Power distribution network reconstruction planning method considering intelligent soft switching and demand response

Abstract

The invention discloses a power distribution network reconstruction planning method considering an intelligent soft switch and demand response, and the method comprises the steps: replacing a conventional tie line switch with the intelligent soft switch, and constructing a multi-objective optimization model through combining renewable energy output, load data and demand response. Aiming at the model, an improved arithmetic optimization algorithm is provided, diversified initial candidate solutions are generated through chaotic mapping and a reverse learning strategy, exploration and development stages are adaptively determined by utilizing a mathematical accelerator function, global search and local fine optimization are performed on the candidate solutions in combination with division, multiplication and addition and subtraction strategies, and the optimal solution is obtained. And meanwhile, applying Cauchy variation to the globally optimal candidate solution to enhance the capability of jumping out of local optimum, and obtaining an optimal power distribution network reconstruction planning solution. And optimizing and adjusting the topological structure of the power distribution network and the configuration of the intelligent soft switch according to the obtained planning solution. The method can significantly improve the voltage stability of the power distribution network, reduce the planning cost, and optimize the economic benefits of the system.

Description

Technical Field

[0001] This invention belongs to the field of distribution network planning technology, and specifically relates to a distribution network reconfiguration planning method that considers smart soft switching and demand response. Background Technology

[0002] Large-scale renewable energy sources such as wind power and solar power are gradually being integrated into distribution networks. While this trend is improving the cleanliness of the energy structure, it also brings significant problems such as voltage fluctuations, increased line losses, and power imbalances, posing fundamental challenges to the planning, design, operation control, and safety of traditional distribution networks. Therefore, under the new circumstances of high-proportion renewable energy integration, traditional distribution networks can no longer meet the requirements for safe, economical, and reliable operation, and urgently need to be redesigned and optimized.

[0003] Network reconfiguration, as a crucial tool for distribution network planning and operation, can optimize power distribution and improve power supply reliability by adjusting feeder structure and switch states. Existing network reconfiguration methods mainly include heuristic rules, intelligent optimization algorithms, and traditional mathematical optimization methods. While these methods can improve the operational performance of distribution networks to some extent, most only address a single optimization objective, and their solution efficiency and applicability remain insufficient, making it difficult to comprehensively address the complex problems brought about by the integration of high proportions of renewable energy.

[0004] For example, in the prior art, Chinese patent CN111682585A discloses a comprehensive planning method and system for intelligent energy storage soft switching in distribution networks. The method includes the following steps: 1) collecting relevant parameters of the distribution system to be planned; 2) establishing an intelligent energy storage soft switching planning model; 3) for a given typical operating scenario of the distribution network and relevant parameters of the distribution system, using a hybrid algorithm combining second-order cone programming and simulated annealing to solve the intelligent energy storage soft switching coordination planning model, and obtaining the optimal intelligent energy storage soft switching planning scheme.

[0005] However, this method has the following limitations: 1. Existing methods mainly focus on the single configuration optimization of smart energy storage and soft switching, without simultaneously considering multiple objectives such as distribution network stability, economy, and renewable energy utilization, thus failing to achieve comprehensive and coordinated optimization. 2. Existing hybrid algorithms rely on a combination of second-order cone programming and simulated annealing, making them susceptible to the influence of the initial solution. The solution process may get trapped in local optima, and computational efficiency is low in large-scale distribution network optimization.

[0006] Therefore, there is an urgent need for a reconfiguration planning method that can comprehensively consider intelligent soft-switching configuration, demand response strategies, multi-objective optimization, and the dynamic operating characteristics of the distribution network. Summary of the Invention

[0007] The purpose of this invention is to overcome the shortcomings of the existing technology and provide a distribution network reconfiguration planning method that considers intelligent soft switching and demand response.

[0008] The objective of this invention can be achieved through the following technical solutions: This invention provides a distribution network reconfiguration planning method considering smart soft switching and demand response, comprising the following steps: replacing traditional distribution network tie-line switches with smart soft switches; based on the replaced smart soft switches, and combining typical renewable energy output scenarios, load data, and demand response, constructing a multi-objective optimization model, wherein the multi-objective optimization model includes multiple optimization objective functions and a set of constraint functions; solving the multi-objective optimization model using an improved arithmetic optimization algorithm to obtain the optimal distribution network reconfiguration planning solution; the distribution network reconfiguration planning solution includes the distribution network topology, smart soft switch configuration, and energy storage configuration; and realizing the reconfiguration planning of the distribution network based on the obtained optimal distribution network reconfiguration planning solution.

[0009] Furthermore, the intelligent soft switch SOP replaces the traditional distribution network tie line switch with a voltage source converter or other power electronic topology to realize intelligent control and dynamic adjustment of the distribution network; The intelligent soft switch satisfies the following model constraints, including: Capacity constraints are expressed as: in, , They are nodes i The active power output of the intelligent soft switch SOP; , They are nodes i The reactive power output of the intelligent soft switch SOP; The total power loss factor for SOP equipment; The constant loss coefficient for SOP equipment; The variable loss coefficient for SOP equipment; Indicates the connection node i With nodes j The capacity of the smart soft switch SOP between them, that is, the maximum power transmission capability of the smart soft switch; The SOP (Standard Operating Procedure) active power balance constraint is expressed as: in, Indicates the connection node i With nodes jThe active power loss of the SOP equipment between them represents the power consumed due to the internal power loss of the SOP equipment; The loss factor for SOP equipment; SOP reactive power constraint is expressed as: in, This is the reactive power limitation factor for SOP.

[0010] Furthermore, the distribution network topology includes the connection methods between nodes in the distribution network, the capacity limitations of each branch, and the deployment location of smart soft switches; the nodes refer to the connection points of various electrical equipment in the distribution network, including substations, distribution boxes, and load terminals, and the nodes are connected through branches; the branches refer to the power lines, transformers, or other electrical facilities connecting the nodes in the distribution network, and the branches have specific power carrying capacity, capable of carrying a certain current and voltage, and their power capacity limitations must be considered in the planning; the smart soft switch configuration includes the type, capacity, installation location of the smart soft switches, and the connection methods between each smart soft switch; the energy storage configuration includes the type, power, capacity, installation location, and charging and discharging strategy of the energy storage device.

[0011] Furthermore, the multiple optimization objective function is expressed as: in, Solution for distribution network reconfiguration planning; , Preset weighting coefficients; The goal is to optimize power grid stability; To minimize planning costs.

[0012] Furthermore, the power grid stability optimization objective aims to minimize the maximum voltage stability index in the distribution network, thereby ensuring voltage stability in all branches of the power grid, and is expressed as: in, This represents the maximum voltage stability index for all branches in a distribution network. These represent the voltage stability indicators of each branch in the distribution network. This represents the total number of all branches in the distribution network, determined based on the number of branches in the distribution network reconfiguration planning solution. The voltage stability index is expressed as: in, Indicates a branch The voltage stability index is used to measure the stability of branch voltage under power flow. Indicates a branch The reactance; , These represent the nodes through which the flow passes. j The active and reactive load power are determined based on the distribution network topology in the distribution network reconfiguration planning solution. Indicates a branch The resistance; Represents a node i The square of the voltage amplitude.

[0013] Furthermore, the goal of minimizing planning costs aims to minimize the investment and operating costs of the distribution network, and is expressed as: in, The total cost of the smart soft switch is expressed as: in, This indicates the investment cost of the intelligent soft switch; This indicates the operation and maintenance cost of the intelligent soft switch; This represents the total number of nodes in the distribution network. For nodes i The set of all adjacent nodes is determined based on the distribution network topology in the distribution network reconfiguration planning solution; Cost per unit capacity of SOP; Indicates the connection node i With nodes j The capacity of the smart soft switch SOP between them is determined based on the smart soft switch configuration in the distribution network reconfiguration plan solution; This is the capital recovery factor. The discount rate is... This refers to the service life; The cost of curtailing wind and solar power is expressed as: in, Indicates the cost of solar power curtailment; Indicates the cost of wind curtailment; These represent the cost per unit of electricity curtailed for solar and wind power, respectively. , They represent s season t Maximum permissible output of photovoltaic and wind power during certain periods; , They represent s season t Photovoltaic active power and wind power active power during the same period; This is the seasonal weighting coefficient; , These represent the sets of nodes in the distribution network that are equipped with photovoltaic power and those that are equipped with wind power, respectively, and are determined based on the distribution network topology in the distribution network reconfiguration planning solution. For time step; Total number of time periods; The energy storage cost is expressed as: in, , These represent the construction cost and operation and maintenance cost of energy storage, respectively. The energy storage discount rate; , Representing nodes respectively i Investment cost per unit power and capacity of energy storage; , Representing nodes respectively i Energy storage power and capacity; This represents the set of nodes in a distribution network where energy storage is installed. This indicates the unit charge / discharge operating cost; , Representing nodes respectively i During the period t Energy storage charging and discharging power.

[0014] Furthermore, the constraint function set includes network reconfiguration constraints, energy storage capacity constraints, topology constraints, distributed power output constraints, system security constraints, demand response constraints, and power transmission constraints.

[0015] Furthermore, the step of solving the constructed multi-objective optimization model using an improved arithmetic optimization algorithm to obtain the optimal solution for the distribution network reconfiguration plan specifically includes: An initial population is constructed using a chaotic inverse learning strategy. Initial solutions are generated through the Tent chaotic map, and inverse solutions are generated based on the inverse learning strategy. The fitness values ​​of each initial solution and inverse solution are calculated, and the best solutions are selected. NIndividuals form an initial population; wherein, the initial solution and the reverse solution are both candidate solutions for the distribution network reconfiguration planning solution; the fitness value is the value of the multi-objective function in the multi-objective optimization model; After obtaining the initial population, the stage of candidate solution update in the current iteration is determined by the mathematical accelerator function FOA. The FOA expression is: The update phase is determined based on the following conditions: in, For the first The accelerator function value for the next iteration; , These represent the maximum and minimum values ​​within the range of the accelerator function, respectively. Indicates the current iteration number; for Random numbers between; This represents the maximum number of iterations. If the determined stage is the exploration stage, then the candidate solutions are updated using a division search strategy or a multiplication search strategy, and the update formula is: in, For the next update i The candidate solution at the th... j Values ​​in each dimension of the decision variable; This indicates that the current globally optimal candidate solution is at the th position. j Values ​​in each decision variable dimension; MOP represents the probability factor of the mathematical optimizer; It is a very small positive number; This is the search amplitude adjustment factor; for Random numbers between; , To be respectively the first in the solution of the distribution network reconfiguration planning j The lower and upper bounds of the search in each decision variable dimension are determined according to the set of constraint functions; the current global optimal candidate solution is the candidate solution with the highest fitness value among all candidate solutions in the current iteration. If the identified stage is the development stage, then an addition / subtraction strategy is used to perform local fine-tuning updates on the candidate solutions. The update formula is: in, for Random numbers between; After completing the update in the exploration or development phase, in order to enhance the ability to escape local optima, a Cauchy mutation operation is performed on the current global optimal candidate solution to obtain a new candidate solution.

[0016] Furthermore, the Tent chaotic mapping is represented as: in, The input variable is a chaotic sequence, and its value range is... , used to generate initial solutions for chaos; This is the output value of the Tent chaotic mapping; The formula for generating the initial solution through Tent chaotic mapping is as follows: in, For the generated initial solution In the j Chaotic generated values ​​across the dimensions of each decision variable; The formula for generating the inverse solution based on the inverse learning strategy is as follows: in, For candidate solutions In the j Backward learning generates values ​​across each decision variable dimension; For candidate solutions In the j Values ​​in each dimension of the decision variable.

[0017] Furthermore, the Cauchy mutation operation is defined by the following formula: in, This represents a new candidate solution after Cauchy mutation; This is the globally optimal candidate solution before the mutation; For positions with 0 as the position parameter, The Cauchy distribution random perturbation is the scale parameter; This indicates that a Cauchy perturbation is applied to the global optimal solution along its dimensions.

[0018] Compared with the prior art, the present invention has the following advantages: (1) Existing technologies lack multi-objective comprehensive optimization capabilities, mainly focusing on the single configuration optimization of smart energy storage and soft switching, failing to simultaneously consider distribution network stability, economy, and renewable energy utilization efficiency. This makes it difficult for existing methods to achieve globally optimal reconfiguration planning when a high proportion of renewable energy is integrated. To address this problem, this invention constructs a multi-objective optimization model that includes grid stability optimization and planning cost optimization objectives, and combines smart soft switching configuration, demand response strategies, and distribution network topology optimization to achieve coordinated optimization of the distribution network in terms of security, economy, and renewable energy utilization, thereby significantly improving the overall performance of distribution network operation.

[0019] (2) In the existing technology, the algorithm's solution efficiency and global optimality need to be improved. It may get stuck in local optima and has low computational efficiency in large-scale distribution network optimization. This invention optimizes the candidate solution generation and search process by adopting an improved arithmetic optimization algorithm, including chaotic back learning initialization, mathematical accelerator functions, exploration and development phase strategies, and Cauchy mutation techniques. This improves the diversity of initial solutions, enhances the algorithm's global search capability, and effectively improves the solution speed and global optimality, thereby enabling the rapid acquisition of the optimal distribution network reconfiguration plan solution.

[0020] (3) Existing technologies often result in uneven distribution of initial solutions during generation, leading to insufficient search space coverage and reducing the probability of finding the global optimum. This invention generates initial candidate solutions through Tent chaotic mapping and combines this with a reverse learning strategy to generate reverse solutions. After calculating the fitness of each candidate solution, the optimal solution is selected to form the initial population. This method effectively improves the diversity and representativeness of the initial solutions, broadens the algorithm's search space coverage, and significantly enhances global optimization capabilities and initial convergence speed.

[0021] (4) Existing algorithms struggle to balance global exploration and local development during iteration, potentially leading to premature local optima. This invention uses a mathematical accelerator function (FOA) to dynamically determine the search phase of candidate solutions. Based on the number of iterations and random numbers, it determines whether the algorithm is currently in the exploration or development phase, thereby adaptively adjusting the search strategy. This achieves an organic combination of global search and local development, improving the convergence of the algorithm and the superiority of the solution.

[0022] (5) Existing methods are inefficient in updating candidate solutions during the global search phase and cannot fully utilize the optimal solution information. This invention introduces a division search strategy and a multiplication search strategy during the exploration phase. By scaling up and multiplying the candidate solutions and combining them with the global optimal solution information, the algorithm effectively guides the candidate solutions to converge to a better region, improves the global search capability of the algorithm, and accelerates the discovery of the optimal solution.

[0023] (6) Existing algorithms are prone to getting stuck in local optima in the later stages of convergence, which limits further optimization of the solution. This invention applies a Cauchy distribution random perturbation to the current global optimal candidate solution, and generates a large amount of random mutation through the long-tail distribution, which enhances the algorithm's ability to escape local optima, thereby effectively expanding the search space, improving the probability of obtaining the global optimal solution and the quality of the final reconstructed planning scheme.

[0024] (7) Intelligent soft switch is a new type of flexible power electronic device and an effective means of planning distribution network. By replacing the original tie line switch with intelligent soft switch to plan distribution network lines, the problem of voltage overrun caused by reverse power flow can be effectively solved. At the same time, the impact of fault current can be avoided and network loss can be effectively reduced. Attached Figure Description

[0025] Figure 1 This is a flowchart of the power distribution network reconfiguration planning method according to an embodiment of the present invention; Figure 2 This is a diagram of the IEEE 33-node system according to an embodiment of the present invention. Figure 3 This is a typical 24-hour power distribution curve of wind and solar load in an embodiment of the present invention; Figure 4 This is a comparison chart of network losses for different schemes in this invention embodiment; Figure 5 This is a comparison diagram of network loss between a conventional network switch and a standard operating procedure (SOP) according to an embodiment of the present invention. Figure 6 This is a schematic diagram of the voltage values ​​of the IEEE 33 node for different algorithms in embodiments of the present invention. Detailed Implementation

[0026] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0027] Example 1: This invention proposes a distribution network reconfiguration planning method that considers intelligent soft switching and demand response. It replaces the original tie-line switches with Standard Operating Procedures (SOPs) to achieve intelligent regulation of the new distribution network. Utilizing typical power output scenarios of wind, solar, and loads, it employs multi-objective planning that considers voltage stability, distribution network planning costs, and operating losses. This improves voltage stability while reducing wind and solar curtailment and network losses, thus promoting the absorption of wind and solar power.

[0028] This embodiment specifically provides a distribution network reconfiguration planning method that considers intelligent soft switching and demand response, such as Figure 1 As shown, it includes the following steps: Step S1: Replace the traditional distribution network tie line switch with a smart soft switch; Soft open point (SOP) is a new type of flexible power electronic device and an effective means of distribution network planning. Its core is to achieve intelligent operation of the distribution network through voltage source converters (VSCs) or other power electronic topologies. Traditional tie-line switches, due to their slow response speed and the need for manual operation, are no longer suitable for the dynamic requirements of high-proportion renewable energy integration in new distribution networks. Replacing traditional tie-line switches with smart soft switches in distribution network planning effectively solves voltage overflow problems caused by reverse power flow and avoids fault current surges, effectively reducing network losses.

[0029] Step S2: Based on the replaced smart soft switch, and combining typical output scenarios, load data and demand response of renewable energy, construct a multi-objective optimization model. The multi-objective optimization model includes multiple optimization objective functions and a set of constraint functions. The constraint function set includes network reconfiguration constraints, energy storage capacity constraints, topology constraints, distributed power output constraints, system security constraints, demand response constraints, and power transmission constraints.

[0030] Network reconfiguration constraints: In the process of distribution network topology optimization, when all lines are connected and form closed loops, the following disconnection strategy can be adopted to achieve network structure optimization and reconfiguration: First, for loop groups with overlapping paths, one shared branch is selected for selective disconnection; second, single-path splitting is performed on non-overlapping independent branches. Specifically, in a network node area with n branches, it must be ensured that the disconnection operation does not exceed m lines (m≤n). This constraint can effectively maintain the radial characteristics of the distribution network topology and avoid islanding or circulating current phenomena. The reconfiguration constraints are as follows: in, Represents the sum of the number of branches in the k-th loop. The set representing the k-th branch, Indicates the total number of loops. This represents the set of surrounding branches connected to the k-th node; Indicates a branch The open and closed states; Indicates the first k A set of branches of a closed loop, wherein the branches in the loop are determined according to the topology of the distribution network; Indicates the first m A set of branches of a closed loop; This represents the total number of closed loops in the distribution network, determined based on the topology in the distribution network reconfiguration planning solution. Represents nodes k The set of all connected branches, including power equipment such as lines and transformers directly connected to the node; This indicates the total number of nodes in the distribution network, including substations, distribution boxes, and load nodes; Energy storage capacity constraints are expressed as: In the formula: , For the first i The lower and upper limits of the capacity of Taiwan's energy storage equipment. For the first i The capacity of the energy storage device.

[0031] Topological constraints: When planning the distribution network structure, a radial constraint should be satisfied. In this case, the total number of branches satisfies: Total number of branches = Total number of nodes - Number of root nodes, as shown in the following formula: In the formula: This represents the collection of all lines in the distribution network. This indicates the open / closed state of the circuit, with 0 representing open and 1 representing closed. This indicates the number of root nodes in the system.

[0032] Distributed power generation output constraints: In the formula: and The actual output of PV at time t are respectively The upper and lower limits.

[0033] System security constraints: In the formula: and These are the upper and lower limits of the voltage at node i, respectively; Let be the voltage magnitude of node i at time t. For nodes The square of the upper limit of the current Demand response constraints: To reduce the peak-to-valley load difference in the distribution network, decrease overall operating costs, and improve economy and reliability, a demand response strategy can be adopted. The load constraints for this strategy are given by the formula: In the formula: For nodes i exist t The price elasticity of electricity demand at any given time; For nodes i exist t Real-time changes in electricity demand before and after implementing demand response; For nodes i exist t Real-time monitoring of changes in electricity consumption before and after demand response; and They are nodes i exist t Implement electricity pricing before and after demand response in real time; For nodes i exist t Always consider the load before demand response. For nodes i exist t Always consider the load after demand response; and They are nodes i exist t The maximum and minimum electricity prices are implemented in real time after demand response.

[0034] Power transfer constraints: In the formula: , These are the upper limits for active and reactive power transmitted through the line, respectively.

[0035] Step S3: Based on the constructed multi-objective optimization model, solve the multi-objective optimization model using an improved arithmetic optimization algorithm to obtain the optimal solution for the distribution network reconfiguration plan. Specifically, this includes: In solving the optimal solution of a programming problem, arithmetic optimization algorithms rely excessively on population initialization, leading to an uneven distribution of individuals in the initial population. To increase the diversity of the initial population, this embodiment employs chaotic mapping and back-learning to initialize the population, selecting the Tent mapping, which has good ergodicity, as the chaotic mapping. The population initialization process is as follows: first, the population is initialized using the Tent mapping; second, the population is updated using a back-learning strategy; finally, the merits of the two population groups (initialized using the Tent mapping and updated using back-learning) are compared and evaluated, and the superior one is retained.

[0036] After obtaining the initial population, the stage of candidate solution update in the current iteration is determined by the mathematical accelerator function FOA. After completing the update during the exploration or development phase, perform a Cauchy mutation operation on the current globally optimal candidate solution to obtain a new candidate solution.

[0037] Step S4: Based on the obtained optimal distribution network reconfiguration plan solution, implement the reconfiguration plan for the distribution network.

[0038] Example 2: This embodiment uses an improved IEEE 33-node power distribution system for analysis. The IEEE 33-node power distribution system is as follows: Figure 2 As shown, based on the wind and solar power output and load in a certain region, the optimal solution obtained through an improved arithmetic optimization algorithm is used in a simulation of the IEEE 33-bus distribution network. The wind and solar power output curves and load output of the entire system are shown in the figure. Figure 3 As shown, the parameters of the relevant planned equipment are as follows: 1) Wind power data: WT installation quantity is 2, installation nodes are 9 and 25, and capacity is 0.5MVA and 1.6MVA.

[0039] 2) Photovoltaic data: There are 2 PV installations, with installation nodes of 17 and 22, and capacities of 1.8MVA and 2MVA respectively.

[0040] 3) Energy Storage Data: Two ESS installations are completed, with 7 and 25 nodes, each with a capacity of 1.5 MVA. The upper and lower limits of state of charge are 0.9 and 0.2, respectively, the charge / discharge efficiency is 0.95, the energy storage construction cost is 1500 yuan / (kW·h), the operation and maintenance cost per unit capacity of energy storage is 0.35 yuan / (kW·h), and the initial state of charge is 0.3 MWh.

[0041] 4) SOP data: The investment cost per unit capacity of SOP is 50 yuan / kVA, and the SOP operation penalty cost coefficient is 0.02.

[0042] 5) Other parameters: The maximum system load is 3715kW + 2300kvar.

[0043] To verify the superiority of the proposed scheme in improving grid voltage stability, reducing investment costs, lowering network losses, and reducing wind and solar curtailment costs, this embodiment sets up four schemes for comparative analysis: Option 1: Original System Option 2: Network reconfiguration without considering demand response Option 3: Consider demand response, but not network reconfiguration. Option 4: Network reconfiguration considering demand response Figure 4The data reflects the network losses of four schemes. Scheme 1 shows that the original system has relatively high network losses, mainly concentrated during peak load periods. During these periods, the overall voltage level is low, leading to increased network losses due to low-voltage power transmission. Scheme 2 considers network reconfiguration, improving the power flow distribution of the distribution network and voltage quality by changing the topology, thereby reducing network losses. Scheme 3 considers demand response, guiding users to reduce electricity consumption during peak load periods and increase consumption during off-peak periods through time-of-use pricing, reducing line overload and thus reducing network losses to some extent. Scheme 4 considers both network reconfiguration and demand response, adjusting load from both temporal and spatial perspectives while improving the topology, further optimizing power flow and effectively reducing network losses.

[0044] from Figure 5 As can be seen, the network loss of traditional tie line switches is significantly higher than that of intelligent soft switches. This is because traditional tie line switches can only physically open and close the line, and cannot actively regulate power flow, easily leading to heavy or light loads on the line, resulting in high network losses. During peak load periods, intelligent soft switches use power electronic devices such as VSCs to regulate the active and reactive power of the line in real time, actively controlling the reactive current circulation path and reducing additional losses caused by reactive current. At the same time, SOPs connect multiple feeders and achieve power flow balance through multi-segment power mutual assistance, thereby reducing line losses caused by uneven load distribution.

[0045] Before and after the distribution network is connected to a high proportion of renewable energy, the per-unit voltage values ​​of different algorithms are as follows: Figure 6 As shown, the voltage curve of IOA is significantly better than that of the curves optimized by AOA and PSO. The lowest per-unit voltage value after IOA optimization is 0.9667 pu, and the average per-unit voltage value is 0.974 pu. The lowest voltage is 0.06541 pu higher than that of AOA, and the average voltage is 0.01356 pu higher. Compared with the PSO algorithm, the lowest voltage and average voltage are 0.07542 pu and 0.02465 pu higher, respectively. The above analysis shows that using the IOA algorithm to optimize the configuration of distribution networks with a high proportion of renewable energy can significantly improve voltage quality, reduce network losses, and improve the power supply reliability of the distribution network.

[0046] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0047] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1.A power distribution network reconfiguration planning method considering smart soft switching and demand response, characterized in that, The method comprises the following steps: An intelligent soft switch is used to replace a traditional power distribution network tie switch; On the basis of the replaced intelligent soft switch, a multi-objective optimization model is constructed in combination with typical output scenarios of renewable energy, load data and demand response, the multi-objective optimization model comprising a multi-objective optimization function and a constraint function set; According to the constructed multi-objective optimization model, the multi-objective optimization model is solved by an improved arithmetic optimization algorithm to obtain an optimal power distribution network reconstruction planning solution, the power distribution network reconstruction planning solution comprising a power distribution network topology, intelligent soft switch configuration and energy storage configuration; According to the obtained optimal power distribution network reconstruction planning solution, reconstruction planning of the power distribution network is realized. 2.The power distribution network reconfiguration planning method considering smart soft switching and demand response according to claim 1, wherein, The intelligent soft switch SOP replaces a traditional power distribution network tie switch through a voltage source converter or other power electronic topology to realize intelligent control and dynamic regulation of the power distribution network; The intelligent soft switch satisfies the following model constraints, comprising: A capacity constraint, expressed as: wherein, , are the active power output by the intelligent soft switch SOP of the node i , , are the reactive power output by the intelligent soft switch SOP of the node i , is the total power loss coefficient of the SOP device; is the constant loss coefficient of the SOP device; is the variable loss coefficient of the SOP device; denotes the capacity of the intelligent soft switch SOP connected between the node i and the node j , i.e. the maximum power transfer capability of the intelligent soft switch; A SOP active power balance constraint, expressed as: wherein, represents the active power loss of the SOP device connecting the nodes i and j , represents the power consumed due to the electrical energy loss inside the SOP device; is the loss coefficient of the SOP device; A SOP reactive power constraint, expressed as: wherein, is the reactive power limit coefficient for the SOP. 3.The power distribution network reconfiguration planning method considering smart soft-switching and demand response of claim 1, wherein, The power distribution network topology comprises a connection mode between nodes in the power distribution network, capacity limitation of each branch and deployment position of the intelligent soft switch; The node refers to a connection point of each electrical equipment in the power distribution network, comprising a substation, a distribution box and a load terminal, and the nodes are connected through branches; The branch refers to a power line, a transformer or other electrical facilities connecting the nodes in the power distribution network, the branch having a specific power carrying capacity and being able to carry a certain current and voltage, and the power capacity limitation of the branch needs to be considered in planning; The intelligent soft switch configuration comprises a type, capacity, installation position of the intelligent soft switch and a connection mode between the intelligent soft switches; The energy storage configuration comprises a type, power, capacity, installation position of the energy storage device and a charging and discharging strategy. 4.The power distribution network reconfiguration planning method considering smart soft switching and demand response of claim 1, wherein, The multi-objective optimization function is expressed as: wherein, is a power distribution network reconfiguration planning solution; , is a preset weight coefficient; is a power grid stability optimization objective; is a minimum planning cost objective. 5.The power distribution network reconfiguration planning method considering smart soft-switching and demand response of claim 4, wherein, The power grid stability optimization objective aims to minimize a maximum voltage stability index in the power distribution network to ensure voltage stability of each branch in the power grid, and is expressed as: wherein, represents the maximum voltage stability index of all branches in the distribution network; respectively represent the voltage stability index of each branch in the distribution network; represents the total number of all branches in the distribution network, which is determined according to the number of branches in the distribution network reconfiguration planning solution; The voltage stability index is expressed as: wherein, represents a voltage stability index of the branch , for measuring the stability of the branch voltage under power flow; represents the reactance of the branch ; , respectively represent the active load power and the reactive load power flowing through the node j , determined according to the power distribution network topology in the power distribution network reconfiguration planning solution; represents the resistance of the branch ; represents the square of the voltage amplitude of the node i . 6.The power distribution network reconfiguration planning method considering smart soft-switching and demand response of claim 4, wherein, The minimum planning cost objective aims to minimize investment and operation cost of the power distribution network, and is expressed as: wherein, represents the total cost of the intelligent soft switch, expressed as: in, This indicates the investment cost of the intelligent soft switch; This indicates the operation and maintenance cost of the intelligent soft switch; This represents the total number of nodes in the distribution network. For nodes i The set of all adjacent nodes is determined based on the distribution network topology in the distribution network reconfiguration planning solution; Cost per unit capacity of SOP; Indicates the connection node i With nodes j The capacity of the smart soft switch SOP between them is determined based on the smart soft switch configuration in the distribution network reconfiguration plan solution; This is the capital recovery factor. The discount rate is... This refers to the service life; The cost of abandoning wind and light is represented as: wherein, represents the photovoltaic curtailment cost; represents the wind curtailment cost; respectively represent the unit photovoltaic curtailment cost and the unit wind curtailment cost; , respectively represent s season t the maximum allowed output of photovoltaic and wind power in the time period; , respectively represent s season t the photovoltaic active power and the wind active power of photovoltaic and wind power in the time period; is a seasonal weight coefficient; , respectively represent the node set in which photovoltaic is installed and the node set in which wind power is installed in the distribution network, which are determined according to the distribution network topology structure in the distribution network reconfiguration planning solution; is a time step; is the total number of time periods; The energy storage cost, denoted as C, is given by: wherein, , respectively represent the storage construction cost and the storage operation cost; is the storage discount rate; , respectively represent the storage unit power and capacity investment cost; i , , respectively represent the storage power and capacity at node i ; represents the set of nodes in the distribution network where storage is installed; represents the unit charge and discharge operation cost; , respectively represent the storage charge and discharge power at node i in period t ; 7.The power distribution network reconfiguration planning method considering smart soft-switching and demand response of claim 1, wherein, The constraint function set comprises network reconstruction constraints, energy storage capacity constraints, topology constraints, distributed power output constraints, system safety constraints, demand response constraints and power transmission constraints. 8.The power distribution network reconfiguration planning method considering smart soft-switching and demand response of claim 1, wherein, According to the constructed multi-objective optimization model, the multi-objective optimization model is solved by an improved arithmetic optimization algorithm to obtain an optimal power distribution network reconstruction planning solution, and specifically comprises: The initial population is constructed by using a chaotic reverse learning strategy, initial solutions are generated by a Tent chaotic mapping, reverse solutions are generated based on the reverse learning strategy, fitness values of the initial solutions and the reverse solutions are calculated, and the first N individuals are selected as the initial population; wherein, the initial solutions and the reverse solutions are candidate solutions of the power distribution network reconstruction planning solution; the fitness value is a multi-optimization objective function value in a multi-objective optimization model. After an initial population is obtained, a mathematical accelerator function FOA is used to determine a stage at which a candidate solution is updated in this iteration, and the FOA expression is: And the update stage is determined according to the following conditions: in, For the first The accelerator function value for the next iteration; , These represent the maximum and minimum values ​​within the range of the accelerator function, respectively. Indicates the current iteration number; for Random numbers between; This represents the maximum number of iterations. If the determined stage is an exploration stage, a division search strategy or a multiplication search strategy is used to update the candidate solution, and the update formula is: in, Waiting for the updated version i The candidate solution at the th... j Values ​​of each decision variable dimension; This indicates that the current globally optimal candidate solution is at the [number]th position. j Values ​​in each decision variable dimension; MOP represents the probability factor of the mathematical optimizer; It is a very small positive number; This is the search amplitude adjustment factor; for Random numbers between; , To be respectively the first in the solution of the distribution network reconfiguration planning j The lower and upper bounds of the search in each decision variable dimension are determined according to the set of constraint functions; the current global optimal candidate solution is the candidate solution with the highest fitness value among all candidate solutions in the current iteration. If the determined stage is a development stage, an addition and subtraction strategy is used to update the candidate solution locally, and the update formula is: wherein is a random number between 0 and 1. After the update in the exploration stage or the development stage is completed, a Cauchy mutation operation is performed on the current global optimal candidate solution to obtain a new candidate solution. 9.The power grid reconfiguration planning method considering smart soft-switching and demand response of claim 8, wherein, The Tent chaotic mapping is expressed as: wherein, is an input variable of the chaotic sequence, and a value range of the input variable is is used for generating a chaotic initial solution; is an output value of the Tent chaotic mapping; The initial solution generated by the Tent chaotic mapping is expressed as: wherein, is the initial solution generated In the first j chaotic generated value in the dimension of the decision variable; The reverse solution generated based on the reverse learning strategy is expressed as: wherein, is a candidate solution reverse learning generates values on the j th decision variable dimension; is a candidate solution values on the j th decision variable dimension. 10.The power grid reconfiguration planning method considering smart soft-switching and demand response according to claim 8, wherein, The Cauchy mutation operation is expressed as: wherein, is the new candidate solution after Cauchy variation; is the global optimal candidate solution before variation; is a Cauchy distribution random perturbation with 0 as the location parameter, is a Cauchy distribution random perturbation with 0 as the location parameter, denotes applying Cauchy perturbation to the global optimal solution by dimension.

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