or implicitly include one or more of the feature. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0096] In the description of the present application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be a connection between the two elements. The terms "vertical", "horizontal", "left", "right", "upper", "lower" and similar expressions used in this article are only for illustrative purposes, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on this application. The term "and / or" used in this article includes any and all combinations of one or more related listed items. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0097] In the description of this application, it should be noted that, unless otherwise defined, all technical and scientific terms used in this application have the same meaning as those commonly understood by those skilled in the art. The terms used in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood by specific circumstances.

[0098] An embodiment of the present application provides a configuration method for an energy storage system. For details, see Figure 1 , Figure 1 A schematic flow chart of a method for configuring an energy storage system in one embodiment of the present application is shown, which includes steps S1-S5:

[0099] S1: Obtain the predicted power demand data of the grid object to be interacted with;

[0100] Preferably, in one embodiment of the present application, the grid object to be interacted includes a data center;

[0101] Among them, the IDC (computer data center) configured with new energy is mainly composed of photovoltaic, wind power, other power sources (non-wind, light and other power sources, such as self-provided diesel engines, and wind and light power deducted from the public power grid), energy storage, load and other resources. When other power sources are not considered and only rely on new energy for power supply, it is considered a "zero-carbon" IDC. Taking the "zero-carbon" IDC as an example, without considering the charging and discharging of energy storage, there is a difference between the power supply of IDC and the load demand, which will cause power abandonment and power gap. The power gap of IDC can be solved through reasonable scheduling and configuration of energy storage, and stable and continuous power supply to IDC can be achieved, and the ability to absorb new energy can be improved.

[0102] In one embodiment of the present application, the calculation method of the total IDC power can be directly calculated by the total power of the IT equipment, as shown in formula (1).

[0103] (1)

[0104] In the formula, is the IDC load demand during period t; IDC energy efficiency, generally between 1.3 and 3; is the total power of the IT equipment of the IDC during the period t. Another calculation method is to calculate the power of the internal electrical equipment of the IDC separately. The internal electrical equipment of the IDC mainly includes electrical equipment, IT equipment and cooling equipment, so we can get formula (2) to describe the total power of the IDC.

[0105] (2)

[0106] In the formula, The load of electrical equipment in period t, mainly including power supply and lighting load, is generally a fixed value; It is the cooling load during period t, which is mainly used to keep the server within normal operating temperature and humidity. It can be further expressed by formula (3).

[0107] (3)

[0108] In the formula, and These are empirical coefficients and are related to the IDC's geographic location, energy utilization, and operating temperature. It can be modeled by a linear regression model as follows:

[0109] (4)

[0110] In the formula, and They are the power of the server when it is idle and when it is working.

[0111] Further, the power demand of the IDC system without considering the configuration of energy storage in period t is obtained , see formula (5).

[0112] (5)

[0113] In the formula, , , They represent the photovoltaic output, wind power output and other power output of the IDC system configuration in period t. When the IDC system has power surplus, the load demand On the contrary, there is a power gap and load demand Therefore, the power demand of the IDC system can be further By power shortage and power surplus Decompose as shown in formula (6).

[0114] (6)

[0115] In the formula, and All are positive numbers.

[0116] On this basis, , The two IDC system supply-demand mismatch evaluation index coefficients represent the IDC system's demand for electricity and the degree of abandonment of renewable energy, respectively, as shown in formulas (7) and (8). From formula (7), we can see that the power gap Supply and demand mismatch coefficient with IDC system In direct proportion, when , it means that the IDC system configuration resources can meet its own power demand; similarly, from formula (8), we can know that the power surplus Supply and demand mismatch coefficient with IDC system In direct proportion, when When , it means that all the electricity of the IDC system is consumed.

[0117] (7)

[0118] (8)

[0119] Considering that as the penetration rate of new energy increases, some new energy is allowed to be abandoned, therefore, compared with the power surplus evaluation index , using the power gap evaluation index It is more practical as a quantitative evaluation indicator of system supply and demand mismatch.

[0120] In one embodiment of the present application, obtaining predicted power demand data of a grid object to be interacted with includes:

[0121] Extract equipment load information and new energy configuration information based on the resource configuration information of the data center;

[0122] Obtain the historical power consumption data of the data center to draw a load characteristic curve, and predict the load data of the data center in the future time period based on the change trend of the load characteristic curve and the equipment load information;

[0123] Predict the new energy output data of the data center in the future time period based on the new energy configuration information and the obtained weather forecast information;

[0124] The power demand data of the data center in the future time period is obtained based on the load data and the new energy output data.

[0125] Among them, resource configuration information covers the various resources required by the data center during construction and operation, including computing resources, storage resources, and network resources. Equipment load information includes the operating status and energy consumption of each device in the data center, including computing load, storage load, and network load. New energy configuration information includes the configuration and situation of the data center in terms of renewable energy utilization, including renewable energy facility data and new energy power generation data.

[0126] Specifically, collect the power consumption records of the data center over the past period of time, including daily, weekly, monthly or annual energy consumption data. Use the historical power consumption data to draw the load characteristic curve of the data center, and obtain the power consumption pattern and trend of the data center based on the load characteristic curve. Combine the equipment load information and the change trend of the load characteristic curve, and use the prediction model (such as time series analysis, machine learning algorithm, etc.) to predict the load data of the data center in the future time period.

[0127] Furthermore, weather forecast data is crucial for predicting the output of renewable energy, because the generation of solar and wind energy is directly affected by weather conditions. Based on the renewable energy configuration information and weather forecast information, an appropriate prediction model (such as a physical model, a statistical model, or a machine learning model) is used to predict the renewable energy output data of the data center in the future time period.

[0128] In one embodiment of the present application, the new energy output data of the data center in the future time period is predicted according to the new energy configuration information and the acquired weather forecast information, including:

[0129] Determine the weather features to be extracted according to the new energy configuration information;

[0130] Extracting features of weather forecast information based on the weather features to be extracted to obtain weather features;

[0131] The weather characteristics are input into the pre-built new energy output prediction model to obtain the new energy output data of the data center in the future time period.

[0132] Specifically, if the data center is equipped with solar photovoltaic panels and wind turbines, the weather features to be extracted include sunshine time, radiation intensity, cloud cover, wind speed, wind direction, and air density.

[0133] Extract these features from weather forecast information. This usually involves parsing, filtering, and transforming weather forecast data to obtain input data suitable for the renewable energy output prediction model. Input these features into a pre-built renewable energy output prediction model. This model is usually a prediction model based on machine learning or deep learning technology, which has been trained to predict renewable energy output based on weather characteristics. The input to the model may include multiple weather features, which are processed, transformed, and combined within the model to ultimately output one or more prediction results, that is, the renewable energy output data of the data center in the future time period.

[0134] Integrate the predicted future load data and renewable energy output data. By comparing the load data and renewable energy output data, calculate the net power demand of the data center in the future time period (i.e., load data minus renewable energy output data).

[0135] S2: Evaluate the electricity demand data, and generate a number of capacity configuration schemes and a number of power configuration schemes for the energy storage system to be configured based on the evaluation results, wherein the evaluation process is designed to evaluate the electricity demand data by time dimension and demand dimension respectively;

[0136] Preferably, in one embodiment of the present application, the electricity demand data is evaluated based on a time series algorithm, and a number of capacity configuration schemes and a number of power configuration schemes for the energy storage system to be configured are generated based on the evaluation results, including:

[0137] Use time series algorithm to analyze the electricity demand data to obtain the peak and valley values ​​and instantaneous change rate of the electricity demand data;

[0138] Design a number of capacity configuration plans based on peak and valley values;

[0139] According to the capacity configuration scheme and the instantaneous change rate, several power configuration schemes are designed.

[0140] Specifically, collect and organize the power demand data of the data center, including historical power records, predicted power demand, etc. These data usually exist in the form of time series, that is, each time point corresponds to a power demand. Clean the data, remove outliers and missing values, and ensure the accuracy and completeness of the data.

[0141] Select appropriate time series algorithms to analyze electricity demand data. Common time series algorithms include ARIMA (autoregressive integrated moving average), LSTM (long short-term memory network), GRU (gated recurrent unit), etc. These algorithms can capture the trend, seasonality and cyclical characteristics of data.

[0142] Through algorithmic analysis, the peak and valley values ​​of power demand data are identified. The peak value represents the maximum power demand of the data center at a specific time point, while the valley value represents the minimum power demand. These values ​​are crucial for the capacity configuration of the energy storage system. In addition to the peak and valley values, the instantaneous rate of change of power demand also needs to be calculated. The instantaneous rate of change reflects the fluctuation of power demand in a short period of time, which can be obtained by calculating the difference between the power demand at adjacent time points and dividing it by the time interval.

[0143] Based on the analysis results of the peak and valley values, determine the maximum capacity requirement that the energy storage system needs to meet. According to the capacity requirements, design a number of capacity configuration schemes. These capacity configuration schemes can include different energy storage media (such as lithium-ion batteries, lead-acid batteries, sodium-sulfur batteries, etc.), different energy storage capacities (such as 100kWh, 200kWh, 300kWh, etc.) and different numbers of energy storage systems (such as single system, dual system, multiple systems in parallel, etc.).

[0144] Based on the analysis results of the instantaneous rate of change, determine the maximum power demand that the energy storage system needs to meet. This is usually equal to or slightly greater than the maximum change in electricity demand in a short period of time to ensure that sufficient power support can be provided when electricity demand changes rapidly. According to the power demand, design a number of power configuration schemes. These schemes can include different charging and discharging powers (such as 10kW, 20kW, 50kW, etc.), different charging and discharging strategies (such as constant power charging and discharging, variable power charging and discharging, etc.) and different energy storage system parallel connection methods.

[0145] S3: respectively inputting the capacity configuration schemes into the pre-built operation models of the grid objects to be interacted, and constructing the discharge power demand curve according to the output results of each model; respectively inputting the power configuration schemes into the pre-built operation models of the grid objects to be interacted, and constructing the discharge capacity demand curve according to the output results of each model;

[0146] Preferably, in one embodiment of the present application, the capacity configuration schemes are respectively input into the pre-built operation models of the grid objects to be interacted, and the discharge power demand curve is constructed according to the obtained output results of each model, including:

[0147] Build an operation model with the goal of minimizing the power shortage in the data center;

[0148] Input the capacity configuration schemes into the operation model respectively to obtain the initial discharge power data set of the energy storage system to be configured;

[0149] The initial discharge power data set is screened to obtain a target discharge power data set that meets a first preset criterion, and a discharge power demand curve is constructed according to the target discharge power data set.

[0150] Preferably, in one embodiment of the present application, the power configuration schemes are respectively input into the pre-built operation models of the grid objects to be interacted, and the discharge capacity demand curve is constructed according to the obtained output results of each model, including:

[0151] Build an operation model with the goal of minimizing the power shortage in the data center;

[0152] Input the power configuration schemes into the operation model respectively to obtain the initial capacity data set of the energy storage system to be configured;

[0153] The initial capacity data set is screened to obtain a target capacity data set that meets a second preset criterion, and a discharge capacity demand curve is constructed according to the target capacity data set.

[0154] Among them, taking electrochemical energy storage (EES) as an example, the charge and discharge power limit constraint of EES is expressed as:

[0155] (9)

[0156] (10)

[0157] in, , Respectively in During the period The charging power and discharging power of the energy storage device to be configured. , Respectively The charging and discharging power limits of energy storage equipment are configured. is a Boolean variable.

[0158] The power state equation of EES is shown in equation (11), and its state constraints are equations (12)-(13).

[0159] (11)

[0160] (12)

[0161] (13)

[0162] In the formula, is the battery status of the i-th EES in period t; , are the maximum and minimum values ​​allowed for the battery status of the i-th EES; represents the charging and discharging efficiency of the i-th EES; is the leakage coefficient, which indicates the percentage of power lost in a period of time; Represents a time interval; is the capacity of the i-th EES; is the battery status of the i-th EES at time t0; is the initial battery state of the i-th EES.

[0163] The running model is expressed as:

[0164] (14)

[0165] in, represents the absolute value of electricity surplus and deficit, , Respectively in The discharge aggregate power and charging aggregate power of the energy storage equipment to be configured in the time period, Indicates power shortage. Indicates a surplus of electricity.

[0166] Furthermore, we can obtain an optimization problem with the minimum of equation (14) as the optimization objective and equations (9)-(13) and (15)-(18) as constraints. , are the discharge aggregate power and charge aggregate power of I EES in period t, both are positive numbers, expressed by equations (15) and (16) respectively. The role of equation (17) is to ensure that the charge aggregate power of EES in period t does not exceed the power surplus of the IDC system; the role of equation (18) is to ensure that the discharge aggregate power of EES in period t does not exceed the power demand of the IDC system.

[0167] (15)

[0168] (16)

[0169] (17)

[0170] (18)

[0171] At the same time, considering the IDC system configuration EES, equation (6) can be modified to equation (19), when When , it means that the IDC system has power surplus in the period t; when , it means that there is a power shortage in the IDC system during the period t.

[0172] (19)

[0173] In the formula, , The discharge and charging powers of the new energy storage aggregates are obtained by solving the optimization problem with the minimum of formula (14) as the optimization objective and formulas (9)-(13) and (15)-(18) as constraints.

[0174] Synchronization requires correction of the supply-demand mismatch evaluation index coefficient, see equations (20)-(21).

[0175] (20)

[0176] (twenty one)

[0177] Specifically, with the goal of minimizing the power shortage of the data center, an operation model of the grid object to be interacted (i.e., the computer data center) is constructed. This model can simulate the operation of the grid under different conditions.

[0178] Input each capacity configuration scheme designed in step S2 into the operation model respectively. For each capacity configuration scheme, the model will simulate the operation of the energy storage system in the power grid, including the charging and discharging process of the energy storage system and the balance of power supply and demand of the power grid. After the model runs, it will output the initial discharge power data set of the energy storage system corresponding to each capacity configuration scheme. The initial discharge power data set is screened to meet the first preset criterion. Through screening, the target discharge power data set can be obtained. According to the target discharge power data set, a discharge power demand curve is constructed.

[0179] Similarly, each pre-designed power configuration scheme is input into the operation model to obtain the target capacity data set. According to the target capacity data set, the discharge capacity demand curve is constructed. The constructed discharge power demand curve and discharge capacity demand curve can be used as a reference for the subsequent energy storage system design and configuration.

[0180] The calculation methods for capacity, discharge power and maximum continuous discharge time requirements are shown in equations (22)-(24) respectively.

[0181] (twenty two)

[0182] (twenty three)

[0183] (twenty four)

[0184] in, is the capacity of EES, is the discharge power of EES, is the maximum continuous discharge duration of EES.

[0185] The first criterion is expressed as:

[0186] or (25)

[0187] By solving and calculating the evaluation index under the kth EES discharge power configuration scheme ,when Satisfying the criterion (25), we can get the capacity of the mth EES The required boundary of EES discharge power of IDC system under configuration .

[0188] The second criterion is expressed as:

[0189] or (26)

[0190] By solving the EES charging and discharging behavior optimization model, the evaluation index under the mth EES capacity configuration scheme is calculated. ,when Satisfying the criterion (26), the discharge power of the kth EES can be obtained Configure the capacity requirement boundary of the IDC system for EES .

[0191] S4: processing the intersection result of the discharge power demand curve and the discharge capacity demand curve according to a preset optimization target to obtain an optimal set;

[0192] Preferably, in one embodiment of the present application, the intersection result of the discharge power demand curve and the discharge capacity demand curve is processed according to a preset optimization target to obtain an optimal set, including:

[0193] According to the intersection results and optimization objectives, select candidate solutions that meet the electricity demand data;

[0194] Optimize candidate solutions based on multi-objective optimization methods and conduct sensitivity analysis on optimization results;

[0195] The optimal set is determined based on the optimization results and the results of sensitivity analysis.

[0196] Specifically, the intersection of the discharge power demand curve and the discharge capacity demand curve represents a discharge strategy that satisfies both power and capacity requirements at a specific time point or time period. These intersections constitute a set of candidate solutions.

[0197] According to the pre-set optimization goals (such as maximizing energy efficiency, minimizing costs, meeting specific power and capacity requirements, etc.), the solutions that meet these goals are screened from the candidate solutions. This step is a preliminary screening, which aims to reduce the computational workload of subsequent optimization.

[0198] For the selected candidate solutions, multi-objective optimization methods (such as genetic algorithms, particle swarm optimization, etc.) can be used for further optimization. Multi-objective optimization methods can consider multiple optimization objectives at the same time and find trade-off solutions between these objectives. During the optimization process, an appropriate fitness function can be defined to evaluate the pros and cons of each candidate solution. The fitness function should be able to reflect the optimization objectives and rank or score the candidate solutions according to these objectives.

[0199] Sensitivity analysis is a method to evaluate the sensitivity of optimization results to changes in input parameters or conditions. In the discharge strategy optimization, sensitivity analysis can determine which parameters or conditions have the greatest impact on the final optimization results to further improve the optimization results. Sensitivity analysis can be achieved by calculating the change in the optimization results when each parameter or condition changes. This can be done through numerical methods (such as finite difference method, perturbation method, etc.) or analytical methods (such as derivatives, partial derivatives, etc.).

[0200] Based on the optimization results and the results of sensitivity analysis, an optimal set is determined. This set contains candidate solutions that are least sensitive to changes in input parameters or conditions while meeting the optimization objectives. These solutions usually have high robustness and reliability and are the first choice for actual discharge strategy formulation.

[0201] S5: Execute the configuration strategy of the energy storage system to be configured constructed by the optimal set.

[0202] After implementing the configuration strategy, the operation of the energy storage system needs to be continuously monitored and evaluated. This includes: monitoring the real-time operating status and performance parameters of the energy storage system; evaluating the economic and social benefits of the energy storage system, such as energy saving effects and cost savings; and making necessary adjustments and optimizations to the configuration strategy based on the evaluation results.

[0203] Another embodiment of the present application provides a configuration device for an energy storage system. For details, see Figure 2 , Figure 2 A schematic diagram of a configuration device of an energy storage system in one embodiment of the present application is shown, which includes:

[0204] An acquisition module 11 is used to acquire the predicted power demand data of the grid object to be interacted with;

[0205] A generating module 12 is used to evaluate the power demand data, and generate a number of capacity configuration schemes and a number of power configuration schemes for the energy storage system to be configured based on the evaluation results, wherein the evaluation process is designed to evaluate the power demand data by time dimension and demand dimension respectively;

[0206] The solution module 13 is used to input the capacity configuration scheme into the pre-built operation model of the power grid object to be interactive, and construct the discharge power demand curve according to the output results of each model; input the power configuration scheme into the pre-built operation model of the power grid object to be interactive, and construct the discharge capacity demand curve according to the output results of each model;

[0207] The optimization module 14 is used to process the intersection result of the discharge power demand curve and the discharge capacity demand curve according to a preset optimization target to obtain an optimal set;

[0208] The configuration module 15 is used to execute the configuration strategy of the energy storage system to be configured constructed by the optimal set.

[0209] Preferably, in one embodiment of the present invention, the grid object to be interacted includes a data center;

[0210] Get the module, specifically for:

[0211] Extract equipment load information and new energy configuration information based on the resource configuration information of the data center;

[0212] Obtain the historical power consumption data of the data center to draw a load characteristic curve, and predict the load data of the data center in the future time period based on the change trend of the load characteristic curve and the equipment load information;

[0213] Predict the new energy output data of the data center in the future time period based on the new energy configuration information and the obtained weather forecast information;

[0214] The power demand data of the data center in the future time period is obtained based on the load data and the new energy output data.

[0215] Preferably, in one embodiment of the present invention, the acquisition module is further used for:

[0216] Determine the weather features to be extracted according to the new energy configuration information;

[0217] Extracting features of weather forecast information based on the weather features to be extracted to obtain weather features;

[0218] The weather characteristics are input into the pre-built new energy output prediction model to obtain the new energy output data of the data center in the future time period.

[0219] Preferably, in one embodiment of the present invention, the generating module is specifically the same as:

[0220] Use time series algorithm to analyze the electricity demand data to obtain the peak and valley values ​​and instantaneous change rate of the electricity demand data;

[0221] Design a number of capacity configuration plans based on peak and valley values;

[0222] According to the capacity configuration scheme and the instantaneous change rate, several power configuration schemes are designed.

[0223] Preferably, in one embodiment of the present invention, the solution module is specifically used for:

[0224] Build an operation model with the goal of minimizing the power shortage in the data center;

[0225] Input the capacity configuration schemes into the operation model respectively to obtain the initial discharge power data set of the energy storage system to be configured;

[0226] The initial discharge power data set is screened to obtain a target discharge power data set that meets a first preset criterion, and a discharge power demand curve is constructed according to the target discharge power data set.

[0227] Preferably, in one embodiment of the present invention, the solution module is specifically used for:

[0228] Build an operation model with the goal of minimizing the power shortage in the data center;

[0229] Input the power configuration schemes into the operation model respectively to obtain the initial capacity data set of the energy storage system to be configured;

[0230] The initial capacity data set is screened to obtain a target capacity data set that meets a second preset criterion, and a discharge capacity demand curve is constructed according to the target capacity data set.

[0231] Preferably, in one embodiment of the present invention, the operation model is expressed as:

[0232]

[0233] in, represents the absolute value of electricity surplus and deficit, , Respectively in The discharge aggregate power and charging aggregate power of the energy storage equipment to be configured in the time period, Indicates power shortage. Indicates a surplus of electricity.

[0234] Preferably, in one embodiment of the present invention, the constraints of the running model are expressed as:

[0235]

[0236]

[0237] in, , Respectively in During the period The charging power and discharging power of the energy storage device to be configured. , Respectively The charging and discharging power limits of energy storage equipment are configured. is a Boolean variable.

[0238] Preferably, in one embodiment of the present invention, the optimization module is specifically used for:

[0239] According to the intersection results and optimization objectives, select candidate solutions that meet the electricity demand data;

[0240] Optimize candidate solutions based on multi-objective optimization methods and conduct sensitivity analysis on optimization results;

[0241] The optimal set is determined based on the optimization results and the results of sensitivity analysis.

[0242] Another embodiment of the present invention provides a configuration device for an energy storage system, see Figure 3 , which is a structural block diagram of a configuration device for an energy storage system provided in an embodiment of the present invention. The configuration device for an energy storage system provided in an embodiment of the present invention comprises a processor 21, a memory 22, and a computer program stored in the memory 22 and configured to be executed by the processor 21. When the processor 21 executes the computer program, the steps in the configuration method embodiment of the energy storage system described above are implemented, for example Figure 1 or, the processor 21 implements the functions of each module in the above-mentioned device embodiments, such as the acquisition module 11, when executing the computer program.

[0243] Exemplarily, the computer program may be divided into one or more modules, which are stored in the memory 22 and executed by the processor 21 to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program in the configuration device of the energy storage system. For example, the computer program may be divided into an acquisition module 11, a generation module 12, a solution module 13, an optimization module 14, and a configuration module 15.

[0244] The configuration device of the energy storage system may include, but is not limited to, a processor 21 and a memory 22. Those skilled in the art will appreciate that the schematic diagram is merely an example of the configuration device of the energy storage system and does not constitute a limitation on the configuration device of the energy storage system. The configuration device may include more or fewer components than shown in the diagram, or a combination of certain components, or different components. For example, the configuration device of the energy storage system may also include input and output devices, network access devices, buses, etc.

[0245] The processor 21 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor 21 is the control center of the configuration device of the energy storage system, and uses various interfaces and lines to connect various parts of the configuration device of the entire energy storage system.

[0246] The memory 22 can be used to store the computer program and / or module. The processor 21 realizes various functions of the configuration device of the energy storage system by running or executing the computer program and / or module stored in the memory 22 and calling the data stored in the memory 22. The memory 22 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory 22 can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0247] Wherein, if the module integrated by the configuration device of the energy storage system is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0248] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0249] Accordingly, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the steps in the configuration method of the energy storage system in the above embodiment, for example Figure 1 Steps S1 to S5 described in .

[0250] Compared with the prior art, the beneficial effects of the embodiments of the present application are at least one of the following:

[0251] (1) By evaluating the electricity demand data in terms of time and quantity, this application can provide a more comprehensive understanding of the electricity consumption characteristics of the power grid, including peak hours, off-peak hours, and changes in demand in different time periods.

[0252] (2) This application uses the pre-built operation model of the grid object to be interacted with, inputs the capacity configuration plan and the power configuration plan into the model respectively, and obtains the model output result. The construction of the operation model makes the configuration of the energy storage system closer to the actual operation status of the grid, and improves the accuracy and practicality of the configuration.

[0253] (3) This application processes the intersection of the discharge power demand curve and the discharge capacity demand curve according to the pre-set optimization goal, aiming to find a storage system configuration that meets both power and capacity requirements. Through optimization processing, configuration solutions that do not meet the grid requirements can be eliminated, the preferred range can be narrowed, and the configuration efficiency can be improved.

[0254] (4) This application achieves the optimal configuration of the energy storage system through accurate prediction, efficient configuration, optimized processing and improvement of comprehensive benefits, which helps to balance the load of the power grid, improve the stability of power supply, reduce operating costs and promote the development of new energy.

[0255] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for configuring an energy storage system, characterized in that: include: Acquire predicted power demand data of the grid object to be interacted with; The grid object to be interacted includes a data center; Evaluate the power demand data, and generate a number of capacity configuration schemes and a number of power configuration schemes for the energy storage system to be configured based on the evaluation results, wherein the evaluation process is designed to evaluate the power demand data by time dimension and demand dimension respectively; Input the capacity configuration schemes into the pre-built operation models of the grid objects to be interacted respectively, and construct the discharge power demand curve according to the output results of each model; Input the power configuration schemes into the pre-built operation models of the grid objects to be interacted respectively, and construct the discharge capacity demand curve according to the output results of each model; The step of respectively inputting the capacity configuration schemes into pre-built operation models of the grid objects to be interacted with, and constructing a discharge power demand curve according to the output results of each model, includes: Constructing the operation model with the goal of minimizing the power shortage of the data center; Inputting the capacity configuration schemes into the operation model respectively to obtain an initial discharge power data set of the energy storage system to be configured; Screening the initial discharge power data set to obtain a target discharge power data set that meets a first preset criterion, and constructing the discharge power demand curve according to the target discharge power data set; The step of respectively inputting the power configuration schemes into pre-built operation models of the grid objects to be interacted with, and constructing a discharge capacity demand curve according to the output results of each model, comprises: Constructing the operation model with the goal of minimizing the power shortage of the data center; Inputting the power configuration schemes into the operation model respectively to obtain an initial capacity data set of the energy storage system to be configured; Screening the initial capacity data set to obtain a target capacity data set that meets a second preset criterion, and constructing the discharge capacity demand curve according to the target capacity data set; Processing the intersection result of the discharge power demand curve and the discharge capacity demand curve according to a preset optimization target to obtain an optimal set; Execute the configuration strategy of the energy storage system to be configured constructed by the optimal set.

2. The method for configuring an energy storage system according to claim 1, characterized in that: The step of obtaining predicted power demand data of the grid object to be interacted with includes: Extracting equipment load information and new energy configuration information according to the resource configuration information of the data center; Obtaining historical power consumption data of the data center to draw a load characteristic curve, and predicting load data of the data center in a future time period according to a change trend of the load characteristic curve and the equipment load information; Predicting the new energy output data of the data center in a future time period according to the new energy configuration information and the acquired weather forecast information; The power demand data of the data center in a future time period is obtained according to the load data and the new energy output data.

3. The method for configuring an energy storage system according to claim 2, characterized in that: The predicting of the new energy output data of the data center in a future time period according to the new energy configuration information and the acquired weather forecast information includes: Determining weather features to be extracted according to the new energy configuration information; Extracting features from the weather forecast information based on the weather features to be extracted to obtain weather features; The weather characteristics are input into a pre-built new energy output prediction model to obtain new energy output data for the data center in a future time period.

4. The method for configuring an energy storage system according to claim 1, characterized in that: The step of evaluating the power demand data and generating a plurality of capacity configuration schemes and a plurality of power configuration schemes for the energy storage system to be configured based on the evaluation results includes: Analyze the power demand data using a time series algorithm to obtain peak and valley values ​​and instantaneous change rates of the power demand data; Designing a number of capacity configuration schemes according to the peak and valley values; According to the capacity configuration scheme and the instantaneous change rate, a number of power configuration schemes are designed.

5. The method for configuring an energy storage system according to claim 1, characterized in that: The operation model is expressed as: in, represents the absolute value of electricity surplus and deficit, , Respectively in The discharge aggregate power and charging aggregate power of the energy storage equipment to be configured in the time period, Indicates power shortage. Indicates a surplus of electricity.

6. The method for configuring an energy storage system according to claim 1, characterized in that: The constraints of the running model are expressed as: in, , Respectively in During the period The charging power and discharging power of the energy storage device to be configured. , Respectively The charging and discharging power limits of energy storage equipment are configured. is a Boolean variable.

7. The method for configuring an energy storage system according to claim 1, characterized in that: The processing of the intersection result of the discharge power demand curve and the discharge capacity demand curve according to the preset optimization target to obtain the optimal set includes: According to the intersection result and the optimization target, a candidate solution that meets the electricity demand data is screened out; Optimizing the candidate solutions based on a multi-objective optimization method, and performing sensitivity analysis on the optimization results; The optimal set is determined according to the optimization result and the result of the sensitivity analysis.

8. A configuration device for an energy storage system, characterized in that: include: An acquisition module, used to acquire predicted power demand data of the grid object to be interacted with; The grid object to be interacted includes a data center; A generation module, used to evaluate the power demand data, and generate a number of capacity configuration schemes and a number of power configuration schemes for the energy storage system to be configured based on the evaluation results, wherein the evaluation process is designed to evaluate the power demand data by time dimension and demand dimension respectively; A solution module, for respectively inputting the capacity configuration scheme into the pre-built operation model of the grid object to be interactive, and constructing a discharge power demand curve according to the output results of each model; respectively inputting the power configuration scheme into the pre-built operation model of the grid object to be interactive, and constructing a discharge capacity demand curve according to the output results of each model; Wherein, the solution module is specifically used for: Constructing the operation model with the goal of minimizing the power shortage of the data center; Inputting the capacity configuration schemes into the operation model respectively to obtain an initial discharge power data set of the energy storage system to be configured; Screening the initial discharge power data set to obtain a target discharge power data set that meets a first preset criterion, and constructing the discharge power demand curve according to the target discharge power data set; Wherein, the solution module is further used for: Constructing the operation model with the goal of minimizing the power shortage of the data center; Inputting the power configuration schemes into the operation model respectively to obtain an initial capacity data set of the energy storage system to be configured; Screening the initial capacity data set to obtain a target capacity data set that meets a second preset criterion, and constructing the discharge capacity demand curve according to the target capacity data set; An optimization module, used for processing the intersection result of the discharge power demand curve and the discharge capacity demand curve according to a preset optimization target to obtain an optimal set; A configuration module is used to execute the configuration strategy of the energy storage system to be configured constructed by the optimal set.

9. The energy storage system configuration device according to claim 8, characterized in that: The acquisition module is specifically used for: Extracting equipment load information and new energy configuration information according to the resource configuration information of the data center; Obtaining historical power consumption data of the data center to draw a load characteristic curve, and predicting load data of the data center in a future time period according to a change trend of the load characteristic curve and the equipment load information; Predicting the new energy output data of the data center in a future time period according to the new energy configuration information and the acquired weather forecast information; The power demand data of the data center in a future time period is obtained according to the load data and the new energy output data.

10. The energy storage system configuration device according to claim 9, characterized in that: The acquisition module is further used for: Determining weather features to be extracted according to the new energy configuration information; Extracting features from the weather forecast information based on the weather features to be extracted to obtain weather features; The weather characteristics are input into a pre-built new energy output prediction model to obtain new energy output data for the data center in a future time period.

11. The energy storage system configuration device according to claim 8, characterized in that: The generation module is specifically the same as: Analyze the power demand data using a time series algorithm to obtain peak and valley values ​​and instantaneous change rates of the power demand data; Designing a number of capacity configuration schemes according to the peak and valley values; According to the capacity configuration scheme and the instantaneous change rate, a number of power configuration schemes are designed.

12. The energy storage system configuration device according to claim 8, characterized in that: The operation model is expressed as: in, represents the absolute value of electricity surplus and deficit, , Respectively in The discharge aggregate power and charging aggregate power of the energy storage equipment to be configured in the time period, Indicates power shortage. Indicates a surplus of electricity.

13. The energy storage system configuration device according to claim 8, characterized in that: The constraints of the running model are expressed as: in, , Respectively in During the period The charging power and discharging power of the energy storage device to be configured. , Respectively The charging and discharging power limits of energy storage equipment are configured. is a Boolean variable.

14. The energy storage system configuration device according to claim 8, characterized in that: The optimization module is specifically used for: According to the intersection result and the optimization target, a candidate solution that meets the electricity demand data is screened out; Optimizing the candidate solutions based on a multi-objective optimization method, and performing sensitivity analysis on the optimization results; The optimal set is determined according to the optimization result and the result of the sensitivity analysis.

15. A configuration device for an energy storage system, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements the energy storage system configuration method according to any one of claims 1 to 7 when executing the computer program.

16. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein when the device where the computer-readable storage medium is located executes the computer program, the configuration method of the energy storage system according to any one of claims 1 to 7 is implemented.

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