Transformer area energy storage configuration method, system and device for improving power supply capacity of power distribution network and storage medium

Abstract

The invention relates to the field of power distribution network systems, in particular to a transformer area energy storage configuration method, system and device for improving the power supply capacity of a power distribution network and a storage medium. Comprising the following steps: acquiring power distribution network data; randomly generating energy storage configuration according to the power distribution network data; establishing a target function by taking the minimum sum of the voltage deviations of the heavy load nodes of the power distribution network as a target based on energy storage configuration, and establishing constraint conditions of the target function according to network power balance and energy storage operation during operation of the power distribution network; and according to the constraint condition, solving the objective function based on a particle swarm algorithm to obtain the optimal district energy storage configuration of the power distribution network. In the solving process, the optimal transformer area energy storage configuration can be selected according to the condition of the power distribution network, so that the energy storage access position and the output scheme are determined, the node voltage quality can be improved, and the power supply capacity of the power distribution network is directly enhanced.

Description

Technical Field

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

[0002] As the pace of urbanization in my country continues to accelerate, the challenges faced by urban power grid planning and construction are becoming increasingly complex and diverse. Among them, the problem of tight power supply is particularly prominent and has become a bottleneck restricting urban development. The problem is that with the continuous expansion of urban population and the sustained rapid growth of the economy, the demand for electricity in cities has shown an explosive growth trend, but the existing power supply capacity is difficult to match it, and the contradiction between supply and demand is becoming increasingly serious.

[0003] The National Development and Reform Commission has made it clear that ensuring safe and reliable power supply is the primary task, and that it will appropriately plan and build distribution networks ahead of time, continuously optimize the grid structure, and maintain a reasonable power supply margin. Therefore, it is necessary to use relevant technical means to improve the power supply capacity of the distribution network to meet the power supply needs of cities.

[0004] At present, distributed power sources, as an important form of new energy, have been widely connected to the distribution network and are playing an increasingly important role in it. As an important part of distributed power sources, the application of substation energy storage in the power system is increasing. Its advantage is that the substation energy storage system can flexibly respond to fluctuations in grid load, especially during peak grid load periods. The energy storage system can meet the load demand by discharging, effectively alleviating voltage fluctuations or power supply interruptions caused by excessive load. At the same time, during the low load period of the grid, the energy storage system can be charged, and the grid load can be balanced by storing electrical energy, further adjusting the voltage, reducing voltage fluctuations and frequency fluctuations, thereby significantly improving the voltage quality and power supply capacity.

[0005] However, how to determine the energy storage access location and output plan is an urgent problem to be solved by people in this field. Traditional solutions include two categories. One is linear programming, which has high computational efficiency. In the optimization configuration problem of energy storage systems, it can be used to determine the optimal charging and discharging strategy, but its applicability is limited by the linear assumption. For problems with nonlinear characteristics, this method is not applicable; the other is dynamic programming, which can be used to solve state transition problems. For energy storage, by defining the battery state of charge and the charge and discharge amount in each time period, the optimal strategy can be gradually solved, but the computational complexity of this method increases sharply with the increase of the state space, which may lead to calculation failure. Summary of the invention

[0006] In response to the problems mentioned in the prior art, the present invention proposes a substation energy storage configuration method, system, device and storage medium for improving the power supply capacity of the distribution network, and adopts a particle swarm algorithm to screen out the optimal solution for efficiently obtaining the energy storage configuration, improve the voltage quality of each node, and thus improve the power supply capacity of the distribution network.

[0007] In order to achieve the above object, the present invention adopts the following technical solution: The present invention provides a method for configuring energy storage in a distribution area to improve the power supply capacity of a distribution network, comprising the following steps: S1. Obtain distribution network data; S2, randomly generate energy storage configuration based on distribution network data; S3, based on the energy storage configuration, establish an objective function with the goal of minimizing the sum of voltage deviations of heavy-load nodes in the distribution network, and establish constraints of the objective function according to the network power balance during the operation of the distribution network and the energy storage operation; S4. According to the constraints, the objective function is solved based on the particle swarm algorithm to obtain the optimal energy storage configuration of the distribution network.

[0008] As a further improvement of the present invention, the distribution network data includes load data, distribution line parameters and network topology data.

[0009] As a further improvement of the present invention, in step S3, an objective function is established with the goal of minimizing the sum of voltage deviations of heavy-load nodes in the distribution network, and the objective function expression is as follows:

[0010] Where: f is the sum of voltage deviations at heavy-load nodes in the distribution network; d Number the nodes of the distribution network; H is the total number of distribution network nodes; U dN For the d The rated voltage of each node; U ds For the d The actual voltage of each node; k d is the coefficient, when d When the node voltage exceeds the limit, k d is 1; otherwise it is 0.

[0011] As a further improvement of the present invention, the constraint conditions in step S3 include network balance constraints and energy storage operation constraints, wherein the network balance constraints are shown in the following formula:

[0012] Where: P inImport power into the distribution grid; P l,i For Node i The load at P bess,j For the j The output of each energy storage; N i is the number of distribution network nodes; N j The number of energy storage devices connected to the distribution network; Energy storage operation constraints include power constraints, energy conservation constraints, and charge and discharge limit constraints. The power constraints are shown in the following formula:

[0013] Where: The real-time remaining percentage of energy storage; is the lower limit of energy storage SOC; The upper limit of energy storage SOC; The energy conservation constraint is expressed as follows:

[0014] Where: express t Charging amount at all times; express t Discharge amount at any time; The charge and discharge limit constraints are as follows:

[0015] Where: is the maximum charge value of the energy storage system; is the maximum discharge value of the energy storage system.

[0016] As a further improvement of the present invention, the specific process of step S4 is: Set the initial parameters of the particle swarm, including the number of populations, spatial dimension, and number of iterations; Solve the objective function in step S3, and select the optimal solution according to the objective function value; Update the particle position, iteratively calculate the function value of updating the particle position, and obtain the iterative solution; The optimal solution is compared with the iterative solution. If the iterative solution is better, the optimal energy storage configuration of the distribution network is output; if the optimal solution is better, the iteration is repeated until the number of iterations is reached, and the optimal solution is output as the optimal energy storage configuration of the distribution network.

[0017] As a further improvement of the present invention, the particle expression is updated as follows:

[0018] Where: For the j Particles in q After +1 iteration, p Position in dimension; For the j Particles in q After the first iteration p The position in the dimension; j Particles in q After the first iteration p Speed ​​in dimension; For the j Particles in q After the first iteration p Individual optimal solutions in dimensions; For the j Particles in q After the first iteration p The overall optimal solution in terms of dimension; c 0 is the inertia weight; c 1 is the self-learning factor; c 2 is the group learning factor; r 1. r 2 are all random numbers on [0,1].

[0019] As a further improvement of the present invention, after performing step S4, it is necessary to verify the optimal energy storage configuration of the distribution network. The verification is to calculate the voltage qualification rate of the distribution network before and after the energy storage is connected. The voltage qualification rate is shown in the following formula:

[0020] Where: t is the voltage monitoring time; The time when the voltage exceeds the upper limit is 1.07; The time when the voltage crosses the lower limit is 0.93; When the voltage qualification rate is greater than 97%, it means that the optimal substation energy storage configuration of the distribution network meets the distribution network operation requirements.

[0021] A substation energy storage configuration system for improving the power supply capacity of a distribution network, comprising: An acquisition module is used to acquire distribution network data; A generation module is used to randomly generate energy storage configurations based on distribution network data; Establishing a module for establishing an objective function based on the energy storage configuration with the goal of minimizing the sum of voltage deviations of heavy-load nodes in the distribution network, and establishing constraints of the objective function according to the network power balance and energy storage operation during the operation of the distribution network; The solution module is used to solve the objective function based on the particle swarm algorithm according to the constraints to obtain the optimal energy storage configuration of the distribution network.

[0022] A substation energy storage configuration device for improving the power supply capacity of a distribution network comprises a processor and a memory, wherein the processor implements the above-mentioned substation energy storage configuration method for improving the power supply capacity of a distribution network when executing a computer program stored in the memory.

[0023] A computer-readable storage medium is used to store a computer program, wherein when the computer program is executed by a processor, the method for configuring energy storage in a substation area for improving the power supply capacity of a distribution network as described above is implemented.

[0024] Compared with the prior art, the present invention has achieved the following technical effects: The present invention discloses a substation energy storage configuration method for improving the power supply capacity of a distribution network. The method establishes an objective function for a randomly generated energy storage configuration scheme by taking the sum of voltage deviations of heavy-load nodes of the distribution network as the minimum as the goal, and establishes constraint conditions of the objective function according to the network power balance and energy storage operation when the distribution network is running. The objective function is then solved based on the constraint conditions to obtain the optimal substation energy storage configuration of the distribution network. In the solution process, the present invention can select the optimal substation energy storage configuration according to the distribution network situation, thereby determining the energy storage access location and output plan, which can not only improve the node voltage quality, thereby directly enhancing the power supply capacity of the distribution network, but also provide a reference basis for relevant departments when formulating plans. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 A flow chart of a method for configuring energy storage in a substation area for improving the power supply capacity of a distribution network according to the present invention; Figure 2 A schematic diagram of the topological structure of the IEEE 33-node distribution network provided in the present invention; Figure 3 is the simulated load variation curve of the present invention; Figure 4 The output and SOC change curve of energy storage No. 1 in the present invention; Figure 5 The output and SOC change curve of energy storage No. 2 in the present invention; Figure 6 The output and SOC change curve of energy storage No. 3 in the present invention; Figure 7 The output and SOC change curve of energy storage No. 4 in the present invention; Figure 8 The output and SOC change curve of energy storage No. 5 in the present invention; Fig. 9 The output and SOC change curve of energy storage No. 6 in the present invention; Fig.10 This is the output and SOC change curve of energy storage No. 7 in the present invention. DETAILED DESCRIPTION

[0026] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. It should also be noted that, for ease of description, only parts related to the present invention, rather than all structures, are shown in the accompanying drawings.

[0027] The present invention will be further explained below with reference to the accompanying drawings and specific embodiments: like Figure 1 As shown, the present invention proposes a method for configuring energy storage in a substation area to improve the power supply capacity of a distribution network, comprising the following steps: S1. Obtain distribution network data; S2, randomly generate energy storage configuration based on distribution network data; S3, based on the energy storage configuration, establish an objective function with the goal of minimizing the sum of voltage deviations of heavy-load nodes in the distribution network, and establish constraints of the objective function according to the network power balance during the operation of the distribution network and the energy storage operation; S4. According to the constraints, the objective function is solved based on the particle swarm algorithm to obtain the optimal energy storage configuration of the distribution network.

[0028] This embodiment first collects distribution network parameter information, including load data, parameters of each line and network topology data. The load data is an important indicator reflecting the power demand of each node or region in the distribution network, including load size, load change rate and load peak period. The parameters of each line include electrical characteristics such as resistance, inductance and capacitance of the line, as well as physical characteristics such as length and cross-sectional area of ​​the line. The network topology data describes the connection relationship between each node, line and transformer in the distribution network, and is the basis for formulating energy storage access and output plans. The energy storage plan is randomly generated by in-depth analysis of the above data.

[0029] In the embodiment, the energy storage scheme preferably uses MATLAB R2020b to randomly generate energy storage access and output schemes.

[0030] In the embodiment, the objective function is to minimize the sum of voltage deviations at overloaded nodes.

[0031] The objective function is established with the goal of minimizing the sum of voltage deviations at heavy-load nodes in the distribution network. The objective function expression is as follows:

[0032] Where: f is the sum of voltage deviations at heavy-load nodes in the distribution network; d Number the nodes of the distribution network; His the total number of distribution network nodes; U dN For the d The rated voltage of each node; U ds For the d The actual voltage of each node; k d is the coefficient, when d When the node voltage exceeds the limit, k d is 1; otherwise it is 0.

[0033] In the embodiment, constraints need to be set for the above functions, where the constraints include network power balance and energy storage operation constraints when the distribution network is running. The constraint formula is as follows: The network balance constraint is as follows:

[0034] Where: P in Import power into the distribution grid; P l,i For Node i The load at P bess,j For the j The output of each energy storage; N i is the number of distribution network nodes; N j The number of energy storage devices connected to the distribution network; Energy storage operation constraints include power constraints, energy conservation constraints, and charge and discharge limit constraints. The power constraints are shown in the following formula:

[0035] Where: The real-time remaining percentage of energy storage; is the lower limit of energy storage SOC; The upper limit of energy storage SOC; The energy conservation constraint is expressed as follows:

[0036] Where: express t Charging amount at all times; express t Discharge amount at any time; The charge and discharge limit constraints are as follows:

[0037] Where: is the maximum charge value of the energy storage system; is the maximum discharge value of the energy storage system.

[0038] The objective function value of randomly generated energy storage access and output schemes is calculated and implemented based on the particle swarm algorithm, and the optimal energy storage access and output scheme is determined through continuous iteration.

[0039] Set the initial parameters of the particle swarm, including the number of populations, spatial dimension, and number of iterations; Solve the objective function. Since the solution of each particle is a storage configuration scheme for the substation, the optimal scheme among all schemes is obtained by screening according to the objective function value. Update the particle position again, iteratively calculate the function value of the updated particle position, and obtain the iterative solution. In the embodiment, the expression for updating the particle is as follows:

[0040] Where: For the j Particles in q After +1 iteration, p Position in dimension; For the j Particles in q After the first iteration p The position in the dimension; j Particles in q After the first iteration p Speed ​​in dimension; For the j Particles in q After the first iteration p Individual optimal solutions in dimensions; For the j Particles in q After the first iteration p The overall optimal solution in terms of dimension; c 0 is the inertia weight; c 1 is the self-learning factor; c 2 is the group learning factor; r 1. r 2 are all random numbers on [0,1].

[0041] The optimal solution is compared with the iterative solution. If the iterative solution is better, the optimal substation energy storage configuration of the distribution network is output; if the optimal solution is better, the iteration is repeated until the number of iterations is met. If the optimal solution is still better, the optimal solution is output as the optimal substation energy storage configuration of the distribution network, otherwise the iterative solution is output as the optimal substation energy storage configuration of the distribution network.

[0042] Calculate the voltage conditions of each node in the distribution network after connecting to the energy storage system to verify the effectiveness of the energy storage connection and output plan.

[0043] The verification in the embodiment is to calculate the voltage qualification rate of the distribution network before and after the energy storage is connected. The voltage qualification rate is shown in the following formula:

[0044] Where: t is the voltage monitoring time; The time when the voltage exceeds the upper limit is 1.07; The time when the voltage crosses the lower limit is 0.93; When the voltage qualification rate is greater than 97%, it means that the optimal substation energy storage configuration of the distribution network meets the distribution network operation requirements.

[0045] This embodiment uses MATLAB R2020b to Figure 2 The 33-node distribution network system shown in the figure is simulated, and the basic distribution network load is the original load of the 33-node distribution system, as shown in Table 1.

[0046] Table 1 Original load of each node in the distribution system

[0047] The distribution network system was simulated for 180 hours. Taking into account the voltage qualification rate and other indicators in the actual distribution network, the load fluctuation range of the distribution network was limited to 0.83 to 0.95 times the basic load. The load changes of the distribution network during the operation time were as follows: Figure 3 The matpower power flow calculation program is called to calculate the voltage qualification rate of the distribution network during the operation time, and the voltage qualification rate when the substation energy storage is not connected is 95.02%.

[0048] The energy storage capacity range of the substation is set to 100kW·h to 200kW·h, and 7 substation energy storages are connected. The energy storage access location and output plan are determined by the particle swarm algorithm. The access location and capacity of each energy storage are shown in Table 2.

[0049] Table 2: Energy storage access location and capacity in the substation area

[0050] The output and SOC changes of the 7 energy storage units at each time are as follows: Figure 4 , Figure 5 , Figure 6 , Figure 7 , Figure 8 , Fig. 9 as well as Fig.10As shown in the figure, after connecting to energy storage, the matpower flow calculation program is called again to calculate the voltage qualification rate of the distribution network during the operation time, and the voltage qualification rate of the distribution network after connecting to the substation energy storage is 97.64%. Compared with the case without connecting to energy storage, the voltage qualification rate of the distribution network has increased by 2.62%, which verifies the feasibility of improving the power supply capacity of the distribution network through substation energy storage.

[0051] Through the above method, the present invention proposes a substation energy storage configuration method for improving the power supply capacity of the distribution network. When the distribution network implements the energy storage configuration and output plan, it can effectively improve the node voltage quality and help improve the power supply capacity of the distribution network.

[0052] Based on the same inventive concept, an embodiment of the present invention also provides a substation energy storage configuration system for improving the power supply capacity of the distribution network. Since the principle of solving the problem by the substation energy storage configuration system for improving the power supply capacity of the distribution network is similar to the aforementioned substation energy storage configuration method for improving the power supply capacity of the distribution network, the implementation of the substation energy storage configuration system for improving the power supply capacity of the distribution network can refer to the implementation of the substation energy storage configuration method for improving the power supply capacity of the distribution network, and the repeated parts will not be repeated.

[0053] In a specific implementation, the substation energy storage configuration system for improving the power supply capacity of the distribution network provided by the embodiment of the present invention specifically includes: An acquisition module is used to acquire distribution network data; A generation module is used to randomly generate energy storage configurations based on distribution network data; Establishing a module for establishing an objective function based on the energy storage configuration with the goal of minimizing the sum of voltage deviations of heavy-load nodes in the distribution network, and establishing constraints of the objective function according to the network power balance and energy storage operation during the operation of the distribution network; The solution module is used to solve the objective function based on the particle swarm algorithm according to the constraints to obtain the optimal energy storage configuration of the distribution network.

[0054] Correspondingly, an embodiment of the present invention further provides a substation energy storage configuration device for improving the power supply capacity of a distribution network, comprising a processor and a memory, wherein the processor implements a substation energy storage configuration method for improving the power supply capacity of a distribution network as provided in an embodiment of the present invention when executing a computer program stored in the memory.

[0055] For more specific processes of the above method, please refer to the corresponding contents disclosed in the aforementioned embodiments, which will not be repeated here.

[0056] Accordingly, an embodiment of the present invention further provides a computer-readable storage medium for storing a computer program, wherein when the computer program is executed by a processor, the method for configuring energy storage in a substation for improving the power supply capacity of a distribution network as provided in an embodiment of the present invention is implemented.

[0057] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the systems, devices, and storage media disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method part.

[0058] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0059] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0060] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0061] The above is a detailed introduction to the substation energy storage configuration method, system, device and storage medium for improving the power supply capacity of the distribution network provided by the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for general technical personnel in this field, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A method for configuring energy storage in a distribution area to improve the power supply capacity of a distribution network, characterized in that: The following steps are involved: S1. Obtain distribution network data; S2, randomly generate energy storage configuration based on distribution network data; S3, based on the energy storage configuration, establish an objective function with the goal of minimizing the sum of voltage deviations of heavy-load nodes in the distribution network, and establish constraints of the objective function according to the network power balance during the operation of the distribution network and the energy storage operation; S4. According to the constraints, the objective function is solved based on the particle swarm algorithm to obtain the optimal energy storage configuration of the distribution network.

2. A method for configuring energy storage in a substation area to improve the power supply capacity of a distribution network according to claim 1, characterized in that: The distribution network data includes load data, distribution line parameters and network topology data.

3. A method for configuring energy storage in a distribution area to improve the power supply capacity of a distribution network according to claim 1, characterized in that: In step S3, an objective function is established with the goal of minimizing the sum of voltage deviations of heavy-load nodes in the distribution network. The objective function expression is as follows: Where: f is the sum of voltage deviations at heavy-load nodes in the distribution network; d Number the nodes of the distribution network; H is the total number of distribution network nodes; U dN For the d The rated voltage of each node; U ds For the d The actual voltage of each node; k d is the coefficient, when d When the node voltage exceeds the limit, k d is 1; otherwise it is 0.

4. A method for configuring energy storage in a substation area to improve the power supply capacity of a distribution network according to claim 1, characterized in that: The constraints in step S3 include network balance constraints and energy storage operation constraints, wherein the network balance constraints are shown in the following formula: Where: P in Import power into the distribution grid; P l,i For Node i The load at P bess,j For the j The output of each energy storage; N i is the number of distribution network nodes; N j The number of energy storage devices connected to the distribution network; Energy storage operation constraints include power constraints, energy conservation constraints, and charge and discharge limit constraints. The power constraints are shown in the following formula: Where: The real-time remaining percentage of energy storage; is the lower limit of energy storage SOC; The upper limit of energy storage SOC; The energy conservation constraint is expressed as follows: Where: express t Charge level at all times; express t Discharge amount at any time; The charge and discharge limit constraints are as follows: Where: is the maximum charge value of the energy storage system; is the maximum discharge value of the energy storage system.

5. A method for configuring energy storage in a substation area to improve the power supply capacity of a distribution network according to claim 1, characterized in that: The specific process of step S4 is: Set the initial parameters of the particle swarm, including the number of populations, spatial dimension, and number of iterations; Solve the objective function in step S3, and select the optimal solution according to the objective function value; Update the particle position, iteratively calculate the function value of updating the particle position, and obtain the iterative solution; The optimal solution is compared with the iterative solution. If the iterative solution is better, the optimal energy storage configuration of the distribution network is output; if the optimal solution is better, the iteration is repeated until the number of iterations is reached, and the optimal solution is output as the optimal energy storage configuration of the distribution network.

6. A method for configuring energy storage in a substation area to improve the power supply capacity of a distribution network according to claim 5, characterized in that: The updated particle expression is as follows: Where: For the j Particles in q After +1 iteration, p Position in dimension; For the j Particles in q After the first iteration p The position in the dimension; j Particles in q After the first iteration p Speed ​​in dimension; For the j Particles in q After the first iteration p Individual optimal solutions in dimensions; For the j Particles in q After the first iteration p The overall optimal solution in terms of dimension; c 0 is the inertia weight; c 1 is the self-learning factor; c 2 is the group learning factor; r 1. r 2 are all random numbers on [0,1].

7. A method for configuring energy storage in a substation area to improve the power supply capacity of a distribution network according to claim 1, characterized in that: After step S4, the optimal energy storage configuration of the distribution network needs to be verified. The verification is to calculate the voltage qualification rate of the distribution network before and after the energy storage is connected. The voltage qualification rate is shown in the following formula: Where: t is the voltage monitoring time; The time when the voltage exceeds the upper limit is 1.07; The time when the voltage crosses the lower limit is 0.93; When the voltage qualification rate is greater than 97%, it means that the optimal substation energy storage configuration of the distribution network meets the distribution network operation requirements.

8. A substation energy storage configuration system for improving the power supply capacity of a distribution network, characterized in that: include: An acquisition module is used to acquire distribution network data; A generation module is used to randomly generate energy storage configurations based on distribution network data; Establishing a module for establishing an objective function based on the energy storage configuration with the goal of minimizing the sum of voltage deviations of heavy-load nodes in the distribution network, and establishing constraints of the objective function according to the network power balance and energy storage operation during the operation of the distribution network; The solution module is used to solve the objective function based on the particle swarm algorithm according to the constraints to obtain the optimal energy storage configuration of the distribution network.

9. A substation energy storage configuration device for improving the power supply capacity of a distribution network, characterized in that: It comprises a processor and a memory, wherein when the processor executes the computer program stored in the memory, it implements the substation energy storage configuration method for improving the power supply capacity of the distribution network as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: Used to store computer programs, wherein when the computer program is executed by a processor, the substation energy storage configuration method for improving the power supply capacity of the distribution network as described in any one of claims 1 to 7 is implemented.

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