Optical storage equipment layout optimization method and system

Abstract

The invention relates to the field of optical storage equipment management, in particular to an optical storage equipment layout optimization method and system, and the method comprises the steps: defining a target function group, defining a constraint condition group, and obtaining an optimal layout scheme through employing an improved multi-target particle swarm algorithm. According to the method, target functions of minimizing economic cost, minimizing demand fluctuation, minimizing voltage deviation and maximizing carbon emission reduction are defined, and unification of economic benefits, power grid quality and environmental protection is realized; the invention provides a comprehensive layout optimization method, which not only brings into the layout position and power configuration of photovoltaic equipment, but also fully considers the integration of charging and discharging strategies of an energy storage system; the improved multi-target particle swarm optimization algorithm is adopted, the diversity and quality of the solution are improved by using the strategy of optimizing the Pareto optimal solution set by using the degree of congestion, and the problem of easy falling into a local optimal solution when the large-scale layout optimization problem of the optical storage system is solved is improved by improving the position updating algorithm of the particles.

Description

Technical Field

[0001] The present invention relates to the field of optical storage equipment management, and in particular to an optical storage equipment layout optimization method and system. Background Art

[0002] Photovoltaic storage equipment refers to photovoltaic energy storage systems, which are composed of photovoltaic power generation equipment and energy storage devices. Photovoltaic power generation equipment converts solar energy into electrical energy through solar panels, while energy storage devices are used to store this part of electrical energy for use when needed. Photovoltaic storage equipment combines photovoltaic power generation with energy storage systems to achieve efficient storage and regulation of electrical energy. On this basis, the optimization of the layout of photovoltaic storage equipment has become a key link in improving the performance and economic benefits of photovoltaic power generation systems. However, the current photovoltaic storage equipment layout optimization method and system have the problem that when seeking the optimal layout solution through a multi-objective optimization algorithm, only economic costs are considered as the objective function, while grid stability and environmental protection are ignored, thereby affecting the overall benefits and sustainable development of the grid. There is a problem that the multi-objective optimization algorithm focuses on the configuration of photovoltaic equipment but fails to fully consider the role and influence of the energy storage system, resulting in the grid being difficult to cope with flexible changes in energy demand. There are multiple objective functions of the photovoltaic storage system layout optimization problem that conflict with each other and the complexity of the photovoltaic storage system topology, resulting in insufficient diversity of solutions of the multi-objective optimization algorithm and easy to fall into the local optimal solution. Summary of the invention

[0003] In view of the above situation, in order to overcome the defects of the prior art, the present invention provides a method and system for optimizing the layout of photovoltaic storage equipment. In view of the problem that when seeking the optimal layout solution through a multi-objective optimization algorithm, only the economic cost is considered as the objective function while the stability of the power grid and environmental protection are ignored, thereby affecting the overall benefits and sustainable development of the power grid, the present invention, on the basis of minimizing the economic cost as the objective function, additionally designs the objective functions of minimizing demand fluctuations, minimizing voltage deviations and maximizing carbon emission reduction in the multi-objective optimization algorithm, thereby achieving a balance between the stability of the optimal layout result of the photovoltaic storage system, environmental protection and economic costs; the multi-objective optimization algorithm focuses on the configuration of photovoltaic equipment but fails to fully consider the role and influence of the energy storage system, resulting in the difficulty of the power grid to cope with flexible changes in energy demand. In order to solve the problem, the present invention proposes a comprehensive photovoltaic storage equipment layout optimization method, which not only incorporates the layout position and power configuration of photovoltaic equipment, but also fully considers the integration of charging and discharging strategies of energy storage system, so as to ensure the continuity and reliability of power supply. In view of the conflict between multiple objective functions of photovoltaic storage system layout optimization problem and the complexity of photovoltaic storage system topology, which leads to the problem that the multi-objective optimization algorithm has insufficient diversity of solutions and is easy to fall into local optimal solutions, the present invention adopts an improved multi-objective particle swarm algorithm. Firstly, the strategy of optimizing Pareto frontier by congestion is used to improve the diversity and quality of layout solutions. Secondly, the problem that the multi-objective particle swarm algorithm is easy to fall into local optimal solutions when solving large-scale layout optimization problems of photovoltaic storage system is improved by improving the particle position update algorithm.

[0004] The technical solution adopted by the present invention is as follows: The present invention provides a method for optimizing the layout of a photovoltaic storage device, which is applied to a power network including photovoltaic equipment, an energy storage system and a grid-connected access point. The specific steps are as follows:

[0005] Step S1: defining an objective function group, wherein the objective function group specifically includes an objective function of minimizing economic cost, an objective function of minimizing demand fluctuation, an objective function of minimizing voltage deviation, and an objective function of maximizing carbon emission reduction;

[0006] Step S2: defining a constraint condition group, wherein the constraint condition group specifically includes a photovoltaic device quantity constraint condition, a voltage constraint condition, and a power constraint condition;

[0007] Step S3: Use the improved multi-objective particle swarm algorithm to obtain the optimal layout solution.

[0008] The objective function of minimizing economic cost is the sum of equipment construction cost, electricity purchase cost and layout cost. The calculation formula is as follows:

[0009] min C=C1+C2+C3;

[0010] Among them, min C represents the objective function of minimizing economic cost, C1 represents the equipment construction cost, C2 represents the electricity purchase cost, and C3 represents the layout cost.

[0011] The calculation formula of the equipment construction cost is as follows:

[0012]

[0013] Where N is the total number of photovoltaic devices, μ i represents the cost coefficient of the ith photovoltaic device, R i represents the service life of the i-th photovoltaic equipment, r is the depreciation rate, B i represents the construction cost of the i-th photovoltaic device.

[0014] The calculation formula of the electricity purchase cost is as follows:

[0015]

[0016] Where M represents the total number of nodes in the power network, T represents the maximum cycle, τ represents the electricity purchase price, represents the load power of the kth node in the tth time period, represents the photovoltaic output power of the kth node in the tth time period, Represents the charging and discharging power of the energy storage system at the kth node in the tth time period.

[0017] The calculation formula of the layout cost is as follows:

[0018]

[0019] Among them, n k represents the number of photovoltaic devices at the kth node, d k represents the distance from the kth node to the nearest grid access point, represents the cable transmission cost from the photovoltaic device of the kth node to the grid access point, ω k It represents the cost of laying cables for the photovoltaic device at the kth node.

[0020] The calculation formula of the objective function of minimizing demand fluctuation is specifically as follows:

[0021]

[0022] Among them, minη represents the objective function of minimizing demand fluctuation.

[0023] The calculation formula of the voltage deviation minimization objective function is as follows:

[0024]

[0025] Where minδ represents the objective function of minimizing voltage deviation, U k , t represents the voltage of the kth node in the tth time period, U N Indicates rated voltage.

[0026] The calculation formula of the maximum carbon emission reduction objective function is as follows:

[0027]

[0028] Among them, max CR represents the maximum carbon emission reduction objective function, θ k represents the carbon emission factor of the kth node. The specific formula is as follows:

[0029]

[0030]

[0031] Among them, E k represents the carbon emission of the kth node, θ′ represents the carbon emission factor of the upper power grid, represents the output power of the traditional generator of the kth node in the tth time period, E′ represents the carbon emissions of the parent power grid, and P′ represents the total output power of the generator of the parent power grid.

[0032] The formula of the photovoltaic equipment quantity constraint condition is as follows:

[0033]

[0034] in, Indicates the maximum number of photovoltaic devices at the kth node.

[0035] The formula of the voltage constraint condition is as follows:

[0036]

[0037] in, and represent the lower limit and upper limit of the voltage of the kth node respectively.

[0038] The formula of the power constraint condition is as follows:

[0039]

[0040]

[0041] in, and They represent the photovoltaic output power of the ith photovoltaic device and the charging and discharging power of the ith energy storage system in the tth time period, and They represent the lower and upper limits of the output power of the ith photovoltaic device, and They respectively represent the lower limit and upper limit of the output power of the i-th energy storage system.

[0042] The specific process of obtaining the optimal layout solution by using the improved multi-objective particle swarm algorithm in step S3 is as follows:

[0043] Step S31: constructing a topological structure vector of the power network, wherein each element in the topological structure vector includes the number of photovoltaic devices corresponding to each node in the power network, the photovoltaic output power in each time period, and the charging and discharging power of the energy storage system in each time period;

[0044] Step S32: Taking the minimization of economic cost objective function, minimization of demand fluctuation objective function, minimization of voltage deviation objective function and maximum carbon emission reduction objective function as objective functions, taking each topological structure vector as a solution, and using the improved multi-objective particle swarm algorithm to obtain the Pareto optimal solution set. The specific improvements are optimizing the Pareto optimal solution set through congestion and the improved particle position update rule.

[0045] The steps of optimizing the Pareto optimal solution set by congestion are specifically as follows:

[0046] Step D1: Obtain the particles in the highest Pareto level in the multi-objective particle swarm algorithm, and sort them respectively on the objective function of minimizing economic cost, the objective function of minimizing demand fluctuation, the objective function of minimizing voltage deviation, and the objective function of maximizing carbon emission reduction;

[0047] Step D2: Calculate the crowding degree of each particle and perform normalization. The specific formula of crowding degree is as follows:

[0048]

[0049] Among them, ρ j represents the crowding degree of the jth particle, A is the number of objective functions, and f a [j+1] is the ath objective function value of the j+1th particle, f a [j-1] is the ath objective function value of the j-1th particle, and are the maximum and minimum values ​​of the ath objective function respectively;

[0050] The normalized calculation formula is as follows:

[0051]

[0052] Among them, ρ′ j represents the normalized ρ j , V represents the total number of particles, ρ o represents the oth particle;

[0053] Step D3: sort the particles according to the congestion degree in terms of minimizing the economic cost objective function, minimizing the demand fluctuation objective function, minimizing the voltage deviation objective function, and maximizing the carbon emission reduction objective function;

[0054] Step D4: Sort the particles in each Pareto level by crowding degree.

[0055] The improved particle position update rule updates the particle position by randomly moving in the search space in each iteration according to the distance between the average value of the individual optimal solution of the particle in all iterations and the current position, combined with the individual optimal solution in the previous iteration. The calculation formula is as follows:

[0056]

[0057] in, is the position of the jth particle in the q+1th iteration, is the position of the jth particle in the qth iteration, represents the individual optimal solution of the jth particle in the qth iteration, rand represents a random number in the interval (0, 1), mean(Pbest j ) represents the average value of the individual optimal solution of the jth particle in all iterations.

[0058] The present invention provides a photovoltaic storage device layout optimization system, which is used to implement a photovoltaic storage device layout optimization method, and specifically includes a multi-objective optimization module, an optimal layout solution module and a user interaction module; the output end of the multi-objective optimization module is connected to the input end of the optimal layout solution module, and the output end of the optimal layout solution module is connected to the input end of the user interaction module;

[0059] The multi-objective optimization module constructs a multi-objective particle swarm optimization model with minimizing economic cost objective function, minimizing demand fluctuation objective function, minimizing voltage deviation objective function and maximizing carbon emission reduction objective function as objective functions, and with photovoltaic equipment quantity constraint condition, voltage constraint condition and power constraint condition as constraint conditions;

[0060] The optimal layout solution module obtains a Pareto optimal solution set and specific values ​​of each optimal solution in each objective function according to a multi-objective particle swarm optimization model;

[0061] The user interaction module maps each optimal solution to the topological structure of the power network, and displays the distribution of photovoltaic storage devices, the photovoltaic output power in each time period, the output power of the energy storage system in each time period, and the specific values ​​of each objective function.

[0062] The beneficial results achieved by the present invention using the above scheme are as follows:

[0063] (1) In order to solve the problem that when seeking the optimal layout solution through a multi-objective optimization algorithm, only the economic cost is considered as the objective function while the grid stability and environmental protection are ignored, thus affecting the overall benefits and sustainable development of the grid, the present invention, on the basis of minimizing the economic cost as the objective function, additionally designs the objective functions of minimizing demand fluctuation, minimizing voltage deviation and maximizing carbon emission reduction in the multi-objective optimization algorithm, thereby achieving a balance between the stability of the optimal layout result of the photovoltaic storage system, environmental protection and economic cost.

[0064] (2) In view of the problem that the multi-objective optimization algorithm focuses on the configuration of photovoltaic equipment but fails to fully consider the role and impact of the energy storage system, making it difficult for the power grid to cope with flexible changes in energy demand, the present invention proposes a comprehensive photovoltaic and energy storage equipment layout optimization method, which not only incorporates the layout location and power configuration of photovoltaic equipment, but also fully considers the integration of the charging and discharging strategy of the energy storage system, ensuring the continuity and reliability of power supply.

[0065] (3) In view of the conflicting multiple objective functions of the photovoltaic storage system layout optimization problem and the complexity of the photovoltaic storage system topological structure, which leads to the problem that the solution diversity of the multi-objective optimization algorithm is insufficient and it is easy to fall into the local optimal solution, the present invention adopts an improved multi-objective particle swarm algorithm. First, the strategy of optimizing the Pareto frontier by congestion is used to improve the diversity and quality of layout solutions. Second, by improving the particle position update algorithm, the problem that the multi-objective particle swarm algorithm is easy to fall into the local optimal solution when solving the large-scale layout optimization problem of photovoltaic storage system is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 A flow chart of a method for optimizing the layout of optical storage equipment proposed by the present invention;

[0067] Figure 2 A flow chart of optimizing the Pareto optimal solution set by congestion proposed by the present invention;

[0068] Figure 3 This is a framework diagram of a system for optimizing the layout of optical storage equipment proposed in the present invention.

[0069] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention. DETAILED DESCRIPTION

[0070] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0071] Example 1, see Figure 1 , Figure 1 This is a flow chart of a method for optimizing the layout of an optical storage device proposed by the present invention. In this embodiment, a method for optimizing the layout of an optical storage device is proposed, and the specific contents are as follows:

[0072] A method for optimizing the layout of photovoltaic storage equipment is applied to a power network including photovoltaic equipment, energy storage system and grid access point. The specific steps are as follows:

[0073] Step S1: defining an objective function group, wherein the objective function group specifically includes an objective function of minimizing economic cost, an objective function of minimizing demand fluctuation, an objective function of minimizing voltage deviation, and an objective function of maximizing carbon emission reduction;

[0074] Step S2: defining a constraint condition group, wherein the constraint condition group specifically includes a photovoltaic device quantity constraint condition, a voltage constraint condition, and a power constraint condition;

[0075] Step S3: Use the improved multi-objective particle swarm algorithm to obtain the optimal layout solution.

[0076] This embodiment specifically distributes the photovoltaic storage system for a power network containing 32 nodes. The first node of the power network is connected to the upper power grid, and the data in the power network is collected every hour.

[0077] Embodiment 2: Based on the above embodiment, this embodiment implements the definition of the objective function group, and the specific contents are as follows:

[0078] The objective function of minimizing economic cost is the sum of equipment construction cost, electricity purchase cost and layout cost. The calculation formula is as follows:

[0079] min C=C1+C2+C3;

[0080] Among them, min C represents the objective function of minimizing economic cost, C1 represents the equipment construction cost, C2 represents the electricity purchase cost, and C3 represents the layout cost.

[0081] The calculation formula of the equipment construction cost is as follows:

[0082]

[0083] Where N is the total number of photovoltaic devices, μ i represents the cost coefficient of the ith photovoltaic device, R i represents the service life of the i-th photovoltaic equipment, r is the depreciation rate, B i represents the construction cost of the ith photovoltaic device. In this embodiment, the cost coefficient of the photovoltaic device is defined as 0.95, and the depreciation rate is defined as 0.045.

[0084] The calculation formula of the electricity purchase cost is as follows:

[0085]

[0086] Where M represents the total number of nodes in the power network, T represents the maximum cycle, τ represents the electricity purchase price, represents the load power of the kth node in the tth time period, represents the photovoltaic output power of the kth node in the tth time period, It represents the charging and discharging power of the energy storage system of the kth node in the tth time period. In this embodiment, the maximum cycle is defined as 24, and the power purchase price is defined as 0.5.

[0087] The calculation formula of the layout cost is as follows:

[0088]

[0089] Among them, n k represents the number of photovoltaic devices at the kth node, d k represents the distance from the kth node to the grid access point, represents the cable transmission cost from the photovoltaic device of the kth node to the grid access point, ω k It represents the cost of laying cables for the photovoltaic device at the kth node.

[0090] The calculation formula of the objective function of minimizing demand fluctuation is specifically as follows:

[0091]

[0092] Among them, minη represents the objective function of minimizing demand fluctuation.

[0093] The calculation formula of the voltage deviation minimization objective function is as follows:

[0094]

[0095] Where minδ represents the objective function of minimizing voltage deviation, Uk，t represents the voltage of the kth node in the tth time period, U N Indicates rated voltage.

[0096] The calculation formula of the maximum carbon emission reduction objective function is as follows:

[0097]

[0098] Among them, max CR represents the maximum carbon emission reduction objective function, θ k represents the carbon emission factor of the kth node. The specific formula is as follows:

[0099]

[0100]

[0101] Among them, E k represents the carbon emission of the kth node, θ′ represents the carbon emission factor of the upper power grid, It represents the output power of the traditional generator of the kth node in the tth time period, E′ represents the carbon emissions of the parent power grid, and P′ represents the total output power of the generator of the parent power grid. The traditional generator is a device that uses conventional energy to generate electricity.

[0102] By executing the operations, in order to solve the problem that when seeking the optimal layout solution through a multi-objective optimization algorithm, only the economic cost is considered as the objective function while the grid stability and environmental protection are ignored, thereby affecting the overall benefits and sustainable development of the grid, on the basis of minimizing the economic cost as the objective function, additional objective functions of minimizing demand fluctuations, minimizing voltage deviations and maximizing carbon emission reduction are designed in the multi-objective optimization algorithm, thereby achieving a balance between the stability of the optimal layout result of the photovoltaic storage system, environmental protection and economic costs; in order to solve the problem that the multi-objective optimization algorithm focuses on the configuration of photovoltaic equipment but fails to fully consider the role and influence of the energy storage system, resulting in the grid being difficult to cope with flexible changes in energy demand, the layout location and power configuration of the photovoltaic equipment are incorporated, and the integration of the charging and discharging strategies of the energy storage system is fully considered to ensure the continuity and reliability of power supply.

[0103] Embodiment 3: Based on the above embodiment, this embodiment implements the definition of a constraint condition group, and the specific contents are as follows:

[0104] The formula of the photovoltaic equipment quantity constraint condition is as follows:

[0105]

[0106] in, Indicates the maximum number of photovoltaic devices at the kth node.

[0107] The formula of the voltage constraint condition is as follows:

[0108]

[0109] in, and represent the lower limit and upper limit of the voltage of the kth node respectively.

[0110] The formula of the power constraint condition is as follows:

[0111]

[0112]

[0113] in, and They represent the photovoltaic output power of the ith photovoltaic device and the charging and discharging power of the ith energy storage system in the tth time period, and They represent the lower and upper limits of the output power of the ith photovoltaic device, and They respectively represent the lower limit and upper limit of the charging and discharging power of the i-th energy storage system.

[0114] Example 4, see Figure 2 , Figure 2 This is a flowchart of optimizing the Pareto optimal solution set by congestion proposed by the present invention. Based on the above embodiments, this embodiment uses an improved multi-objective particle swarm algorithm to obtain the optimal layout solution. The specific contents are as follows:

[0115] The specific process of obtaining the optimal layout solution by using the improved multi-objective particle swarm algorithm in step S3 is as follows:

[0116] Step S31: constructing a topological structure vector of the power network, wherein each element in the topological structure vector includes the number of photovoltaic devices corresponding to each node in the power network, the photovoltaic output power in each time period, and the charging and discharging power of the energy storage system in each time period;

[0117] Step S32: Taking the minimization of economic cost objective function, minimization of demand fluctuation objective function, minimization of voltage deviation objective function and maximum carbon emission reduction objective function as objective functions, taking each topological structure vector as a solution, and using the improved multi-objective particle swarm algorithm to obtain the Pareto optimal solution set. The specific improvements are optimizing the Pareto optimal solution set through congestion and the improved particle position update rule.

[0118] In this embodiment, the population size of the multi-objective particle swarm is set to 150, the maximum number of iterations is set to 800, the inertia weight is set to 0.4, the learning factor cl is set to 1.5, and the learning factor c2 is set to 2.

[0119] The steps of optimizing the Pareto optimal solution set by congestion are specifically as follows:

[0120] Step D1: Obtain the particles in the highest Pareto level in the multi-objective particle swarm algorithm, and sort them respectively on the objective function of minimizing economic cost, the objective function of minimizing demand fluctuation, the objective function of minimizing voltage deviation, and the objective function of maximizing carbon emission reduction;

[0121] Step D2: Calculate the crowding degree of each particle and perform normalization. The specific formula of crowding degree is as follows:

[0122]

[0123] Among them, ρ j represents the crowding degree of the jth particle, A is the number of objective functions, and f a [j+1] is the ath objective function value of the j+1th particle, f a [j-1] is the ath objective function value of the j-1th particle, and are the maximum and minimum values ​​of the ath objective function respectively;

[0124] The normalized calculation formula is as follows:

[0125]

[0126] Among them, ρ′ j represents the normalized ρ j , V represents the total number of particles, ρ o represents the oth particle;

[0127] Step D3: sort the particles according to the congestion degree in terms of minimizing the economic cost objective function, minimizing the demand fluctuation objective function, minimizing the voltage deviation objective function, and maximizing the carbon emission reduction objective function;

[0128] Step D4: Sort the particles in each Pareto level by crowding degree.

[0129] The improved particle position update rule is to randomly move the particle in the search space to update the particle position in each iteration according to the distance between the average value of the individual optimal solution of the particle in all iterations and the current position, and to combine the individual optimal solution in the previous iteration. The calculation formula is as follows:

[0130]

[0131] in, is the position of the jth particle in the q+1th iteration, is the position of the jth particle in the qth iteration, represents the individual optimal solution of the jth particle in the qth iteration, rand represents a random number in the interval (0, 1), mean(Pbest j ) represents the average value of the individual optimal solution of the jth particle in all iterations.

[0132] By executing the operations, an improved multi-objective particle swarm algorithm is adopted to address the problem that the solutions of the multi-objective optimization algorithm are insufficiently diverse and easily fall into the local optimal solution due to the conflicts among multiple objective functions of the photovoltaic storage system layout optimization problem and the complexity of the photovoltaic storage system topological structure. Firstly, the strategy of optimizing the Pareto frontier by congestion is utilized to improve the diversity and quality of layout solutions. Secondly, the problem that the multi-objective particle swarm algorithm is easily trapped in the local optimal solution when solving the large-scale layout optimization problem of the photovoltaic storage system is improved by improving the particle position update algorithm.

[0133] Example 5, see Figure 3 , Figure 3 This is a framework diagram of a system for optimizing the layout of optical storage equipment proposed by the present invention. This embodiment implements a system for optimizing the layout of optical storage equipment based on the above embodiments. The specific contents are as follows:

[0134] The present invention provides a photovoltaic storage device layout optimization system, which is used to implement a photovoltaic storage device layout optimization method, and specifically includes a multi-objective optimization module, an optimal layout solution module and a user interaction module; the output end of the multi-objective optimization module is connected to the input end of the optimal layout solution module, and the output end of the optimal layout solution module is connected to the input end of the user interaction module;

[0135] The multi-objective optimization module constructs a multi-objective particle swarm optimization model with minimizing economic cost objective function, minimizing demand fluctuation objective function, minimizing voltage deviation objective function and maximizing carbon emission reduction objective function as objective functions, and with photovoltaic equipment quantity constraint condition, voltage constraint condition and power constraint condition as constraint conditions;

[0136] The optimal layout solution module obtains a Pareto optimal solution set and specific values ​​of each optimal solution in each objective function according to a multi-objective particle swarm optimization model;

[0137] The user interaction module maps each optimal solution to the topological structure of the power network, and displays the distribution of photovoltaic storage devices, the photovoltaic output power in each time period, the charging and discharging power of the energy storage system in each time period, and the specific values ​​of each objective function.

Claims

1. A method for optimizing the layout of photovoltaic and energy storage equipment, applied to a power network including photovoltaic equipment, energy storage system and grid-connected access points, characterized in that: The following steps are involved: Step S1: defining an objective function group, wherein the objective function group specifically includes an objective function of minimizing economic cost, an objective function of minimizing demand fluctuation, an objective function of minimizing voltage deviation, and an objective function of maximizing carbon emission reduction; Step S2: defining a constraint condition group, wherein the constraint condition group specifically includes a photovoltaic device quantity constraint condition, a voltage constraint condition, and a power constraint condition; Step S3: Using the improved multi-objective particle swarm algorithm to obtain the optimal layout solution. The specific process is as follows: Step S31: constructing a topological structure vector of the power network, wherein each element in the topological structure vector includes the number of photovoltaic devices corresponding to each node in the power network, the photovoltaic output power in each time period, and the charging and discharging power of the energy storage system in each time period; Step S32: taking each topological structure vector as a solution, and using the improved multi-objective particle swarm algorithm to obtain the Pareto optimal solution set, the improvement specifically includes optimizing the Pareto optimal solution set by the congestion degree and improving the particle position update rule.

2. The optical storage device layout optimization method according to claim 1, characterized in that: The objective function of minimizing economic cost is the sum of equipment construction cost, electricity purchase cost and layout cost. The calculation formula is as follows: min C=C1+C2+C3; Among them, min C represents the objective function of minimizing economic cost, C1 represents the equipment construction cost, C2 represents the electricity purchase cost, and C3 represents the layout cost; The calculation formula of the equipment construction cost is as follows: Where N is the total number of photovoltaic devices, μ i represents the cost coefficient of the ith photovoltaic device, R i represents the service life of the i-th photovoltaic equipment, r is the depreciation rate, B i represents the construction cost of the i-th photovoltaic device; The calculation formula of the electricity purchase cost is as follows: Where M represents the total number of nodes in the power network, T represents the maximum cycle, τ represents the electricity purchase price, represents the load power of the kth node in the tth time period, represents the photovoltaic output power of the kth node in the tth time period, represents the charging and discharging power of the energy storage system at the kth node in the tth time period; The calculation formula of the layout cost is as follows: Among them, n k represents the number of photovoltaic devices at the kth node, d k represents the distance from the kth node to the grid access point, represents the cable transmission cost from the photovoltaic device of the kth node to the grid access point, ω k It represents the cost of laying cables for the photovoltaic device at the kth node.

3. The optical storage device layout optimization method according to claim 1, characterized in that: The calculation formula of the objective function of minimizing demand fluctuation is as follows: Among them, minη represents the objective function of minimizing demand fluctuation.

4. The optical storage device layout optimization method according to claim 1, characterized in that: The calculation formula of the voltage deviation minimization objective function is as follows: Where minδ represents the objective function of minimizing voltage deviation, U k,t represents the voltage of the kth node in the tth time period, U N Indicates rated voltage.

5. The method for optimizing the layout of optical storage devices according to claim 1, characterized in that: The calculation formula of the maximum carbon emission reduction objective function is as follows: Among them, max CR represents the maximum carbon emission reduction objective function, θ k represents the carbon emission factor of the kth node. The specific formula is as follows: Among them, E k represents the carbon emission of the kth node, θ' represents the carbon emission factor of the parent power grid, represents the output power of the traditional generator of the kth node in the tth time period, E' represents the carbon emissions of the parent power grid, and P' represents the total output power of the generator of the parent power grid.

6. The method for optimizing the layout of optical storage devices according to claim 1, characterized in that: The specific formula of the improved particle position update rule is as follows: in, is the position of the i-th particle in the q+1-th iteration, is the position of the i-th particle in the q-th iteration, represents the individual optimal solution of the i-th particle in the q-th iteration, rand represents a random number in the interval (0,1), mean(Pbest i ) represents the average value of the individual optimal solution of the i-th particle in all iterations.

7. A system for optimizing the layout of a photovoltaic storage device, used to implement a method for optimizing the layout of a photovoltaic storage device according to any one of claims 1 to 6, characterized in that: It includes multi-objective optimization module, optimal layout solution module and user interaction module; The multi-objective optimization module constructs a multi-objective particle swarm optimization model with minimizing economic cost objective function, minimizing demand fluctuation objective function, minimizing voltage deviation objective function and maximizing carbon emission reduction objective function as objective functions, and with photovoltaic equipment quantity constraint condition, voltage constraint condition and power constraint condition as constraint conditions; The optimal layout solution module obtains a Pareto optimal solution set and specific values ​​of each optimal solution in each objective function according to a multi-objective particle swarm optimization model; The user interaction module maps each optimal solution to the topological structure of the power network, and displays the distribution of photovoltaic storage devices, the photovoltaic output power in each time period, the charging and discharging power of the energy storage system in each time period, and the specific values ​​of each objective function.

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