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

Abstract

The application discloses a kind of energy storage system configuration method, device, equipment and computer readable storage medium, the application relates to electric energy management technical field, the energy storage system configuration method includes obtaining historical power generation data and historical power consumption data of user side;Based on historical power generation data, historical power consumption data and preset constraint condition, the parameter to be configured of energy storage system in preset objective function is solved, and the target parameter value corresponding to the parameter to be configured is obtained;Based on target parameter value, the energy storage system is configured, to supply user side with configured energy storage system, wherein, configured energy storage system satisfies preset cost requirement.The application can improve the accuracy of energy storage system configuration result.

Description

Technical Field

[0001] This application relates to the field of electric energy management technology, and in particular to a method, apparatus, equipment and computer-readable storage medium for configuring an energy storage system. Background Technology

[0002] With the continuous development of technology, the functions and applications of energy storage systems used on the user side (i.e., electricity users) are becoming increasingly diverse. Energy storage systems can not only make full use of the peak-valley electricity price difference and reduce electricity costs through peak shaving and valley filling strategies, but also effectively avoid economic losses caused by power outages, thereby providing users with a more stable and reliable power service.

[0003] Currently, common energy storage system configuration methods typically rely on users' annual load data. The objective function is the maximum net present value (NPV) of the project throughout the year (which includes energy storage investment costs, peak shaving and valley filling costs on the first day, capacity fee revenue, and power outage load losses). An optimization model is established with constraints such as investment scale, energy storage power constraints, and energy storage capacity. The objective function is then solved using mixed-integer linear programming to obtain the optimal energy storage system configuration. However, because this configuration method considers only a limited range of data affecting the energy storage system configuration, it suffers from low accuracy in the configuration results.

[0004] Therefore, improving the accuracy of energy storage system configuration results is an urgent problem that needs to be solved. Summary of the Invention

[0005] The main objective of this application is to provide a method, apparatus, device, and computer-readable storage medium for configuring an energy storage system, with the aim of improving the accuracy of the configuration results of the energy storage system.

[0006] To achieve the above objectives, this application provides a method for configuring an energy storage system, the method comprising:

[0007] Obtain historical power generation and historical electricity consumption data from the user side;

[0008] Based on the historical power generation data, the historical power consumption data, and the preset constraints, the parameters to be configured for the energy storage system in the preset objective function are solved to obtain the target parameter values ​​corresponding to the parameters to be configured.

[0009] The energy storage system is configured based on the target parameter values ​​so that the user side can use the configured energy storage system, wherein the configured energy storage system meets the preset cost requirements.

[0010] In one embodiment, the step of solving for the configurable parameters of the energy storage system in a preset objective function based on the historical power generation data, the historical power consumption data, and preset constraints, and obtaining the target parameter values ​​corresponding to the configurable parameters, includes:

[0011] Based on the historical power generation data and the historical power consumption data, determine the instance function corresponding to the preset objective function;

[0012] Based on the preset constraints, the parameters to be configured for the energy storage system in the instance function are solved to obtain the target parameter values ​​corresponding to the parameters to be configured.

[0013] In one embodiment, the step of determining the instance function corresponding to the preset objective function based on the historical power generation data and the historical power consumption data includes:

[0014] Based on the historical power generation data, the historical power consumption data, and the parameters to be configured, a first function and a second function are determined, wherein the first function is used to calculate the normal power purchase cost of the energy storage system, and the second function is used to calculate the demand power purchase cost of the energy storage system.

[0015] Based on the unit cost of the energy storage system, the equipment depreciation factor, the maintenance cost factor, and the parameters to be configured, a third function is determined, wherein the third function is used to calculate the initial purchase cost and maintenance cost of the energy storage system;

[0016] Based on the first function, the second function, and the third function, determine the instance function corresponding to the preset target function.

[0017] In one embodiment, the parameters to be configured include rated charge and discharge power, and the step of determining the first function and the second function based on the historical power generation data, the historical power consumption data, and the parameters to be configured includes:

[0018] Cluster the historical power generation data and the historical power consumption data to obtain each typical load scenario and its respective weight;

[0019] The result of subtracting the historical power generation data from the historical power consumption data is determined, and the result value is added to the rated charging and discharging power to obtain a first expression, wherein the first expression represents the power supply of the power grid on the user side;

[0020] Based on each of the typical load scenarios, each of the weights, the first expression, and the preset time-of-use electricity price, the first function is determined, and based on each of the typical load scenarios, each of the weights, the first expression, and the preset demand electricity price, the second function is determined.

[0021] In one embodiment, the step of clustering the historical power generation data and the historical power consumption data to obtain each typical load scenario and its respective weight includes:

[0022] The historical power generation data and the historical power consumption data are divided into multiple load scenario data according to a preset time interval;

[0023] Clustering is performed on the data from multiple load scenarios to obtain multiple typical load scenarios;

[0024] The ratio of the number of load scenario data corresponding to the typical load scenario to the total number of all load scenario data is used as the weight of the typical load scenario.

[0025] In one embodiment, the parameters to be configured further include energy storage capacity, and the unit cost includes a first unit cost of the energy storage capacity and a second unit cost of the charge / discharge rated power;

[0026] The step of determining the third function based on the unit cost of the energy storage system, the equipment depreciation factor, the maintenance cost factor, and the parameters to be configured includes:

[0027] The product of the energy storage capacity and the first unit cost is added to the product of the rated charge / discharge power and the second unit cost to obtain a second expression, wherein the second expression represents the initial purchase cost;

[0028] The third function is determined based on the second expression, the equipment depreciation factor, and the maintenance cost factor.

[0029] In one embodiment, the preset constraints include a first constraint for constraining the energy storage capacity and a second constraint for constraining the rated charging and discharging power. The first constraint is determined based on a preset system capacity limit, and the second constraint is determined based on the number of times and time periods of peak electricity prices within a preset duration.

[0030] Furthermore, to achieve the above objectives, this application also provides an energy storage system configuration device, the energy storage system configuration device comprising:

[0031] The acquisition module is used to acquire historical power generation data and historical electricity consumption data from the user side;

[0032] The solution module is used to solve the parameters to be configured in the energy storage system in the preset objective function based on the historical power generation data, the historical power consumption data and preset constraints, and to obtain the target parameter values ​​corresponding to the parameters to be configured.

[0033] A configuration module is used to configure the energy storage system based on the target parameter values ​​so that the user side can use the configured energy storage system, wherein the configured energy storage system meets preset cost requirements.

[0034] In addition, to achieve the above objectives, this application also provides a storage medium, which is a computer-readable storage medium, on which a program implementing an energy storage system configuration method is stored, and the program implementing the energy storage system configuration method is executed by a processor to implement the steps of the energy storage system configuration method as described above.

[0035] In addition, to achieve the above objectives, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the energy storage system configuration method described above.

[0036] This application provides a method for configuring an energy storage system. The method involves acquiring historical power generation data and historical electricity consumption data from the user side, and then solving for the parameters to be configured in the preset objective function based on the historical power generation data, historical electricity consumption data, and preset constraints. This yields the target parameter values ​​corresponding to the parameters to be configured. The method then configures the energy storage system based on the target parameter values ​​so that the user side can use the configured energy storage system. The configured energy storage system meets preset cost requirements.

[0037] Thus, compared to the traditional method of configuring energy storage systems based on a single data type, this application combines historical power generation data and historical electricity consumption data to solve for the target parameter values ​​that enable the configured energy storage system to meet the preset cost requirements, thereby improving the comprehensiveness of the basic data and thus improving the accuracy of the energy storage system configuration results. Attached Figure Description

[0038] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0039] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 This is a flowchart illustrating the first embodiment of the energy storage system configuration method of this application;

[0041] Figure 2 This is a schematic diagram of a typical load scenario involved in an embodiment of the energy storage system configuration method of this application;

[0042] Figure 3 This is a schematic diagram of the energy storage system configuration process according to an embodiment of the energy storage system configuration method of this application;

[0043] Figure 4 This is a schematic diagram of the modular structure of the energy storage system configuration device in this application;

[0044] Figure 5 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the energy storage system configuration method in this application embodiment.

[0045] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0046] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0047] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0048] The main solution of this application is as follows: acquiring historical power generation data and historical power consumption data from the user side; based on the historical power generation data, the historical power consumption data, and preset constraints, solving the parameters to be configured in the preset objective function of the energy storage system to obtain the target parameter values ​​corresponding to the parameters to be configured; configuring the energy storage system based on the target parameter values ​​so that the user side can use the configured energy storage system, wherein the configured energy storage system meets preset cost requirements.

[0049] Currently, common energy storage system configuration methods typically use users' annual load data as the objective function, with the maximum net present value (NPV) of the project during the period (including energy storage investment costs, peak shaving and valley filling first-day revenue, capacity fee revenue, and power outage load losses) as the objective function. An optimization model is established with constraints such as investment scale, energy storage power constraints, and energy storage capacity. Then, a mixed-integer linear programming method is used to solve the objective function to obtain the optimal energy storage system configuration. However, because the above-mentioned energy storage system configuration method considers only a limited range of data affecting the configuration, it suffers from low accuracy in the configuration results.

[0050] Thus, compared to the traditional method of configuring energy storage systems based on a single data type, this application combines historical power generation data and historical electricity consumption data to solve for the target parameter values ​​that enable the configured energy storage system to meet the preset cost requirements, thereby improving the comprehensiveness of the basic data and thus improving the accuracy of the energy storage system configuration results.

[0051] It should be noted that, in the various embodiments of the energy storage system configuration method of this application, the executing entity of the method can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an energy storage system configuration device capable of realizing the above functions. This embodiment does not specifically limit this. The following uses the configuration system as the executing entity as an example to describe this embodiment and the following embodiments. For ease of description, the executing entity is omitted in the following description.

[0052] Based on this, this application proposes a first embodiment of an energy storage system configuration method, please refer to... Figure 1 The energy storage system configuration method includes steps S10 to S30:

[0053] Step S10: Obtain historical power generation data and historical electricity consumption data from the user side;

[0054] It should be noted that the application scenario of this application is to optimize the configuration of energy storage systems used by factories that consume electricity, so as to reduce the total operating cost of the energy storage system, that is, to maximize the factory's profits. The aforementioned user side refers to factories or other enterprises that use energy storage systems and consume electricity. In the embodiments of this application, historical power generation data and historical electricity consumption data of the user side can be obtained within a preset period of time (hereinafter referred to as the first preset period of time for distinction). This application does not limit the specific duration of the first preset period of time. In this embodiment of the application, the first preset period of time is set to 1 year. Historical power generation data refers to the power generation data of the user side within the past first preset time period. This historical power generation data includes wind power processing data and / or solar power output data. Specifically, wind power output data is obtained by processing the raw wind power data generated by the user side during the first preset time period. The raw wind power data consists of the three-phase active power during wind power generation, and the average active power for each hour in the raw wind power data is calculated to obtain the aforementioned wind power output data. Similarly, solar power output data is obtained by processing the raw photovoltaic data generated by the user side during the first preset time period. Specifically, the raw photovoltaic data consists of the three-phase active power during photovoltaic power generation, and the average active power for each hour in the raw photovoltaic data is calculated to obtain the aforementioned solar power output data. Historical electricity consumption data is obtained by processing the raw electricity consumption data actually used by the user side during the first preset time period. Specifically, the raw electricity consumption data consists of the actual three-phase active power used by the user side during the first preset time period, and the average active power for each hour in the raw electricity consumption data is calculated to obtain the aforementioned user load data. It should be noted that the total electricity consumption on the user side is the sum of wind power supply, solar power supply, and grid power supply.

[0055] In one feasible implementation, before step S10, the method further includes: acquiring raw wind power data, raw photovoltaic data, and raw electricity consumption data of the user corresponding to the energy storage system within a first preset time period; calculating the first average active power of the raw wind power data per hour, and determining the first average active power corresponding to each hour as wind power output data; calculating the second average active power of the raw photovoltaic data per hour, and determining the second average active power corresponding to each hour as photovoltaic output data; and calculating the third average active power of the raw electricity consumption data per hour, and determining the third average active power corresponding to each hour as historical electricity consumption data.

[0056] Step S20: Based on the historical power generation data, the historical power consumption data, and the preset constraints, solve the parameters to be configured in the preset objective function of the energy storage system to obtain the target parameter values ​​corresponding to the parameters to be configured.

[0057] It should be noted that the preset objective function is a function derived manually based on experimental data, representing the total operating cost of the energy storage system. The preset constraints are functions derived manually based on experimental data to constrain the preset objective function. The preset objective function includes the configurable parameters of the energy storage system. When the preset objective function represents the total operating cost of the energy storage system, the configurable parameters of the energy storage system described in the preset objective function are solved with the goal of minimizing the total operating cost, yielding the target parameter values ​​corresponding to the configurable parameters. These target parameter values ​​ensure that the energy storage system configured based on these target parameter values ​​meets the preset cost requirements. The configurable parameters of the energy storage system include energy storage capacity and rated charge / discharge power.

[0058] In this embodiment, step S20 may include steps S201 to S202:

[0059] Step S201: Based on the historical power generation data and the historical power consumption data, determine the instance function corresponding to the preset objective function;

[0060] It should be noted that the preset objective function can be a formula without actual values ​​or a formula with some actual values ​​substituted. After obtaining the historical power generation and consumption data from the user side, the historical power generation and consumption data are substituted into the preset objective function to obtain the instance function.

[0061] In one feasible embodiment, step S201 may include steps A10 to A30:

[0062] Step A10: Based on the historical power generation data, the historical power consumption data, and the parameters to be configured, construct a first function and a second function, wherein the first function is used to calculate the normal power purchase cost, and the second function is used to calculate the demand power purchase cost;

[0063] It should be noted that the total operating cost of an energy storage system includes normal electricity purchase cost, demand-based electricity purchase cost, initial purchase cost, and maintenance cost. Normal electricity purchase cost refers to the cost incurred by the user in purchasing electricity from the power company within a first preset period; demand-based electricity purchase cost refers to the fee charged by the power company to the user for the maximum electricity demand (usually measured in kilowatts or kilovolt-amperes) within a specific period (such as a month or a quarter); initial purchase cost is the cost of purchasing a new energy storage system; and maintenance cost is the cost of maintaining the energy storage system.

[0064] Based on historical power generation data, historical power consumption data, and the parameters to be configured for the energy storage system, the formulas for calculating normal power purchase cost (hereinafter referred to as the first function for distinction) and demand-based power purchase cost (hereinafter referred to as the second function for distinction) are determined.

[0065] Step A20: Based on the unit cost of the energy storage system, the equipment depreciation factor, the maintenance cost factor, and the parameters to be configured, determine a third function, wherein the third function is used to calculate the initial purchase cost and the maintenance cost;

[0066] It should be noted that the unit cost of an energy storage system includes the unit cost of the energy storage capacity (hereinafter referred to as the first unit cost for distinction) and the unit cost of the rated charge / discharge power (hereinafter referred to as the second unit cost for distinction). The equipment depreciation factor is determined based on the equipment depreciation rate and service life of the energy storage system. Specifically, the equipment depreciation factor is expressed as follows:

[0067]

[0068] Where r is the equipment depreciation rate of the energy storage system, and T is the service life of the energy storage system. It should be understood that the first preset duration is the service life of the energy storage system.

[0069] Based on the unit cost of the energy storage system, the equipment depreciation factor, the maintenance cost factor, and the parameters to be configured, the calculation formulas for the initial purchase cost and maintenance cost are determined (hereinafter referred to as the third function for distinction).

[0070] Step A30: Based on the first function, the second function, and the third function, determine the instance function corresponding to the preset target function.

[0071] The sum of the first, second, and third functions is used as the objective function.

[0072] In this embodiment, the parameter to be configured includes the rated charge / discharge power, and step A10 may include steps A101 to A103:

[0073] Step A101: Cluster the historical power generation data and the historical power consumption data to obtain each typical load scenario and its respective weight;

[0074] It should be noted that the historical power generation data and historical power consumption data are clustered to obtain multiple cluster centers. The mean vector of each cluster center corresponds to a typical load scenario, and the weight of the cluster center corresponds to the occurrence ratio of the typical load scenario.

[0075] In this embodiment, step A101 may include steps A1011 to A1013:

[0076] Step A1011: Divide the historical power generation data and the historical power consumption data into multiple load scenario data according to a preset time interval;

[0077] Step A1012: Cluster the data of multiple load scenarios to obtain multiple typical load scenarios;

[0078] Step A1013: The ratio of the number of load scenario data corresponding to the typical load scenario to the total number of all load scenario data is used as the weight of the typical load scenario.

[0079] It should be noted that this application does not limit the specific duration of the preset time interval. The preset time interval can be any duration that meets actual needs. In this embodiment of the application, the preset time interval is 24 hours, that is, one day.

[0080] In this embodiment, wind power output data, solar power output data, and historical electricity consumption data are divided into multiple load scenario data according to preset time intervals. It should be understood that each load scenario data includes 24 hours of wind power output data, 24 hours of solar power output data, and 24 hours of historical electricity consumption data. A Gaussian mixture model is used to cluster the multiple load scenario data to obtain multiple typical load scenarios. The ratio of the number of load scenario data corresponding to each typical load scenario to the total number of all load scenario data is used as the weight of each typical load scenario.

[0081] It should be noted that the number of typical load scenarios can be preset. This application does not limit the number of typical load scenarios. In this embodiment, the number of typical load scenarios is set to 12.

[0082] For example, the annual wind power output data, solar power output data, and historical electricity consumption data are divided into 365 samples per day. Each sample represents a load scenario for one day. Then, a Gaussian mixture model is used to cluster the 365 samples, resulting in n cluster centers and a weight p for each cluster center. The mean vector of each cluster center corresponds to a typical load scenario, and the weight of the cluster center corresponds to the proportion of occurrence of that typical load scenario. It should be understood that the number of typical load scenarios is represented by n.

[0083] For example, such as Figure 2 The diagram shows a typical load scenario, which includes load curves corresponding to 10 typical load scenarios. The horizontal axis of the coordinate system for each load curve is time, and the vertical axis is active power, with the unit of active power being kW.

[0084] Step A102: Determine the result value of subtracting the wind power output data and the photovoltaic power output data from the historical electricity consumption data, and add the result value and the rated charging and discharging power to obtain a first expression, wherein the first expression represents the power supply of the grid on the user side;

[0085] Subtracting wind power output data and solar power output data from historical electricity consumption data yields a result value. This result value is then added to the rated charging and discharging power to obtain an expression (hereinafter referred to as the first expression for distinction). This first expression represents the grid power supply on the user side. It should be noted that the grid power supply is the electricity purchased by the user from the power company, for which payment is required. For example, the first expression can be expressed as: (P di (t)-P gi (t)-P fi (t)+P e ), where P di (t), P gi (t) and P fi (t) represents the historical electricity consumption data (i.e., the third average active power in hour t), photovoltaic power output data (i.e., the second average active power in hour t), and wind power output data (i.e., the first average active power in hour t) for the i-th typical load scenario, respectively, in hour t. Where i∈[1, n], t∈[1, 24]. P e Rated power for charging and discharging the energy storage system (positive during charging, negative during discharging).

[0086] Step A103: Based on each of the typical load scenarios, each of the weights, the first expression, and the preset time-of-use electricity price, construct the first function; and based on each of the typical load scenarios, each of the weights, the first expression, and the preset demand electricity price, construct the second function.

[0087] It should be noted that the time-of-use electricity price is the power company's normal electricity price.

[0088] Based on each typical load scenario, its corresponding weight, a first expression, and a preset time-of-use electricity price, a first function is constructed. For example, the first function can be expressed as:

[0089]

[0090] Among them, C d R(t) represents the normal electricity purchase cost that the user needs to pay within the first preset time period, where R(t) is the time-of-use electricity price, T is the service life of the energy storage system, and p i Let be the weight of the i-th typical load scenario.

[0091] Based on each typical load scenario, its corresponding weight, the first expression, and the preset demand price, a second function is constructed. For example, the second function can be expressed as:

[0092]

[0093] Among them, C x R represents the demand-based electricity purchase cost that the user needs to pay within the first preset time period. c This refers to demand-based electricity pricing. It should be noted that this application does not restrict the specific system for charging demand-based electricity fees. The method for calculating demand-based electricity fees differs depending on the charging system. For example, in one feasible implementation, the demand-based electricity purchase cost can be a basic demand cost plus a separate charge for the amount exceeding the basic demand cost.

[0094] Thus, this application constructs an objective function by combining demand-based electricity purchase costs, making the final allocation result more accurate.

[0095] In this embodiment, the parameters to be configured further include energy storage capacity, and the unit cost includes a first unit cost of the energy storage capacity and a second unit cost of the rated charge / discharge power. Step A20 may include steps A201 to A202:

[0096] Step A201: Add the product of the energy storage capacity and the first unit cost to the product of the rated charge / discharge power and the second unit cost to obtain a second expression, wherein the second expression represents the initial purchase cost;

[0097] It should be noted that the unit cost of an energy storage system includes the unit cost of the energy storage capacity (hereinafter referred to as the first unit cost for distinction) and the unit cost of the rated charge and discharge power (hereinafter referred to as the second unit cost for distinction).

[0098] Adding the product of the energy storage capacity and the first unit cost in the parameters to be configured, and the product of the rated charge / discharge power and the second unit cost, yields an expression (hereinafter referred to as the second expression for distinction). The second expression represents the initial purchase cost of the energy storage system. For example, the second expression can be expressed as: (C1·E + C2·P) e ), where C1 (i.e., the first unit cost) and C2 (i.e., the second unit cost) are the unit costs of energy storage capacity and rated charge / discharge power, respectively, and E is the capacity of the energy storage system.

[0099] For example, as shown in Table 1 below, the system unit price and demand unit price are listed. Specifically, the rated power unit price (i.e., the first unit cost) of the energy storage system is 1000 yuan / KW, the capacity unit price (i.e., the second unit cost) of the energy storage system is 2000 yuan / KW, and the demand unit price (i.e., the demand electricity price) is 44.8 yuan / KW.

[0100] Table 1

[0101] 1 Unit price per rated power of energy storage system (RMB / kW) 1000 2 Energy storage system unit price (RMB / kWh) 2000 3 Price per kW (RMB) 44.8

[0102] Step A202: Based on the second expression, the equipment depreciation factor, and the maintenance cost factor, construct the third function.

[0103] The third function is determined based on the second expression, the equipment depreciation factor, and the maintenance cost factor. For example, the expression for the third function is:

[0104]

[0105] Where Ce represents the system purchase cost for users to purchase a new energy storage system and the maintenance cost for maintaining the energy storage system during its service life, and α represents the energy storage system maintenance cost coefficient.

[0106] Step S202: Solve for the configurable parameters of the energy storage system in the instance function based on the preset constraints to obtain the target parameter values ​​corresponding to the configurable parameters.

[0107] In one feasible implementation, step S202 may include step B10: using a Bayesian optimization algorithm to solve for the parameters to be configured in the instance function of the energy storage system based on the preset constraints, and obtaining the target parameter values ​​corresponding to the parameters to be configured.

[0108] It's important to note that Bayesian optimization is a sequential design strategy for global optimization, particularly suitable for situations where function evaluation costs are high. In Bayesian optimization, a probabilistic model (typically a Gaussian process) is used to simulate the objective function, and the model is iteratively improved by sampling new candidate points in regions of highest uncertainty. Thus, by employing the Bayesian optimization algorithm based on the constraints of the objective function, the configurable parameters within the objective function can be solved to obtain the optimal parameter values ​​corresponding to the parameters to be configured.

[0109] Step S30: Configure the energy storage system based on the target parameter values ​​so that the user side can use the configured energy storage system, wherein the configured energy storage system meets the preset cost requirements.

[0110] The energy storage system is configured based on target parameter values, specifically by configuring its energy storage capacity and rated charge / discharge power. This results in a configured energy storage system that meets preset cost requirements. These preset cost requirements refer to minimizing or reducing the total operating cost of the energy storage system while satisfying preset constraints. In other words, the target parameter values ​​are the configuration values ​​that minimize the total operating cost of the energy storage system.

[0111] For example, such as Figure 3 The diagram illustrates the configuration process of an energy storage system. First, wind power output data, solar power output data, and historical electricity consumption data are acquired within a first preset time period. Then, based on the acquired data and the parameters to be configured in the energy storage system, a first function and a second function are determined. Simultaneously, a third function is determined based on the unit cost of the energy storage system, equipment depreciation coefficient, maintenance cost coefficient, and the parameters to be configured. The first, second, and third functions are added together to determine the instance function. The instance function is then solved based on preset constraints. Finally, the optimal parameter values ​​for the parameters to be configured are obtained.

[0112] This application provides a method for configuring an energy storage system. This method involves acquiring historical power generation data and historical electricity consumption data from the user side, and based on the historical power generation data, historical electricity consumption data, and preset constraints, solving for the parameters to be configured in the preset objective function of the energy storage system to obtain the target parameter values ​​corresponding to the parameters to be configured. Then, the energy storage system is configured based on the target parameter values ​​so that the user side can use the configured energy storage system. The configured energy storage system meets preset cost requirements.

[0113] Thus, compared to the traditional method of configuring energy storage systems based on a single data type, the embodiments of this application combine historical power generation data and historical electricity consumption data to solve for the target parameter values ​​that enable the configured energy storage system to meet the preset cost requirements, thereby improving the comprehensiveness of the basic data and thus improving the accuracy of the energy storage system configuration results.

[0114] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in the first embodiment can be referred to the above description and will not be repeated hereafter. On this basis, the constraints include a first constraint for constraining the energy storage capacity and a second constraint for constraining the rated charging and discharging power. The first constraint is determined based on a preset system capacity limit, and the second constraint is determined based on the number of times and time period of peak electricity prices within a preset time.

[0115] It should be noted that, in order to protect the energy storage devices of the energy storage system, the capacity of the energy storage system is limited, and a constraint condition (hereinafter referred to as the first constraint condition for distinction) is set as follows: 0.1·E≤E(t)≤0.9·E, where E(t) is the actual amount of electricity to be stored. According to the peak shaving and valley filling strategy, the energy storage system is used to discharge during peak electricity price periods, that is, to use the electricity pre-stored by the energy storage system, and then used to charge during off-peak electricity price periods, in order to save electricity costs. Therefore, the rated charging and discharging power of the energy storage system is limited, and a constraint condition (hereinafter referred to as the second constraint condition for distinction) is set as follows:

[0116]

[0117] T 充 = Number of times the peak occurred; T 放 =Number of times peak occurrence

[0118] That is, the second constraint consists of two expressions, which are used to limit the energy storage system's evaluation charge from being completely discharged during the peak period, and the number of times the energy storage system charges and discharges per day is equal to the number of times the peak occurs, i.e., the aforementioned T. 充 and T 放 T 充 For the number of charging cycles, T 放 This represents the number of discharges.

[0119] For example, as shown in Table 2, which is a time-of-use electricity price table, the peak electricity prices for each day in January or December are 9:00–11:00 and 18:00–20:00; the peak electricity prices for each day in July or August are 14:00–15:00 and 20:00–21:00; the peak electricity prices for each day are 8:00–11:00 and 17:00–22:00; the average electricity prices for each day are 11:00–17:00 and 22:00–24:00; and the lowest electricity prices are 24:00–8:00.

[0120] Table 2

[0121]

[0122] Thus, this application embodiment calculates typical load scenarios on the user side, comprehensively considers wind power, photovoltaic, energy storage and user load, and establishes an energy storage system configuration optimization model with minimizing project cost as the objective function, thereby discovering the optimal configuration of the energy storage system to maximize user benefits.

[0123] This application also provides an energy storage system configuration device, please refer to... Figure 4 The energy storage system configuration device includes:

[0124] Module 10 is used to acquire historical power generation data and historical power consumption data from the user side;

[0125] The solution module 20 is used to solve the parameters to be configured in the preset objective function of the energy storage system based on the historical power generation data, the historical power consumption data and preset constraints, and to obtain the target parameter values ​​corresponding to the parameters to be configured.

[0126] The configuration module 30 is used to configure the energy storage system based on the target parameter values ​​so that the user side can use the configured energy storage system, wherein the configured energy storage system meets the preset cost requirements.

[0127] Optionally, the solution module 20 is further configured to:

[0128] Based on the historical power generation data and the historical power consumption data, determine the instance function corresponding to the preset objective function;

[0129] Based on the preset constraints, the parameters to be configured for the energy storage system in the instance function are solved to obtain the target parameter values ​​corresponding to the parameters to be configured.

[0130] Optionally, the solution module 20 is further configured to:

[0131] Based on the historical power generation data, the historical power consumption data, and the parameters to be configured, a first function and a second function are determined, wherein the first function is used to calculate the normal power purchase cost of the energy storage system, and the second function is used to calculate the demand power purchase cost of the energy storage system.

[0132] Based on the unit cost of the energy storage system, the equipment depreciation factor, the maintenance cost factor, and the parameters to be configured, a third function is determined, wherein the third function is used to calculate the initial purchase cost and maintenance cost of the energy storage system;

[0133] Based on the first function, the second function, and the third function, determine the instance function corresponding to the preset target function.

[0134] Optionally, the parameters to be configured include the rated charge / discharge power, and the solving module 20 is further used for:

[0135] Cluster the historical power generation data and the historical power consumption data to obtain each typical load scenario and its respective weight;

[0136] The result of subtracting the historical power generation data from the historical power consumption data is determined, and the result value is added to the rated charging and discharging power to obtain a first expression, wherein the first expression represents the power supply of the power grid on the user side;

[0137] Based on each of the typical load scenarios, each of the weights, the first expression, and the preset time-of-use electricity price, the first function is determined, and based on each of the typical load scenarios, each of the weights, the first expression, and the preset demand electricity price, the second function is determined.

[0138] Optionally, the solution module 20 is further configured to:

[0139] The historical power generation data and the historical power consumption data are divided into multiple load scenario data according to a preset time interval;

[0140] Clustering is performed on the data from multiple load scenarios to obtain multiple typical load scenarios;

[0141] The ratio of the number of load scenario data corresponding to the typical load scenario to the total number of all load scenario data is used as the weight of the typical load scenario.

[0142] Optionally, the parameters to be configured further include energy storage capacity, and the unit cost includes a first unit cost of the energy storage capacity and a second unit cost of the rated charge and discharge power;

[0143] The solution module 30 is also used for:

[0144] The product of the energy storage capacity and the first unit cost is added to the product of the rated charge / discharge power and the second unit cost to obtain a second expression, wherein the second expression represents the initial purchase cost;

[0145] The third function is determined based on the second expression, the equipment depreciation factor, and the maintenance cost factor.

[0146] Optionally, the preset constraints include a first constraint for constraining the energy storage capacity and a second constraint for constraining the rated charging and discharging power. The first constraint is determined based on a preset system capacity limit, and the second constraint is determined based on the number of times and time periods of peak electricity prices within a preset time period.

[0147] The energy storage system configuration device provided in this application, employing the energy storage system configuration method described in the above embodiments, can solve the technical problem of how to improve the accuracy of energy storage system configuration results. Compared with the prior art, the beneficial effects of the energy storage system configuration device provided in this application are the same as those of the energy storage system configuration method provided in the above embodiments, and other technical features in the energy storage system configuration device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0148] This application provides an energy storage system configuration device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the energy storage system configuration method in the above embodiment 1.

[0149] The following is for reference. Figure 5 The diagram illustrates a structural schematic of an energy storage system configuration device suitable for implementing embodiments of this application. The energy storage system configuration device in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 5 The energy storage system configuration shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0150] like Figure 5As shown, the energy storage system configuration device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the energy storage system configuration device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. Communication device 1009 allows the energy storage system configuration device to communicate wirelessly or wiredly with other devices to exchange data. Although the figure shows an energy storage system configuration device with various systems, it should be understood that implementation or possession of all the systems shown is not required. More or fewer systems may be implemented alternatively.

[0151] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0152] The energy storage system configuration device provided in this application, employing the energy storage system configuration method described in the above embodiments, can solve the technical problems of energy storage system configuration. Compared with the prior art, the beneficial effects of the energy storage system configuration device provided in this application are the same as those of the energy storage system configuration method provided in the above embodiments, and other technical features in this energy storage system configuration device are the same as those disclosed in the method of the previous embodiment, and will not be repeated here.

[0153] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0154] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0155] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the energy storage system configuration method in the above embodiments.

[0156] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0157] The aforementioned computer-readable storage medium may be included in the energy storage system configuration device; or it may exist independently and not be assembled into the energy storage system configuration device.

[0158] The aforementioned computer-readable storage medium carries one or more programs. When these programs are executed by the energy storage system configuration device, the energy storage system configuration device causes the following: it acquires historical power generation data and historical power consumption data from the user side; based on the historical power generation data, the historical power consumption data, and preset constraints, it solves for the configuration parameters of the energy storage system in a preset objective function to obtain the target parameter values ​​corresponding to the configuration parameters; and it configures the energy storage system based on the target parameter values ​​so that the user side can use the configured energy storage system, wherein the configured energy storage system meets preset cost requirements.

[0159] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0160] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0161] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0162] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described energy storage system configuration method, thereby solving the technical problem of energy storage system configuration. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the energy storage system configuration method provided in the above embodiments, and will not be repeated here.

[0163] This application provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the energy storage system configuration method described above.

[0164] The computer program product provided in this application can solve the technical problem of how to improve the heat dissipation efficiency of a configuration system. Compared with the prior art, the beneficial effects of the computer program product provided in the embodiments of this application are the same as the beneficial effects of the energy storage system configuration method provided in the above embodiments, and will not be repeated here.

[0165] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent scope of this application.

Claims

1. A method for configuring an energy storage system, characterized in that, The energy storage system configuration method includes: Acquire historical power generation data and historical electricity consumption data from the user side, wherein the historical power generation data includes wind power output data and solar power output data; Based on the historical power generation data and the historical power consumption data, an instance function corresponding to the preset objective function is determined, wherein the preset objective function is the minimization of the instance function, and the instance function is the sum of the first function, the second function, and the third function; Based on preset constraints, the Bayesian optimization algorithm is used to solve the configurable parameters of the energy storage system in the instance function to obtain the target parameter values ​​corresponding to the configurable parameters. The configurable parameters include energy storage capacity and rated charging and discharging power. The preset constraints include a first constraint for constraining the energy storage capacity and a second constraint for constraining the rated charging and discharging power. The energy storage system is configured based on the target parameter values ​​so that the user side can use the configured energy storage system, wherein the configured energy storage system meets the preset cost requirements; Among them, based on the historical power generation data and the historical power consumption data, the instance function corresponding to the preset objective function is determined, including: Based on the historical power generation data and the historical power consumption data, a Gaussian mixture model is used for clustering to obtain multiple cluster centers. The mean vector of each cluster center corresponds to a typical load scenario, and the weight of each cluster center is the proportion of occurrence of the corresponding typical load scenario. Based on each typical load scenario, each weight, a first expression, and a preset time-of-use electricity price, a first function is determined to calculate the normal electricity purchase cost of the energy storage system. A second function is determined based on each typical load scenario, each weight, the first expression, and a preset demand electricity price to calculate the demand electricity purchase cost of the energy storage system. The first expression represents the grid power supply on the user side. The first function is: in, This represents the normal electricity purchase cost that the user needs to pay within the first preset time period; For time-of-use electricity pricing; T represents the lifespan of the energy storage system. The weight of the i-th typical load scenario; the first expression is , , and These are the historical electricity consumption data, photovoltaic power output data, and wind power output data for the t-th hour under the i-th typical load scenario, respectively. Rated power for charging and discharging the energy storage system; The second function is: in, This represents the demand-based electricity purchase cost that the user needs to pay within the first preset time period. The demand-based electricity price; and based on the unit cost of the energy storage system, the equipment depreciation factor, the maintenance cost factor, and the parameters to be configured, a third function is determined, which is used to calculate the initial purchase cost and maintenance cost of the energy storage system.

2. The method as described in claim 1, characterized in that, The energy storage system configuration method includes: clustering the historical power generation data and the historical power consumption data to obtain each typical load scenario and its respective weight; The historical power generation data and historical power consumption data are clustered to obtain each typical load scenario and its respective weight, including: The historical power generation data and the historical power consumption data are divided into multiple load scenario data according to a preset time interval; Clustering is performed on the data from multiple load scenarios to obtain multiple typical load scenarios; The ratio of the number of load scenario data corresponding to the typical load scenario to the total number of all load scenario data is used as the weight of the typical load scenario.

3. The method as described in claim 1, characterized in that, The unit cost includes a first unit cost of the energy storage capacity and a second unit cost of the rated charge and discharge power. The step of determining the third function based on the unit cost of the energy storage system, the equipment depreciation factor, the maintenance cost factor, and the parameters to be configured includes: The product of the energy storage capacity and the first unit cost is added to the product of the rated charge / discharge power and the second unit cost to obtain a second expression, wherein the second expression represents the initial purchase cost; The third function is determined based on the second expression, the equipment depreciation factor, and the maintenance cost factor.

4. The method as described in claim 1, characterized in that, The first constraint is determined based on a preset system capacity limit, and the second constraint is determined based on the number of times and time periods of peak electricity prices within a preset time period.

5. An energy storage system configuration device, characterized in that, The energy storage system configuration device includes: The acquisition module is used to acquire historical power generation data and historical electricity consumption data from the user side, wherein the historical power generation data includes wind power output data and solar power output data; The solution module is used to determine an instance function corresponding to a preset objective function based on the historical power generation data and the historical power consumption data. The preset objective function is the minimization of the instance function, which is the sum of a first function, a second function, and a third function. Based on preset constraints, a Bayesian optimization algorithm is used to solve for the configurable parameters of the energy storage system in the instance function, obtaining the target parameter values ​​corresponding to the configurable parameters. The configurable parameters include energy storage capacity and rated charge / discharge power. The preset constraints include a first constraint on the energy storage capacity and a second constraint on the rated charge / discharge power. The solution is based on the historical power generation data and the historical power consumption data. The process involves determining instance functions corresponding to a preset objective function, including: clustering historical power generation data and historical electricity consumption data using a Gaussian mixture model to obtain multiple cluster centers, where the mean vector of each cluster center corresponds to a typical load scenario, and the weight of each cluster center is the proportion of occurrence of the corresponding typical load scenario; determining a first function for calculating the normal electricity purchase cost of the energy storage system based on each typical load scenario, each weight, a first expression, and a preset time-of-use electricity price; and determining a second function for calculating the demand electricity purchase cost of the energy storage system based on each typical load scenario, each weight, the first expression, and a preset demand electricity price; wherein the first expression represents the grid power supply on the user side. The first function is: in, This represents the normal electricity purchase cost that the user needs to pay within the first preset time period; For time-of-use electricity pricing; T represents the lifespan of the energy storage system. The weight of the i-th typical load scenario; the first expression is , , and These are the historical electricity consumption data, photovoltaic power output data, and wind power output data for the t-th hour under the i-th typical load scenario, respectively. Rated power for charging and discharging the energy storage system; The second function is: in, This represents the demand-based electricity purchase cost that the user needs to pay within the first preset time period. The demand electricity price; and based on the unit cost of the energy storage system, the equipment depreciation factor, the maintenance cost factor and the parameters to be configured, a third function is determined, which is used to calculate the initial purchase cost and maintenance cost of the energy storage system; A configuration module is used to configure the energy storage system based on the target parameter values ​​so that the user side can use the configured energy storage system, wherein the configured energy storage system meets preset cost requirements.

6. An energy storage system configuration device, characterized in that, The energy storage system configuration device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the energy storage system configuration method as described in any one of claims 1 to 4.

7. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the energy storage system configuration method as described in any one of claims 1 to 4.

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