Optimal configuration method for energy storage system of power distribution network containing wind and light

Abstract

The invention relates to the field of power distribution network energy storage, in particular to an optimal configuration method for a power distribution network energy storage system containing wind and light. According to the method, a double-layer optimization modeling framework is adopted, an outer-layer model is used for determining site selection and capacity configuration of the energy storage system, the aim is to comprehensively optimize energy storage investment cost and operation performance indexes of the power distribution network, and configuration benefits are maximized on the basis of meeting operation constraints. An energy storage optimization benefit balance index is introduced into the outer layer model to serve as an evaluation index, and an adaptive variation particle swarm optimization algorithm is adopted to solve the configuration position and capacity of the energy storage system. And the inner layer model is constructed based on a second-order cone programming method and is used for optimizing a charging and discharging plan of the energy storage system under a given energy storage configuration condition. And the active power loss and the voltage deviation of the system are comprehensively considered by the inner-layer objective function so as to realize the optimal operation benefit. Through double-layer collaborative optimization, combined design of configuration and scheduling of the energy storage system is realized, and the operation stability and economy of the power distribution network under the access of renewable energy sources are improved.

Description

Technical Field

[0001] The present invention relates to the field of distribution network energy storage, and particularly to an optimal configuration method for a distribution network energy storage system with wind and light. Background Art

[0002] Wind power and photovoltaic power generation have the characteristics of intermittency and volatility. After their large-scale access to the distribution network, it brings significant challenges to the safe and stable operation of the distribution network. On the one hand, the peak output of local wind and light power generation may cause reverse power flow, resulting in complex power flow distribution and increasing the difficulty of system control; on the other hand, the randomness of renewable energy exacerbates voltage fluctuations and puts forward higher requirements for voltage regulation ability. The root cause of these problems lies in the insufficient regulation ability of the distribution network to the output fluctuations of renewable energy, which is likely to lead to a decline in operation stability.

[0003] Energy storage systems, with advantages such as fast response and two-way power regulation, are widely used to alleviate the fluctuations of renewable energy, improve power quality, and enhance the operation stability of the power grid. Against the background of the increasingly widespread grid connection of distributed wind power and photovoltaic power, the reasonable configuration of energy storage systems can achieve peak shaving and valley filling, suppress power fluctuations, improve the consumption capacity of renewable energy, and enhance the safety and reliability of the operation of the distribution network. Therefore, optimizing the location and capacity of energy storage systems in the distribution network has become an important research direction for improving the operation performance of the system.

[0004] At present, the optimal configuration methods of energy storage systems mainly include two categories: analytical methods and intelligent optimization algorithms. Analytical methods usually evaluate each node based on grid sensitivity indicators, such as power sensitivity, voltage sensitivity, etc., to determine the configuration location of energy storage. For example, some studies calculate the node power sensitivity factor through the Jacobian matrix to determine the optimal layout of energy storage; there are also methods that use voltage-load sensitivity analysis to evaluate the improvement effect of energy storage on the voltage of the end node. However, when dealing with complex problems such as the location and capacity determination of multiple energy storage devices, the analytical method has a high computational complexity and is difficult to effectively solve the non-linear optimization model with mixed variables.

[0005] In contrast, intelligent optimization algorithms are widely used in the research on the optimal configuration of energy storage systems due to their advantages in dealing with multi-objective and multi-constraint optimization problems. Common intelligent algorithms include genetic algorithms, particle swarm optimization algorithms, artificial bee colony algorithms, grey wolf algorithms, etc. Relevant research shows that these algorithms have good performance in reducing system losses, optimizing voltage quality, and improving the economy of the power grid. However, intelligent algorithms also have certain limitations, such as being prone to falling into local optima, having a slow convergence speed, being sensitive to parameter selection, etc., which limit their application effects in large-scale complex systems.

[0006] In summary, although certain achievements have been made in the existing energy storage configuration methods, in the face of large-scale distributed access scenarios with wind and solar power, problems such as insufficient regulation capacity, uneconomical configuration, and low solution efficiency still exist. Therefore, there is an urgent need to propose an optimal configuration method that can balance the operation stability of the distribution network and the economy of the energy storage system, so as to improve the friendly access ability of wind and solar renewable energy and enhance the flexibility and resilience of the distribution network. Summary of the Invention

[0007] To solve the problems of insufficient regulation capacity, poor configuration economy, and low solution efficiency caused by large output fluctuations, power reverse transmission, and voltage instability during the access of wind power and photovoltaic power to the distribution network, the present invention provides an optimal configuration method for an energy storage system in a distribution network with wind and solar power, which is implemented based on a two-layer optimization model. The two-layer optimization model includes: an outer layer model and an inner layer model, and the outer layer model and the inner layer model are jointly operated in an iterative nested manner: the preliminary energy storage configuration scheme generated by the outer layer model is input into the inner layer model for operation optimization, and the optimized results of the daily operation parameters are fed back to the outer layer model to update the energy storage configuration scheme.

[0008] The outer layer model is used to determine the optimal siting and capacity configuration of the energy storage system, that is, to obtain the energy storage configuration scheme; comprehensively considering the investment cost index of energy storage equipment, the power purchase cost index of the distribution network, and the operation performance index of the distribution network, and introducing the "energy storage system optimization benefit balance index" as a comprehensive evaluation criterion, the adaptive mutation particle swarm optimization algorithm is used to solve the configuration location and capacity of the energy storage system, and on the basis of meeting the operation constraints, the configuration benefit is maximized to achieve the balanced optimization of economic cost and operation benefit.

[0009] The inner layer model is used to construct an SOCP operation optimization model with the minimum node voltage deviation and active power loss as the objectives for the energy storage configuration scheme output by the outer layer model, considering the active power loss and voltage deviation of the energy storage system to achieve the optimal operation benefit.

[0010] A storage device, the storage device stores instructions and data for implementing the optimal configuration method for the energy storage system in the distribution network with wind and solar power.

[0011] An optimal configuration device for an energy storage system in a distribution network with wind and solar power, including: a processor and a storage device; the processor loads and executes the instructions and data in the storage device for implementing the optimal configuration method for the energy storage system in the distribution network with wind and solar power.

[0012] The beneficial effects brought by the technical solution provided by the present invention are as follows: By introducing a dynamic weight mechanism, optimizing the adaptive adjustment of algorithm parameters, and combining the characteristics of efficient solution of second-order cone programming, the present invention uses an adaptive mutation particle swarm optimization algorithm to solve the configuration location and capacity of the energy storage system. The second-order cone relaxation method is used to convexly optimize the power flow problem of the distribution network, improving the model solution accuracy and efficiency. The inner-layer objective function comprehensively considers the active power loss and voltage deviation of the system to achieve the optimal operation benefit. The outer-layer solution algorithm introduces an adaptive mutation particle swarm algorithm to enhance the search ability, avoid falling into local optima, and improve the global optimization performance. Through the double-layer collaborative optimization of the inner and outer-layer models, the outer layer performs energy storage site selection and capacity configuration, and the inner layer optimizes the energy storage operation strategy, strengthening the coupling coordination between the configuration decision and operation control, realizing the joint design of energy storage system configuration and scheduling, and achieving the coordination of economy and operation stability. By introducing an operation optimization benefit balance index, the unified consideration of technical performance and economic cost is realized; by combining the adaptive mutation particle swarm algorithm and convex optimization technology, the solution efficiency and convergence quality are improved. By optimizing the energy storage site selection and capacity configuration, the present invention significantly suppresses the fluctuations of wind and light output, improves the voltage quality, reduces the system loss, and enhances the operation stability; by comprehensively considering the investment cost, operation loss, and renewable energy consumption revenue, the optimal life-cycle cost of the energy storage system is achieved, enhancing the economy; by combining the advantages of the analytical method and intelligent algorithms, the calculation complexity is reduced, the solution efficiency is improved, and local optima are avoided, which is applicable to large-scale distribution networks and scenarios with multiple renewable energy accesses, and has wide adaptability; the present invention provides an efficient and reliable energy storage configuration scheme for distribution networks with high proportions of renewable energy access, which is of great significance for promoting the transformation of the energy structure and constructing a new power system, and has outstanding technical innovation and market application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The present invention will be further described below in conjunction with the drawings and embodiments. In the drawings:

[0014] Figure 1 is a schematic diagram of the double-layer optimization model architecture in the embodiment of the present invention;

[0015] Figure 2 is a schematic diagram of the voltage amplitude of the distribution network nodes before and after the energy storage access in the embodiment of the present invention;

[0016] Figure 3 is a schematic diagram of the change of the active power loss curve before and after the energy storage access in the embodiment of the present invention;

[0017] Figure 4 is a schematic diagram of the operation of the hardware device in the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] In order to have a clearer understanding of the technical features, objectives, and effects of the present invention, the specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0019] Embodiment 1

[0020] Please refer to Figure 1 , Figure 1 which is a schematic diagram of the two - layer optimization model architecture of the optimization configuration method for a wind - solar - integrated distribution network energy storage system in an embodiment of the present invention. The two - layer optimization model includes an outer - layer model and an inner - layer model. The two form an interactive collaborative optimization process through an information feedback mechanism to achieve the coordinated optimization of the economy of the energy storage system and the operation stability of the distribution network, taking into account both the construction investment cost and the operation benefit of the energy storage, and having good applicability and popularization value for improving the comprehensive performance of the distribution network.

[0021] The outer - layer model is used to determine the optimal siting and capacity configuration of the energy storage system. Its objective function comprehensively considers economic indicators such as the investment cost of energy storage equipment, the power purchase cost of the distribution network, and the operation performance indicators of the distribution network, and introduces the "energy storage system optimization benefit balance index" as a comprehensive evaluation criterion to maximize the configuration benefit on the basis of meeting operation constraints and achieve the balanced optimization of economic cost and operation benefit. The decision variables are: the installed capacity and location of the energy storage battery. The constraint conditions are: energy storage location constraint, energy storage rated power and capacity constraint. The adaptive mutation particle swarm optimization algorithm is used to solve the configuration location and capacity of the energy storage system to improve the model convergence speed and the global optimality of the solution.

[0022] The inner - layer model is constructed based on the second - order cone programming method and is used to optimize the charge - discharge plan of the energy storage system under the given energy storage configuration conditions. The inner - layer model takes the real - time operation optimization of the energy storage system as the core. For the energy storage configuration scheme output by the outer - layer model, a second - order cone programming (SOCP) operation optimization model with the minimization of node voltage deviation and active power loss as the objective (i.e., Figure 1 the energy storage system optimization operation model in

[0023] During the entire solution process, the outer-layer model and the inner-layer model are jointly operated in an iterative nested manner: the preliminary energy storage configuration plan generated by the outer-layer model is input into the inner-layer model for operation optimization, and the optimization results of daily operation parameters (such as node voltage levels and network loss indicators) are fed back to the outer-layer model to update the configuration plan. Through multiple rounds of iteration, the model finally converges to the optimal energy storage configuration plan that balances economy and operation stability. The present invention effectively improves the operation stability and economy of the distribution network under the access of renewable energy, and is applicable to the optimization configuration problem of energy storage systems in scenarios with high-penetration wind and light access.

[0024] 1. Regarding the energy storage system optimization configuration model in the outer-layer model

[0025] It aims to globally optimize the access location and configuration capacity of the energy storage system in the distribution network to achieve the coordinated unity of operation benefits and investment economy. As the outer-layer model in the two-layer optimization structure, this model is responsible for providing the optimal initial configuration plan and serving as the input basis for the inner-layer operation optimization model.

[0026] This outer-layer model constructs a multi-objective optimization function with the "energy storage system optimization benefit balance index" as the core. The multi-objective optimization function comprehensively considers the following key factors:

[0027]

[0028] C total =(C op +C om +C gd ) / 365(3.3)

[0029] In the formula, F is the daily operation optimization value of the distribution network, C total,pri , C total,op are the total operation costs before and after energy storage configuration respectively; C total is the total operation cost; C op is the initial investment cost of the energy storage system; C om is the energy storage operation and maintenance cost; C gd is the main network power purchase cost; CP represents the energy storage system optimization benefit balance index.

[0030] To avoid investment redundancy and system operation performance degradation caused by excessive investment in the number or capacity of distributed energy storage systems, the present invention proposes a capacity and power constraint strategy for energy storage systems for distribution network applications. This strategy introduces multiple technical constraint conditions during the optimization process to achieve the coordinated balance between the scientific selection of energy storage systems and the economy of distribution network operation.

[0031] (1) Energy storage system location constraint

[0032] To limit the maximum number of energy storage systems connected, control the equipment scale, and improve the investment efficiency, the following location constraint conditions are set:

[0033]

[0034] In the formula, is the flag for the energy storage connected to node b; bus is the node in the distribution network where the energy storage can be connected; is the maximum allowable number of energy storage systems connected.

[0035] (2) Energy storage system capacity constraint

[0036] To ensure that the capacity of the configured energy storage system is within the feasible operation range, and to avoid low operation efficiency caused by too small capacity or investment waste caused by too large capacity, the following upper and lower capacity constraints are set:

[0037]

[0038] In the formula, is the capacity of the energy storage system; are the maximum and minimum capacities of the energy storage system respectively.

[0039] (3) Energy storage system power constraint

[0040] To ensure the operation stability of the energy storage system during the charging and discharging process, the upper and lower limits of the charging and discharging power are set as follows:

[0041]

[0042] In the formula, is the charging and discharging power of the energy storage; are the maximum and minimum charging powers of the energy storage system respectively.

[0043] By jointly introducing the above constraint conditions, the optimization model described in the present invention can effectively control the configuration scale of the energy storage system, improve the rationality and economy of resource allocation, and at the same time ensure the safe and stable operation of the system, providing stable boundary condition support for subsequent operation optimization and scheduling.

[0044] 2. Regarding the optimized operation model of the energy storage system in the inner layer model

[0045] The present invention proposes an optimized operation model of the inner layer energy storage system based on second-order cone programming, which is used to optimize the operation strategy of the energy storage system in the distribution network on the basis of a given energy storage system configuration scheme. The main objective of this model is to improve the system operation quality and reduce the system operation loss, so as to further improve the comprehensive benefit of the energy storage system.

[0046] The optimized operation model takes the voltage deviation and active power loss in the operation process of the distribution network as the joint optimization objectives, and constructs the following bi-objective optimization function:

[0047] minF = α 1 f 1 +α 2 f 2 (3.7)

[0048] In the formula, f 1 and f 2 are respectively the daily operation optimization values of the voltage deviation and active power loss after the energy storage system is connected to the distribution network. Among them, α 1 and α 2 are the weight coefficients of each objective, and α 1 +α 2 = 1.

[0049] (1) Voltage deviation objective function

[0050] To restrict the influence of the energy storage access on the node voltage level, the following voltage deviation objective function is defined:

[0051]

[0052] In the formula, U s,pri and U s,op are respectively the voltage deviation amounts of the distribution network before and after the energy storage system is connected; U n,t is the voltage of node n at time t; U ref is the reference voltage of the distribution network; N bus is the number of nodes in the distribution network.

[0053] (2) Active power loss

[0054]

[0055] In the formula, P loss,pri and P loss,op are respectively the active power losses of the distribution network before and after the energy storage system is connected; R l is the resistance of branch l; L line is the branch set of the distribution network; I l,t is the current of branch l at time t; Δt represents the scheduling unit time interval.

[0056] To meet the actual operating conditions of the distribution network and ensure the safe and stable operation of the system, a systematic constraint mechanism is introduced into the inner-layer daily operation optimization model of the present invention. This constraint system comprehensively considers the power flow power balance constraint of the distribution network, the operation safety constraints such as node voltage and line power, and the operation constraints such as the charge and discharge power, capacity, and state transfer of the energy storage system, thereby constructing a feasible solution space for the optimization problem and ensuring that the optimization scheme has engineering feasibility and physical rationality.

[0057] Among them, the power flow constraint of the distribution network is processed by the Second-Order Cone Relaxation (SOCR) method. This method can effectively address the non-convex constraint problem existing in the traditional DistFlow power flow model. By transforming the non-linear power flow equation into a second-order cone programming problem in convex optimization form, it significantly improves the solvability and computational efficiency of the problem. The SOCR method not only reduces the complexity of problem solving but also improves the convergence speed and accuracy of the model in medium and large-scale distribution network systems, providing theoretical and algorithmic support for the efficient operation of the inner-layer optimization model.

[0058] 3. Adaptive Particle Swarm Optimization Algorithm Design

[0059] In view of the characteristics of integer and continuous mixed variables in the outer-layer energy storage system siting and sizing problem of the present invention, an improved Adaptive Mutation Particle Swarm Optimization (AMPSO) algorithm is proposed to efficiently solve the optimal installation location and capacity configuration of energy storage devices.

[0060] Based on the traditional Particle Swarm Optimization (PSO) algorithm, the AMPSO algorithm introduces a dynamic adjustment mechanism for individual and social learning factors, as well as a mutation operation strategy, thereby enhancing the exploration ability in the early stage of the search and the convergence stability in the later stage, realizing the search and evolution process of "exploration - jump out - re-convergence", and improving the global optimization ability of the algorithm.

[0061] (1) Dynamic Learning Factor Update Mechanism

[0062] To improve the adaptability of particles in the search process, the present invention dynamically updates the individual and social learning factors through a linear function. The specific update formula is as follows:

[0063] c 1 = c 1,i + d × (c 1,f - c 1,i ) / d max (3.12)

[0064] c 2 = c 2,i+d×(c 2,f -c 2,i ) / d max (3.13)

[0065] where d and d max are the iteration number and the maximum iteration number of the particle respectively; c 1,i and c 2,i are the initial values of the individual and social learning factors respectively; c 1,f and c 2,f are the final values of the individual and social learning factors respectively; c 1 and c 2 represent the individual and social learning factors respectively. Through linear adjustment, the particle focuses on local search in the early stage and tends to stable convergence in the later stage, which helps to improve the overall search efficiency and stability.

[0066] (2) Adaptive Mutation Strategy Design

[0067] To prevent the particle from falling into the local optimal solution, the present invention introduces a mutation mechanism to increase the population diversity. Under the condition of meeting the set probability threshold, a part of the particles are selected for perturbation mutation operation, and its update method is as follows:

[0068]

[0069] where λ is the adaptive step size; P m is the adaptive mutation rate.

[0070]

[0071] where P m,max is the maximum mutation rate.

[0072] Through the above mutation mechanism, the algorithm can maintain a strong global jumping ability in the early stage of the search and reduce the mutation probability in the later stage to improve the convergence stability, thus taking into account both the convergence speed and the global optimization performance.

[0073] The AMPSO algorithm described in the present invention combines the velocity-position update mechanism, dynamic factor adjustment and adaptive mutation strategy, significantly improving the convergence accuracy and global search ability under high-dimensional mixed integer problems.

[0074] 4. Solution Process

[0075] The present invention uses an improved particle swarm optimization algorithm (IPSO) to solve the bilevel optimization model, where: the outer model optimization uses an adaptive mutation particle swarm algorithm to search for the energy storage configuration scheme; the inner model scheduling uses second-order cone programming to solve the operation optimization problem; an iterative solution process is introduced to realize the information interaction and optimization convergence between the inner and outer models.

[0076] An optimization configuration method for a distribution network energy storage system with wind and light proposed by the present invention is implemented based on the above-mentioned two-layer optimization model, and specifically includes the following steps:

[0077] Step S1: Input data preparation

[0078] Obtain the basic input parameters related to the energy storage optimization configuration model, including the output scenarios of distributed wind power and distributed photovoltaic power on a typical day, the basic load data of each node, the distribution network topology structure information, and the relevant control parameters of AMPSO.

[0079] Step S2: Initialization of the outer layer model

[0080] In the outer layer optimization model, initialize the configuration scheme of the energy storage system, including the installation location, capacity, and rated power of the energy storage device, as the initial solution of the particle swarm algorithm.

[0081] Step S3: Parameter transfer to the inner layer model

[0082] Transfer the energy storage configuration scheme corresponding to the current particle to the inner layer operation optimization model. The inner layer model aims to optimize the distribution network operation parameters and optimize the charge and discharge strategy of the energy storage system to ensure the minimization of the objective function such as operation cost, voltage deviation, or network loss while meeting the system operation constraints.

[0083] Step S4: Feedback of the optimization result to the outer layer model

[0084] Feed back the operation results output by the inner layer model, including the power purchase power from the superior power grid at each moment and the key operation parameters of the distribution network (such as node voltage, power flow distribution, etc.), to the outer layer model for subsequent particle swarm fitness calculation.

[0085] Step S5: Calculate the optimization benefit index

[0086] The outer layer model calculates the optimization benefit balance index of the energy storage system for the current particle scheme based on the total cost of energy storage configuration, the power purchase cost generated during operation, and other operation parameters, which is used to measure the comprehensive balance degree between the economy and operation benefit of the current configuration scheme.

[0087] Step S6: Iterative optimization

[0088] Based on the velocity update, position update mechanism, and adaptive mutation strategy of the AMPSO algorithm, generate a new generation of particle swarm (i.e., a new energy storage configuration scheme), and repeat the calculation process of steps S3 to S5 to continuously perform iterative optimization.

[0089] Step S7: Result output

[0090] When the number of iterations reaches the set termination condition, or the fitness of the particle swarm converges to the preset threshold, the optimal energy storage location and sizing scheme is output as the final optimization configuration result of the distributed energy storage system.

[0091] The method of the present invention realizes the joint design of energy storage system configuration and scheduling through the collaborative optimization of the inner and outer layer models, effectively improving the operation stability and economy of the distribution network under the access of renewable energy, and is applicable to the optimization configuration problem of energy storage systems in scenarios with high-penetration wind and light access.

[0092] To verify the practicability of the proposed two-layer optimization configuration model of the present invention, a typical distribution network system is selected, and three scenarios with different numbers of energy storage access are set for simulation comparison. Specifically, they include: Scenario 1 (single energy storage), Scenario 2 (double energy storage), and Scenario 3 (triple energy storage). The operation results are shown in Table 3.1.

[0093] Table 3.1 Index comparison of different scenarios

[0094]

[0095]

[0096] From the comparison results, it can be seen that Scenario 2, that is, the scheme of accessing two energy storage systems, performs the best in all indicators. On the basis of moderately increasing the configuration capacity, it effectively reduces the system's electricity purchase cost and active power loss, while improving the voltage operation quality. The optimized benefit balance index is 4.03%, which is the highest among the three scenarios, comprehensively indicating that this configuration scheme achieves a good balance between economy and operation performance.

[0097] In contrast, although the investment cost of Scenario 1 is low, due to only accessing one energy storage system, the regulation ability is insufficient, making it difficult to meet the voltage regulation requirements of multiple nodes in the distribution network during high-load periods, resulting in an increase in the electricity purchase cost and an aggravation of voltage deviation, and the system operation effect is limited. Although Scenario 3 further increases the configuration capacity, the improvement of the operation index is not obvious, while the investment cost increases significantly, and the overall cost performance decreases instead, indicating that excessive configuration of energy storage quantity will lead to resource waste and cannot achieve the optimal coordination of system operation.

[0098] Figure 2 For the voltage amplitude of the distribution network nodes before and after the energy storage access, Figure 2 (a) of it is a schematic diagram before the energy storage access, Figure 2 (b) of it is a schematic diagram before the energy storage access, Figure 3 It shows the change of the active power loss curve before and after the energy storage access. After the energy storage access, the voltage deviation amplitude of multiple nodes is significantly narrowed, and the overall operating voltage is more stable. The present invention shows that the energy storage has good dynamic regulation ability.

[0099] In summary, the double-layer optimization model constructed by the present invention can achieve the coordinated optimization of the configuration of the energy storage system, taking into account both economy and operating performance, and has good engineering applicability and popularization value.

[0100] Embodiment 2

[0101] An optimized configuration device 401 for a distribution network energy storage system with wind and light, as Figure 4 shown, includes: a processor 402 and a storage device 403; the processor 402 loads and executes instructions and data in the storage device 403 to implement the method for optimizing the configuration of the distribution network energy storage system with wind and light.

[0102] Embodiment 3

[0103] A storage device that stores instructions and data for implementing the method for optimizing the configuration of the distribution network energy storage system with wind and light.

[0104] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for optimizing the configuration of a distribution network energy storage system including wind and solar power, based on a two-layer optimization model, characterized in that: The two-layer optimization model includes: an outer model and an inner model. The outer model and the inner model are jointly operated in an iterative nesting manner: the preliminary energy storage configuration plan generated by the outer model is input into the inner model for operation optimization, and the daily operation parameter optimization results are fed back to the outer model for updating the energy storage configuration plan; The outer model is used to determine the optimal location and capacity configuration of the energy storage system, that is, to obtain the energy storage configuration plan; it comprehensively considers the energy storage equipment investment cost index, the distribution network power purchase cost index, and the distribution network operation performance index, and introduces the "energy storage system optimization benefit balance index" as a comprehensive evaluation criterion. The adaptive mutation particle swarm optimization algorithm is used to solve the configuration location and capacity of the energy storage system, maximize the configuration benefit on the basis of satisfying the operation constraints, and achieve a balanced optimization of economic cost and operation benefit; The inner model is used to construct a SOCP operation optimization model based on the energy storage configuration plan output by the outer model, with the goal of minimizing node voltage deviation and active power loss. The active network loss and voltage deviation of the energy storage system are taken into account to achieve the optimal operation efficiency.

2. The method for optimizing the configuration of a wind-solar distribution network energy storage system according to claim 1, characterized in that: The decision variables in the outer model are: the installation capacity and location of the energy storage battery, and the constraints are: energy storage system location constraint, energy storage system rated power constraint and energy storage system capacity constraint. The adaptive mutation particle swarm optimization algorithm is used to solve the configuration location and capacity of the energy storage system to improve the convergence speed of the outer model and the global optimality of the solution.

3. The method for optimizing the configuration of a wind-solar distribution network energy storage system according to claim 1, characterized in that: The decision variables in the inner model are: the output characteristics of each power generation unit, and the constraints are: system safety constraints, charging and discharging power constraints, and power flow constraints; this inner model effectively improves the voltage quality and energy utilization efficiency of the distribution network, and enhances the system's adaptability to the volatility of renewable energy.

4. The method for optimizing the configuration of a wind-solar distribution network energy storage system according to claim 1, characterized in that: The multi-objective optimization function of the outer model is: C total =(C op +C om +C gd ) / 365 Where F is the daily operation optimization value of the distribution network, C total,pri , C total,op are the total operating costs before and after energy storage configuration; C total is the total operating cost; C op is the initial investment cost of the energy storage system; C om is the energy storage operation and maintenance cost; C gd is the cost of purchasing electricity from the main grid, and CP represents the optimization benefit balance index of the energy storage system.

5. The method for optimizing the configuration of a wind-solar distribution network energy storage system as claimed in claim 2, characterized in that: (1) The location constraint of the energy storage system is: In the formula, It is the sign of node b accessing the energy storage system; bus is the node of the distribution network that can access the energy storage system; The maximum number of energy storage systems allowed to be connected; (2) The capacity constraint of the energy storage system is: In the formula, is the capacity of the energy storage system; are the maximum and minimum capacities of the energy storage system, respectively; (3) The power constraint of the energy storage system is: P i ess,min ≤P i ess ≤P i ess,max Where P i ess P is the energy storage charging and discharging power; i ess,max , P i ess,min are the maximum and minimum charging power of the energy storage system respectively.

6. The method for optimizing the configuration of a wind-solar distribution network energy storage system according to claim 1, characterized in that: The dual-objective optimization function of the SOCP operation optimization model is: minF=α1f1+α2f2 In the formula, f1 and f2 are the daily operation optimization values ​​of voltage deviation and active network loss after the energy storage system is connected to the distribution network, α1 and α2 are the weight coefficients of each target, and α1+α2=1; (1) The voltage deviation objective function is: Where U s,pri , U s,op are the voltage deviations of the distribution network before and after the energy storage system is connected; U n,t is the voltage of node n at time t; U ref is the reference voltage of the distribution network; N bus is the number of distribution network nodes; (2) The objective function of active network loss is: Where P loss,pri , P loss,op are respectively the active network loss of the distribution network before and after the energy storage system is connected; R l is the resistance of branch l; L line I is the branch set of the distribution network; l,t is the current of branch l at time t; Δt represents the scheduling unit time interval.

7. The method for optimizing the configuration of a wind-solar distribution network energy storage system according to claim 1, characterized in that: The calculation process using the adaptive particle swarm optimization algorithm is as follows: (1) Dynamic learning factor update mechanism In order to improve the adaptability of particles in the search process, the individual and social learning factors are dynamically updated through linear functions. The specific update formula is as follows: c1=c 1,i +d×(c 1,f -c 1,i ) / d max c2=c 2,i +d×(c 2,f -c 2,i ) / d max Among them, d and d max are the number of iterations and the maximum number of iterations of the particle respectively; c 1,i and c 2,i are the initial values ​​of individual and social learning factors respectively; c 1,f and c 2,f are the final values ​​of individual and social learning factors, respectively; c1 and c2 represent individual and social learning factors, respectively; (2) Adaptive mutation strategy design In order to prevent particles from falling into the local optimal solution, a mutation mechanism is introduced to increase population diversity. Under the condition of meeting the set probability threshold, some particles are selected for perturbation mutation operation. The update method is as follows: Among them, λ is the adaptive step size; P m is the adaptive mutation rate; Indicates the latest position; is the particle position before participating in the adaptive mutation strategy; N(0,1) is a normal distribution; rand(0,1) represents a random number of 0 or 1; Among them, P m,max is the maximum mutation rate.

8. The method for optimizing the configuration of a wind-solar distribution network energy storage system according to claim 1, characterized in that: The implementation of the distribution network energy storage system optimization configuration method includes the following steps: Step S1: Input data preparation Obtain basic input parameters related to the energy storage optimization configuration model, including typical daily distributed wind power and distributed photovoltaic output scenarios, basic load data of each node, distribution network topology information, and relevant control parameters of AMPSO; Step S2: Initialization of the outer model In the outer model, the configuration scheme of the energy storage system is initialized, including the installation location, capacity and rated power of the energy storage equipment, as the initial solution of the particle swarm algorithm; Step S3: Parameters are passed to the inner model The energy storage configuration scheme corresponding to the current particle is passed to the inner model. The inner model optimizes the charging and discharging strategy of the energy storage system with the goal of optimizing the distribution network operation parameters to ensure that the system operation constraints are met while minimizing the objective functions such as operating cost, voltage deviation or network loss. Step S4: Feedback the optimization results of the inner model to the outer model Feedback the operation results output by the inner model, including the power purchased from the upper power grid at each moment and the key operation parameters of the distribution network, to the outer model for subsequent particle swarm fitness calculation; the key operation parameters of the distribution network include node voltage and power flow distribution; Step S5: Calculate the optimization benefit index The outer model calculates the optimal benefit balance index of the energy storage system of the current particle scheme based on the total cost of energy storage configuration, the electricity purchase cost generated during operation, and other operating parameters. It is used to measure the comprehensive balance between the economy and operating benefits of the current configuration scheme. Step S6: Iterative optimization Based on the speed update, position update mechanism and adaptive mutation strategy of the AMPSO algorithm, a new generation of particle swarms, i.e., a new energy storage configuration scheme, is generated, and the calculation process of steps S3-S5 is repeated to continuously perform iterative optimization; Step S7: Result output When the number of iterations reaches the set termination condition, or the fitness of the particle swarm converges to the preset threshold, the optimal energy storage site selection and capacity solution is output as the final optimization configuration result of the distributed energy storage system.

9. A storage device, characterized in that: The storage device stores instructions and data for implementing the method for optimizing configuration of a distribution network energy storage system including wind and solar power as described in any one of claims 1 to 8.

10. An optimized configuration device for a distribution network energy storage system including wind and solar power, characterized in that: include: Processor and storage device; the processor loads and executes instructions and data in the storage device to implement the method for optimizing configuration of a distribution network energy storage system containing wind and solar power as described in any one of claims 1 to 8.

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