A method and system for site selection and capacity determination of a distributed energy storage system

Abstract

The application discloses a kind of distributed energy storage system's site selection and system, method includes the following steps: obtaining distribution network node data and carrying out cluster analysis, obtain typical daily load and photovoltaic curve;Obtain preliminary installation site selection;Establish the multi-objective optimization model of energy storage site selection and capacity, determine objective function and constraint condition;Using typical distribution network IEEE33 node model, input line parameters, load level, network topology connection relationship, system node voltage and branch current, and the quantitative parameter of energy storage system, typical daily load and photovoltaic curve, preliminary site selection are input to multi-objective optimization model, and improved grey wolf algorithm is used to solve, obtain optimal solution set;Analysis optimal solution set, obtain the optimal energy storage position and configuration capacity size of energy storage system.The application can optimize site selection efficiency and accuracy, realize energy storage system investment cost minimization, effectively improve the power quality of distribution network.

Description

Technical Field

[0001] This invention mainly relates to the field of power distribution network technology, and specifically to a method and system for site selection and capacity determination of a distributed energy storage system. Background Technology

[0002] Photovoltaic power generation is susceptible to environmental influences, exhibiting significant fluctuations, randomness, and intermittency in its output. Large-scale grid integration of photovoltaic power can severely impact the safe and stable operation of the power grid. Battery energy storage systems (BESSs) possess rapid power regulation capabilities, effectively mitigating fluctuations in photovoltaic output, improving voltage quality, and reducing peak-to-valley differences. With the advancement of demonstration and commercial projects, energy storage will exhibit multi-point distribution characteristics within the power grid. Through unified power system dispatch, multi-point distributed energy storage systems can achieve orderly aggregation, fulfilling not only local application needs but also providing emergency power support, enhancing grid security and stability, effectively improving the grid's capacity to absorb renewable energy, and enriching ancillary services such as peak shaving, frequency regulation, and voltage regulation. This makes the power system more "flexible" and "intelligent," promoting a transformation in grid development models. Therefore, the selection of the access location, installed power, and capacity of battery energy storage systems directly affects the efficient and economical operation of the distribution network.

[0003] Currently, there are several unresolved issues regarding the site selection and capacity optimization of distributed energy storage systems: 1. The optimization objectives are not comprehensive, and the investment cost of battery energy storage systems can restrict their large-scale application. 2. The comprehensive cost model for battery energy storage is an effective way to minimize the investment cost of energy storage systems, but it does not consider additional subsidies. 3. Traditional intelligent optimization algorithms have poor globality and uniformity, and are prone to getting trapped in local optima. Summary of the Invention

[0004] The technical problem to be solved by this invention is to provide a method and system for site selection and capacity determination of a distributed energy storage system with high site selection efficiency and high accuracy, in response to the technical problems existing in the prior art.

[0005] To solve the above-mentioned technical problems, the technical solution proposed by this invention is as follows:

[0006] A method for site selection and capacity determination of a distributed energy storage system includes the following steps:

[0007] Acquire photovoltaic output data, generator output data, and load data of distribution network nodes, and perform cluster analysis to obtain typical daily load and photovoltaic curves of the distributed energy storage system;

[0008] The impact of photovoltaic (PV) grid connection on the voltage of distribution network nodes is analyzed, the nodes are divided into zones, vulnerable node groups are identified, and preliminary site selection for energy storage system installation is obtained.

[0009] A multi-objective optimization model for energy storage site selection and capacity determination was established. The objective function was determined to optimize the economic efficiency of the energy storage system and minimize voltage and load fluctuations. The constraints were also determined.

[0010] Using a typical distribution network IEEE33 node model, line parameters, load levels, network topology connections, system node voltages, and branch currents are input respectively. The quantitative parameters of the energy storage system, typical daily loads, photovoltaic curves, and preliminary site selection are input into the multi-objective optimization model for energy storage site selection and capacity determination. An improved Grey Wolf algorithm is used to solve the multi-objective optimization model to obtain the optimal solution set.

[0011] By analyzing the optimal solution set, the optimal energy storage location and configuration capacity of the energy storage system can be obtained.

[0012] Preferably, the specific process of solving the multi-objective optimization model using the improved Grey Wolf algorithm is as follows:

[0013] 1) Enter the initial value;

[0014] 2) Set the relevant parameters for the improved Grey Wolf algorithm;

[0015] 3) Initialize the gray wolf population using Tent chaotic mapping;

[0016] 4) Determine if the iteration count has been reached. If so, skip to step 9); otherwise, calculate the fitness of the initial population, sort them according to their fitness, record the Pareto optimal solution, and assign the three best individuals to wolves α, β, and δ in turn.

[0017] 5) Update the weight coefficient parameters of the current population;

[0018] 6) Determine if the population size has been reached. If so, use the gray wolf algorithm to find the candidate wolves. Otherwise, modify step 4).

[0019] 7) Calculate the neighborhood of the candidate wolf, and then perform a cross-pollination of individuals within the neighborhood to select new candidate wolves;

[0020] 8) Compare the candidate wolves obtained by the gray wolf algorithm with the candidate wolves after the hybrid crossover, select the candidate wolves with high fitness to update the population and replace the external solution set;

[0021] 9) End, and obtain the final optimal solution set.

[0022] Preferably, during the initial site selection, sensitivity analysis of the voltage of the distribution network nodes is performed, and the Pearson correlation coefficient between each node is calculated. The most vulnerable node group is obtained through comprehensive analysis, and a site selection and capacity determination analysis is performed on this node group to install the energy storage system at the nodes with weak system voltage stability.

[0023] Preferably, the sensitivity index is:

[0024]

[0025] Where: P i Q i Let ΔP be the active and reactive power of node i; i ΔQ i Let V be the change in active and reactive power at node i; U be the set of power grid nodes; j be any power grid node within the set of power grid nodes; V j Let be the node voltage at node j.

[0026] Preferably, the Pearson correlation coefficient between the nodes is:

[0027]

[0028] Where V i Let be the node voltage of node i; This represents the average voltage at each node.

[0029] Preferably, the multi-objective optimization model for energy storage site selection and capacity determination is as follows:

[0030]

[0031] In the formula: f1, f2, and f3 are the optimization space formed by the three optimization objective functions, and w(x) and v(x) are the constraints of the functions, which constitute the feasible region for solving the functions.

[0032] Preferably, the multi-objective optimization model for energy storage site selection and capacity determination also includes a comprehensive economic model for the energy storage system, a node voltage fluctuation model, and a load fluctuation model; wherein the comprehensive economic model for the energy storage system is:

[0033] C Ess =C inv +C rep +C ps +C om +C scr -C res -C sub -C rst

[0034] Where C Ess For the total cost, C inv For the initial investment cost, Crep To cover replacement costs, C ps For charging and discharging costs, C om For system operation and maintenance costs, C scr It is the cost of disposal, C res To recover residual value, C sub It is a government subsidy or allowance, C rst It delays the overall benefits of power grid upgrades.

[0035] Preferably, the constraints include power balance constraints, power flow constraints, voltage constraints, total active and reactive power limits injected into the distribution network from the upstream power grid, battery system charge and discharge constraints, battery SOC constraints, and constraints on battery installation location and number.

[0036] Preferably, the quantitative parameters include unit price, energy storage system service life, discount rate, and time-of-use electricity price.

[0037] The present invention also discloses a location and capacity determination system for a distributed energy storage system, comprising a memory and a processor connected to each other, wherein the memory stores a computer program, and the computer program executes the steps of the method described above when run by the processor.

[0038] Compared with the prior art, the advantages of the present invention are as follows:

[0039] This invention aims to optimize the economic efficiency, voltage fluctuation, and load fluctuation of the energy storage system in the distribution network. It establishes an optimal location and capacity model for the battery energy storage system and uses an improved gray wolf algorithm to solve the model, thereby minimizing the investment cost of the energy storage system and effectively improving the power quality of the distribution network.

[0040] The energy storage site selection and capacity optimization method based on the improved gray wolf algorithm of this invention, compared with other fixed site selection and capacity optimization methods, first considers the relationship between node voltage fluctuations before and after photovoltaic access, divides the nodes into zones for screening, optimizes site selection efficiency and accuracy, and optimizes the energy storage system economic model by taking into account the minimization of economic investment and voltage and load fluctuations, while also considering the system operation safety. Finally, the model is solved and analyzed based on the improved gray wolf algorithm to obtain the optimal site location and capacity configuration of the energy storage system. Attached Figure Description

[0041] Figure 1 This is a flowchart of an embodiment of the site selection and sizing method of the present invention.

[0042] Figure 2 This is a topology diagram of a typical IEEE 33-node distribution network in an embodiment of the present invention.

[0043] Figure 3 The flowchart shows the improved gray wolf algorithm of the present invention in an embodiment. Detailed Implementation

[0044] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0045] like Figure 1 As shown, the location and capacity determination method for a distributed energy storage system according to an embodiment of the present invention includes the following steps:

[0046] Obtain photovoltaic output data, generator output data, and load data of a distribution network node from a State Grid power supply company or statistics bureau, and perform cluster analysis to obtain typical daily load and photovoltaic curves for the distributed energy storage system;

[0047] The impact of photovoltaic (PV) grid connection on the voltage of distribution network nodes is analyzed, the nodes are divided into zones, vulnerable node groups are identified, and preliminary site selection for energy storage system installation is obtained.

[0048] A multi-objective optimization model for energy storage site selection and capacity determination was established using the MATLAB platform. The objective function was determined to be the optimal economic efficiency of the energy storage system and the minimum voltage and load fluctuations. The model also considered constraints such as the safe operation state of the distribution network, power flow state, energy storage system capacity, safe operation, and other constraints.

[0049] Using a typical distribution network IEEE33 node model, line parameters, load levels, network topology connections, system node voltages, and branch currents are input respectively. The quantitative parameters of the energy storage system, typical daily loads, photovoltaic curves, and preliminary site selection are input into the multi-objective optimization model for energy storage site selection and capacity determination. An improved Grey Wolf algorithm is used to solve the multi-objective optimization model to obtain the optimal solution set.

[0050] By analyzing the optimal solution set, the optimal energy storage location and configuration capacity of the energy storage system can be obtained.

[0051] In one specific embodiment, the improved grey wolf optimization (IGWO) algorithm is used to solve the multi-objective optimization model. The specific steps are as follows:

[0052] 1) Enter the initial value;

[0053] 2) Set the relevant parameters for IGWO, such as population size N=30, maximum number of iterations Tmax=200, etc.

[0054] 3) Initialize the gray wolf population using Tent chaotic mapping;

[0055] 4) Determine if the iteration count has been reached. If so, skip to step 9); otherwise, calculate the fitness of the initial population, sort them according to their fitness, record the Pareto optimal solution, and assign the three best individuals to wolves α, β, and δ in turn.

[0056] 5) Update the weight coefficients and other parameters of the current population;

[0057] 6) Determine if the population size has been reached. If so, use the gray wolf algorithm to find the candidate wolves. Otherwise, modify step 4).

[0058] 7) Calculate the neighborhood of the candidate wolf, and then perform a cross-pollination of individuals within the neighborhood to select new candidate wolves;

[0059] 8) Compare the candidate wolves obtained by the gray wolf algorithm with the candidate wolves after the hybrid crossover, select the candidate wolves with high fitness to update the population and replace the external solution set;

[0060] 9) The algorithm ends, and the final optimal solution set is obtained.

[0061] This invention aims to optimize the economic efficiency, voltage fluctuation, and load fluctuation of the energy storage system in the distribution network. It establishes an optimal location and capacity model for the battery energy storage system and uses an improved gray wolf algorithm to solve the model, thereby minimizing the investment cost of the energy storage system and effectively improving the power quality of the distribution network.

[0062] The energy storage site selection and capacity optimization method based on the improved gray wolf algorithm of this invention, compared with other fixed site selection and capacity optimization methods, first considers the relationship between node voltage fluctuations before and after photovoltaic access, divides the nodes into zones for screening, optimizes site selection efficiency and accuracy, and optimizes the energy storage system economic model by taking into account the minimization of economic investment and voltage and load fluctuations, while also considering the system operation safety. Finally, the model is solved and analyzed based on the improved gray wolf algorithm to obtain the optimal site location and capacity configuration of the energy storage system.

[0063] This invention also discloses a location and capacity determination system for a distributed energy storage system, including an interconnected memory and a processor. The memory stores a computer program, which, when run by the processor, executes the steps of the method described above. The location and capacity determination system of this invention, corresponding to the aforementioned location and capacity determination method, also possesses the advantages described above.

[0064] To better understand the above technical solution, the following will provide a detailed description of the technical solution in conjunction with the accompanying drawings and specific implementation methods:

[0065] Step 1: Analyze the impact of photovoltaic system integration on distribution network node voltages. Distributed photovoltaic integration will alter the voltage values ​​at distribution network nodes. By performing sensitivity analysis on node voltages and calculating the Pearson correlation coefficients between various nodes, a comprehensive analysis is conducted to identify the most vulnerable node group. Site selection and capacity assessment are then performed on this node group. Installing the energy storage system at nodes with weak system voltage stability can better improve system voltage stability and effectively increase planning time and efficiency.

[0066] The sensitivity index for the degree of influence of distribution network node voltage is shown in equation (1):

[0067]

[0068] Where: P i Q i Let ΔP be the active and reactive power of node i; i ΔQ i Let V be the change in active and reactive power at node i; U be the set of power grid nodes; j be any power grid node within the set of power grid nodes; V j Let be the node voltage at node j.

[0069] The Pearson correlation coefficient between the voltages of two adjacent nodes in the distribution network is shown in equation (2):

[0070]

[0071] Where V i Let be the node voltage of node i; This represents the average voltage of each node; nodes with strong correlation can be selected as important nodes for further processing.

[0072] Step 2: Considering the randomness of photovoltaic and load output, the K-means clustering algorithm is used to analyze the historical photovoltaic and load data in Step 1 to obtain four typical daily curves of load and photovoltaic. The correlation between photovoltaic and load fluctuations is then combined to obtain the four clustering days with the highest correlation.

[0073] Step 3: Establish a multi-objective optimization model for energy storage site selection and capacity determination, determine the objective function that optimizes the economic efficiency of the energy storage system, minimizes voltage fluctuation and load fluctuation, and considers the constraints of distribution network operation safety state, power flow state, energy storage system capacity, safe operation, and other constraints.

[0074] The multi-objective optimization model for energy storage location and capacity is shown in Equation (3), which includes minimizing the overall cost, voltage fluctuation and power fluctuation of the energy storage system.

[0075]

[0076] Where f1, f2, and f3 are the optimization space formed by the three objective functions, and w(x) and v(x) are the constraints of the functions, which constitute the feasible region for solving the functions.

[0077] The multi-objective optimization model and its constraints are as follows:

[0078] a. Comprehensive economic model of energy storage systems

[0079] The comprehensive investment cost of the energy storage system is shown in equation (4):

[0080] C Ess =C inv +C rep +C ps +C om +C scr -C res -C sub -C rst (4)

[0081] Where C Ess For the total cost, C inv For the initial investment cost, C rep To cover replacement costs, C ps For charging and discharging costs, C om For system operation and maintenance costs, C scr It is the cost of disposal, C res To recover residual value, C sub It is a government subsidy or allowance, C rst It delays the overall benefits of power grid upgrades.

[0082] The initial investment cost of the battery energy storage system is shown in equation (5):

[0083]

[0084] The initial investment cost of a battery energy storage system is related to the number, capacity, and power of the batteries installed. Where N... BESS This indicates the number of batteries installed, E BESS,m and P BESS,m Let represent the installed capacity and power of the m-th BESS, respectively; α and β represent the unit capacity cost and unit power cost of BESSs, respectively. This invention selects lithium-ion batteries with high overall performance as the energy storage element for BESSs, and the cost parameters are shown in Table 1.

[0085] Table 1 Initial Investment Cost Parameters of Battery Energy Storage System

[0086] Unit capacity cost (ten thousand yuan / MWh) 127 Unit power cost (ten thousand yuan / MW) 110

[0087] The replacement cost of the battery energy storage system is shown in equation (6):

[0088]

[0089] In the formula, η represents the discount rate for battery installation, T represents the service life of the battery energy storage system, n represents the number of battery cycles, and k represents the one-time payment coefficient. Specific parameters are shown in Table 2.

[0090] Table 2 Replacement Coefficients for Battery Energy Storage Systems

[0091] Battery energy storage system service life 10 Discount factor 6.333％

[0092] The charging and discharging cost of the battery energy storage system, i.e. the cost of purchasing and selling electricity, is shown in equation (7):

[0093]

[0094] In the formula, Rd represents the typical scenario of the distribution network analyzed by clustering in step two, Dr represents the number of days in a year for the r-th scenario, and h(t) is the price difference between purchasing and selling electricity in a day based on time-of-use pricing, as shown in Table 3.

[0095] Table 3 Electricity Purchase / Sale Prices

[0096] 00：00—08：00 0.17 0.13 08：00—11：00 0.49 0.38 11：00—16：00 0.83 0.65 16：00—19：00 0.49 0.38 9：00—22：00 0.83 0.65 22：00—24：00 0.49 0.38

[0097] The operation and maintenance cost of the battery energy storage system is shown in Equation (8), and is taken as 5% of the system cost.

[0098] C om =C inv ×5% (8)

[0099] The disposal cost of the battery energy storage system is shown in equation (9):

[0100] C scr =(α·E) pscr +β·E escr (9)

[0101] In the formula, E pscr With E escr These represent the disposal costs per unit of power and per unit of capacity, respectively. Where E... pscr The usual price is 465 yuan / kW.

[0102] The financial subsidies for installing battery energy storage systems are shown in formula (10):

[0103]

[0104] In the formula, σ represents the operating subsidy provided by the government to BESSs for the electricity sold, typically taken as 0.12 yuan / kWh; P dis(t) represents the power generation of the energy storage system.

[0105] In summary, the overall cost of a battery energy storage system can be expressed as:

[0106] f1 = C Ess =C inv +C rep +C ps +C om +C scr -C res -C sub -C rst (11)

[0107] b. Node voltage fluctuation model

[0108] The integration of energy storage systems can effectively mitigate the problem of node voltage fluctuations after photovoltaic systems are integrated.

[0109]

[0110] In the formula: N is the number of system nodes; V m (t) represents the voltage at node m at time t; Let m be the average voltage at node m;

[0111] c. Load Fluctuation Model

[0112] Similarly, the integration of energy storage systems can effectively suppress load fluctuations in the power distribution network and maintain power stability.

[0113] The load fluctuation model of the power distribution system is shown in equation (13):

[0114]

[0115]

[0116] In the formula, P load (t), P pv (t) and P cha / dis (t) represents the load power, photovoltaic output, and energy storage battery charging and discharging power during time period t, respectively. This represents the average value of the load curve.

[0117] d. Constraints

[0118] (1) Power balance constraint

[0119]

[0120] Where: n pv These represent the number of photovoltaic installations; P pv,m (t) represents the active power output of the m-th photovoltaic cell at time t; Pload (t) represents the total load at time t.

[0121] (2) Current constraints

[0122]

[0123] In the formula: P i (t), Q i (t) represent the active and reactive power injected into the node, respectively; V i (t) represents the voltage at node i at time t; V j (t) represents the voltage at node j at time t; G ij B ij This refers to the conductance and susceptance between nodes i and j; θ ij This refers to the phase difference between nodes i and j.

[0124] (3) Voltage constraint

[0125] V i min ≤V i ≤V i max (16)

[0126] In the formula, and These are the upper and lower voltage limits for node i, respectively.

[0127] (4) Constraints on the total active and reactive power injected into the distribution network from the upper-level power grid.

[0128]

[0129] In the formula, These represent the upper and lower limits of the active power (alyx) and reactive power of the tie line, respectively.

[0130] (5) Battery system charge and discharge constraints

[0131]

[0132] In the formula, P cha,i (t) and P dis,i (t) refers to the charging and discharging power of the i-th energy storage battery, η cha With η dis This refers to the battery's charge and discharge efficiency, P. BESS,i It is the rated power of the i-th battery.

[0133] (6) Battery SOC constraint

[0134] SOC min ≤SOC i (t)≤SOC max(19)

[0135] In the formula, SOC min With SOC max This refers to the maximum and minimum values ​​of the battery's charge level (state of charge).

[0136] (7) Battery installation location and number constraints

[0137]

[0138] In the formula L BESS,i This is the location where the battery is installed, N. BESS,min With N BESS,max This refers to the minimum and maximum number of batteries that can be installed. L grid This refers to the structure of the power grid, N nodes This refers to the number of nodes in the distribution network.

[0139] Step 4: This invention uses a typical IEEE 33-node distribution network system for simulation analysis, and its topology is as follows: Figure 2 As shown, the line parameters and network topology, system node voltage and branch current limits, and initial values ​​of system reference voltage and reference power are set in the IEEE 33 nodes. The quantized parameters of the energy storage system, typical daily load and photovoltaic curves, and preliminary site selection are input into the multi-objective optimization model for energy storage site selection and capacity determination. The improved greywolf optimization (IGWO) algorithm is used to solve the model to obtain the optimal solution set. The quantized parameters include unit price, battery energy storage system service life, discount rate, time-of-use electricity price, operation, and power constraints.

[0140] like Figure 3 As shown, the specific process of solving the model using the improved Grey Wolf algorithm is as follows:

[0141] 1) Enter the initial value;

[0142] 2) Set the relevant parameters for IGWO, such as population size N=30, maximum number of iterations Tmax=200, etc.

[0143] 3) Initialize the gray wolf population using Tent chaotic mapping;

[0144] 4) Determine if the number of iterations has been reached. If yes, skip to step 9); otherwise, calculate the fitness of the initial population, sort them according to their fitness, record the Pareto optimal solution, and assign the three best individuals to wolves α, β, and δ in turn.

[0145] 5) Update the weight coefficients and other parameters of the current population;

[0146] 6) Determine if the population size has been reached. If so, use the gray wolf algorithm to find the candidate wolves. Otherwise, modify step 4).

[0147] 7) Calculate the neighborhood of the candidate wolf, and then perform a cross-pollination of individuals within the neighborhood to select new candidate wolves;

[0148] 8) Compare the candidate wolves obtained by the gray wolf algorithm with the candidate wolves after the hybrid crossover, select the candidate wolves with high fitness to update the population and replace the external solution set;

[0149] 9) The algorithm ends, and the final solution set is obtained.

[0150] Step 5: Establish weights based on information entropy, dynamically update the solution set in Step 4, select the optimal solution using the TOPSIS method, determine the capacity configuration and site layout of the energy storage system, and achieve the best trade-off between the overall cost, voltage fluctuation and load fluctuation of the battery energy storage system.

[0151] This invention establishes an optimal addressing and capacity model for battery energy storage systems, determines the optimization objectives and solution algorithms, thereby reducing the economic investment of distributed energy storage systems and improving the power quality of distribution networks.

[0152] The above are merely preferred embodiments of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should be considered within the scope of protection of the present invention.

Claims

1. A method for site selection and capacity determination of a distributed energy storage system, characterized in that, Including the following steps: Acquire photovoltaic output data, generator output data, and load data of distribution network nodes, and perform cluster analysis to obtain typical daily load and photovoltaic curves of the distributed energy storage system; The impact of photovoltaic (PV) grid connection on the voltage of distribution network nodes is analyzed, the nodes are divided into zones, vulnerable node groups are identified, and preliminary site selection for energy storage system installation is obtained. A multi-objective optimization model for energy storage site selection and capacity determination was established. The objective function was determined to optimize the economic efficiency of the energy storage system and minimize voltage and load fluctuations. The constraints were also determined. Using a typical distribution network IEEE 33-node model, line parameters, load levels, network topology connections, system node voltages, and branch currents are input respectively. The quantitative parameters of the energy storage system, typical daily load and photovoltaic curves, and preliminary site selection are input into the multi-objective optimization model for energy storage site selection and capacity determination. An improved Grey Wolf algorithm is used to solve the multi-objective optimization model to obtain the optimal solution set. Analyze the optimal solution set to obtain the optimal energy storage location and configuration capacity of the energy storage system; The specific process of solving the multi-objective optimization model using the improved Grey Wolf algorithm is as follows: 1) Enter the initial value; 2) Set the relevant parameters for the improved Grey Wolf algorithm; 3) Initialize the gray wolf population using Tent chaotic mapping; 4) Determine if the iteration count has been reached; if so, skip to step 9); otherwise, calculate the fitness of the initial population, sort them according to their fitness, record the Pareto optimal solution, and assign the three best individuals to the wolves in turn. α , β , δ ; 5) Update the weight coefficient parameters of the current population; 6) Determine if the population size has been reached. If so, use the gray wolf algorithm to find candidate wolves; otherwise, modify step 4). 7) Calculate the neighborhood of each candidate wolf, and then perform cross-pollination on the individuals within that neighborhood to select new candidate wolves; 8) Compare the candidate wolves obtained by the gray wolf algorithm with the candidate wolves after the hybrid crossover, select the candidate wolves with high fitness to update the population and replace the external solution set; 9) End, obtaining the final optimal solution set; During the initial site selection, sensitivity analysis of the voltage of the distribution network nodes is performed and the Pearson correlation coefficient between each node is calculated. The most vulnerable node group is obtained through comprehensive analysis, and the site selection and capacity determination analysis is performed on this node group to install the energy storage system at the node with weak system voltage stability. The sensitivity index is as follows: in: P i , Q i For nodes i Active power and reactive power; ΔP i , ΔQ i For nodes i The changes in active power and reactive power; U A set of power grid nodes; j For any power grid node within the set of power grid nodes; V j For nodes j Node voltage; The Pearson correlation coefficients between the nodes are: in V i For nodes i Node voltage; This represents the average voltage at each node.

2. The method for site selection and capacity determination of a distributed energy storage system according to claim 1, characterized in that, The multi-objective optimization model for energy storage site selection and capacity determination is as follows: In the formula: f 1. f 2. f 3 is the optimization space formed by the three objective functions, w( x ) and v( x The constraints of the function are denoted as , which together constitute the feasible region for solving the function.

3. The method for site selection and capacity determination of a distributed energy storage system according to claim 2, characterized in that, The multi-objective optimization model for energy storage site selection and capacity determination also includes a comprehensive economic model for the energy storage system, a node voltage fluctuation model, and a load fluctuation model; among which, the comprehensive economic model for the energy storage system is: in C Ess For total cost, C inv For initial investment costs, C rep To replace the cost, C ps For charging and discharging costs, C om For system operation and maintenance costs, C scr It is the cost of scrapping and disposal. C res In order to recover residual value, C sub It is a government subsidy or allowance. C rst It delays the overall benefits of power grid upgrades.

4. The method for site selection and capacity determination of a distributed energy storage system according to claim 3, characterized in that, The constraints include power balance constraints, power flow constraints, voltage constraints, total active and reactive power limits injected into the distribution network from the upstream grid, battery system charge and discharge constraints, battery SOC constraints, and constraints on battery installation location and number.

5. The method for site selection and capacity determination of a distributed energy storage system according to any one of claims 1-4, characterized in that, The quantitative parameters include unit price, energy storage system service life, discount rate, and time-of-use electricity price.

6. A location and capacity determination system for a distributed energy storage system, comprising an interconnected memory and a processor, wherein the memory stores a computer program, characterized in that, The computer program, when run by a processor, performs the steps of the method as described in any one of claims 1 to 5.

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