An electronic flexible soft-switch interface location selection and capacity configuration method and device

Abstract

The present invention discloses a method and device for location selection and capacity configuration of an electronic flexible soft-switching interface, which relates to the technical field of power systems and is used to solve the technical problem that the existing method for location selection and capacity configuration of an electronic flexible soft-switching interface results in poor overall reliability of the distribution network. The method includes obtaining historical data of wind and light output and historical fault data of the distribution network, and constructing a fuzzy set of wind and light output according to the historical data of wind and light output by using a preset metric tool; 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; taking the minimum sum of the annual investment cost of the electronic flexible soft-switching 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; based on the fault scenario of the distribution network, using the particle swarm optimization 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 generating a location selection and capacity configuration scheme for the electronic flexible soft-switching interface.

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. In this context, the application of flexible soft switches (Soft Open Point, SOP) for distribution networks has become increasingly common.

[0003] The SOP technology mainly replaces the traditional 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 conventional feeder flexible interconnection, 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), thereby achieving complementary advantages.

[0005] The 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, resulting in an unreasonable configuration of the soft switch and being unable to fully reflect the actual needs of the power grid, leading to 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 measurement 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] Take the minimum of the 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 construct 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 optimization 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 comparison results corresponding to each distribution network line;

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

[0016] Use the operation status 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 optimization algorithm and the column and constraint generation algorithm are used to solve the upper and lower layer model according to the wind-solar power 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-solar power 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 operation cost on the fault days corresponding to each particle according to 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;

[0024] Under the wind-solar power 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 operation cost on the non-fault days corresponding to each particle according to 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;

[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 days corresponding to each particle, and the operation cost on the non-fault days;

[0026] Calculate the fitness value corresponding to each particle 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;

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

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

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

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

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

[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 planning scheme parameters of the electronic flexible soft switch as the location selection and capacity configuration scheme of the electronic flexible soft switch interface.

[0034] Optionally, the energy storage and 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:

[0035] ;

[0036] 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; 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 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.

[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 output data and historical distribution network fault data, and construct a fuzzy set of wind and light output according to the historical wind and light 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 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 construct an upper and lower layer model with upper and lower layer planning operation constraints;

[0044] An output scheme module, configured to solve the upper and lower layer models according to the fuzzy set of wind and light power generation based on the distribution network fault scenario by using a particle swarm optimization algorithm and a column and constraint generation algorithm, and generate a location selection and capacity configuration scheme for 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 location selection and capacity configuration of an electronic flexible soft-switch interface. First, historical data of wind and light output and historical fault data of the distribution network are obtained, and a fuzzy set of wind and light output is constructed according to the historical data of wind and light output by using a preset measurement tool. 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. 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 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 optimization 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 light output, and a location selection and capacity configuration scheme for the electronic flexible soft-switch interface is generated. Based on the above solution, a fuzzy set of wind and light output is constructed according to the obtained historical data of wind and light output by using a preset measurement tool. 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 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, a particle swarm optimization algorithm and a column and constraint generation algorithm are combined to solve the upper and lower layer models according to the fuzzy set of wind and light output, and a location selection and capacity configuration scheme for the electronic flexible soft-switch interface is generated. In this process, the present invention takes into account the uncertainty of wind and light output, can cope with different load and demand changes, realizes faster power exchange and power flow regulation, and thus improves 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 drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can also obtain other drawings based on these drawings without creative efforts.

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

[0052] Figure 2 It is a schematic diagram of the iteration process based on the particle swarm optimization 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 5It is a structural block diagram of a device for location selection and capacity configuration of an electronic flexible soft switch interface provided in Embodiment 2 of the present invention. Detailed implementation manners

[0056] Embodiments of the present invention provide a method and a device for location selection and capacity configuration of an electronic flexible soft switch interface, which are used to solve the technical problem that the existing method for location selection and capacity configuration of an electronic flexible soft switch interface results in poor overall reliability of a distribution network.

[0057] In order to make the invention 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 belong to the scope of protection of the present invention.

[0058] Please refer to Figure 1 , Figure 1 It is a step flowchart of a method for location selection and capacity configuration of an electronic flexible soft switch interface provided in Embodiment 1 of the present invention.

[0059] A method for location selection and capacity configuration 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 a 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 a 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 for 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] (i.e., the random number of the distribution network line) is generated for the ij-th distribution line, and it 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] where 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] Furthermore, repeat the above sampling process for all distribution lines with historical fault conditions in the distribution network 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 that in 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 realized 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. Among them, 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 positions of the E-SOP (location selection of the electronic flexible soft switch interface positions) 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 the installation. Therefore, the upper layer planning constraints refer to a series of restrictive and prescriptive conditions set 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 positions of the E-SOP and the capacity configuration planning, etc.) to ensure the rationality, feasibility, and compliance with specific goals and actual situations of the planning results; 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 the value 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 the value 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 among the power flowing through the energy storage during the t period; is the charging power of the E-SOP energy storage among 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 node i and node j during the t period; is the reactive power upper limit coefficient.

[0088] Based on the above, for the convenience of calculation, for the above radical expressions, that is, for and perform second-order cone relaxation to obtain the following expressions:

[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 among 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 among 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 energy storage; is the maximum 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 on 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 at 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 on 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 at node i at time t; is the power value of the starting node 1 of the distribution network at time t; is the magnitude of the power provided by the superior power grid at time t.

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

[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 in a conducting state during period t. , indicating that line ij is in an open state 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 fault line can be repaired within the same period. 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 fault lines within the same period, that is, at most h lines can be repaired every T f time. The line repair constraints are as follows:

[0113] ;

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

[0115] period. This situation of repaired lines should be less than or equal to h lines.

[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 time. Common transferable loads include electric vehicle charging stations, some industrial loads, etc. Their incentive response model can be expressed as:

[0128] ;

[0129] ;

[0130] ;

[0131] where, 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 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 use 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] where, 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 parameters of the energy storage and electronic flexible soft-switching planning scheme include the converter capacity of the flexible soft-switching, the location of the flexible soft-switching converter, the size of the energy storage, the power flowing through the energy storage, and the location of the electronic flexible soft-switching converter; the upper-layer model is specifically:

[0136] ;

[0137] where, is the annual investment cost of the electronic flexible soft-switching; r is the discount rate; y is the operation years of the electronic flexible soft-switching; is the total number of nodes in the distribution network; The cost per unit capacity of the converter for the flexible soft switch; The capacity of the converter for the flexible soft switch between node i and node j; Indicates whether a flexible soft switch converter is installed between node i and node j, Randomly takes values 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; The installation and fixing cost of the flexible soft switch converter; The cost per unit capacity of the flexible soft switch energy storage; The energy storage size between node i and node j; The cost per unit power of the flexible soft switch energy storage; The power size flowing through the energy storage between node i and node j; 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 takes values 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; The installation and fixing cost of the flexible soft switch energy storage.

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

[0139] ;

[0140] Among them, 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; The probability of the nth scenario under the actual wind and light output distribution; K is the number of fault days; The operating cost on the fault day; The operating 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 output fuzzy set, and generate the siting and capacity configuration scheme of the electronic flexible soft switch interface position.

[0142] It should be noted that for the upper-layer particle swarm algorithm: The particle swarm optimization (PSO) 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 flocks or other biological groups in the search space. For the lower-layer C&CG algorithm (Column and Constraint Generation Algorithm) solution: 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 based on 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 optimizes the dispatching operation of the distribution network under the worst wind and light power output scenarios.

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

[0145] Step S41: Based on the distribution network fault scenarios, determine the number of days of faults.

[0146] It should be noted that based on the operation state data of the distribution lines in the distribution network fault scenarios, the number of days of faults is determined. For example, the operation state data of the distribution lines in the distribution network fault scenarios includes 120 days, among which the operation state data of 6 days shows faults. The number of days of faults 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 schemes 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 schemes corresponding to each particle.

[0149] Step S44: Under the fuzzy set of wind and light output, 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, the column and constraint generation algorithm is used 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 fuzzy set of wind and light output, based on the operation constraints of the electronic flexible soft switch, power balance constraints, radial operation constraints, controllable load constraints, and transferable load constraints, the column and constraint generation algorithm is used 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: The lower-layer model is used to calculate the annual load loss cost of each particle in the 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: The fitness value of each particle is calculated using the annual load loss cost of each particle in the 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 values corresponding to each particle, the individual historical energy storage and electronic flexible soft switch planning scheme parameters and individual optimal fitness values corresponding to each particle are determined;

[0154] Step S49: Traverse the individual historical energy storage and electronic flexible soft switch planning scheme parameters and individual optimal fitness values 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, the initial energy storage and electronic flexible soft switch planning scheme parameters corresponding to multiple particles are updated to determine the updated energy storage and electronic flexible soft switch planning scheme parameters corresponding to each particle, and the update times are counted in real time;

[0156] Step S411: According to the updated energy storage and electronic flexible soft switch planning scheme parameters corresponding to each particle, the updated fitness value corresponding to each particle is determined;

[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-switching planning scheme parameters corresponding to the minimum value in the fitness values as the new individual historical optimal energy storage and electronic flexible soft-switching 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-switching planning scheme parameters, and use the new individual historical optimal energy storage and electronic flexible soft-switching planning scheme parameters corresponding to the minimum value in the fitness values as the new global optimal energy storage and electronic flexible soft-switching 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-switching 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-solar power output. Under the wind-solar 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-switching planning scheme parameters corresponding to each particle, the converter capacity of the electronic flexible soft-switch), find the wind-solar 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 superior 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 is worth mentioning that the iterative process based on the column and constraint generation (C&CG) algorithm can refer to the step process of the existing technology, and the present invention will not elaborate too much.

[0170] Furthermore, please refer to Figure 2 . Under the upper-layer 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; among them, if it is the first iteration currently, record the initial energy storage and electronic flexible soft switch planning scheme parameters and its fitness value 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. Among them, V is the number of iterations (i.e., the number of updates).

[0171] Furthermore, 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 count 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. Then, 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 are used 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, and thus significantly improve the overall response speed of the system. At the same time, 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 assisted regulation. These specific requirements will promote the diversified development of SOP in terms of structure and function in the future. 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 SOP to jointly enhance the flexibility of the distribution network 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, thus achieving 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 issues to be solved urgently. At the same time, the reasonable configuration of the location selection of the multi-terminal interface of E-SOP and the allowable power capacity and internal energy storage size passing through it 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 output, and generates the line faults of the distribution network through Monte Carlo sampling 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 the whole year, the operating 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 considered respectively, and the multi-terminal interface location selection and capacity configuration of E-SOP are determined under the comprehensive weight.

[0177] Combined with the joint action 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, according to the load size of each time period, the location selection of the multi-terminal interface of E-SOP in the upper layer and the configuration planning of the capacity size are carried out, and the lower layer optimizes the dispatching operation of the distribution network under the worst wind and light output scenario. The upper layer planning and the 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 to cluster into certain wind and light output scenarios according to historical wind and light output data. Monte Carlo simulation, Monte Carlo simulation 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 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-solar 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 system, the operation costs of the multi-terminal interface location selection and capacity configuration of the E-SOP in the distribution network system on 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-solar 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 an 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 an 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-solar output and the historical fault data of the distribution network, and construct a fuzzy set of the wind-solar output according to the historical data of the wind-solar output by using a preset measurement tool;

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

[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 an upper and lower layer model with 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 fault scenario of the distribution network by using the particle swarm optimization algorithm and the column and constraint generation algorithm according to the fuzzy set of the wind-solar output, and generate a location selection and capacity configuration scheme for the electronic flexible soft switch interface.

[0187] Furthermore, 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 of each distribution network line;

[0189] Compare the random numbers of each distribution network line with the fault probability 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] Construct a distribution network fault scenario by using the operation status data corresponding to each distribution network line.

[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 the operation constraints of electronic flexible soft switches, 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 the planning scheme parameters of the electronic flexible soft switch 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 the planning scheme parameters of the electronic flexible soft switch corresponding to each particle;

[0198] Under the fuzzy set of wind and light output, based on the operation constraints of electronic flexible soft switches, 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 days 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 the planning scheme parameters of the electronic flexible soft switch corresponding to each particle;

[0199] Under the fuzzy set of wind and light output, based on the operation constraints of electronic flexible soft switches, 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 days 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 the planning scheme parameters of the electronic flexible soft switch 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 operation cost of the fault day corresponding to each particle, and the operation 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 capacity of the converter 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 capacity of the converter of the flexible soft switch, the location of the converter of the flexible soft switch, the size of the energy storage, the power flowing through the energy storage, 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 energy storage of the flexible soft switch; is the energy storage size between node i and node j; is the cost per unit power of the energy storage of the flexible soft switch; 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 energy storage of the flexible soft switch.

[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 the fault day; is the operation cost on the non-fault day.

[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 locating and capacity configuring the position of the electronic flexible soft switch interface as described in the first embodiment above.

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

[0219] In several embodiments provided by the present application, it should be understood that the disclosed device and method can be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of units is only a logical function division, and there may be other division methods in actual implementation. 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 couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of devices or units can be in electrical, mechanical or other forms.

[0220] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or may 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, and are not intended 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 locating the position and configuring the capacity of an electronic flexible soft-switch interface, characterized in that Including: 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 measurement tool; Construct a distribution network fault scenario according to the historical fault data of the distribution network by using the Monte Carlo sampling method; 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 goal, and constructing an upper and lower layer model with upper and lower layer planning operation constraints; Based on the distribution network fault scenario, use the particle swarm optimization 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; The historical fault data of the distribution network includes the fault probabilities of multiple distribution network lines; The constructing a distribution network fault scenario according to the historical fault data of the distribution network by using the Monte Carlo sampling method includes: Based on the Monte Carlo sampling method, determine the random numbers of each distribution network line; Compare the random numbers and fault probabilities of each distribution network line to generate comparison results corresponding to each distribution network line; Generate operation state data corresponding to each distribution network line according to the comparison results corresponding to each distribution network line; Construct a distribution network fault scenario by using the operation state data corresponding to each distribution network line; The upper and lower layer model includes an upper layer model and a lower layer model; The upper layer model is specifically: ； Among them, 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; indicates 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 fixation 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; 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 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 fixation cost of the flexible soft switch energy storage; The lower layer model is specifically: ； 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 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 operating cost on the fault day; is the operating cost on non-fault days; ； ； ； ； ； ； ； Among them, and are the operating cost on the fault day and the operating cost on the non - fault day; , , are respectively the cost of purchasing electricity from the superior, the load shedding penalty, and the power loss cost of E - SOP within 24 hours; and are respectively the line maintenance cost and the transferred load cost; is the set of distribution network lines; , , , and are respectively the unit electricity purchase cost coefficient, the load shedding penalty coefficient, the E - SOP power loss cost coefficient, the line maintenance cost coefficient, and the transferred load cost coefficient; is the situation where branch k fails, is the fault, is the non - fault; is the magnitude of the power provided by the superior power grid within time period t; is the active load shed by bus i within time period t; is the active power loss injected between node i and node j by the SOP converter within time period t; and are respectively the loads transferred in and out within time period t; is the time step; is the time of the transferable load; is the total number of nodes in the distribution network.

2. The method for locating the position and configuring the capacity of the electronic flexible soft-switch interface according to claim 1, characterized in that The upper and lower layer planning operation constraints include upper layer planning constraints and lower layer operation constraints; 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.

3. The method for locating the position and configuring the capacity of the electronic flexible soft-switch interface according to claim 2, characterized in that, The generating a location selection and capacity configuration scheme for the electronic flexible soft switch interface by using the particle swarm optimization 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 based on the distribution network fault scenario includes: Based on the distribution network fault scenario, determine the number of fault days; 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; The parameters of the initial energy storage and electronic flexible soft switch planning scheme include the converter capacity of the flexible soft switch, the location selection of the flexible soft switch converter, the energy storage size, the power flowing through the energy storage, and the location selection of the flexible soft switch converter; 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; Under the fuzzy set of wind and light output, based on the electronic flexible soft switch operation constraints, the power balance constraints, the line maintenance constraints, the radial operation constraints, the controllable load constraints, and the transferable load constraints, use the column and constraint generation algorithm to calculate the fault day operation cost corresponding to each particle according to 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; Under the fuzzy set of wind and light output, 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, the column and constraint generation algorithm is used to calculate the operation cost of each 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; The lower-layer model is used to calculate the annual load loss cost of the distribution network corresponding to each particle according to the number of fault days, the operation cost of each fault day corresponding to each particle, and the operation cost of non-fault days; 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; Based on the initial energy storage and electronic flexible soft switch planning scheme parameters and fitness values corresponding to each particle, the individual historical energy storage and electronic flexible soft switch planning scheme parameters and individual optimal fitness values corresponding to each particle are determined; Traverse the individual historical energy storage and electronic flexible soft switch planning scheme parameters and individual optimal fitness values 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, the initial energy storage and electronic flexible soft switch planning scheme parameters corresponding to multiple particles are updated to determine the updated energy storage and electronic flexible soft switch planning scheme parameters corresponding to each particle, and the update times are counted in real time; According to the updated energy storage and electronic flexible soft switch planning scheme parameters corresponding to each particle, the updated fitness value corresponding to each particle is determined; 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; 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; 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 electronic flexible soft switch planning scheme parameters are used as the location selection and capacity configuration scheme of the electronic flexible soft switch interface.

4. An electronic flexible soft-switch interface location selection and capacity configuration device, which is applied to the electronic flexible soft-switch interface location selection and capacity configuration method described in claim 1, and is characterized in that, Including: The data acquisition module is used to acquire the historical data of wind and light output and the 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 measurement tool; The scenario construction module is used to construct a distribution network fault scenario according to the historical fault data of the distribution network by using the Monte Carlo sampling method; The model construction module is used 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 switch and the annual load loss cost of the distribution network, and with the upper and lower layer planning operation constraints. The output scheme module is used to solve the upper and lower layer model based on the distribution network fault scenario, using the particle swarm algorithm and the column and constraint generation algorithm according to the fuzzy set of wind and light output, and generate the location selection and capacity configuration scheme of the electronic flexible soft switch interface.

5. A computer device, characterized in that, It includes a memory and a processor. When the computer program stored in the memory is executed by the processor, the processor executes the steps of the method for location selection and capacity configuration of the electronic flexible soft switch interface according to any one of claims 1-3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed, it implements the method for location selection and capacity configuration of the electronic flexible soft switch interface according to any one of claims 1-3.

7. A computer program product, characterized in that, The computer program product 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 executes the method for location selection and capacity configuration of the electronic flexible soft switch interface according to any one of claims 1-3.

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