Power distribution area energy storage control and capacity configuration method, system, equipment and medium

Abstract

The invention discloses a power distribution area energy storage control and capacity configuration method, system, equipment and medium, and the method comprises the steps: firstly, setting a plurality of different energy storage control strategies according to a preset discharge lower limit and a preset charge upper limit of an energy storage device of a power distribution area in combination with the residual capacity condition of the energy storage device; then, on the basis of time-of-use electricity price data and load data of the power distribution area, according to an energy storage control strategy, an objective function composed of the minimum operation cost of the power distribution area, the maximum photovoltaic consumption rate of the power distribution area and the minimum net load variance of the power distribution area is constructed; solving the objective function through an optimization algorithm to obtain a charging and discharging power vector of the energy storage device; and finally, calculating the optimal configuration capacity of the energy storage device according to the charging and discharging power vector of the energy storage device. According to the invention, the problems of too low photovoltaic consumption rate and too high total cost are solved, and the stability of the power grid is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution substations, and in particular to a method, system, device and medium for energy storage control and capacity configuration in a power distribution substation. Background Art

[0002] At present, distributed power sources in distribution substations, mainly photovoltaic power generation, are developing rapidly. Their output is characterized by randomness, volatility, and intermittency. The difficulty and uncertainty of distribution substation system planning and operation have increased significantly. It is urgent to reduce the redundancy of substation grid operation through reasonable regulation of energy storage devices, and alleviate the impact of distributed power output characteristics on the safety, economy, and reliability of user power supply of substation grid operation. Summary of the Invention

[0003] The present invention provides a method, system, device and medium for energy storage control and capacity configuration in a distribution station area, which are used to solve the problems of low photovoltaic absorption rate and high total cost, and improve the stability of the power grid.

[0004] In view of this, a first aspect of the present invention provides a method for energy storage control and capacity configuration in a distribution station area, the method comprising:

[0005] According to the preset lower discharge limit and upper charge limit of the energy storage device in the distribution station area, and in combination with the remaining capacity of the energy storage device, several different energy storage control strategies are set, and the energy storage device is controlled according to the energy storage control strategies;

[0006] Based on the time-of-use electricity price data and load data of the distribution substation, construct an objective function consisting of minimizing the operating cost of the distribution substation, maximizing the photovoltaic absorption rate of the distribution substation, and minimizing the net load variance of the distribution substation according to the energy storage control strategy, and set constraints for the objective function;

[0007] Based on the constraints of the objective function, solving the objective function corresponding to each of the energy storage control strategies through an optimization algorithm to obtain the optimal solutions corresponding to different energy storage control strategies, selecting a final solution from each of the optimal solutions, and obtaining a final energy storage device charge and discharge power vector;

[0008] The optimal configuration capacity of the energy storage device is calculated according to the charge and discharge power vector of the energy storage device, and the energy storage device is configured according to the optimal configuration capacity.

[0009] Optionally, the energy storage control strategy includes:

[0010] A natural day of 24 hours , The power generation gap or surplus in the distribution station area at the moment is , The energy storage device power in the distribution station area at that moment is , the maximum capacity of the energy storage device in the distribution station area is , the minimum capacity of the energy storage device in the distribution station area is ;

[0011] like If the power generation is insufficient at any time, ,like If there is surplus power generation at any time, ;

[0012] Strategy 1: When When the power distribution area sells Power; when When the power distribution area purchases power;

[0013] Strategy 2: When When the energy storage device is charged Power, when When power;

[0014] Strategy three: When When the energy storage device is charged Power, sold from the distribution area to the external grid Power, when When Power, the distribution area purchases from the external power grid power.

[0015] Optionally, the constraints of the objective function include: a state of charge constraint of the energy storage device and an output constraint of the photovoltaic cell.

[0016] Optionally, based on the constraint of the objective function, solving the objective function corresponding to each energy storage control strategy through an optimization algorithm to obtain the optimal solution corresponding to the different energy storage control strategies, selecting a final solution from each optimal solution, and obtaining the final energy storage device charge and discharge power vector includes:

[0017] Based on the constraints of the objective function, a non-dominated sorting genetic algorithm is used in combination with 2-norm normalization to solve the objective function corresponding to each energy storage control strategy, and the optimal solution corresponding to the different energy storage control strategies is obtained. A final solution is selected from each optimal solution to obtain the final energy storage device charging and discharging power vector.

[0018] Optionally, calculating the optimal configuration capacity of the energy storage device according to the charge and discharge power vector of the energy storage device includes:

[0019] Lagrange interpolation is performed on the charge and discharge power vectors of each energy storage device to obtain a Lagrange interpolation polynomial, and the Lagrange interpolation polynomial is integrated to obtain the optimal configuration capacity of the energy storage device.

[0020] A second aspect of the present invention provides a distribution substation energy storage control and capacity configuration system, the system comprising:

[0021] a setting unit, configured to set a plurality of different energy storage control strategies based on a preset lower discharge limit and upper charge limit of an energy storage device in a distribution station area and in combination with a remaining capacity of the energy storage device, and to control the energy storage device according to the energy storage control strategies;

[0022] A construction unit is configured to construct, based on the time-of-use electricity price data and load data of the distribution substation, an objective function consisting of minimizing the operating cost of the distribution substation, maximizing the photovoltaic absorption rate of the distribution substation, and minimizing the net load variance of the distribution substation according to the energy storage control strategy, and set constraints for the objective function;

[0023] a solving unit, configured to solve the objective function corresponding to each of the energy storage control strategies using an optimization algorithm based on the constraints of the objective function, obtain the optimal solutions corresponding to different energy storage control strategies, select a final solution from each of the optimal solutions, and obtain a final energy storage device charge and discharge power vector;

[0024] A calculation unit is used to calculate the optimal configuration capacity of the energy storage device according to the charge and discharge power vector of the energy storage device, and configure the energy storage device according to the optimal configuration capacity.

[0025] Optionally, the energy storage control strategy includes:

[0026] A natural day of 24 hours , The power generation gap or surplus in the distribution station area at the moment is , The energy storage device power in the distribution station area at that moment is , the maximum capacity of the energy storage device in the distribution station area is , the minimum capacity of the energy storage device in the distribution station area is ;

[0027] like If the power generation is insufficient at any time, ,like If there is surplus power generation at any time, ;

[0028] Strategy 1: When When the power distribution area sells Power; when When the power distribution area purchases power;

[0029] Strategy 2: When When the energy storage device is charged Power, when When power;

[0030] Strategy three: When When Power, sold from the distribution area to the external grid Power, when When Power, the distribution area purchases from the external power grid power.

[0031] Optionally, the solving unit is specifically configured to:

[0032] Based on the constraints of the objective function, a non-dominated sorting genetic algorithm is used in combination with 2-norm normalization to solve the objective function corresponding to each energy storage control strategy, and the optimal solution corresponding to the different energy storage control strategies is obtained. A final solution is selected from each optimal solution to obtain the final energy storage device charging and discharging power vector.

[0033] A third aspect of the present invention provides a device for energy storage control and capacity configuration in a distribution station area, the device comprising a processor and a memory:

[0034] The memory is used to store program code and transmit the program code to the processor;

[0035] The processor is configured to execute the steps of the method for energy storage control and capacity configuration in a distribution station area as described in the first aspect above according to the instructions in the program code.

[0036] A fourth aspect of the present invention provides a computer-readable storage medium for storing program code, wherein the program code is used to execute the method for energy storage control and capacity configuration in a distribution station area described in the first aspect.

[0037] It can be seen from the above technical solutions that the present invention has the following advantages:

[0038] The energy storage control and capacity configuration method of the distribution substation area of ​​this embodiment first sets several different energy storage control strategies based on the preset lower discharge limit and upper charge limit of the energy storage device in the distribution substation area, and in combination with the remaining capacity of the energy storage device. Then, according to the energy storage control strategy, an objective function is established to minimize the total cost of the distribution substation area, minimize the photovoltaic absorption rate, and minimize the net load variance; the objective function is solved by an optimization algorithm to obtain the charge and discharge power vector of the energy storage device in the distribution substation area; finally, based on the charge and discharge power vector of the energy storage device, the required energy storage device capacity is calculated. The present invention controls the charging and discharging of the energy storage device in the distribution substation area by setting an energy storage control strategy, and considers factors such as time-of-use electricity prices and the impact load faced by the distribution substation area. In view of the problem of photovoltaic absorption in the distribution substation area, an objective function of the energy storage device in the distribution substation area, i.e., a control model, is given. By solving the objective function, the optimal configuration capacity of the energy storage device is obtained, thereby solving the problems of too low photovoltaic absorption rate and too high total cost, and improving the stability of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0040] Figure 1 A schematic flow chart of a method for controlling energy storage and configuring capacity in a distribution station area provided by an embodiment of the present invention;

[0041] Figure 2 A schematic diagram of the structure of a distribution substation energy storage control and capacity configuration system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0042] In order to make the purpose, features, and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0043] See also Figure 1 , a method for controlling energy storage and configuring capacity in a distribution station area provided in an embodiment of the present invention includes:

[0044] Step 101: According to the preset lower discharge limit and upper charge limit of the energy storage device in the distribution station area and in combination with the remaining capacity of the energy storage device, several different energy storage control strategies are set, and the energy storage device is controlled according to the energy storage control strategies.

[0045] It should be noted that the energy storage device in the distribution station area is set with a lower discharge limit and an upper charge limit; whether the remaining capacity of the energy storage device is lower than the upper charge limit is determined. If so, the energy storage device can be charged; otherwise, the energy storage device cannot be charged; and whether the remaining capacity of the energy storage device is higher than the lower discharge limit is determined. If so, the energy storage device can be discharged; otherwise, the energy storage device cannot be discharged. Specific energy storage control strategies are described in the following embodiments.

[0046] Step 102: Based on the time-of-use electricity price data and load data of the distribution substation, an objective function is constructed according to the energy storage control strategy, which consists of minimizing the operating cost of the distribution substation, maximizing the photovoltaic absorption rate of the distribution substation, and minimizing the net load variance of the distribution substation, and constraints are set for the objective function.

[0047] It should be noted that the present invention takes into account factors such as time-of-use electricity prices and the impact load faced by distribution stations, and considers the problem of photovoltaic absorption in distribution stations, and provides an objective function (control model) for the energy storage device in the distribution station area. Specifically, the total cost function of the distribution station area is calculated; the photovoltaic absorption rate function of the distribution station area is calculated; the net load variance function of the distribution station area is calculated; and the three established objective functions constitute an objective function vector. Here, a negative value is taken as the second element of the objective function vector, because the photovoltaic absorption rate of the distribution station area is required to obtain a maximum value. By taking a negative value, the minimum value can be obtained like the other two objective functions. The construction of the objective function is described in detail in the following embodiment. The constraints of the objective function set in this embodiment include: the state of charge of the energy storage device and the output constraint of the photovoltaic cell. The constraint setting process is described in detail in the following embodiment.

[0048] Step 103: Based on the constraints of the objective function, the objective function corresponding to each energy storage control strategy is solved by an optimization algorithm to obtain the optimal solution corresponding to different energy storage control strategies. A final solution is selected from each optimal solution to obtain the final energy storage device charge and discharge power vector.

[0049] It can be understood that the "optimal solution" (i.e., the best of the best) is selected from each optimal solution (charging and discharging power vector of the energy storage device) as the final solution, that is, the final charging and discharging power vector of the energy storage device is obtained.

[0050] It should be noted that, based on the constraints of the objective function, an optimization algorithm is used to solve the objective function corresponding to each energy storage control strategy, obtaining the Pareto optimal frontier, i.e., the charge and discharge power vectors of the energy storage devices in the distribution station area. It can be understood that an optimization algorithm is a type of algorithm used to find the minimum or maximum value of the objective function under given constraints. The objective function can be linear, nonlinear, convex, or non-convex, and the optimization algorithm selects a corresponding solution method based on different characteristics. Those skilled in the art can select different optimization algorithms based on actual circumstances, and this is not limited here.

[0051] Step 104: Calculate the optimal configuration capacity of the energy storage device according to the charge and discharge power vector of the energy storage device, and configure the energy storage device according to the optimal configuration capacity.

[0052] It should be noted that the energy storage device charge and discharge power vectors obtained above are discrete points over a daily period and do not accurately reflect the actual changes in the energy storage device over time. Therefore, it is necessary to make the energy storage device charge and discharge power vectors change continuously over time to obtain the desired energy storage device capacity.

[0053] The energy storage control and capacity configuration method of the distribution substation area of ​​this embodiment first sets several different energy storage control strategies based on the preset lower discharge limit and upper charge limit of the energy storage device in the distribution substation area, and in combination with the remaining capacity of the energy storage device. Then, according to the energy storage control strategy, an objective function is established to minimize the total cost of the distribution substation area, minimize the photovoltaic absorption rate, and minimize the net load variance; the objective function is solved by an optimization algorithm to obtain the charge and discharge power vector of the energy storage device in the distribution substation area; finally, based on the charge and discharge power vector of the energy storage device, the required energy storage device capacity is calculated. The present invention controls the charging and discharging of the energy storage device in the distribution substation area by setting an energy storage control strategy, and considers factors such as time-of-use electricity prices and the impact load faced by the distribution substation area. In view of the problem of photovoltaic absorption in the distribution substation area, an objective function of the energy storage device in the distribution substation area, i.e., a control model, is given. By solving the objective function, the optimal configuration capacity of the energy storage device is obtained, thereby solving the problems of too low photovoltaic absorption rate and too high total cost, and improving the stability of the power grid.

[0054] In one embodiment, the energy storage control strategy in step 101 includes:

[0055] A natural day of 24 hours , The power generation gap or surplus in the distribution station area at the moment is , The energy storage device power in the distribution station area at that moment is , the maximum capacity of the energy storage device in the distribution station area is , the minimum capacity of the energy storage device in the distribution station area is ;

[0056] like If the power generation is insufficient at any time, ,like If there is surplus power generation at any time, ;

[0057] Strategy 1: When When the power distribution area sells Power; when When the power distribution area purchases power;

[0058] Strategy 2: When When Power, when When power;

[0059] Strategy three: When When Power, sold from the distribution area to the external grid Power, when When Power, the distribution area purchases from the external power grid power.

[0060] In one embodiment, in step 102, based on the time-of-use electricity price data and load data of the distribution substation, an objective function is constructed according to the energy storage control strategy, which includes minimizing the operating cost of the distribution substation, maximizing the photovoltaic absorption rate of the distribution substation, and minimizing the net load variance of the distribution substation, including:

[0061] Distribution area operating costs: ;

[0062] Where, represents the total cost of the distribution area, Indicates the distribution station area The cost of purchasing power from the external grid at all times, express The penalty cost of constantly giving up light, express The operating cost of the energy storage device in the distribution station area at any time, Represents the investment and construction cost of the energy storage device.

[0063] The specific expressions of each cost in the total cost of the distribution area are as follows:

[0064] ;

[0065] ;

[0066] ;

[0067] Where, express Time-of-use electricity prices at different times, express The unit power abandonment cost at the moment, express The unit power operating cost of the energy storage device at the time, express The power purchased by the distribution area from the external power grid at any given moment, express The abandoned optical power at the moment, express The remaining power of the energy storage device at the moment.

[0068] Photovoltaic absorption rate of distribution station area: ;

[0069] In the formula, in order to unify the optimization objectives of the multi-objective optimization model to the minimum value, the objective function is given The photovoltaic absorption rate takes a negative value.

[0070] Where, express Photovoltaic power generation at the time.

[0071] Net load variance: ;

[0072] ;

[0073] Where, express Net load of the distribution area at the moment, Indicates the mean value of the net load.

[0074] In one embodiment, in step 102 , constraints of the objective function are set, including: state of charge of the energy storage device and output constraints of the photovoltaic cell.

[0075] Energy storage device state of charge constraints:

[0076] ;

[0077] Where, express The remaining power of the energy storage device at the moment, Indicates the upper limit of charging of the energy storage device, Indicates the lower discharge limit of the energy storage device, Indicates the upper capacity limit of the energy storage device, Indicates the lower capacity limit of the energy storage device.

[0078] Photovoltaic cell power generation power constraints:

[0079] ;

[0080] In one embodiment, step 103 includes:

[0081] Based on the constraints of the objective function, a non-dominated sorting genetic algorithm combined with 2-norm normalization is used to solve the objective function corresponding to each energy storage control strategy, and the optimal solution corresponding to different energy storage control strategies is obtained. The final solution is selected from each optimal solution to obtain the final energy storage device charging and discharging power vector.

[0082] It should be noted that the optimization algorithm of this embodiment adopts a non-dominated sorting genetic algorithm. The solution steps of the non-dominated sorting genetic algorithm are as follows:

[0083] S101. Initialize the parameters of NSGA-Ⅱ, including the population size , maximum number of iterations , crossover probability , mutation probability and other parameters;

[0084] S102. Calculate the fitness. For each chromosome, calculate its fitness in the objective function space. The fitness calculation is the three elements of the objective function vector;

[0085] S103, non-dominated sorting, divides the individuals in the population into multiple fronts. This sorting process determines the non-dominated level of each chromosome to identify which chromosomes occupy different positions on the Pareto front. Individuals with higher levels are not dominated by individuals with lower levels;

[0086] S104. Crowding distance calculation: To maintain the diversity of the Pareto frontier, the crowding distance is calculated to measure the density of individuals in the target space. The larger the crowding distance, the greater the distance between individuals, which helps to maintain a dispersed distribution on the Pareto frontier.

[0087] S105. Selection operation: Use a binary tournament as the selection operation. In each generation, first use the binary tournament selection operation to randomly select two individuals from the current population, and then select the individual with the higher non-dominated level. This selection process is performed by comparing the non-dominated level and crowding distance of the individuals.

[0088] S106, crossover / mutation operation, generates the parent population through crossover and mutation operations on the selected individuals. The parent and offspring are merged into a larger candidate population;

[0089] S107, iterate, re-perform non-dominated sorting, crowding distance calculation, selection, crossover, and mutation on the candidate population to generate a new offspring population, and iterate until the stopping condition is met (such as reaching the maximum number of iterations or converging to a satisfactory Pareto front solution), and obtain the charging and discharging power vector of the energy storage device on the Pareto optimal front;

[0090] Furthermore, it should be noted that for the substation after energy storage control and capacity configuration optimization, the fitness vectors of the three objective functions are calculated respectively. Considering that the units and orders of magnitude of the three objective functions are different, it is not comparable to use the original data directly. In this paper, 2-norm normalization is used, that is, for dimensional vector , the 2-norm is ;

[0091] The 2-norm normalization of the three objective functions is as follows:

[0092] ;

[0093] Give the 2-norm normalized objective function vector endowment The weights of the two vectors are then calculated. The inner product F is used as the only criterion for measuring the multi-objective function optimization model. The chromosome with the smallest inner product F is selected to obtain the charging and discharging power vector of the energy storage device.

[0094] In one embodiment, step 104 includes:

[0095] Lagrange interpolation is used for the charging and discharging power vectors of each energy storage device to obtain a Lagrange interpolation polynomial. The Lagrange interpolation polynomial is integrated to obtain the optimal configuration capacity of the energy storage device.

[0096] It should be noted that Lagrange interpolation:

[0097] Generally, if it is known In different points The function value at (That is, the function passes this points), we can construct a points, and no more than Polynomial .

[0098] in, Become the basis functions of the Lagrange polynomials, ;

[0099] Energy storage device charge and discharge power curve through For these 24 points, the polynomial function obtained by Lagrange interpolation is: ;

[0100] Integrate the above energy storage device charge and discharge function , and finally determine the optimal capacity configuration of the energy storage device.

[0101] The above is a method for controlling energy storage and configuring capacity in a distribution substation area provided in an embodiment of the present invention. The following is a system for controlling energy storage and configuring capacity in a distribution substation area provided in an embodiment of the present invention.

[0102] See also Figure 2 , an embodiment of the present invention provides a distribution substation energy storage control and capacity configuration system, comprising:

[0103] The setting unit 201 is used to set several different energy storage control strategies according to the preset lower discharge limit and upper charge limit of the energy storage device in the distribution station area and in combination with the remaining capacity of the energy storage device.

[0104] The construction unit 202 is used to construct an objective function consisting of minimizing the operating cost of the distribution area, maximizing the photovoltaic absorption rate of the distribution area, and minimizing the net load variance of the distribution area based on the time-of-use electricity price data and load data of the distribution area and according to the energy storage control strategy, and set constraints for the objective function.

[0105] The solving unit 203 is used to solve the objective function corresponding to each energy storage control strategy through an optimization algorithm based on the constraints of the objective function, obtain the optimal solution corresponding to different energy storage control strategies, select the final solution from each optimal solution, and obtain the final energy storage device charging and discharging power vector.

[0106] The calculation unit 204 is configured to calculate the optimal configuration capacity of the energy storage device according to the charge and discharge power vector of the energy storage device.

[0107] Furthermore, an embodiment of the present invention also provides a power distribution area energy storage control and capacity configuration device, the device including a processor and a memory:

[0108] The memory is used to store program code and transmit the program code to the processor;

[0109] The processor is configured to execute the steps of the method for energy storage control and capacity configuration in a distribution station area as described in the above method embodiment according to the instructions in the program code.

[0110] Furthermore, an embodiment of the present invention also provides a computer-readable storage medium for storing program code, and the program code is used to execute the power distribution substation area energy storage control and capacity configuration method described in the above method embodiment.

[0111] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0112] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interface, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0113] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0114] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0115] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the method of the present invention. The aforementioned storage medium includes various media that can store program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0116] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for energy storage control and capacity configuration in a distribution station area, characterized in that: include: According to the preset lower discharge limit and upper charge limit of the energy storage device in the distribution station area, and in combination with the remaining capacity of the energy storage device, several different energy storage control strategies are set, and the energy storage device is controlled according to the energy storage control strategies; Based on the time-of-use electricity price data and load data of the distribution substation, construct an objective function consisting of minimizing the operating cost of the distribution substation, maximizing the photovoltaic absorption rate of the distribution substation, and minimizing the net load variance of the distribution substation according to the energy storage control strategy, and set constraints for the objective function; Based on the constraints of the objective function, solving the objective function corresponding to each of the energy storage control strategies through an optimization algorithm to obtain the optimal solutions corresponding to different energy storage control strategies, selecting a final solution from each of the optimal solutions, and obtaining a final energy storage device charge and discharge power vector; The optimal configuration capacity of the energy storage device is calculated according to the charge and discharge power vector of the energy storage device, and the energy storage device is configured according to the optimal configuration capacity.

2. The method for energy storage control and capacity configuration in a distribution station area according to claim 1, characterized in that: The energy storage control strategy includes: A natural day of 24 hours , The power generation gap or surplus in the distribution station area at the moment is , The energy storage device power in the distribution station area at that moment is , the maximum capacity of the energy storage device in the distribution station area is , the minimum capacity of the energy storage device in the distribution station area is ; like If the power generation is insufficient at any time, ,like If there is surplus power generation at any time, ; Strategy 1: When When the power distribution area sells Power; when When the power distribution area purchases power; Strategy 2: When When Power, when When power; Strategy three: When When Power, sold from the distribution area to the external grid Power, when When Power, the distribution area purchases from the external power grid power.

3. The method for energy storage control and capacity configuration in a distribution station area according to claim 1, characterized in that: The constraints of the objective function include: a state of charge constraint of the energy storage device and an output constraint of the photovoltaic cell.

4. The method for energy storage control and capacity configuration in a distribution substation area according to claim 1, characterized in that: The objective function corresponding to each energy storage control strategy is solved by an optimization algorithm based on the constraint of the objective function to obtain the optimal solution corresponding to the different energy storage control strategies, and a final solution is selected from each optimal solution to obtain the final energy storage device charge and discharge power vector, including: Based on the constraints of the objective function, a non-dominated sorting genetic algorithm is used in combination with 2-norm normalization to solve the objective function corresponding to each energy storage control strategy, and the optimal solution corresponding to the different energy storage control strategies is obtained. A final solution is selected from each optimal solution to obtain the final energy storage device charging and discharging power vector.

5. The method for energy storage control and capacity configuration in a distribution substation area according to claim 1, characterized in that: Calculating the optimal configuration capacity of the energy storage device according to the charge and discharge power vector of the energy storage device includes: Lagrange interpolation is performed on the charge and discharge power vectors of each energy storage device to obtain a Lagrange interpolation polynomial, and the Lagrange interpolation polynomial is integrated to obtain the optimal configuration capacity of the energy storage device.

6. A distribution substation energy storage control and capacity configuration system, characterized in that: include: a setting unit, configured to set a plurality of different energy storage control strategies based on a preset lower discharge limit and upper charge limit of an energy storage device in a distribution station area and in combination with a remaining capacity of the energy storage device, and to control the energy storage device according to the energy storage control strategies; A construction unit is configured to construct, based on the time-of-use electricity price data and load data of the distribution substation, an objective function consisting of minimizing the operating cost of the distribution substation, maximizing the photovoltaic absorption rate of the distribution substation, and minimizing the net load variance of the distribution substation according to the energy storage control strategy, and set constraints for the objective function; a solving unit, configured to solve the objective function corresponding to each of the energy storage control strategies using an optimization algorithm based on the constraints of the objective function, obtain the optimal solutions corresponding to different energy storage control strategies, select a final solution from each of the optimal solutions, and obtain a final energy storage device charge and discharge power vector; A calculation unit is used to calculate the optimal configuration capacity of the energy storage device according to the charge and discharge power vector of the energy storage device, and configure the energy storage device according to the optimal configuration capacity.

7. The power distribution area energy storage control and capacity configuration system according to claim 6, characterized in that: The energy storage control strategy includes: A natural day of 24 hours , The power generation gap or surplus in the distribution station area at the moment is , The energy storage device power in the distribution station area at that moment is , the maximum capacity of the energy storage device in the distribution station area is , the minimum capacity of the energy storage device in the distribution station area is ; like If the power generation is insufficient at any time, ,like If there is surplus power generation at any time, ; Strategy 1: When When the power distribution area sells Power; when When the power distribution area purchases power; Strategy 2: When When Power, when When power; Strategy three: When When Power, sold from the distribution area to the external grid Power, when When Power, the distribution area purchases from the external power grid power.

8. The power distribution area energy storage control and capacity configuration system according to claim 6, characterized in that: The solving unit is specifically used for: Based on the constraints of the objective function, a non-dominated sorting genetic algorithm is used in combination with 2-norm normalization to solve the objective function corresponding to each energy storage control strategy, and the optimal solution corresponding to the different energy storage control strategies is obtained. A final solution is selected from each optimal solution to obtain the final energy storage device charging and discharging power vector.

9. A distribution area energy storage control and capacity configuration device, characterized in that: The device includes a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the distribution station area energy storage control and capacity configuration method according to any one of claims 1-5 according to the instructions in the program code.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium is used to store program code, and the program code is used to execute the distribution station area energy storage control and capacity configuration method according to any one of claims 1 to 5.

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