Optimized operation method, device, equipment and medium applicable to flexible distribution network

Abstract

The present invention belongs to the field of smart grid technology, and specifically relates to an optimization operation method, device, equipment and medium suitable for flexible distribution network. The optimization operation method suitable for flexible distribution network proposed by the present invention takes the minimum voltage fluctuation of distribution network as the goal, optimizes decision variables by using particle swarm algorithm, obtains a first optimization result; uses the first optimization result as the search starting point X0, adopts iterative search method, based on the optimal step size g m A linear search of the objective function along each direction of the unit vector e is performed sequentially to obtain the optimal solution for the flexible multi-state switch FDS operation. By coordinating global and local searches, the global search efficiently explores the entire solution space, determining the approximate location of the optimal solution. Based on this, a refined local search is performed, thereby improving the local optimization capability. This avoids local optimality and speeds up the calculation.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system automation, and in particular relates to an optimized operation method, device, equipment and medium applicable to a flexible distribution network. Background Art

[0002] The increasing penetration rate of distributed generation (DG) and the continuous growth of load have put higher demands on the operation of distribution networks. How to efficiently utilize renewable energy, accommodate diversified electricity demands, and improve power supply quality and reliability while ensuring economical power distribution are important goals of the development of smart distribution networks.

[0003] With the rapid development of power electronics and control technology, flexible DC technology based on voltage source converters has been widely used at the transmission network level. In recent years, to meet the demand for high-quality power supply, flexible DC technology has gradually expanded to the distribution level. Existing research has shown that flexible DC distribution technology can effectively improve power quality, enhance power supply reliability, and facilitate the flexible integration of renewable energy, with broad development prospects. In particular, to address the shortcomings of existing primary distribution equipment, which is mostly limited by capacity, adjustment times, and continuity, international scholars have pioneered the concept of flexible interconnection of distribution networks based on flexible distribution multi-state switches (FDS). Implemented based on fully controlled power electronic devices, FDS is used to connect two or more distribution lines and has the ability to quickly and continuously adjust active and reactive power. When the system is operating normally, FDS can effectively balance the load and alleviate network congestion by adjusting the power output of each port, allowing distribution assets to be fully utilized. It can also reduce network losses, improve power quality, and achieve economical and efficient operation of the system. When a system fault occurs, FDS can quickly isolate the fault without increasing the short-circuit current, providing voltage and frequency support for the outage area, thereby improving the safety and reliability of the system.

[0004] Most existing studies have either considered the impact of DG access on distribution network losses and voltage or FDS alone, but have not yet fully analyzed the impact of the increasing DG penetration rate and the access of FDS on distribution network operations. At the same time, the optimization algorithms currently used are mainly traditional computational intelligence methods or linear programming methods. Traditional computational intelligence methods have the problems of premature maturity and poor local optimization capabilities, and linear programming methods have the disadvantage of easily falling into local extreme values. Summary of the Invention

[0005] The purpose of the present invention is to provide an optimization operation method, device, equipment and medium suitable for a flexible distribution network to solve the drawbacks brought about by the above-mentioned traditional optimization methods.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] A first aspect of the present invention provides an optimization operation method applicable to a flexible distribution network, comprising the following steps:

[0008] Obtain network topology data and operating parameters of the target distribution network;

[0009] Determine the operating constraints of the target distribution network;

[0010] Determine the operating parameters of the flexible multi-state switch FDS as decision variables;

[0011] Perform power flow calculation based on the network topology data and operating parameters of the target distribution network to obtain the target distribution network voltage value;

[0012] Based on the target distribution network voltage value, with the goal of minimizing the distribution network voltage fluctuation, and under the operating constraints of the target distribution network, a particle swarm algorithm is used to optimize the decision variables, and the decision variables corresponding to the optimal objective function are taken as the first optimization result;

[0013] Create a unit vector e=[e1,e2,…,e i ,…,e M ] T ; Where i = 1, 2, ..., M, M is the dimension of the decision variable;

[0014] The first optimization result is used as the search starting point X0, and an iterative search method is used based on the optimal step size g m A linear search of the objective function is performed along each direction of the unit vector e in turn. The search ends when the preset conditions are met, and the decision variables corresponding to the search points obtained at this time are used as the optimal solution for the flexible multi-state switch FDS operation.

[0015] Furthermore, in the step of determining the operating constraints of the target distribution network, the operating constraints of the target distribution network at least include upper and lower voltage limit constraints.

[0016] Furthermore, the steps of optimizing the decision variables using the particle swarm algorithm include the following:

[0017] Step 1: Initialize the particle swarm, including initializing the elements contained in each particle and the elements contained in all particles;

[0018] Step 2: Calculate the current flow based on the position of each particle to obtain the initial fitness of each particle;

[0019] Step 3: For a particle, take its initial fitness as the current local optimal value, and take the position corresponding to each initial fitness as the position of the local optimal value of each particle;

[0020] Step 4: For all particles, select the best initial fitness from each initial fitness, take the best initial fitness as the current global optimal value, and take the position corresponding to the best initial fitness as the position of the global optimal value;

[0021] Step 5: Update the current speed of each particle;

[0022] Step 6: Update the current position of each particle;

[0023] Step 7: For a particle, compare whether the fitness of the current particle is better than the historical local optimal value. If so, take the fitness of the current particle as the local optimal value of the particle, and its corresponding position as the location of the local optimal value of the particle;

[0024] Step 8: Find the global optimal value in the current group, and use the position corresponding to the current global optimal value as the position of the global optimal value of the particle swarm. The various decision variables represented by the position of the global optimal value constitute the first optimization result.

[0025] Furthermore, based on the optimal step size g m In the step of linearly searching the objective function along each direction of the unit vector e, the following formula is used to update the search point:

[0026] X i =X i +g m e

[0027] Among them, g m represents the optimal step size; e represents the unit vector; X i Indicates the search point;

[0028] In the first iteration, the search initial point X0 is substituted into the above formula as the starting point, and the search point is continuously updated through the above formula. When the search in all directions is completed, the final search point X of the current iteration is finally obtained. M .

[0029] Furthermore, the optimal step length g m Calculate according to the following formula:

[0030]

[0031] Among them, θ i It is an operator; is the gradient of the objective function; is the modulus of the objective function; is θ i The transpose of .

[0032] Furthermore, the operator θ iThe expression is as follows:

[0033]

[0034] Further, update the unit vector e to search for point X M As the new search starting point, along the acceleration direction X M -X0 performs a linear search on the target function again.

[0035] The updated unit vectors are as follows:

[0036] e=[e2,e3,...,e M ,||X M -X0||] T

[0037] In each iteration, when updating the unit vector e, the first direction in the unit vector e is removed and a new search direction is added.

[0038] A second aspect of the present invention provides an optimized operation device applicable to a flexible distribution network, comprising the following steps:

[0039] An acquisition module is used to obtain network topology data and operating parameters of the target distribution network;

[0040] A first determination module is used to determine the operation constraints of the target distribution network;

[0041] A second determination module is used to determine the operating parameters of the flexible multi-state switch FDS as decision variables;

[0042] The power flow calculation module is used to perform power flow calculation based on the network topology data and operating parameters of the target distribution network to obtain the target distribution network voltage value;

[0043] a preliminary optimization module for optimizing decision variables using a particle swarm optimization algorithm based on the target distribution network voltage value and with the goal of minimizing distribution network voltage fluctuations while satisfying the operating constraints of the target distribution network, and taking the decision variables corresponding to the optimal objective function as the first optimization result;

[0044] Vector building block, used to build the unit vector e = [e1, e2, ..., e i ,…,e M ] T ; Where i = 1, 2, ..., M, M is the dimension of the decision variable;

[0045] The final optimization module is used to use the first optimization result as the search starting point X0, and adopt an iterative search method based on the optimal step size g mA linear search of the objective function is performed along each direction of the unit vector e in turn. The search ends when the preset conditions are met, and the decision variables corresponding to the search points obtained at this time are used as the optimal solution for the flexible multi-state switch FDS operation.

[0046] According to a third aspect of the present invention, an electronic device is provided, comprising a processor and a memory, wherein the processor is configured to execute a computer program stored in the memory to implement an optimized operation method applicable to a flexible distribution network.

[0047] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by a processor, an optimized operation method applicable to a flexible distribution network is implemented.

[0048] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0049] The optimization operation method for flexible distribution network proposed in the present invention takes the minimum voltage fluctuation of distribution network as the goal, optimizes the decision variables by adopting particle swarm algorithm, and obtains the first optimization result; takes the first optimization result as the search initial point X0, adopts iterative search method, and obtains the optimal step size g based on the optimal step size g. m A linear search of the objective function along each direction of the unit vector e is performed sequentially to obtain the optimal solution for the flexible multi-state switch FDS operation. By coordinating global and local searches, the global search efficiently explores the entire solution space, determining the approximate location of the optimal solution. Based on this, a refined local search is performed, thereby improving the local optimization capability. This avoids local optimality and speeds up the calculation. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The accompanying drawings, which constitute part of this application, are intended to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are intended to explain the present invention and do not constitute an undue limitation of the present invention. In the accompanying drawings:

[0051] Figure 1 This is an algorithm flow chart of an optimization operation method applicable to a flexible distribution network according to an embodiment of the present invention;

[0052] Figure 2 The following is a flowchart of an optimization operation method applicable to a flexible distribution network according to an embodiment of the present invention;

[0053] Figure 3 This is a simplified structural diagram of an optimization operation device applicable to a flexible distribution network according to an embodiment of the present invention;

[0054] Figure 4 The figure is a structural block diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0055] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that, unless there is a conflict, the embodiments and features in the embodiments of the present application can be combined with each other.

[0056] The following detailed description is an exemplary description, which is intended to provide further detailed description of the present invention. Unless otherwise indicated, all technical terms used in the present invention have the same meaning as those generally understood by those skilled in the art. The terms used in the present invention are only for describing specific embodiments, and are not intended to limit the exemplary embodiments according to the present invention.

[0057] Example 1

[0058] Embodiment 1 of the present invention provides an optimization operation method suitable for a flexible distribution network, which is suitable for a distribution network containing FDS under high DG penetration. It solves the problems of premature maturity and local extreme values of the current mainstream traditional computational intelligence methods and linear programming methods, and improves the convergence speed and calculation accuracy compared with traditional methods.

[0059] This paper studies the optimized operation of flexible interconnected distribution networks under the background of high DG penetration, comprehensively analyzes high DG penetration and the access of FDS, solves the precise control of FDS with the goal of improving voltage distribution, and improves the stability of distribution network operation under high DG penetration.

[0060] like Figure 1 As shown, an optimization operation method applicable to a flexible distribution network includes the following steps:

[0061] S1. Obtain the network topology data and operating parameters of the target distribution network.

[0062] The target distribution network includes a distributed power source DG and a flexible multi-state switch FDS, and the installation positions of the distributed power source DG and the flexible multi-state switch FDS are determined in the network topology data.

[0063] The operating parameters of the target distribution network include line length, rated voltage, rated current, rated capacity of transformers in the substation, active and reactive power data of each substation, etc., and also include the output of distributed power generation DG.

[0064] This solution also obtains data such as the voltage level of the target distribution network, the active and reactive loads of each node, and the rated power of the FDS.

[0065] S2. Determine the operating constraints of the target distribution network.

[0066] The operation constraints of the target distribution network include upper and lower voltage limit constraints, reliability constraints, etc.

[0067] S3. Determine the operating parameters of the flexible multi-state switch FDS as decision variables.

[0068] It should be noted that the operating parameters of the flexible multi-state switch FDS in this scheme are the target quantities that need to be optimized. The final optimization result is the optimal operating parameters. When the FDS operates according to the optimal operating parameters, the voltage fluctuation of the target distribution network is minimized.

[0069] S4. Perform power flow calculation based on the network topology data and operating parameters of the target distribution network to obtain the target distribution network voltage value.

[0070] S5. Based on the target distribution network voltage value, with the goal of minimizing the distribution network voltage fluctuation, and under the operating constraints of the target distribution network, the decision variables are optimized using the particle swarm algorithm, and the decision variables corresponding to the optimal objective function are taken as the first optimization result.

[0071] Specifically, the preliminary optimization using the particle swarm algorithm can include the following:

[0072] Step 1: Initialize the particle swarm, including initializing the elements contained in each particle and the elements contained in all particles.

[0073] Initialize the elements contained in each particle:

[0074] (1) Current position: (x1, x2, ..., xn); where x1, x2, ..., xn are decision variables.

[0075] (2) The objective function value at the current moment (also called fitness): f(x1,x2,...,xn);

[0076] (3) The historical optimal position of the particle: (x1_pbest, x2_pbest, ..., xn_pbest);

[0077] (4) The historical optimal objective function value of the particle: f_pbest = f(x1_pbest, x2_pbest, ..., xn_pbest);

[0078] Initialize the elements contained in all particles:

[0079] (1) Total number of particles: n;

[0080] (2) Total number of iterations: N;

[0081] (3) Global optimal position: (x1_gbest,x2_gbest,...,xn_gbest);

[0082] (4) Global optimal objective function value: f_gbest = f(x1_gbest, x2_gbest, ..., xn_gbest);

[0083] (5) Upper and lower limits of position randomization: xmin, xmax;

[0084] Specifically, at the beginning of the iteration and during the iteration, the positions of the particles are randomly distributed, and the upper and lower limits of the random distribution are set to prevent the random distribution from deviating too far and affecting the optimization results;

[0085] (6) Upper and lower limits of speed: Vmin, Vmax;

[0086] Specifically, since the speed also has a certain degree of randomness during the iteration process, this solution limits the speed to a certain range to prevent the speed value from being too large and affecting the optimization results;

[0087] (7) Speed calculation parameters: c1, c2.

[0088] Step 2: Calculate the flow based on the position of each particle to obtain the initial fitness of each particle.

[0089] Step 3: For a particle, take its initial fitness as the current local optimal value, and take the position corresponding to each initial fitness as the position of the local optimal value of each particle.

[0090] Step 4: For all particles, select the best initial fitness from each initial fitness, take the best initial fitness as the current global optimal value, and take the position corresponding to the best initial fitness as the position of the global optimal value.

[0091] Step 5: Update the current velocity of each particle.

[0092] Specifically, update the current speed of each particle according to the following formula:

[0093]

[0094] Among them, w is the inertia weight, which represents the tendency of the particle to continue the current motion direction; c1 and c2 are learning factors. c1 adjusts the direction of the optimal position of the individual particle, and c2 adjusts the direction of the optimal position of the cluster; r1 and r2 are random numbers in the interval [0,1], which are used to avoid local convergence. is the velocity of the particle at the next moment, is the particle's current velocity, is a local optimum, is the current position of the particle, is the global optimal.

[0095] In this solution, when updating the current speed of each particle, the speed of each particle is limited so that it cannot exceed the set maximum speed.

[0096] Step 6: Update the current position of each particle.

[0097] Specifically, the current position of each particle is updated according to the following formula:

[0098]

[0099] in, is the position of the particle at the next moment, is the current position of the particle, is the velocity of the particle at the next moment.

[0100] Step 7: For a particle, compare whether the fitness of the current particle is better than the historical local optimal value. If so, take the fitness of the current particle as the local optimal value of the particle, and its corresponding position as the location of the local optimal value of the particle.

[0101] Step 8: Find the global optimal value in the current group, and use the position corresponding to the current global optimal value as the position of the global optimal value of the particle swarm. The various decision variables represented by the position of the global optimal value constitute the first optimization result.

[0102] S6. Create a unit vector e=[e1,e2,…,e i ,…,e M ] T ; Where i = 1, 2,…, M, where M is the dimension of the decision variable.

[0103] S6, take the first optimization result as the search starting point X0, adopt the iterative search method, based on the optimal step length g m A linear search of the objective function is performed along each direction of the unit vector e in turn. The search ends when the preset conditions are met, and the decision variables corresponding to the search points obtained at this time are used as the optimal solution for the flexible multi-state switch FDS operation.

[0104] Specifically, when performing a linear search on the objective function, the following formula is used to update the search point:

[0105] X i =X i-1 +g m e

[0106] Among them, g m represents the optimal step size; e represents the unit vector; X i Represents the search point, which is a matrix.

[0107] In the first iteration, the search initial point X0 is substituted into the above formula as the starting point, and the search point is continuously updated through the above formula. When the search in all directions of the unit vector is completed, the final search point X of the current iteration is finally obtained. M ;

[0108] Specifically, the optimal step length g m It can be calculated as follows:

[0109]

[0110] Among them, θ i It is an operator; is the gradient of the objective function; is the modulus of the objective function; is θ i The transpose of . Operator θ i The expression is as follows:

[0111]

[0112] Search point X M As the new search starting point, along the acceleration direction X M -X0 performs linear search on the objective function again and updates the unit vector e;

[0113] The updated unit vectors are as follows:

[0114] e=[e2,e3,...,e M ,||X M -X0||] T In each iteration, when updating the unit vector e, always remove the first direction in the unit vector e and add a new search direction When the maximum number of iterations is reached, or the difference between the objective function value obtained in the iter-th iteration and the objective function value corresponding to the previous iteration is less than the preset termination value, the search ends; the decision variable corresponding to the current search point is used as the optimal solution for the FDS operation.

[0115] Specifically, the optimal solution involved in this embodiment is the relevant operating parameters of the FDS, and the objective function value corresponding to the optimal solution is the voltage value of the distribution network, which is used to evaluate the effectiveness of the optimal solution.

[0116] The optimization operation method for flexible distribution networks provided by the present invention is divided into two parts: global search calculation and local search calculation: (1) Global search calculation: First, a particle swarm optimization (PSO) algorithm with simple algorithm parameter settings and fast convergence speed is used to efficiently explore the entire solution space and determine the approximate location of the optimal solution. (2) Local search calculation: A simple and fast-converging Powell method is used to perform one-dimensional optimization on a multivariable function in the conjugate direction to gradually approach the optimal solution. The fitness of the comparison result is compared to see whether it meets the output requirements. If it meets the requirements, the result is directly output. If it does not meet the requirements, steps (1) and (2) are repeated to continue iterating until the fitness meets the requirements and the result is output.

[0117] Example 2

[0118] like Figure 3 As shown, based on the same inventive concept as the above embodiment, embodiment 2 of the present application further provides an optimized operation device applicable to a flexible distribution network, including the following:

[0119] An acquisition module is used to obtain network topology data and operating parameters of the target distribution network;

[0120] The first determination module is used to determine the operation constraints of the target distribution network.

[0121] The second determining module is used to determine the operating parameters of the flexible multi-state switch FDS as decision variables.

[0122] The power flow calculation module is used to perform power flow calculation based on the network topology data and operating parameters of the target distribution network to obtain the target distribution network voltage value.

[0123] The preliminary optimization module is used to optimize the decision variables based on the target distribution network voltage value, with the goal of minimizing the distribution network voltage fluctuation, and under the operating constraints of the target distribution network, using the particle swarm algorithm to optimize the decision variables, and taking the decision variables corresponding to the optimal objective function as the first optimization result.

[0124] Vector building block, used to build the unit vector e = [e1, e2, ..., e i ,…,e M ] T ; Where i = 1, 2,…, M, where M is the dimension of the decision variable.

[0125] The final optimization module is used to use the first optimization result as the search starting point X0, and adopt an iterative search method based on the optimal step size g mA linear search of the objective function is performed along each direction of the unit vector e in turn. The search ends when the preset conditions are met, and the decision variables corresponding to the search points obtained at this time are used as the optimal solution for the flexible multi-state switch FDS operation.

[0126] In the first determination module, the operation constraints of the target distribution network include at least upper and lower voltage limit constraints.

[0127] Preliminary optimization module, specifically used for:

[0128] Step 1: Initialize the particle swarm, including initializing the elements contained in each particle and the elements contained in all particles;

[0129] Step 2: Calculate the current flow based on the position of each particle to obtain the initial fitness of each particle;

[0130] Step 3: For a particle, take its initial fitness as the current local optimal value, and take the position corresponding to each initial fitness as the position of the local optimal value of each particle;

[0131] Step 4: For all particles, select the best initial fitness from each initial fitness, take the best initial fitness as the current global optimal value, and take the position corresponding to the best initial fitness as the position of the global optimal value;

[0132] Step 5: Update the current speed of each particle;

[0133] Step 6: Update the current position of each particle;

[0134] Step 7: For a particle, compare whether the fitness of the current particle is better than the historical local optimal value. If so, take the fitness of the current particle as the local optimal value of the particle, and its corresponding position as the location of the local optimal value of the particle;

[0135] Step 8: Find the global optimal value in the current group, and use the position corresponding to the current global optimal value as the position of the global optimal value of the particle swarm. The various decision variables represented by the position of the global optimal value constitute the first optimization result.

[0136] In the final optimization module, the following formula is used to update the search point:

[0137] X i =X i-1 +g m e

[0138] Among them, g m represents the optimal step size; e represents the unit vector; Xi represents the search point;

[0139] In the first iteration, the search initial point X0 is substituted into the above formula as the starting point, and the search point is continuously updated through the above formula. When the search in all directions is completed, the final search point X of the current iteration is finally obtained. M .

[0140] Good step length g m Calculate according to the following formula:

[0141]

[0142] Among them, θ i It is an operator; is the gradient of the objective function; is the modulus of the objective function; is θ i The transpose of .

[0143] Operator θ i The expression is as follows:

[0144]

[0145] Update the unit vector e to search for point X M As the new search starting point, along the acceleration direction X M -X0 performs a linear search on the target function again.

[0146] The updated unit vectors are as follows:

[0147] e=[e2,e3,...,e M ,||X M -X0||] T In each iteration, when updating the unit vector e, the first direction in the unit vector e is removed and a new search direction is added.

[0148] Example 3

[0149] like Figure 4 As shown, the present invention further provides an electronic device 100 for implementing an optimized operation method applicable to a flexible distribution network; the electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on the at least one processor 102, and at least one communication bus 104. The memory 101 can be used to store the computer program 103. The processor 102 implements the steps of an optimized operation method applicable to a flexible distribution network in Example 1 by running or executing the computer program stored in the memory 101 and calling data stored in the memory 101.

[0150] The memory 101 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data (such as audio data) created according to the use of the electronic device 100. In addition, the memory 101 may include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device.

[0151] The at least one processor 102 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 102 may be a microprocessor or any conventional processor. The processor 102 is the control center of the electronic device 100 and connects various parts of the entire electronic device 100 using various interfaces and lines.

[0152] The memory 101 in the electronic device 100 stores a plurality of instructions to implement an optimized operation method applicable to a flexible distribution network. The processor 102 can execute the plurality of instructions to implement:

[0153] Obtain network topology data and operating parameters of the target distribution network;

[0154] Determine the operating constraints of the target distribution network;

[0155] Determine the operating parameters of the flexible multi-state switch FDS as decision variables;

[0156] Perform power flow calculation based on the network topology data and operating parameters of the target distribution network to obtain the target distribution network voltage value;

[0157] Based on the target distribution network voltage value, with the goal of minimizing the distribution network voltage fluctuation, and under the operating constraints of the target distribution network, a particle swarm algorithm is used to optimize the decision variables, and the decision variables corresponding to the optimal objective function are taken as the first optimization result;

[0158] Create a unit vector e=[e1,e2,…,e i ,…,e M ] T ; Where i = 1, 2, ..., M, M is the dimension of the decision variable;

[0159] The first optimization result is used as the search starting point X0, and an iterative search method is used based on the optimal step size g m A linear search of the objective function is performed along each direction of the unit vector e in turn. The search ends when the preset conditions are met, and the decision variables corresponding to the search points obtained at this time are used as the optimal solution for the flexible multi-state switch FDS operation.

[0160] Example 4

[0161] If the module / unit integrated in the electronic device 100 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. Computer-readable media may include: any entity or device that can carry computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory and read-only memory (ROM, Read-Only Memory).

[0162] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0163] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0164] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0165] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0166] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0167] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. An optimization operation method applicable to a flexible distribution network, characterized in that: Flexible distribution networks include distributed generation and flexible multi-state switches; The method comprises the following steps: Obtain the network topology data and operating parameters of the target distribution network; operating parameters include line length, rated voltage, rated current, rated capacity of transformers in each substation, and active and reactive power data for each substation, as well as the output of distributed generation. Determine the operation constraints of the target distribution network; wherein the operation constraints at least include upper and lower voltage limit constraints; Determine the operating parameters of the flexible multi-state switch FDS as decision variables; Perform power flow calculation based on the network topology data and operating parameters of the target distribution network to obtain the target distribution network voltage value; Based on the target distribution network voltage value, with the goal of minimizing the distribution network voltage fluctuation, and under the operating constraints of the target distribution network, a particle swarm algorithm is used to optimize the decision variables, and the decision variables corresponding to the optimal objective function are taken as the first optimization result; Create a unit vector e=[e1,e2,…,e i ,…,e M ] T ; Where i = 1, 2, ..., M, M is the dimension of the decision variable; The first optimization result is used as the search starting point X0, and an iterative search method is used based on the optimal step size g m A linear search of the objective function is performed along each direction of the unit vector e in turn. The search ends when the preset conditions are met, and the decision variables corresponding to the search points obtained at this time are used as the optimal solution for the flexible multi-state switch FDS operation.

2. The optimization operation method applicable to a flexible distribution network according to claim 1, characterized in that: The steps of optimizing decision variables using particle swarm optimization include the following: Step 1: Initialize the particle swarm, including initializing the elements contained in each particle and the elements contained in all particles; Step 2: Calculate the current flow based on the position of each particle to obtain the initial fitness of each particle; Step 3: For a particle, take its initial fitness as the current local optimal value, and take the position corresponding to each initial fitness as the position of the local optimal value of each particle; Step 4: For all particles, select the best initial fitness from each initial fitness, take the best initial fitness as the current global optimal value, and take the position corresponding to the best initial fitness as the position of the global optimal value; Step 5: Update the current speed of each particle; Step 6: Update the current position of each particle; Step 7: For a particle, compare whether the fitness of the current particle is better than the historical local optimal value. If so, take the fitness of the current particle as the local optimal value of the particle, and its corresponding position as the location of the local optimal value of the particle; Step 8: Find the global optimal value in the current group, and use the position corresponding to the current global optimal value as the position of the global optimal value of the particle swarm. The various decision variables represented by the position of the global optimal value constitute the first optimization result.

3. The optimization operation method applicable to a flexible distribution network according to claim 1, characterized in that: Based on the optimal step size g m In the step of linearly searching the objective function along each direction of the unit vector e, the following formula is used to update the search point: X i =X i-1 +g m e Among them, g m represents the optimal step size; e represents the unit vector; X i Indicates the search point; In the first iteration, the search initial point X0 is substituted into the above formula as the starting point, and the search point is continuously updated through the above formula. When the search in all directions is completed, the final search point X of the current iteration is finally obtained. M .

4. The optimization operation method applicable to a flexible distribution network according to claim 3, characterized in that: Optimal step length g m Calculate according to the following formula: Among them, θ i It is an operator; is the gradient of the objective function; is the modulus of the objective function; is θ i The transpose of .

5. The optimization operation method applicable to a flexible distribution network according to claim 4, characterized in that: Operator θ i The expression is as follows:

6. The optimization operation method applicable to a flexible distribution network according to claim 4, characterized in that: Update the unit vector e to search for point X M As the new search starting point, along the acceleration direction X M -X0 performs linear search on the target function again; The updated unit vectors are as follows: e=[e2,e3,…,e M ,||X M -X0||] T In each iteration, when updating the unit vector e, the first direction in the unit vector e is removed and a new search direction is added.

7. An optimized operation device applicable to a flexible distribution network, used to implement the optimized operation method applicable to a flexible distribution network according to claim 1, characterized in that: These include: An acquisition module is used to obtain network topology data and operating parameters of the target distribution network; A first determination module is used to determine the operation constraints of the target distribution network; A second determination module is used to determine the operating parameters of the flexible multi-state switch FDS as decision variables; The power flow calculation module is used to perform power flow calculation based on the network topology data and operating parameters of the target distribution network to obtain the target distribution network voltage value; a preliminary optimization module for optimizing decision variables using a particle swarm optimization algorithm based on the target distribution network voltage value and with the goal of minimizing distribution network voltage fluctuations while satisfying the operating constraints of the target distribution network, and taking the decision variables corresponding to the optimal objective function as the first optimization result; Vector building block, used to build the unit vector e = [e1, e2, ..., e i ,…,e M ] T ; Where i = 1, 2, ..., M, M is the dimension of the decision variable; The final optimization module is used to use the first optimization result as the search starting point X0, and adopt an iterative search method based on the optimal step size g m A linear search of the objective function is performed along each direction of the unit vector e in turn. The search ends when the preset conditions are met, and the decision variables corresponding to the search points obtained at this time are used as the optimal solution for the flexible multi-state switch FDS operation.

8. An electronic device, characterized in that: The system comprises a processor and a memory, wherein the processor is configured to execute a computer program stored in the memory to implement the optimization operation method applicable to a flexible distribution network as claimed in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by a processor, the optimization operation method applicable to a flexible distribution network according to any one of claims 1 to 6 is implemented.

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