Electronic flexible soft switch interface location selection and capacity configuration method and device

Abstract

The invention discloses an electronic flexible soft switch interface position site selection and capacity configuration method and device, relates to the technical field of power systems, and is used for solving the technical problem of poor overall reliability of a power distribution network caused by the existing electronic flexible soft switch interface position site selection and capacity configuration method. The method comprises the following steps: acquiring wind and light output historical data and power distribution network historical fault data, and constructing a wind and light output fuzzy set by adopting a preset measurement tool according to the wind and light output historical data; adopting a Monte Carlo sampling method to construct a power distribution network fault scene according to the historical fault data of the power distribution network; taking the minimum sum of the annual investment cost of the electronic flexible soft switch and the annual load loss cost of the power distribution network as a target, and constructing upper and lower layer models according to upper and lower layer planning operation constraint conditions; and based on a power distribution network fault scene, solving the upper-layer model and the lower-layer model by adopting a particle swarm algorithm and a column and constraint generation algorithm according to the wind-light output fuzzy set, and generating an electronic flexible soft switch interface location selection and capacity configuration scheme.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and particularly to a method and device for locating the position and configuring the capacity of an electronic flexible soft-switch interface. Background Art

[0002] With the rapid development of the economy, the intermittency and randomness of distributed power sources pose challenges to power quality and the stability of power systems. Against this background, the application of flexible soft switches (Soft Open Point, SOP) for distribution networks is becoming increasingly common.

[0003] The SOP technology mainly replaces the traditional circuit breaker-based tie switch with controllable power electronic devices to achieve a normal flexible "soft connection" between feeders. This innovative method can flexibly respond to changes in different loads and demands, provide faster power exchange and power flow regulation, thus significantly improving the overall response speed of the system. At the same time, the SOP also has high-precision power control capabilities, enabling the power system to more effectively manage and distribute electrical energy.

[0004] The flexible interconnection technology of intelligent distribution networks needs to adapt to diverse system topologies and operating modes. On the basis of the conventional flexible interconnection of feeders, it must also meet the flexible interconnection requirements in different scenarios such as multi-line power supply, multi-voltage-level power supply, multi-level substation interconnection, and energy storage-assisted regulation. These specific requirements will drive the diversified development of future SOPs in terms of structure and function. As an energy element, energy storage can significantly improve the flexibility of the distribution network through flexible charging and discharging methods. However, a single-node fixed-access energy storage system has limitations in regulating the problems of new energy surplus or insufficient power supply occurring on other feeders of the distribution network, that is, its spatial regulation ability is insufficient. Combining energy storage with SOP to enhance the flexibility of the distribution network jointly in the dimensions of time and space, energy and power is an effective solution. By connecting the energy storage to the DC port of the SOP, the two form a multi-feeder shared energy storage system based on E-SOP (Electronic Soft Open Point), thus achieving complementary advantages.

[0005] Existing methods for locating the position and configuring the capacity of electronic flexible soft-switch interfaces are mainly based on node voltage deviation analysis or sensitivity analysis to determine the interface position and capacity. However, as a fully controlled power electronic device, the soft switch has a high investment cost, and the above two analysis methods only target a single index (such as voltage or power flow), ignoring multi-dimensional factors such as economy, reliability, and equipment utilization rate. This makes the configuration of the soft switch unreasonable and unable to fully reflect the actual needs of the power grid, resulting in poor overall reliability of the distribution network. Summary of the Invention

[0006] The present invention provides a method and device for location selection and capacity configuration of an electronic flexible soft-switch interface, which is used to solve the technical problem that the existing methods for location selection and capacity configuration of an electronic flexible soft-switch interface result in poor overall reliability of the distribution network.

[0007] A method for location selection and capacity configuration of an electronic flexible soft-switch interface provided in the first aspect of the present invention includes:

[0008] Obtain historical data of wind and light output and historical fault data of the distribution network, and construct a fuzzy set of wind and light output according to the historical data of wind and light output by using a preset metric tool;

[0009] Construct a fault scenario of the distribution network according to the historical fault data of the distribution network by using the Monte Carlo sampling method;

[0010] Taking the minimum sum of the annual investment cost of the electronic flexible soft-switch and the annual load loss cost of the distribution network as the objective, and constructing an upper and lower layer model with upper and lower layer planning operation constraints;

[0011] Based on the fault scenario of the distribution network, use the particle swarm algorithm and the column and constraint generation algorithm to solve the upper and lower layer model according to the fuzzy set of wind and light output, and generate a location selection and capacity configuration scheme for the electronic flexible soft-switch interface.

[0012] Optionally, the historical fault data of the distribution network includes the fault probabilities of multiple distribution network lines; the step of constructing a fault scenario of the distribution network according to the historical fault data of the distribution network by using the Monte Carlo sampling method includes:

[0013] Based on the Monte Carlo sampling method, determine the random numbers of each distribution network line;

[0014] Compare the random numbers and fault probabilities of each distribution network line to generate a comparison result corresponding to each distribution network line;

[0015] Generate operation state data corresponding to each distribution network line according to the comparison result corresponding to each distribution network line;

[0016] Use the operation state data corresponding to each distribution network line to construct a fault scenario of the distribution network.

[0017] Optionally, the upper and lower layer planning operation constraints include upper layer planning constraints and lower layer operation constraints;

[0018] The lower layer operation constraints include electronic flexible soft-switch operation constraints, power balance constraints, line maintenance constraints, radial operation constraints, controllable load constraints, and transferable load constraints.

[0019] Optionally, the upper and lower layer model includes an upper layer model and a lower layer model; based on the distribution network fault scenario, the particle swarm algorithm and the column and constraint generation algorithm are used to solve the upper and lower layer model according to the wind and light output fuzzy set, and a location selection and capacity configuration scheme for the electronic flexible soft switch interface is generated, including:

[0020] Based on the distribution network fault scenario, determine the number of days of fault occurrence;

[0021] Under the upper layer planning constraints, generate a particle swarm, and initialize the parameters of the initial energy storage and electronic flexible soft switch planning scheme corresponding to each particle in the particle swarm;

[0022] Use the upper layer model to calculate the annual investment cost of the electronic flexible soft switch corresponding to each particle according to the parameters of the initial energy storage and electronic flexible soft switch planning scheme corresponding to each particle;

[0023] Under the wind and light output fuzzy set, based on the operation constraints of the electronic flexible soft switch, the power balance constraint, the line maintenance constraint, the radial operation constraint, the controllable load constraint, and the transferable load constraint, use the column and constraint generation algorithm to calculate the power flowing through the energy storage and the converter capacity of the flexible soft switch in the parameters of the initial energy storage and electronic flexible soft switch planning scheme corresponding to each particle, and calculate the operation cost on the fault day corresponding to each particle;

[0024] Under the wind and light output fuzzy set, based on the operation constraints of the electronic flexible soft switch, the power balance constraint, the radial operation constraint, the controllable load constraint, and the transferable load constraint, use the column and constraint generation algorithm to calculate the power flowing through the energy storage and the converter capacity of the flexible soft switch in the parameters of the initial energy storage and electronic flexible soft switch planning scheme corresponding to each particle, and calculate the operation cost on the non-fault day corresponding to each particle;

[0025] Use the lower layer model to calculate the annual load loss cost of the distribution network corresponding to each particle according to the number of days of fault occurrence, the operation cost on the fault day corresponding to each particle, and the operation cost on the non-fault day;

[0026] Use the annual load loss cost of the distribution network corresponding to each particle and the annual investment cost of the electronic flexible soft switch to calculate the fitness value corresponding to each particle;

[0027] Based on the parameters of the initial energy storage and electronic flexible soft switch planning scheme corresponding to each particle and the fitness value, determine the parameters of the individual historical energy storage and electronic flexible soft switch planning scheme and the individual optimal fitness value corresponding to each particle;

[0028] Traverse the individual historical energy storage, electronic flexible soft switch planning scheme parameters, and individual optimal fitness values corresponding to each of the particles to determine the global optimal energy storage and electronic flexible soft switch planning scheme parameters;

[0029] Based on the individual historical energy storage and electronic flexible soft switch planning scheme parameters of each of the individuals and the global optimal energy storage and electronic flexible soft switch planning scheme parameters, update the initial energy storage and electronic flexible soft switch planning scheme parameters corresponding to multiple of the particles to determine the updated energy storage and electronic flexible soft switch planning scheme parameters corresponding to each of the particles, and count the update times in real time;

[0030] According to the updated energy storage and electronic flexible soft switch planning scheme parameters corresponding to each of the particles, determine the updated fitness values corresponding to each of the particles;

[0031] Compare the updated fitness values corresponding to each of the particles with the individual optimal fitness values, and use the energy storage and electronic flexible soft switch planning scheme parameters corresponding to the minimum value in the fitness values as the new individual historical optimal energy storage and electronic flexible soft switch planning scheme parameters;

[0032] Traverse the individual optimal fitness values corresponding to each of the new individual historical optimal energy storage and electronic flexible soft switch planning scheme parameters, and use the new individual historical optimal energy storage and electronic flexible soft switch planning scheme parameters corresponding to the minimum value in the fitness values as the new global optimal energy storage and electronic flexible soft switch planning scheme parameters;

[0033] When the update times reach the preset iteration times threshold, use the location of the electronic flexible soft switch and the converter capacity of the flexible soft switch in the new global optimal energy storage and electronic flexible soft switch planning scheme parameters as the electronic flexible soft switch interface location selection and capacity configuration scheme.

[0034] Optionally, the energy storage and electronic flexible soft switch planning scheme parameters include the converter capacity of the flexible soft switch, the location of the converter of the flexible soft switch, the energy storage size, the power size flowing through the energy storage, the location of the converter of the electronic flexible soft switch; the upper-layer model is specifically:

[0035] ;

[0036] Among them, is the annual investment cost of the electronic flexible soft switch; r is the discount rate; y is the operating life of the electronic flexible soft switch; is the total number of nodes in the distribution network; is the cost per unit capacity of the converter of the flexible soft switch; is the converter capacity of the flexible soft switch between node i and node j; is whether to install a converter of the flexible soft switch between node i and node j, Randomly take values of 0 or 1. When , it indicates installation and is recorded as the siting location of the flexible soft-switching converter. When , it means no installation; is the installation and fixed cost of the flexible soft-switching converter; is the unit capacity cost of the flexible soft-switching energy storage; is the energy storage size between node i and node j; is the unit power cost of the flexible soft-switching energy storage; is the power size flowing through the energy storage between node i and node j; is whether to further install energy storage on the basis of installing a flexible soft-switching converter between node i and node j to form an electronic flexible soft-switching converter, Randomly take values of 0 or 1. When , it indicates installation and is recorded as the siting location of the electronic flexible soft-switching converter. When , it means no installation; is the installation and fixed cost of the flexible soft-switching energy storage.

[0037] Optionally, the lower-layer model is specifically:

[0038] ;

[0039] Among them, is the annual load loss cost of the distribution network; D is a vector composed of uncertainty parameters, representing the fuzzy set of wind and light power output; M is the Mth wind and light power output scenario; is the probability of the nth scenario under the actual wind and light power output distribution; K is the number of fault days; is the operation cost on fault days; is the operation cost on non-fault days.

[0040] An electronic flexible soft-switching interface location siting and capacity configuration device provided in the second aspect of the present invention includes:

[0041] A data acquisition module, configured to acquire historical wind and light power output data and historical distribution network fault data, and construct a fuzzy set of wind and light power output according to the historical wind and light power output data by using a preset measurement tool;

[0042] A scenario construction module, configured to construct a distribution network fault scenario according to the historical distribution network fault data by using the Monte Carlo sampling method;

[0043] A model construction module, configured to construct an upper and lower layer model with the goal of minimizing the sum of the annual investment cost of the electronic flexible soft-switching and the annual load loss cost of the distribution network, and based on the upper and lower layer planning operation constraints;

[0044] An output solution module, configured to solve the upper and lower layer models according to the fuzzy set of wind and light output by using a particle swarm algorithm and a column and constraint generation algorithm based on the distribution network fault scenario, and generate a solution for the location selection and capacity configuration of the electronic flexible soft switch interface.

[0045] A computer device provided in the third aspect of the present invention includes a memory and a processor. A computer program is stored in the memory. When the computer program is executed by the processor, the processor is caused to execute the steps of the method for location selection and capacity configuration of the electronic flexible soft switch interface as described in any one of the above.

[0046] A computer-readable storage medium provided in the fourth aspect of the present invention has a computer program stored thereon. When the computer program is executed, the steps of the method for location selection and capacity configuration of the electronic flexible soft switch interface as described in any one of the above are implemented.

[0047] A computer program product provided in the fifth aspect of the present invention includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer is caused to execute the steps of the method for location selection and capacity configuration of the electronic flexible soft switch interface as described in any one of the above.

[0048] It can be seen from the above technical solutions that the present invention has the following advantages:

[0049] The above solution of the present invention provides a method for locating the interface position and configuring the capacity of an electronic flexible soft switch. First, historical data of wind and solar power output and historical fault data of the distribution network are obtained, and a fuzzy set of wind and solar power output is constructed using a preset metric tool based on the historical data of wind and solar power output. Then, a Monte Carlo sampling method is used to construct a fault scenario of the distribution network according to the historical fault data of the distribution network. With the goal of minimizing the sum of the annual investment cost of the electronic flexible soft switch and the annual load loss cost of the distribution network, and based on the upper and lower layer planning operation constraints, an upper and lower layer model is constructed. Finally, based on the fault scenario of the distribution network, a particle swarm algorithm and a column and constraint generation algorithm are used to solve the upper and lower layer models according to the fuzzy set of wind and solar power output, generating a solution for locating the interface position and configuring the capacity of the electronic flexible soft switch. Based on the above solution, a fuzzy set of wind and solar power output is constructed using a preset metric tool according to the obtained historical data of wind and solar power output. With the goal of minimizing the sum of the annual investment cost of the electronic flexible soft switch and the annual load loss cost of the distribution network, and based on the upper and lower layer planning operation constraints, an upper and lower layer model is constructed. Based on the fault scenario of the distribution network, combined with a particle swarm algorithm and a column and constraint generation algorithm, the upper and lower layer models are solved according to the fuzzy set of wind and solar power output, generating a solution for locating the interface position and configuring the capacity of the electronic flexible soft switch. In this process, the present invention takes into account the uncertainty of wind and solar power output, can cope with different load and demand changes, achieve faster power exchange and power flow regulation, thereby improving the overall reliability of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0051] Figure 1 It is a flowchart of the steps of a method for locating the interface position and configuring the capacity of an electronic flexible soft switch provided in Embodiment 1 of the present invention;

[0052] Figure 2 It is a schematic diagram of the iterative process based on the particle swarm algorithm and the column and constraint generation (C&CG) algorithm provided in Embodiment 1 of the present invention;

[0053] Figure 3 It is a schematic diagram of the typical SOP structure provided in Embodiment 1 of the present invention;

[0054] Figure 4 It is a schematic diagram of the multi-port E-SOP structure provided in Embodiment 1 of the present invention;

[0055] Figure 5This is a structural block diagram of a device for locating the position and configuring the capacity of an electronic flexible soft switch interface provided in the second embodiment of the present invention. Detailed implementation manners

[0056] The embodiments of the present invention provide a method and a device for locating the position and configuring the capacity of an electronic flexible soft switch interface, which are used to solve the technical problem that the existing methods for locating the position and configuring the capacity of an electronic flexible soft switch interface result in poor overall reliability of the distribution network.

[0057] To make the objectives, features, and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the embodiments described below are only a 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 those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0058] Please refer to Figure 1 , Figure 1 This is a step flowchart of a method for locating the position and configuring the capacity of an electronic flexible soft switch interface provided in the first embodiment of the present invention.

[0059] A method for locating the position and configuring the capacity of an electronic flexible soft switch interface provided by the present invention includes:

[0060] Step 101: Obtain historical data of wind and light output and historical fault data of the distribution network, and construct a fuzzy set of wind and light output according to the historical data of wind and light output by using a preset metric tool.

[0061] It should be noted that the reference distributed output is generated according to the historical data of wind and light output , assuming that there are N pieces of historical data of wind and light output that can be classified into M output scenarios, and there are , , , samples in each scenario, then the corresponding probability is , . Using a preset metric tool, that is, the Kullback-Leibler (KL) divergence, the fuzzy set of wind and light output (wind and light output fuzzy set) is obtained as shown in the following formula:

[0062] ;

[0063] where D is the fuzzy set of wind and light output; d is the risk threshold; is the KL divergence between the actual distributed output P of wind and light and the reference distributed output ; is the upper quantile of the chi-square distribution with M - 1 degrees of freedom a. The threshold d is selected in this way to ensure that the probability that the actual distribution is not lower than a is included in the fuzzy set D. In the present invention, a takes the value of 0.95; N is the number of historical data of wind and light output.

[0064] Step 102: Use the Monte Carlo sampling method to construct a distribution network fault scenario based on the historical fault data of the distribution network.

[0065] The historical fault data of the distribution network includes the fault probabilities of multiple distribution network lines.

[0066] Specifically, step 102 may include the following sub-steps S21 - S24:

[0067] Step S21: Based on the Monte Carlo sampling method, determine the random numbers of each distribution network line;

[0068] Step S22: Compare the random numbers of each distribution network line with the fault probabilities to generate comparison results corresponding to each distribution network line;

[0069] Step S23: Generate operation state data corresponding to each distribution network line according to the comparison results corresponding to each distribution network line;

[0070] Step S24: Use the operation state data corresponding to each distribution network line to construct a distribution network fault scenario.

[0071] It should be noted that the line faults are summarized according to the historical fault conditions and the fault probabilities of the corresponding multiple lines (i.e., the fault probabilities of multiple distribution network lines) is the fault probability of the ij-th distribution line on the t-th day. On the t-th day, according to the Monte Carlo sampling method, a random number uniformly distributed between [0, 1] is generated for the ij-th distribution line (i.e., the random number of the distribution network line), which is compared with the failure rate of the distribution network line at this moment. According to the comparison results corresponding to each distribution network line, the operation state data corresponding to each distribution network line is generated. This process can be expressed as:

[0072] ;

[0073] Among them, is the operation state data of the ij-th distribution line on the t-th day, 0 represents a fault, and 1 represents normal operation; is a random number uniformly distributed between [0, 1] generated for the ij-th distribution line on the t-th day.

[0074] Further, repeat the above sampling process for all distribution lines in the distribution network with historical fault conditions to determine the faulty lines in the distribution network and the corresponding fault occurrence times, generate a distribution network fault scenario, and finally obtain a set of fault scenarios through Monte Carlo simulation for subsequent solution of the scheme.

[0075] Step 103: With the goal of minimizing the sum of the annual investment cost of the electronic flexible soft switch and the annual load loss cost of the distribution network, and based on the upper and lower layer planning operation constraints, construct an upper and lower layer model.

[0076] The upper and lower layer planning operation constraints refer to the collaborative optimization of the location selection and capacity configuration of the electronic flexible soft switch interface. The constraints are divided into upper layer planning constraints and lower layer operation constraints, and the collaborative optimization of planning economy and operation reliability is achieved through hierarchical modeling.

[0077] The lower layer operation constraints include the operation constraints of the electronic flexible soft switch, power balance constraints, line maintenance constraints, radial operation constraints, controllable load constraints, and transferable load constraints.

[0078] The upper and lower layer model includes an upper layer model and a lower layer model. The upper and lower layer model is a hierarchical modeling framework used in the present invention for the collaborative optimization of the location selection and capacity configuration of the electronic flexible soft switch interface. The upper layer model aims to minimize the annual investment cost, and determines the location selection and capacity configuration scheme of the E-SOP through the upper layer planning constraints; the lower layer model is based on the upper layer planning results, under the fuzzy set of wind and light output and fault scenarios, aims to minimize the load loss cost, and optimizes the dispatching strategy through the lower layer operation constraints. Finally, the iterative solution of the two-layer model is realized through the particle swarm algorithm and the column and constraint generation algorithm, and an E-SOP configuration scheme that takes into account economy and reliability is generated.

[0079] It should be noted that the upper layer model mainly conducts the location selection of the multi-terminal interface position of the E-SOP (location selection of the electronic flexible soft switch interface position) and the configuration planning of the capacity size. It should be considered that the capacity of a single energy storage and the SOP converter should be kept within a reasonable range, and the total number of installed SOP converters should be equal to the number of multi-terminal interfaces of the E-SOP, and there should also be an upper limit arrangement for the total investment cost of installation. Therefore, the upper layer planning constraints refer to a series of restrictive and prescriptive conditions set to ensure the rationality, feasibility, and compliance with specific goals and actual situations when conducting the upper layer planning (such as the above-mentioned upper layer planning work related to the location selection of the multi-terminal interface position of the E-SOP and the capacity configuration planning, etc.); the upper layer planning constraints are specifically:

[0080] ;

[0081] Among them, is the annual investment cost of the electronic flexible soft switch; Based on whether to further install energy storage on the basis of installing a flexible soft-switching converter between node i and node j to form an electronic flexible soft-switching converter, Randomly take values of 0 or 1. When , it means installation and is recorded as the siting location of the electronic flexible soft-switching converter. When , it means no installation; is the minimum value of the power allowed to flow through the energy storage; is the magnitude of the power flowing through the energy storage between node i and node j; is the maximum value of the power allowed to flow through the energy storage; is the minimum value of the energy storage; is the magnitude of the energy storage between node i and node j; is the maximum value of the energy storage; is the minimum capacity value; is the converter capacity of the flexible soft-switch between node i and node j; is the maximum capacity value; is the total number of SOP converters (flexible soft-switching converters) allowed to be installed on the distribution network; is the total number of nodes in the distribution network; is the upper limit of the annual investment cost of E-SOP; is whether to install a flexible soft-switching converter between node i and node j, Randomly take values of 0 or 1. When , it means installation and is recorded as the siting location of the flexible soft-switching converter. When , it means no installation.

[0082] Furthermore, the lower layer is mainly for the optimal dispatching operation of the distribution network under the most severe wind and light output scenarios. For the operation constraints of the electronic flexible soft-switch, specifically: When the E-SOP is connected to the distribution network for operation, the following constraint principles should be followed:

[0083] ;

[0084] ;

[0085] ;

[0086] ;

[0087] Among them, is the active power injected by the SOP converter between node i and node j (the ijth distribution line, i.e., branch ij) during the t-th time period; is the active power loss of the SOP converter injected between node i and node j during the t-th time period; is the discharge power of the E-SOP energy storage in the power flowing through the energy storage during the t period; is the charging power of the E-SOP energy storage in the power flowing through the energy storage during the t period; is the loss coefficient of SOP; is the reactive power injected by the SOP converter between nodes i and j during the t period; is the upper limit coefficient of reactive power.

[0088] Based on the above, for the convenience of calculation, for the above formula with a square root, that is, for and perform second-order cone relaxation to obtain the following formula:

[0089] ;

[0090] ;

[0091] Among them, the energy storage part in E-SOP should follow the following constraints:

[0092] ;

[0093] ;

[0094] ;

[0095] ;

[0096] ;

[0097] Among them, is the energy storage size during the t period; is the energy storage size during the t-1 period; is the time step; is the charging efficiency; is the charging power of the E-SOP energy storage in the power flowing through the energy storage during the t-1 period; is the discharge efficiency; is the discharge power of the E-SOP energy storage in the power flowing through the energy storage during the t-1 period; E is the capacity of the ESS (Energy Storage System) in E-SOP; is the minimum value of the energy storage; is the maximum value of the energy storage; is the maximum charging power; is; is the maximum discharge power; and are binary variables, representing the charge and discharge states respectively. When the ESS is in the discharge state, is 0, is 1, indicating that the ESS cannot be in the charging and discharging states simultaneously.

[0098] Furthermore, for the power balance constraint, specifically: the active and reactive power balance constraints, that is, the amount of power flowing into each bus is equal to the amount flowing out:

[0099] ;

[0100] ;

[0101] ;

[0102] where is the set of branches with power flowing into bus i; is the set of branches with power flowing out of bus i; is the active power output of wind and solar power generation at time t under a typical day, and the typical day is the load data obtained by clustering the annual load conditions; is the active power flowing into or out of node i at time t; is the active load removed from bus i at time t; is the active power injected by the SOP converter into node i at time t; is the active load of node i at time t; is the reactive power flowing into or out of node i at time t; is the reactive power output of wind and solar power generation at time t under a typical day; is the reactive load removed from bus i at time t; is the reactive power injected by the SOP converter into node i at time t; is the reactive load of node i at time t; is the power value of the starting node 1 of the distribution network at time t; is the power provided by the superior power grid at time t.

[0103] Furthermore, the transmission capacity limits of line active and reactive power:

[0104] ;

[0105] where and are the resistance and reactance of branch ij respectively; is the system base voltage; and represent the voltage values at nodes j and i at time t respectively; and represent the active power and reactive power transmitted on line (branch) ij at time t respectively; Indicates the on / off state of line ij during period t. , indicating that line ij is a conducting path during period t. , indicating that line ij is an open circuit during period t; J is an infinitely large value.

[0106] Range limitation of node voltage:

[0107] ;

[0108] Among them, is the voltage value of node i during period t; is the minimum value of the voltage of node i; is the maximum value of the voltage of node i.

[0109] Furthermore, for line maintenance constraints, when a fault persists, only one faulty line can be repaired within the same period of time. The on / off state of the model fault represents the maintenance strategy constraints as follows. The initial on / off state of the distribution network lines:

[0110] ;

[0111] ;

[0112] During the restoration process of the distribution network, maintenance personnel can only repair h faulty lines within the same period of time, that is, at most h lines can be repaired every T f time. The repair line constraints are as follows:

[0113] ;

[0114] Among them, h is the maximum number of lines that can be repaired within the same period of time; is the time spent on repairing h lines; is the on / off state of line ij during period t. When is 0, line ij is in the open state. When is 1, line ij is in the conducting state. And the total number of on / off operations of line ij during period minus the total number of on / off operations of line ij during period t is the situation of the lines repaired by the maintenance team during

[0115] Furthermore, for the radial operation constraints, the distribution network is usually designed as a closed loop and operated as an open loop. When a fault occurs, the lost-load area can be restored through the dynamic reconfiguration of the distribution network and DG scheduling. Regardless of the power flow direction, the distribution network can ensure the operation of the radial structure, which can be expressed as:

[0116] ;

[0117] Among them, , is a binary variable representing the branch state. When the power flow is from branch i to branch j, is 1, otherwise it is 0. Similarly, when the power flow is from branch j to branch i, is 1, otherwise it is 0; is a binary variable representing the fault state. If branch k fails during time period t, then is 0, otherwise it is 1.

[0118] The distribution network is a radial network, and each sub-bus cannot be connected to multiple parent buses simultaneously. The constraints are as follows:

[0119] ;

[0120] ;

[0121] ;

[0122] Among them, is the set of root buses directly connected to the power plant, is the set of root buses directly connected to DG (Distributed Generation), is the set of root buses directly connected to MEG (Micro Energy Generator), is a binary variable. When there is power exchange at bus i during time period t, that is, the load at bus i is energized, then is 1, otherwise it is 0. Because the power flow can only flow out from the root bus and cannot flow back, so when , is zero.

[0123] Furthermore, strengthen the constraint on the branch power flow, indicating that when there is no power flow between the buses directly connected to branch k, that is, the branch state , are all 0, and are also 0. Apply the large method to decouple two unconnected buses:

[0124] ;

[0125] ;

[0126] Among them, is the active power of branch k during time period t; is the reactive power of branch k during time period t, and J is an infinitely large value.

[0127] For the transferable load constraint, the controllability of the transferable load is very strong. The load with a variable power supply time can be changed according to the plan, realizing the same total power supply but variable power supply periods. Common transferable loads include electric vehicle charging stations, some industrial loads, etc. Their incentive response model can be expressed as:

[0128] ;

[0129] ;

[0130] ;

[0131] Among them, is the total transferable load size of node i; and are the loads transferred in and out within a given time respectively; is the binary state variable indicating whether node i transfers the load. , representing the load transfer of node i, , representing that the load of node i does not transfer; and are the time of the transferable load and its maximum transferable time respectively.

[0132] Furthermore, since the investment and usage time of E-SOP is calculated in years, the annual investment cost of E-SOP and the annual load loss cost of the distribution network are mainly considered in this invention. The objective function is established with the minimum sum as the goal:

[0133] ;

[0134] Among them, is the sum of the annual investment cost of E-SOP and the annual load loss cost of the distribution network , represents the fitness value.

[0135] Furthermore, the planning scheme parameters of the energy storage and electronic flexible soft switch include the converter capacity of the flexible soft switch, the location of the flexible soft switch converter, the size of the energy storage, the power flowing through the energy storage, and the location of the electronic flexible soft switch converter; the upper-layer model is specifically:

[0136] ;

[0137] Among them, is the annual investment cost of the electronic flexible soft switch; r is the discount rate; y is the operation years of the electronic flexible soft switch; is the total number of nodes in the distribution network; $C_{inv}$ is the cost per unit capacity of the converter for the flexible soft switch; $C_{ij}^{inv}$ is the converter capacity of the flexible soft switch between node $i$ and node $j$; $x_{ij}$ indicates whether to install a flexible soft switch converter between node $i$ and node $j$, randomly taking values of 0 or 1. When , it means installation and is denoted as the siting location of the flexible soft switch converter. When , it means no installation; $C_{fix}^{inv}$ is the installation and fixation cost of the flexible soft switch converter; $C_{es}$ is the cost per unit capacity of the flexible soft switch energy storage; $E_{ij}$ is the energy storage size between node $i$ and node $j$; $C_{ep}$ is the cost per unit power of the flexible soft switch energy storage; $P_{ij}$ is the power magnitude flowing through the energy storage between node $i$ and node $j$; $y_{ij}$ indicates whether to further install energy storage on the basis of installing a flexible soft switch converter between node $i$ and node $j$ to form an electronic flexible soft switch converter, randomly taking values of 0 or 1. When , it means installation and is denoted as the siting location of the electronic flexible soft switch converter. When , it means no installation; $C_{fix}^{es}$ is the installation and fixation cost of the flexible soft switch energy storage.

[0138] Furthermore, the lower - layer model is specifically:

[0139] ;

[0140] wherein, $C_{loss}$ is the annual load loss cost of the distribution network; $D$ is a vector composed of uncertainty parameters, representing the fuzzy set of wind and light power output; $M$ is the $M$-th wind and light power output scenario; $p_{n}$ is the probability of the $n$-th scenario under the actual wind and light power output distribution; $K$ is the number of fault days; $C_{f}$ is the operation cost on fault days; $C_{nf}$ is the operation cost on non - fault days.

[0141] Step 104: Based on the distribution network fault scenarios, use the particle swarm optimization algorithm and the column - and - constraint generation algorithm to solve the upper - and lower - layer models according to the wind and light power output fuzzy set, and generate the siting and capacity configuration scheme of the electronic flexible soft switch interface location.

[0142] It should be noted that for the upper-layer particle swarm optimization algorithm: The particle swarm optimization (PSO) algorithm is an optimization algorithm based on the concept of swarm intelligence. It is a simulation algorithm proposed by British scientists Eberhart and Kennedy in 1995, in which a large number of particles simulate the behavior of bird or other biological groups in the search space. For the solution of the lower-layer C&CG algorithm (Column and Constraint Generation Algorithm): Robust optimization is an optimization method to cope with data uncertainty, but single-stage robust optimization is too conservative. To solve this problem, two-stage robust optimization and more general multi-stage robust optimization are introduced. The core idea is to divide the decision-making problem into two stages. The first stage is to make a preliminary decision, and the second stage is to formulate a better decision-making strategy according to the decision result of the first stage to cope with the impact of data uncertainty, thereby reducing conservatism and improving robustness.

[0143] Furthermore, the present invention aims to minimize the sum of the annual investment cost of E-SOP and the annual load loss cost of the distribution network. According to the load magnitudes in each time period, the siting of the multi-terminal interface positions of the upper-layer E-SOP and the configuration planning of the capacity sizes are carried out, while the lower layer conducts the optimal dispatching operation of the distribution network under the worst wind and light output scenarios.

[0144] Specifically, step 104 may include the following sub-steps S41-S4:

[0145] Step S41: Based on the distribution network fault scenario, determine the number of days of fault occurrence;

[0146] It should be noted that based on the operation status data of the distribution lines in the distribution network fault scenario, the number of days of fault occurrence is determined. For example, the distribution network fault scenario contains the operation status data of the distribution lines for 120 days, among which the operation status data of the distribution lines for 6 days shows faults. The number of days of fault occurrence can be determined as 6, and the number of non-fault days is determined as 114.

[0147] Step S42: Under the upper-layer planning constraint conditions, generate a particle swarm and initialize the parameters of the initial energy storage and electronic flexible soft switch planning scheme corresponding to each particle in the particle swarm;

[0148] Step S43: Use the upper-layer model to calculate the annual investment cost of the electronic flexible soft switch corresponding to each particle according to the parameters of the initial energy storage and electronic flexible soft switch planning scheme corresponding to each particle;

[0149] Step S44. Under the wind and light output fuzzy set, based on the operation constraints of the electronic flexible soft switch, power balance constraints, line maintenance constraints, radial operation constraints, controllable load constraints, and transferable load constraints, use the column and constraint generation algorithm to calculate the operation cost of each particle on the fault day according to the power flowing through the energy storage and the converter capacity of the flexible soft switch in the initial energy storage and electronic flexible soft switch planning scheme parameters corresponding to each particle;

[0150] Step S45. Under the wind and light output fuzzy set, based on the operation constraints of the electronic flexible soft switch, power balance constraints, radial operation constraints, controllable load constraints, and transferable load constraints, use the column and constraint generation algorithm to calculate the operation cost of each particle on the non-fault day according to the power flowing through the energy storage and the converter capacity of the flexible soft switch in the initial energy storage and electronic flexible soft switch planning scheme parameters corresponding to each particle;

[0151] Step S46. Use the lower-layer model to calculate the annual load loss cost of each particle's corresponding distribution network according to the number of fault days, the operation cost of each particle on the fault day, and the operation cost of each particle on the non-fault day;

[0152] Step S47. Calculate the fitness value of each particle according to the annual load loss cost of each particle's corresponding distribution network and the annual investment cost of the electronic flexible soft switch;

[0153] Step S48. Based on the initial energy storage and electronic flexible soft switch planning scheme parameters and fitness value corresponding to each particle, determine the individual historical energy storage and electronic flexible soft switch planning scheme parameters and individual optimal fitness value corresponding to each particle;

[0154] Step S49. Traverse the individual historical energy storage and electronic flexible soft switch planning scheme parameters and individual optimal fitness value corresponding to each particle to determine the global optimal energy storage and electronic flexible soft switch planning scheme parameters;

[0155] Step S410. Based on the individual historical energy storage and electronic flexible soft switch planning scheme parameters and the global optimal energy storage and electronic flexible soft switch planning scheme parameters of each individual, update the initial energy storage and electronic flexible soft switch planning scheme parameters corresponding to multiple particles, determine the updated energy storage and electronic flexible soft switch planning scheme parameters corresponding to each particle, and count the update times in real time;

[0156] Step S411. Determine the updated fitness value corresponding to each particle according to the updated energy storage and electronic flexible soft switch planning scheme parameters corresponding to each particle;

[0157] Step S412: Compare the updated fitness values corresponding to each particle with the individual optimal fitness values, and use the energy storage and electronic flexible soft-switch planning scheme parameters corresponding to the minimum value in the fitness values as the new individual historical optimal energy storage and electronic flexible soft-switch planning scheme parameters;

[0158] Step S413: Traverse the individual optimal fitness values corresponding to the new individual historical optimal energy storage and electronic flexible soft-switch planning scheme parameters, and use the new individual historical optimal energy storage and electronic flexible soft-switch planning scheme parameters corresponding to the minimum value in the fitness values as the new global optimal energy storage and electronic flexible soft-switch planning scheme parameters;

[0159] Step S414: When the number of updates reaches the preset iteration number threshold, use the location of the electronic flexible soft-switch and the converter capacity of the flexible soft-switch in the new global optimal energy storage and electronic flexible soft-switch planning scheme parameters as the electronic flexible soft-switch interface location selection and capacity configuration scheme.

[0160] It should be noted that the annual load loss cost of the distribution network , mainly refers to the operating cost of the distribution network system under the multi-terminal interface location selection and capacity configuration of the E-SOP on the fault day and non-fault day considering the worst wind and light power output. Under the wind and light power output data set, the column and constraint generation (C&CG) algorithm is used for iteration to dynamically identify the worst scenario, that is, for the current planning scheme (the power magnitude flowing through the energy storage in the initial energy storage and electronic flexible soft-switch planning scheme parameters corresponding to each particle, the converter capacity of the electronic flexible soft-switch), find the wind and light power output scenario (the worst scenario) that makes the loss cost the highest, and calculate the cost that meets all constraints. Among them, the calculation formulas for the operating cost on the fault day and the operating cost on the non-fault day are specifically as follows:

[0161] ;

[0162] ;

[0163] ;

[0164] ;

[0165] ;

[0166] ;

[0167] ;

[0168] Among them, and are the operating cost on the fault day and the operating cost on the non-fault day; 、 , are the upper-level power purchase cost, load shedding penalty, and E-SOP power loss cost within 24 hours, respectively; and are the line maintenance cost and the transferred load cost, respectively; is the set of distribution network lines; , , , and are the unit power purchase cost coefficient, load shedding penalty coefficient, E-SOP power loss cost coefficient, line maintenance cost coefficient, and transferred load cost coefficient, respectively; is the situation where branch (distribution network line) k fails, is the fault, is non-fault.

[0169] It should be noted that the iterative process based on the column and constraint generation (C&CG) algorithm can refer to the step process of the prior art, and the present invention will not elaborate further.

[0170] Further, please refer to Figure 2 . Under the upper-level planning constraint conditions, generate a particle swarm, and initialize the initial energy storage and electronic flexible soft switch planning scheme parameters corresponding to each particle in the particle swarm. Then, after further calculating the fitness value of each particle, based on the initial energy storage and electronic flexible soft switch planning scheme parameters and the fitness value corresponding to each particle, determine the individual historical energy storage and electronic flexible soft switch planning scheme parameters and the individual optimal fitness value corresponding to each particle; wherein, if it is the first iteration currently, record the initial energy storage and electronic flexible soft switch planning scheme parameters and their fitness values of each particle as the individual historical optimal energy storage and electronic flexible soft switch planning scheme parameters and the individual optimal fitness value of each particle respectively; if it is not the first iteration currently, it is necessary to compare the fitness value corresponding to each particle with the individual optimal fitness value to determine the individual historical optimal energy storage and electronic flexible soft switch planning scheme parameters and the individual optimal fitness value of each particle. Wherein, V is the number of iterations (i.e., the number of updates).

[0171] Further, traverse the individual historical energy storage and electronic flexible soft switch planning scheme parameters and the individual optimal fitness value corresponding to each particle to determine the global optimal energy storage and electronic flexible soft switch planning scheme parameters; based on the individual historical energy storage and electronic flexible soft switch planning scheme parameters and the global optimal energy storage and electronic flexible soft switch planning scheme parameters, update the initial energy storage and electronic flexible soft switch planning scheme parameters corresponding to multiple particles to determine the updated energy storage and electronic flexible soft switch planning scheme parameters corresponding to each particle (the existing update process can be referred to), and statistically record the number of updates in real time.

[0172] Further, when the number of updates reaches the preset iteration number threshold, the siting location of the electronic flexible soft switch and the converter capacity of the flexible soft switch in the new global optimal energy storage and electronic flexible soft switch planning scheme parameters are used as the siting and capacity configuration scheme for the electronic flexible soft switch interface location. When the number of updates does not reach the preset iteration number threshold, jump to step S410 to continue the iteration until the number of updates reaches the preset iteration number threshold, and use the siting location of the electronic flexible soft switch and the converter capacity of the flexible soft switch in the new global optimal energy storage and electronic flexible soft switch planning scheme parameters determined when the number of updates reaches the preset iteration number threshold as the siting and capacity configuration scheme for the electronic flexible soft switch interface location.

[0173] As a comparison of technical effects, it can be referenced in combination with the existing technology. With the rapid development of the economy, in order to reduce environmental pollution, by actively promoting the development of sustainable energy, distributed power sources based on renewable energy have become an important part of the modern power grid. However, while fully exploring its potential value, the intermittency and randomness of distributed power sources pose challenges to power quality and the stability of the power system. Against this background, the application of intelligent soft switches (Soft Open Point, SOP) for distribution networks is becoming more and more common. The SOP technology mainly replaces the traditional breaker-based tie switch with a controllable power electronic device to achieve a normal flexible "soft connection" between feeders. This innovative method can flexibly respond to changes in different loads and demands, provide faster power exchange and power flow regulation, thus significantly improving the overall response speed of the system. At the same time, the SOP also has high-precision power control capabilities, enabling the power system to more effectively manage and distribute electric energy. The typical SOP structure is as follows Figure 3 shown.

[0174] The flexible interconnection technology of intelligent distribution networks needs to adapt to diverse system topologies and operation modes. On the basis of the flexible interconnection of conventional feeders, it must also meet the flexible interconnection requirements in different scenarios such as multi-line power supply, multi-voltage level power supply, multi-level substation interconnection, and energy storage auxiliary regulation. These specific requirements will promote the diversified development of the future SOP in terms of structure and function. As an energy element, energy storage can significantly improve the flexibility of the distribution network through flexible charging and discharging methods. However, the energy storage system with a single-node fixed access has limitations in regulating the problems of new energy surplus or insufficient power supply occurring on other feeders of the distribution network, that is, its spatial regulation ability is insufficient. Combining energy storage with the SOP to enhance the flexibility of the distribution network jointly in the dimensions of time and space, energy and power is an effective solution. By connecting the energy storage to the DC port of the SOP, the two form a multi-feeder shared energy storage system based on E-SOP, so as to achieve complementary advantages. The multi-port E-SOP structure is as follows Figure 4 shown.

[0175] Therefore, in the use of E-SOP devices, how to flexibly arrange the opening and closing of the SOP converter switch and the power passing through it, and how to flexibly charge and discharge the internal energy storage of the device to cope with different load and demand changes, achieve faster power exchange and power flow regulation, reduce the load loss of the distribution network, and improve the overall reliability of the distribution network have become key problems to be solved urgently. At the same time, the reasonable configuration of the multi-terminal interface location of E-SOP in terms of its location selection and the allowable power capacity and internal energy storage size that can pass through to improve the energy utilization efficiency is also an important challenge currently faced.

[0176] To address the above problems, the present invention proposes a method for location selection and capacity configuration of an electronic flexible soft-switching interface, which uses KL divergence to obtain the fuzzy set of wind and light power output, and generates a Monte Carlo sampling of line faults in the distribution network based on historical fault conditions. Taking the load demand of a single typical day as an example, according to the proportion of fault days and non-fault days in a whole year, the operation costs of the multi-terminal interface location selection and capacity configuration of E-SOP in the distribution network system under fault days and non-fault days are respectively considered, and the multi-terminal interface location selection and capacity configuration of E-SOP are determined under the comprehensive weight.

[0177] Considering the combined effects of the transferable load constraint, line topology reconstruction constraint, power flow operation constraint, node voltage constraint, and power upper and lower limit constraints of the distribution network, with the goal of minimizing the sum of the annual investment cost of E-SOP and the annual load loss cost of the distribution network, the location selection of the upper-layer E-SOP multi-terminal interface and the configuration planning of the capacity size are carried out according to the load size in each time period, and the lower layer conducts the optimal dispatching operation of the distribution network under the worst wind and light power output scenarios. The upper-layer planning and lower-layer dispatching optimization are solved by the particle swarm algorithm and the C&CG algorithm respectively.

[0178] In summary, it plays a core role in the optimization process of the model, is used to measure the difference between two probability distributions, and helps cluster into certain wind and light power output scenarios according to historical wind and light power output data. Monte Carlo simulation, which simulates uncertain or random factors through random sampling. By generating a large number of random samples, a comprehensive statistical analysis of the problem can be carried out. Solving with the upper-layer planning particle swarm algorithm and the lower-layer optimization C&CG algorithm: Under the requirements of the upper-layer planning and lower-layer optimization objectives, minimize the sum of the annual investment cost of E-SOP and the annual load loss cost of the distribution network as much as possible.

[0179] In the embodiment of the present invention, the present invention aims to minimize the sum of the annual investment cost of the E-SOP and the annual load loss cost of the distribution network. The KL divergence is used to obtain the fuzzy set of the wind and light output, and the Monte Carlo sampling of the line faults of the distribution network is generated according to the historical fault conditions. Taking the load demand of a single typical day as an example, by considering the proportion of the fault days and non-fault days in the whole year, and comprehensively considering the combined effects of the transferable load constraint, line topology reconstruction constraint, power flow operation constraint, node voltage constraint, and power upper and lower limit constraints of the distribution network, the operation costs of the multi-terminal interface location selection and capacity configuration of the E-SOP in the distribution network system under fault days and non-fault days are respectively considered, and the multi-terminal interface location selection and capacity configuration of the E-SOP are determined under the comprehensive weight.

[0180] The upper-layer planning and lower-layer scheduling optimization are solved by the particle swarm optimization algorithm and the C&CG algorithm respectively. For the upper layer, the location selection of the multi-terminal interface of the E-SOP and the configuration planning of the capacity size are carried out, and for the lower layer, the optimal scheduling operation of the distribution network is carried out under the worst wind and light output scenario. Thus, the optimal utility result of investing in and constructing the E-SOP is obtained.

[0181] Please refer to Figure 5 , Figure 5 which is the structural block diagram of a device for the location selection and capacity configuration of the electronic flexible soft switch interface provided in the second embodiment of the present invention.

[0182] A device for the location selection and capacity configuration of the electronic flexible soft switch interface provided by the present invention includes:

[0183] A data acquisition module 501, configured to acquire the historical data of the wind and light output and the historical fault data of the distribution network, and construct a fuzzy set of the wind and light output according to the historical data of the wind and light output by using a preset measurement tool;

[0184] A scenario construction module 502, configured to construct a distribution network fault scenario according to the historical fault data of the distribution network by using the Monte Carlo sampling method;

[0185] A model construction module 503, configured to aim at minimizing the sum of the annual investment cost of the electronic flexible soft switch and the annual load loss cost of the distribution network, and construct upper and lower layer models based on the upper and lower layer planning operation constraint conditions;

[0186] An output scheme module 504, configured to solve the upper and lower layer models based on the distribution network fault scenario by using the particle swarm optimization algorithm and the column and constraint generation algorithm according to the fuzzy set of the wind and light output, and generate a location selection and capacity configuration scheme for the electronic flexible soft switch interface.

[0187] Further, the historical fault data of the distribution network includes the fault probabilities of multiple distribution network lines; the scenario construction module 502 is specifically configured to:

[0188] Based on the Monte Carlo sampling method, determine the random numbers for each distribution network line;

[0189] Compare the random numbers and fault probabilities of each distribution network line to generate the comparison results corresponding to each distribution network line;

[0190] Generate the operation status data corresponding to each distribution network line according to the comparison results corresponding to each distribution network line;

[0191] Use the operation status data corresponding to each distribution network line to construct a distribution network fault scenario.

[0192] Furthermore, the upper and lower layer planning operation constraints include upper layer planning constraints and lower layer operation constraints;

[0193] The lower layer operation constraints include electronic flexible soft switch operation constraints, power balance constraints, line maintenance constraints, radial operation constraints, controllable load constraints, and transferable load constraints.

[0194] Furthermore, the upper and lower layer models include an upper layer model and a lower layer model; The output scheme module 504 is specifically used for:

[0195] Based on the distribution network fault scenario, determine the number of days of fault occurrence;

[0196] Under the upper layer planning constraints, generate a particle swarm and initialize the initial energy storage and electronic flexible soft switch planning scheme parameters corresponding to each particle in the particle swarm;

[0197] Use the upper layer model to calculate the annual investment cost of the electronic flexible soft switch corresponding to each particle according to the initial energy storage and electronic flexible soft switch planning scheme parameters corresponding to each particle;

[0198] Under the fuzzy set of wind and light output, based on the electronic flexible soft switch operation constraints, power balance constraints, line maintenance constraints, radial operation constraints, controllable load constraints, and transferable load constraints, use the column and constraint generation algorithm to calculate the operation cost on the fault day corresponding to each particle according to the power flowing through the energy storage and the converter capacity of the flexible soft switch in the initial energy storage and electronic flexible soft switch planning scheme parameters corresponding to each particle;

[0199] Under the fuzzy set of wind and light output, based on the electronic flexible soft switch operation constraints, power balance constraints, radial operation constraints, controllable load constraints, and transferable load constraints, use the column and constraint generation algorithm to calculate the operation cost on the non-fault day corresponding to each particle according to the power flowing through the energy storage and the converter capacity of the flexible soft switch in the initial energy storage and electronic flexible soft switch planning scheme parameters corresponding to each particle;

[0200] The lower-layer model calculates the annual load loss cost of the distribution network corresponding to each particle according to the number of days of fault occurrence, the operating cost of the fault day corresponding to each particle, and the operating cost of the non-fault day;

[0201] The fitness value corresponding to each particle is calculated by using the annual load loss cost of the distribution network corresponding to each particle and the annual investment cost of the electronic flexible soft switch;

[0202] Based on the initial energy storage and the planning scheme parameters of the electronic flexible soft switch corresponding to each particle and the fitness value, the individual historical energy storage and the planning scheme parameters of the electronic flexible soft switch corresponding to each particle and the individual optimal fitness value are determined;

[0203] Traverse the individual historical energy storage and the planning scheme parameters of the electronic flexible soft switch corresponding to each particle and the individual optimal fitness value, and determine the global optimal energy storage and the planning scheme parameters of the electronic flexible soft switch;

[0204] Based on the individual historical energy storage and the planning scheme parameters of the electronic flexible soft switch corresponding to each individual and the global optimal energy storage and the planning scheme parameters of the electronic flexible soft switch, the initial energy storage and the planning scheme parameters of the electronic flexible soft switch corresponding to multiple particles are updated, the updated energy storage and the planning scheme parameters of the electronic flexible soft switch corresponding to each particle are determined, and the update times are counted in real time;

[0205] According to the updated energy storage and the planning scheme parameters of the electronic flexible soft switch corresponding to each particle, the updated fitness value corresponding to each particle is determined;

[0206] Compare the updated fitness value corresponding to each particle with the individual optimal fitness value, and use the energy storage and the planning scheme parameters of the electronic flexible soft switch corresponding to the minimum value in the fitness value as the new individual historical optimal energy storage and the planning scheme parameters of the electronic flexible soft switch;

[0207] Traverse the individual optimal fitness values corresponding to the new individual historical optimal energy storage and the planning scheme parameters of the electronic flexible soft switch, and use the energy storage and the planning scheme parameters of the new individual historical optimal electronic flexible soft switch corresponding to the minimum value in the fitness value as the new global optimal energy storage and the planning scheme parameters of the electronic flexible soft switch;

[0208] When the update times reach the preset iteration times threshold, the location of the electronic flexible soft switch and the converter capacity of the flexible soft switch in the new global optimal energy storage and the planning scheme parameters of the electronic flexible soft switch are used as the location selection and capacity configuration scheme of the electronic flexible soft switch interface.

[0209] Furthermore, the energy storage and the planning scheme parameters of the electronic flexible soft switch include the converter capacity of the flexible soft switch, the location of the converter of the flexible soft switch, the energy storage size, the power size flowing through the energy storage, and the location of the converter of the electronic flexible soft switch; the upper-layer model is specifically:

[0210] ;

[0211] Wherein, is the annual investment cost of the electronic flexible soft switch; r is the discount rate; y is the operation life of the electronic flexible soft switch; is the total number of nodes in the distribution network; is the cost per unit capacity of the converter of the flexible soft switch; is the converter capacity of the flexible soft switch between node i and node j; is whether to install a flexible soft switch converter between node i and node j, randomly takes values of 0 or 1. When , it means installation and is recorded as the siting location of the flexible soft switch converter. When , it means no installation; is the installation and fixing cost of the flexible soft switch converter; is the cost per unit capacity of the flexible soft switch energy storage; is the energy storage size between node i and node j; is the cost per unit power of the flexible soft switch energy storage; is the power size flowing through the energy storage between node i and node j; is whether to further install energy storage on the basis of installing a flexible soft switch converter between node i and node j to form an electronic flexible soft switch converter, randomly takes values of 0 or 1. When , it means installation and is recorded as the siting location of the electronic flexible soft switch converter. When , it means no installation; is the installation and fixing cost of the flexible soft switch energy storage.

[0212] Furthermore, the lower-layer model is specifically:

[0213] ;

[0214] Wherein, is the annual load loss cost of the distribution network; D is a vector composed of uncertainty parameters, representing the fuzzy set of wind and light output; M is the Mth wind and light output scenario; is the probability of the nth scenario under the actual wind and light output distribution; K is the number of fault days; is the operation cost on fault days; is the operation cost on non-fault days.

[0215] Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the above-described devices and modules can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0216] An embodiment of the present invention further provides a computer device, including a memory and a processor, where a computer program is stored in the memory; when the computer program is executed by the processor, the processor executes the steps of the method for selecting the location and configuring the capacity of the electronic flexible soft switch interface in the first embodiment as described above.

[0217] An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program / instructions is stored, and when the computer program / instructions is executed by the processor, the steps of the method for selecting the location and configuring the capacity of the electronic flexible soft switch interface in the first embodiment as described above are implemented.

[0218] An embodiment of the present invention further provides a computer program product, including a computer program / instructions, and when the computer program / instructions is executed by the processor, the steps of the method for selecting the location and configuring the capacity of the electronic flexible soft switch interface in the first embodiment as described above are implemented.

[0219] In several embodiments provided by the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces. The indirect coupling or communication connection of devices or units can be in an electrical, mechanical or other form.

[0220] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place, or they can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0221] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for selecting the location and configuring the capacity of an electronic flexible soft switch interface, characterized in that: include: Acquire historical wind and solar power output data and historical distribution network fault data, and use a preset measurement tool to construct a wind and solar power output fuzzy set based on the historical wind and solar power output data; A distribution network fault scenario is constructed based on the historical fault data of the distribution network using a Monte Carlo sampling method; The goal is to minimize the sum of the annual investment cost of the electronic flexible soft switch and the annual load loss cost of the distribution network, and to construct the upper and lower layer models based on the upper and lower layer planning and operation constraints. Based on the distribution network fault scenario, the particle swarm algorithm and column and constraint generation algorithm are used to solve the upper and lower layer models according to the wind and solar output fuzzy sets to generate the electronic flexible soft switch interface location selection and capacity configuration plan.

2. The method for selecting the location and configuring the capacity of an electronic flexible soft switch interface according to claim 1, characterized in that: The distribution network historical fault data includes the failure probabilities of multiple distribution network lines; The Monte Carlo sampling method is used to construct a distribution network fault scenario according to the distribution network historical fault data, including: Determining random numbers for each distribution network line based on the Monte Carlo sampling method; Comparing the random number and the fault probability of each of the distribution network lines to generate a comparison result corresponding to each of the distribution network lines; Generate operation status data corresponding to each of the distribution network lines according to the comparison results corresponding to each of the distribution network lines; The operating status data corresponding to each of the distribution network lines is used to construct a distribution network failure scenario.

3. The method for selecting the location and configuring the capacity of an electronic flexible soft switch interface according to claim 1, characterized in that: The upper and lower layer planning and operation constraints include upper layer planning constraints and lower layer operation constraints; The lower-level operating constraints include electronic flexible soft switch operating constraints, power balance constraints, line maintenance constraints, radial operating constraints, controllable load constraints, and transferable load constraints.

4. The method for selecting the location and configuring the capacity of an electronic flexible soft switch interface according to claim 3, characterized in that: The upper and lower layer models include an upper layer model and a lower layer model; based on the distribution network fault scenario, the particle swarm algorithm and the column and constraint generation algorithm are used to solve the upper and lower layer models according to the wind and solar output fuzzy set to generate the electronic flexible soft switch interface location selection and capacity configuration plan, including: Based on the distribution network fault scenario, determining the number of days the fault will occur; Under the upper-level planning constraints, a particle swarm is generated, and initialization of the initial energy storage and electronic flexible soft switch planning scheme parameters corresponding to each particle in the particle swarm; The upper model is used to calculate the annual investment cost of the electronic flexible soft switch corresponding to each particle according to the initial energy storage corresponding to each particle and the electronic flexible soft switch planning scheme parameters; Under the wind and solar output fuzzy set, based on the electronic flexible soft switch operation constraint, the power balance constraint, the line maintenance constraint, the radial operation constraint, the controllable load constraint, and the transferable load constraint, the column and constraint generation algorithm is used to calculate the fault day operation cost corresponding to each particle according to the initial energy storage corresponding to each particle and the power size of the energy storage flow in the electronic flexible soft switch planning scheme parameters and the converter capacity of the flexible soft switch; Under the wind and solar output fuzzy set, based on the electronic flexible soft switch operation constraint, the power balance constraint, the radial operation constraint, the controllable load constraint, and the transferable load constraint, the column and constraint generation algorithm is used to calculate the non-fault day operation cost corresponding to each particle according to the initial energy storage corresponding to each particle and the power flowing through the energy storage in the electronic flexible soft switch planning scheme parameters and the converter capacity of the flexible soft switch; The lower model is used to calculate the annual load loss cost of the distribution network corresponding to each particle according to the number of days when the fault occurs, the fault day operation cost corresponding to each particle, and the non-fault day operation cost; The fitness value corresponding to each particle is calculated by using the annual load loss cost of the distribution network and the annual investment cost of the electronic flexible soft switch corresponding to each particle; Based on the initial energy storage and electronic flexible soft switch planning scheme parameters and fitness values ​​corresponding to each of the particles, determine the individual historical energy storage and electronic flexible soft switch planning scheme parameters and individual optimal fitness values ​​corresponding to each of the particles; Traversing the individual historical energy storage and electronic flexible soft switch planning scheme parameters and individual optimal fitness values ​​corresponding to each of the particles, and determining the global optimal energy storage and electronic flexible soft switch planning scheme parameters; Based on the individual historical energy storage and electronic flexible soft switch planning scheme parameters and the global optimal energy storage and electronic flexible soft switch planning scheme parameters, the initial energy storage and electronic flexible soft switch planning scheme parameters corresponding to the plurality of particles are updated, the updated energy storage and electronic flexible soft switch planning scheme parameters corresponding to the particles are determined, and the number of updates is counted in real time; Determining the updated fitness value corresponding to each particle according to the updated energy storage and electronic flexible soft switch planning scheme parameters corresponding to each particle; Comparing the updated fitness values ​​corresponding to each of the particles with the individual optimal fitness value, and taking the energy storage and electronic flexible soft switch planning scheme parameters corresponding to the minimum value of the fitness values ​​as new individual historical optimal energy storage and electronic flexible soft switch planning scheme parameters; Traversing the individual optimal fitness values ​​corresponding to the new individual historical optimal energy storage and electronic flexible soft switch planning scheme parameters, and taking the new individual historical optimal energy storage and electronic flexible soft switch planning scheme parameters corresponding to the minimum value in the fitness values ​​as the new global optimal energy storage and electronic flexible soft switch planning scheme parameters; When the update number reaches a preset iteration number threshold, the electronic flexible soft switch site selection position and the converter capacity of the flexible soft switch in the new global optimal energy storage and electronic flexible soft switch planning scheme parameters are used as the electronic flexible soft switch interface site selection and capacity configuration scheme.

5. The method for selecting the location and configuring the capacity of an electronic flexible soft switch interface according to claim 4, characterized in that: The parameters of the energy storage and electronic flexible soft switch planning scheme include the converter capacity of the flexible soft switch, the location of the flexible soft switch converter, the energy storage size, the power size of the energy storage flow, and the location of the electronic flexible soft switch converter; the upper model is specifically: ； in, is the annual investment cost of the electronic flexible soft switch; r is the discount rate; y is the operating life of the electronic flexible soft switch; is the total number of nodes in the distribution network; is the cost per unit capacity of the converter with flexible soft switching; is the converter capacity of the flexible soft switch between nodes i and j; Whether to install a flexible soft switch converter between node i and node j, Randomly take a value of 0 or 1. , it means installation and is recorded as the location of the flexible soft switch converter. , indicating that it is not installed; Fixed cost for installation of flexible soft-switching converters; is the unit capacity cost of flexible soft switch energy storage; is the energy storage size between node i and node j; is the unit power cost of flexible soft switching energy storage; is the power of energy storage flow between node i and node j; Whether to further install energy storage to form an electronic flexible soft switching converter on the basis of installing a flexible soft switching converter between node i and node j, Randomly take a value of 0 or 1. , it means installation and is recorded as the location of the electronic flexible soft switch converter. , indicating that it is not installed; Fixed costs for installing energy storage for flexible soft switches.

6. The method for selecting the location and configuring the capacity of an electronic flexible soft switch interface according to claim 4, characterized in that: The lower layer model is specifically: ； in, is the annual load loss cost of the distribution network; D is a vector composed of uncertainty parameters, representing the fuzzy set of wind and solar output; M is the Mth wind and solar output scenario; is the probability of the nth scenario under the actual wind and solar power output distribution; K is the number of fault days; is the operating cost on the failure day; It is the operating cost on a non-fault day.

7. An electronic flexible soft switch interface location selection and capacity configuration device, characterized in that: include: A data acquisition module is used to acquire historical wind and solar power output data and historical distribution network fault data, and to construct a wind and solar power output fuzzy set based on the historical wind and solar power output data using a preset measurement tool; A scenario building module is used to build a distribution network fault scenario based on the historical fault data of the distribution network using a Monte Carlo sampling method; A model building module is used to build upper and lower models with the goal of minimizing the sum of the annual investment cost of the electronic flexible soft switch and the annual load loss cost of the distribution network, and based on the upper and lower planning and operation constraints; The output scheme module is used to solve the upper and lower layer models according to the wind and solar output fuzzy sets based on the distribution network fault scenario, and generate the electronic flexible soft switch interface location selection and capacity configuration scheme.

8. A computer device, characterized in that: It includes a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the steps of the method for selecting the location and configuring the capacity of the electronic flexible soft switch interface as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed, the method for selecting the location and configuring the capacity of the electronic flexible soft switch interface as described in any one of claims 1 to 6 is implemented.

10. A computer program product, characterized in that The computer program product includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions, wherein when the program instructions are executed by a computer, the computer executes the electronic flexible soft switch interface location selection and capacity configuration method as described in any one of claims 1-6.

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