Photovoltaic bearing capacity optimization method and system based on weight evaluation multiple targets

Abstract

The invention belongs to the field of power systems, and particularly discloses a photovoltaic bearing capacity optimization method and system based on weight evaluation multiple targets. The method comprises the following steps: constructing an optimization objective function for the purpose of safe operation of a power distribution area, and constructing a corresponding constraint condition and a penalty function at the same time; establishing another optimization objective function through the safety index evaluation weight and the fuzzy relation matrix; determining an index weight between the optimization objective functions; the optimization objective function and the penalty function are combined; firstly, a multi-target particle swarm optimization algorithm is used to optimize a combined optimization target function; then, a roulette-weight gradually-strong combination method is adopted to correct the probability of particle selection; and finally, selecting a particle with a globally optimal solution and a penalty function of 0 to perform single-target optimization, and outputting a distributed photovoltaic locating and sizing method which maximizes the photovoltaic bearing capacity of a power distribution area. According to the invention, the photovoltaic access method with the maximum photovoltaic bearing capacity can be obtained.

Description

Technical Field

[0001] This application belongs to the field of power systems, and more specifically, relates to a photovoltaic carrying capacity optimization method and system based on weighted evaluation of multiple objectives. Background Technique

[0002] Distributed power sources such as photovoltaic and wind power are in the stage of vigorous development. However, with the continuous development of distributed power sources, problems such as unqualified power quality and excessive reverse load rates of equipment have emerged in various places due to the large-scale access of distributed photovoltaic to the distribution network, and the photovoltaic carrying capacity of the distribution network is insufficient. Therefore, it is urgent to improve the photovoltaic carrying capacity of the distribution network through reasonable distributed photovoltaic access planning.

[0003] Currently, the existing technologies for improving the distributed photovoltaic carrying capacity of distribution substations mainly use the method of reasonable photovoltaic site selection and capacity determination. Generally, one or two indicators are selected as the optimization objects for direct optimization to determine a site selection and capacity determination scheme. However, this approach does not combine the evaluation of carrying capacity as the basis for selecting the optimal solution, and the number of selected indicators is often small and cannot represent the carrying capacity of the distribution substation, lacking scientificity. Moreover, the obtained optimal solution is only a set of non-dominated solutions, without a high confidence level, and cannot achieve the goal of maximizing the photovoltaic carrying capacity of the distribution substation through site selection and capacity determination.

[0004] There are other technical solutions that can combine a comprehensive evaluation model and select the solution with the highest evaluation value among non-dominated solutions as the output result. This technical solution is more scientific in the selection of objectives, but it cannot reflect the evaluation model in the process of carrying capacity optimization. The role time of the evaluation model in site selection and capacity determination optimization is short, and the relationship between the two is relatively disjointed. Moreover, the constraint conditions are not defined based on the requirements of various safety indicators of the distribution substation, and the obtained results cannot represent the optimization results of maximizing the photovoltaic carrying capacity of the distribution substation. Summary of the Invention

[0005] Aiming at the defects of the existing technology, the purpose of this application is to provide a photovoltaic carrying capacity optimization method and system based on weighted evaluation of multiple objectives, aiming to solve the problem that the existing technologies for improving the distributed photovoltaic carrying capacity of distribution substations do not dynamically integrate the carrying capacity evaluation into the optimization process, resulting in the inability to output the global optimal solution of maximizing the photovoltaic carrying capacity.

[0006] To achieve the above object, in the first aspect, this application provides a photovoltaic carrying capacity optimization method based on weighted evaluation of multiple objectives, including the following steps: Step S1: Construct an optimization objective function for the safe operation of the distribution substation, and at the same time delimit the constraint conditions for the safe operation of the distribution substation and the corresponding penalty functions; Step S2: Establish the evaluation weight and fuzzy relation matrix of the security index, construct the loss function of the security index, and use it as another optimization objective function; Step S3: Determine the index weights between the optimization objective functions in Step S1 and Step S2; and use the penalty function in Step S1 as another optimization objective function, and combine it with the optimization objective functions in Step S1 and Step S2; Step S4: Use the multi-objective particle swarm optimization algorithm in the first n iterations to optimize the combined optimization objective function to obtain the global optimal solution; one particle corresponds to a distributed photovoltaic access method; Step S5: In the (n + 1)-th to q-th iterations, combine the index weights between the optimization objective functions in Step S3 to obtain the optimization objective correction term, and then use the roulette wheel-weight strengthening combination method to combine the penalty function to affect the correction coefficient to correct the selection probability of the particle; Step S6: Select the particle with the global optimal solution and a penalty function of 0 in Step S5 for single-objective optimization, and output the distributed photovoltaic siting and sizing method that maximizes the photovoltaic carrying capacity of the distribution substation area; where n and q are set values, and q is greater than n.

[0007] Further preferably, Step S1 specifically includes the following steps: Step S1.1: Under the conditions that the distribution substation area transformer is continuously not overloaded and the power quality index does not exceed the standard, select the maximum value of the distributed photovoltaic capacity accepted by the distribution substation area as the first optimization objective function; and take the minimum value of the line loss in the distribution substation area as the second optimization objective function; Step S1.2: Taking the network itself power flow equation as the constraint condition, construct the voltage deviation constraint condition, three-phase unbalance degree constraint condition and reverse load rate constraint condition, and obtain the corresponding penalty function.

[0008] Further preferably, Step S2 specifically includes the following steps: Step S2.1: Taking the voltage deviation, three-phase unbalance degree and reverse load rate as security indexes, establish the evaluation matrix of the bearing capacity security index; Step S2.2: Based on the evaluation matrix of the bearing capacity security index, use the geometric mean method to calculate the evaluation weight of the bearing capacity security index; And based on the security indexes in Step S2.1, construct the comment set of the photovoltaic bearing capacity security index, calculate the membership degree of each security index to the comment set of the photovoltaic bearing capacity security index, and then obtain the fuzzy relation matrix; Step S2.3: Perform fuzzy synthesis on the security index evaluation weight and the fuzzy relation matrix in Step S2.2 to obtain the evaluation membership vector of the bearing capacity security index, and calculate the fuzzy score of the bearing capacity security index; Step S2.4: Based on the fuzzy scoring of the bearing capacity safety index in Step S2.3, obtain the safety index loss function as the third optimization objective function.

[0009] Further preferably, Step S4 specifically includes the following steps: Step S4.1: Normalize all optimization objective functions, use the multi-objective particle swarm algorithm to optimize all optimization objective functions, and store the non-dominated solutions in all optimization objective functions in each iteration in the Pareto front optimal solution set; Step S4.2: Divide each dimension corresponding to the optimization objective function in the Pareto front optimal solution space into N parts by the grid method, and use the roulette wheel method to select the global optimal solution position of the particle; Wherein, N is an integer greater than or equal to 2.

[0010] Further preferably, Step S5 specifically includes the following steps: Step S5.1: Based on the index weights between the optimization objective functions in Step S3, calculate the weight score of the i-th particle in the iteration process, and then obtain the influence parameter of the optimization objective correction term; Step S5.2: Calculate the influence weight of the penalty function by the method of exponential decay, combine the influence parameter of the optimization objective correction term in Step S5.1, and use the roulette wheel-weight strengthening combination method to correct the probability of particle selection.

[0011] In a second aspect, the present application provides a photovoltaic bearing capacity optimization system based on weight evaluation multi-objectives, including: An objective function construction module, used to construct an optimization objective function for the purpose of safe operation of the distribution substation area, and at the same time construct corresponding constraint conditions and penalty functions; construct a safety index loss function through the safety index evaluation weight and the fuzzy relationship matrix, and use it as another optimization objective function; An objective function processing module, used to determine the index weights between the optimization objective functions in the objective function construction module, use the penalty function as another optimization objective function, and combine all optimization objective functions; A first iteration module, used to use the multi-objective particle swarm optimization algorithm in the first n iterations to optimize the combined optimization objective functions to obtain the global optimal solution; wherein, one particle corresponds to a distributed photovoltaic access method; A second iteration module, used to combine the index weights between the optimization objective functions in the objective function processing module to obtain three optimization objective correction terms, and then use the roulette wheel-weight strengthening combination method to combine the penalty function influence correction coefficient to correct the probability of particle selection; The third iteration module is used to select the particles with the global optimal solution and a penalty function of 0 in the second iteration module for single-objective optimization, and output a distributed photovoltaic siting and sizing method that maximizes the photovoltaic carrying capacity of the distribution transformer area.

[0012] Further preferably, the objective function construction module includes a constraint condition construction unit, a first objective function construction unit, and a second objective function construction unit; The first objective function construction unit is used to select the maximum value of the distributed photovoltaic capacity accepted by the distribution transformer area as the first optimization objective function under the conditions that the distribution transformer area transformer is continuously not overloaded and the power quality index does not exceed the standard; The second objective function construction unit is used to take the minimum value of the line loss in the distribution transformer area as the second optimization objective function; The constraint condition construction unit is used to construct voltage deviation constraint conditions, three-phase unbalance degree constraint conditions, and reverse load rate constraint conditions with the network itself power flow equation as the constraint condition, and obtain the corresponding penalty function.

[0013] Further preferably, the objective function construction module further includes a third optimization objective function construction unit, and the third optimization objective function construction unit includes: a first calculation subunit, a second calculation subunit, a third calculation subunit, a fourth calculation subunit, and a fifth calculation subunit; The first calculation subunit is used to establish a carrying capacity safety index evaluation matrix with voltage deviation, three-phase unbalance degree, and reverse load rate as safety indexes; The second calculation subunit is used to calculate the carrying capacity safety index evaluation weight by using the geometric mean method based on the carrying capacity safety index evaluation matrix; The third calculation subunit is used to construct a photovoltaic carrying capacity safety index comment set based on the safety indexes, calculate the membership degree of each safety index to the photovoltaic carrying capacity safety index comment set, and then obtain a fuzzy relation matrix; The fourth calculation subunit is used to perform fuzzy synthesis on the safety index evaluation weight and the fuzzy relation matrix to obtain a carrying capacity safety index evaluation membership vector, and calculate the carrying capacity safety index fuzzy score; The fifth calculation subunit is used to obtain a safety index loss function based on the carrying capacity safety index fuzzy score as the third optimization objective function.

[0014] Further preferably, the first iteration module includes: a multi-objective particle swarm optimization unit and a particle position selection unit; The multi-objective particle swarm optimization unit is used to perform normalization processing on all optimization objective functions, use the multi-objective particle swarm algorithm to optimize all optimization objective functions, and store the non-dominated solutions in all optimization objective functions in each iteration in the Pareto front optimal solution set; The particle position selection unit is used to divide each dimension corresponding to the optimization objective function in the Pareto front optimal solution space into N parts by the grid method, and use the roulette method to select the global optimal solution position of the particle; where N is an integer greater than or equal to 2.

[0015] Further preferably, the second iteration module includes: a first correction unit, a second correction unit, and a third correction unit; The first correction unit is used to calculate the weight score of the i-th particle in the iteration process based on the index weights between the optimization objective functions, and then obtain the influence parameter of the optimization objective correction term; The second correction unit is used to calculate the influence weight of the penalty function by the method of exponential decay; The third correction unit is used to combine the influence weight of the penalty function and the influence parameter of the optimization objective correction term, and use the roulette-weight increasing combination method to correct the probability of particle selection.

[0016] In a third aspect, the present application provides an electronic device, including: at least one memory for storing a program; at least one processor for executing the program stored in the memory. When the program stored in the memory is executed, the processor is used to execute the method described in the first aspect or any possible implementation manner of the first aspect.

[0017] In a fourth aspect, the present application provides a computer-readable storage medium. The computer-readable storage medium stores a computer program. When the computer program runs on a processor, the processor is caused to execute the method described in the first aspect or any possible implementation manner of the first aspect.

[0018] In a fifth aspect, the present application provides a computer program product. When the computer program product runs on a processor, the processor is caused to execute the method described in the first aspect or any possible implementation manner of the first aspect.

[0019] It can be understood that the beneficial effects of the above second aspect to fifth aspect can refer to the relevant descriptions in the above first aspect, and will not be elaborated here.

[0020] Generally speaking, compared with the prior art through the above technical solutions conceived by the present application, the following beneficial effects are obtained: The present application provides a photovoltaic carrying capacity optimization method based on weight evaluation for multiple objectives. In the early stage of the iteration of the carrying capacity optimization algorithm, the penalty function is used as a part of the optimization function, and the multi-objective particle swarm algorithm is used for optimization, fully retaining the global search ability of the algorithm and effectively avoiding local optimal solutions.

[0021] The present application provides a photovoltaic carrying capacity optimization method based on weighted evaluation of multiple objectives. In the middle stage of the iteration of the carrying capacity optimization algorithm, the gradually increasing influence of the evaluation system weight is introduced, and as the number of iterations increases, the influence of the carrying capacity evaluation system on each optimization objective is strengthened, making the optimization gradually have directionality and purpose. In the later stage of iteration, a weighted scoring method is used for rapid local convergence, and a unique global optimal solution can be obtained. The optimization result obtained by this method has obvious specific significance, and it can enable the distribution transformer area to maximize the photovoltaic access method defined by the evaluation model.

[0022] The present application provides a photovoltaic carrying capacity optimization method based on weighted evaluation of multiple objectives, which fully considers the actual operation conditions of the distribution transformer area, takes the safe and stable operation of the distribution transformer area as the constraint condition, and takes the maximization of the distributed photovoltaic carrying capacity and the optimal operation safety of the transformer area as the basis for selecting optimization objectives. The established optimization model has engineering guiding significance. Brief Description of the Drawings

[0023] Figure 1 is a schematic flow chart of the photovoltaic carrying capacity optimization method based on weighted evaluation of multiple objectives provided by an embodiment of the present application; Figure 2 is the topological structure of the IEEE33-node test case provided by an embodiment of the present application; Figure 3 is a schematic structural diagram of an electronic device provided by an embodiment of the present application. Detailed Embodiments

[0024] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0025] The term "and / or" in this article is a relational term describing associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. The symbol " / " in this article represents an "or" relationship between associated objects. For example, A / B represents A or B.

[0026] The terms "first" and "second" etc. in the specification and claims of this article are used to distinguish different objects, rather than to describe a specific order of objects.

[0027] In the embodiments of the present application, words such as "exemplary" or "for example" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as "exemplary" or "for example" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of words such as "exemplary" or "for example" is intended to present relevant concepts in a specific manner.

[0028] In the description of the embodiments of the present application, unless otherwise specified, the meaning of "a plurality" refers to two or more.

[0029] The embodiments of the present application will be described below with reference to the accompanying drawings in the embodiments of the present application.

[0030] The present application provides a method and system for optimizing photovoltaic carrying capacity based on weighted evaluation of multiple objectives. The purpose is to construct a multi-index weight evaluation system that can be used to evaluate the photovoltaic carrying capacity of a distribution substation area through reasonable index selection, and establish a multi-objective optimization model. With the maximization of the photovoltaic carrying capacity of the substation area as the goal, fully considering the constraint conditions for the safe and stable operation of the distribution substation area, the index weight evaluation system and the constraint conditions of the safety index are incorporated into each iteration of the optimization model, so that the optimization iteration result of the distributed photovoltaic site selection and capacity determination can maximize the photovoltaic carrying capacity of the distribution substation area on the premise of ensuring the safe and stable operation of the distribution substation area.

[0031] As Figure 1 shown, the present application provides a method for optimizing photovoltaic carrying capacity based on weighted evaluation of multiple objectives, including the following steps: Step 1: Select the optimization objective, calculate the loss function of the objective, and at the same time delimit the set of constraint conditions for the safe and stable operation of the distribution substation area, list all constraint equations, and calculate the constraint penalty function through the constraint equations; Step 2: Based on the selected optimization objective, adopt the improved analytic hierarchy process-fuzzy comprehensive evaluation, calculate the photovoltaic carrying capacity scoring index in combination with the safety index, list the loss function, and combine the loss function with the optimization objective and the constraint penalty function in Step 1 to form a multi-objective optimization set; Step 3: Based on the multi-objective optimization set, use the improved analytic hierarchy process to determine the index weights between the optimization objectives combined in Step 2; Step 4: Use the multi-objective particle swarm optimization algorithm in the early stage of iteration to optimize the multi-objective optimization set obtained in Step 2, retain the non-dominated solutions in the multi-objective optimization set, store them in the Pareto front, and select the global optimal solution of the particle through the grid method and the roulette wheel method; where each particle corresponds to a distributed photovoltaic access method; Step 5: In the middle and late stages of iteration, introduce the gradually strengthening combination method of constraint penalty function, and fully consider the importance between the constraints of boundary conditions and optimization indicators when selecting the global optimal solution at each step of iteration until the iteration is completed, and output the distributed photovoltaic siting and sizing method that can maximize the photovoltaic carrying capacity of the distribution substation area; Step 6: Repeat Step 4 and Step 5, perform multiple iterative solutions, compare the multi-objective optimization set scores of each optimization solution, and select the one with the highest score as the final result.

[0032] More specifically, Step 1 specifically includes the following steps: Step 1.1: According to the definition of the photovoltaic carrying capacity of the distribution network, that is, under the conditions that the equipment is continuously not overloaded and the power quality indicators do not exceed the standard, the distribution network accepts the maximum capacity of distributed photovoltaics, and select the first optimization objective function f 1 is the maximum value of the distributed photovoltaic capacity accepted by the distribution substation area, that is:

[0033] Among them, S PV is the total capacity of the connected photovoltaics; Since line loss is the main factor affecting the operation of the distribution substation area during the operation of the distribution substation area, therefore, select the second optimization objective function as the minimum value of the line loss of the distribution substation area, that is:

[0034] Among them, is the line loss between node i and j ; U i and U j are the voltage vectors of node i and node j , I ij is the current vector between node i and node j ; A node is each position and electrical section in the distribution substation area topology with exact voltage and phase and balanced incoming and outgoing power; as Figure 2 shows, each black dot represents a node.

[0035] Step 1.2: Based on the constraint conditions such as the equipment being continuously not overloaded and the power quality indicators not exceeding the standard in Step 1.1, determine all the constraint conditions for problem solving; The first constraint condition is the constraint of the network's own power flow equation; Secondly, there are constraints on non-exceedance of power quality indicators. From the perspective of data acquisition difficulty and feasibility, inequality constraints are written for non-exceedance of node voltage deviation and unbalance degree. The voltage deviation does not exceed ± ΔU max , and the unbalance degree does not exceed Δε max . Then the inequality constraints can be written in the following form:

[0036]

[0037] Among them, is the voltage deviation of the i th m phase of the node; U im is the voltage of the i th m phase of the node; is the rated voltage of the bus; ε i is the voltage unbalance degree of the node i ; U i(2) is the negative sequence component of the voltage of the node i ; U i(1) is the positive sequence component of the voltage of the node i ; is the maximum allowable bus voltage deviation; is the maximum allowable unbalance degree deviation; Finally, for the constraint condition of continuous non-overloading of equipment, the load rate constraint condition is listed. According to the requirement of equipment thermal stability, it is required that the reverse load rate of the equipment should not exceed 80% of the equipment capacity as the reverse load rate constraint. Therefore, this constraint condition can be written as:

[0038] Among them, λ is the reverse load rate, P D is the output of distributed power sources; P L is the equivalent power consumption load at the same time, that is, the load minus the output of other power sources except distributed power sources; Se is the actual operation limit of the transformer or line; Step 1.3: Based on the inequality constraints in Step 1.2, write the penalty function objective. For the voltage deviation constraint, the penalty function can be written as:

[0039] Among them, the definitions of the variables are the same as those in Step 1.2; For the three-phase unbalance constraint, the penalty function can be written as:

[0040] Among them, the definitions of the variables are the same as those in Step 1.2; For the reverse load rate constraint, the penalty function can be written as:

[0041] Among them, the definitions of the variables are the same as those in Step 1.2; Normalize the three penalty functions respectively. The normalized penalty functions are dup , and write the total penalty function constraint as:

[0042]

[0043] Among them, 、 、 are the normalized penalty functions; Step 2 includes the following steps: Step 2.1: Based on the reverse load rate and safety indexes such as power quality obtained in Step 1, establish a bearing capacity evaluation system; First, use the improved Analytic Hierarchy Process (AHP) to obtain the evaluation weights among the three indexes of voltage deviation, three-phase unbalance degree and reverse load rate. Use the improved 9 / 9 - 9 / 1 scale to compare the importance of two indexes pairwise. If the scale is 9 / 9, it indicates that the two indexes have the same importance. If the scale is 9 / 7, it indicates that the former is slightly more important than the latter, and so on. Fill these pairwise comparison values into the following comparison matrix:

[0044] Among them, is the scale between two evaluation indexes, i = 1, 2, 3; j = 1, 2, 3; Step 2.2: Use the geometric mean method to obtain the evaluation weights of the bearing capacity safety indexes. The calculation method is as follows:

[0045] Among them, n = 3, ω i is the i weight of the th index, and the definition of is the same as that in 2.1, and kWith the indicators j The scale between two indicators; Step 2.3: Based on the security indicators in three dimensions, construct N groups of comment sets U for the photovoltaic bearing capacity security indicators, calculate the membership degrees of each security indicator to each comment set, and fill the membership degrees into the corresponding positions of the fuzzy relation matrix R :

[0046] Among them, the matrix element r mn is the fuzzy membership degree of indicator m to comment n, where m = 1, 2, 3 and n = 1, 2,..., N; Step 2.4: Conduct fuzzy synthesis on the security indicator evaluation weights obtained in Step 2.2 and the fuzzy relation matrix obtained in Step 2.3 to obtain the evaluation membership vector K of the bearing capacity security indicator, and calculate the fuzzy score of the bearing capacity security indicator through K:

[0047]

[0048] Among them, k 1, k 2, … , k N are the membership degrees of the 1st, 2nd,..., Nth groups of comment sets; U 1, U 2, … , U N is the median of each group of comment sets, P is the fuzzy score of the bearing capacity security indicator; Step 2.5: Based on the fuzzy score of the bearing capacity security indicator obtained in Step 2.4, write the loss function of the security indicator, and determine the third optimization objective as:

[0049] Based on the optimization objectives and penalty functions obtained in Step 1, form an extended multi-objective optimization set, and the extended multi-objective optimization function is as follows:

[0050] Step 3 includes the following steps: Based on the extended multi-objective optimization function obtained in Step 2, determine the weights f 1 、f 2 、f among the three optimization objectives w 1 、w 2 、w3. The method for determining the weight is the same as that in Steps 2.1 and 2.2; Step 4 uses the multi-objective particle swarm optimization algorithm in the early stage of iteration to optimize the multi-objective optimization set obtained in Step 2, retain the non-dominated solutions in the multi-objective optimization set, store them in the Pareto front, and select the global optimal solution of the particle through the grid method and the roulette wheel method; it specifically includes the following steps: Step 4.1: In the early stage of iteration, normalize the four optimization objectives in Step 2, and use the multi-objective particle swarm algorithm to optimize the four optimization objectives; store the non-dominated solutions among the four objectives in each iteration in the Pareto front optimal solution set. There is no situation where the four indicators of a set of optimization solutions are simultaneously better than those of another set of solutions among the non-dominated solutions; Step 4.2: Divide each index dimension of the Pareto front optimal solution space into N parts through the grid method. The boundaries of each dimension are determined by the maximum and minimum values of that index dimension. Then, for the four index dimensions, the optimal solution space is divided into a four-dimensional grid with a total number of N×N×N×N; each set of optimal solutions in the optimal solution space will surely fall into one specific grid; adopt the roulette wheel method to select the global optimal solution position of the particle, that is, determine the probability of each grid being selected according to the number of optimal solutions in each grid. The fewer the number of solutions, the lower the probability of the grid being selected; Let the number of optimal solutions in the i th grid where there are optimal solutions be n i , then the probability of this grid being selected is:

[0051] where, p i is the probability of the i th grid where there are optimal solutions being selected, N ’ is the total number of grids where there are optimal solutions; when there are multiple optimal solutions in the selected grid, the probability of each optimal solution being selected is equal; Step 5 In the middle and late stages of iteration, introduce the gradually strengthening constraint of the penalty function and the weight constraint of the optimization index in Step 3, replace the roulette wheel method in Step 4 with the roulette wheel-weight gradually strengthening combination method, and fully consider the constraint of the boundary conditions and the importance among the optimization indexes when selecting the global optimal solution in each iteration until the iteration is completed, and output the distributed photovoltaic siting and sizing method that can maximize the photovoltaic carrying capacity of the distribution transformer area; it specifically includes the following steps: Step 5.1: Judge that the iteration enters the middle stage, and calculate the weight score of the i th particle in the iteration process based on the optimization objective evaluation weight in Step 3:

[0052] Among them, f 1i 、f 2i 、f 3i are the three optimized objective values after normalization for the i-th particle, g i is the i weighted score of the k -th particle; all particles are sorted according to the weighted score. Let the sorting of the particle score be k , then the weight score correction term of this particle is 1 / gen 1, the end criterion for the middle stage of iteration is gen 2, and the current iteration number is t , then the influence parameter of the optimized objective correction term can be written as:

[0053] Among them, α i is the i influence parameter of the optimized objective correction term for the -th particle;

[0054] Step 5.2: Calculate the influence weight of the penalty function by using the method of exponential decay. The influence of the penalty function correction term can be written as: β i is the influence correction parameter of the penalty function for the i-th particle; Step 5.3: Modify the roulette wheel selection method in Step 4.2 to become the roulette wheel - weight strengthening combination method. The probability of each grid being selected is corrected to:

[0055] Among them, k is the influence degree parameter of the optimized objective; then continue to carry out the multi-objective optimization search of the particle swarm; Step 5.4: Judge that the iteration enters the later stage and convert it to single-objective optimization; select the global optimal solution as the particle with the highest score ranking calculated in Step 5.1 and a penalty function of 0 to accelerate the local convergence process.

[0056] To sum up, compared with the prior art, the present application has the following advantages: The present application fully considers the actual operation conditions of the distribution transformer area, takes the safe and stable operation of the distribution transformer area as the constraint condition, and takes the maximization of the distributed photovoltaic carrying capacity and the optimal operation safety of the transformer area as the basis for selecting the optimization objectives. The established optimization model has engineering guiding significance.

[0057] In the early stage of the iteration of the bearing capacity optimization algorithm, the penalty function is used as a part of the optimization function, and the multi-objective particle swarm algorithm is used for optimization, fully retaining the global search ability of the algorithm and effectively avoiding local optimal solutions.

[0058] In the middle stage of the iteration of the bearing capacity optimization algorithm, the gradually increasing influence of the evaluation system weight is introduced. As the number of iterations increases, the bearing capacity evaluation system's effect on each optimization objective is strengthened, making the optimization gradually directional and purposeful. In the later stage of the iteration, weighted scoring is used for rapid local convergence, and a unique global optimal solution can be obtained. The optimization result obtained by this method has obvious specific significance, and a photovoltaic access method that maximizes the photovoltaic bearing capacity defined by the distribution transformer area through the evaluation model can be obtained.

[0059] The following describes the photovoltaic bearing capacity optimization system based on weight evaluation and multi-objectives provided by the present application. The photovoltaic bearing capacity optimization system based on weight evaluation and multi-objectives described below can be correspondingly referred to the photovoltaic bearing capacity optimization method described above.

[0060] It should be understood that the above system is used to execute the method in the above embodiments. For the corresponding program modules in the system, their implementation principles and technical effects are similar to those described in the above method. The working process of this system can refer to the corresponding process in the above method, which will not be elaborated here.

[0061] Based on the method in the above embodiments, as Figure 3 shown, an embodiment of the present application provides an electronic device, which may include: a processor (Processor) 810, a communication interface (Communications Interface) 820, a memory (Memory) 830, and a communication bus 840. Among them, the processor 810, the communication interface 820, and the memory 830 communicate with each other through the communication bus 840. The processor 810 can call the logical instructions in the memory 830 to execute the method in the above embodiments.

[0062] In addition, when the logical instructions in the above memory 830 are implemented in the form of software functional units and sold or used as an independent product, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art or a part of this technical solution can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present application.

[0063] Based on the method in the above embodiments, an embodiment of the present application provides a computer-readable storage medium. The computer-readable storage medium stores a computer program. When the computer program runs on a processor, the processor is caused to execute the method in the above embodiments.

[0064] Based on the method in the above embodiments, an embodiment of the present application provides a computer program product. When the computer program product runs on a processor, the processor is caused to execute the method in the above embodiments.

[0065] It can be understood that the processor in the embodiments of the present application may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. The general-purpose processor may be a microprocessor or any conventional processor.

[0066] The method steps in the embodiments of the present application may be implemented in a hardware manner or may be implemented by a processor executing software instructions. The software instructions may be composed of corresponding software modules. The software modules may be stored in a random access memory (RAM), flash memory, read-only memory (ROM), programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), registers, hard disk, removable hard disk, CD-ROM, or any other form of storage medium well known in the art. An exemplary storage medium is coupled to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium may also be a component of the processor. The processor and the storage medium may be located in an ASIC.

[0067] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable systems. The computer instructions can be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center in a wired manner (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or a wireless manner (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that includes one or more integrated available media. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, a magnetic tape), an optical medium (such as a DVD), or a semiconductor medium (such as a solid state disk (SSD)), etc.

[0068] It can be understood that the various numerical numbers involved in the embodiments of the present application are only for the convenience of description and are not used to limit the scope of the embodiments of the present application.

[0069] Those skilled in the art can easily understand that the above is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A photovoltaic carrying capacity optimization method based on weighted evaluation of multiple objectives, characterized in that: The following steps are involved: Step S1: construct an optimization objective function for the purpose of safe operation of the distribution station area, and construct corresponding constraints and penalty functions; Step S2: construct a safety index loss function through the safety index evaluation weight and the fuzzy relationship matrix, and use it as another optimization objective function; Step S3: Determine the index weight between the optimization objective functions in step S1 and step S2; and use the penalty function in step S1 as another optimization objective function, and combine it with the optimization objective functions in step S1 and step S2; Step S4: In the first n iterations, a multi-objective particle swarm optimization algorithm is used to optimize the combined optimization objective function to obtain a global optimal solution; wherein one particle corresponds to one distributed photovoltaic access method; Step S5: In the n+1~qth iterations, the optimization objective correction term is obtained by combining the indicator weights between the optimization objective functions in step S3, and then the probability of particle selection is corrected by combining the roulette-weight gradual strengthening combination method with the penalty function influence correction coefficient; Step S6: Select the particle with the global optimal solution and penalty function of 0 in step S5 for single-objective optimization, and output a distributed photovoltaic site selection and capacity determination method that maximizes the photovoltaic carrying capacity of the distribution station area; wherein n and q are set values, and q is greater than n.

2. The photovoltaic carrying capacity optimization method according to claim 1, characterized in that: Step S1 specifically includes the following steps: Step S1.1: Under the condition that the transformer in the distribution station area is continuously not overloaded and the power quality index does not exceed the standard, the maximum value of the distributed photovoltaic capacity accepted by the distribution station area is selected as the first optimization objective function, and the minimum value of the line loss in the distribution station area is selected as the second optimization objective function; Step S1.2: Taking the power flow equation of the network itself as the constraint condition, construct the voltage deviation constraint condition, the three-phase unbalance constraint condition and the reverse load rate constraint condition, and obtain the corresponding penalty function.

3. The photovoltaic carrying capacity optimization method according to claim 2, characterized in that: Step S2 specifically includes the following steps: Step S2.1: Taking voltage deviation, three-phase unbalance and reverse load rate as safety indicators, a load capacity safety index evaluation matrix is ​​constructed; Step S2.2: Based on the bearing capacity safety index evaluation matrix, the bearing capacity safety index evaluation weight is calculated using the geometric mean method; Based on the safety index in step S2.1, a photovoltaic carrying capacity safety index evaluation set is constructed, and the membership degree of each safety index to the photovoltaic carrying capacity safety index evaluation set is calculated, thereby obtaining a fuzzy relationship matrix; Step S2.3: fuzzy synthesis is performed on the safety index evaluation weight and the fuzzy relationship matrix in step S2.2 to obtain the bearing capacity safety index evaluation membership vector, and the bearing capacity safety index fuzzy score is calculated; Step S2.4: Based on the fuzzy score of the bearing capacity safety index in step S2.3, obtain the safety index loss function as the third optimization objective function.

4. The photovoltaic carrying capacity optimization method according to any one of claims 1 to 3, characterized in that: Step S4 specifically includes the following steps: Step S4.1: normalize all optimization objective functions, use multi-objective particle swarm algorithm to optimize all optimization objective functions, and store the non-dominated solutions in all optimization objective functions in each iteration in the Pareto frontier optimal solution set; Step S4.2: Divide the dimension corresponding to each optimization objective function in the Pareto frontier optimal solution space into N parts by using the grid method, and select the global optimal solution position of the particle by using the roulette method; Wherein, N is an integer greater than or equal to 2.

5. The photovoltaic carrying capacity optimization method according to claim 4, characterized in that: Step S5 specifically includes the following steps: Step S5.1: Based on the indicator weights between the optimization objective functions in step S3, the weight score of the i-th particle in the iteration process is calculated, and then the influencing parameters of the optimization objective correction term are obtained; Step S5.2: The influence weight of the penalty function is calculated using the exponential decay method, and the probability of particle selection is corrected using the roulette-weighted gradual strengthening combination method in combination with the influence parameter of the optimization target correction term in step S5.

1.

6. A photovoltaic carrying capacity optimization system based on weighted evaluation of multiple objectives, characterized in that: include: The objective function construction module is used to construct the optimization objective function for the purpose of safe operation of the distribution station area, and to construct the corresponding constraint conditions and penalty functions; Through the safety index evaluation weight and fuzzy relationship matrix, a safety index loss function is constructed and used as another optimization objective function; An objective function processing module is used to determine the indicator weights between the optimization objective functions in the objective function building module, and to use the penalty function as another optimization objective function to combine all the optimization objective functions; The first iteration module is used to use the multi-objective particle swarm optimization algorithm in the first n iterations to optimize the combined optimization objective function and obtain the global optimal solution; one particle corresponds to one distributed photovoltaic access method; The second iteration module is used to combine the indicator weights between the objective functions optimized in the objective function processing module to obtain three optimization objective correction items, and then correct the probability of particle selection by combining the roulette-weight gradual strengthening combination method with the penalty function influence correction coefficient; The third iterative module is used to select the particles with the global optimal solution and penalty function of 0 in the second iterative module for single-objective optimization, and output a distributed photovoltaic site selection and capacity determination method that maximizes the photovoltaic carrying capacity of the distribution station area.

7. The photovoltaic carrying capacity optimization system according to claim 6, characterized in that: The objective function construction module includes a constraint condition construction unit, a first objective function construction unit, and a second objective function construction unit; The first objective function construction unit is used to select the maximum value of the distributed photovoltaic capacity accepted by the distribution station area as the first optimization objective function under the condition that the transformer in the distribution station area is continuously not overloaded and the power quality index does not exceed the standard; The second objective function construction unit is used to take the minimum value of the line loss in the distribution station area as the second optimization objective function; The constraint condition construction unit is used to construct voltage deviation constraint conditions, three-phase unbalance constraint conditions and reverse load rate constraint conditions with the power flow equation of the network itself as constraint conditions, and obtain corresponding penalty functions.

8. The photovoltaic carrying capacity optimization system according to claim 7, characterized in that: The objective function construction module also includes a third optimization objective function construction unit, and the third optimization objective function construction unit includes: a first calculation subunit, a second calculation subunit, a third calculation subunit, a fourth calculation subunit and a fifth calculation subunit; The first calculation subunit is used to establish a bearing capacity safety index evaluation matrix based on voltage deviation, three-phase unbalance and reverse load rate as safety indicators; The second calculation subunit is used to calculate the bearing capacity safety index evaluation weight by using the geometric mean method based on the bearing capacity safety index evaluation matrix; The third calculation subunit is used to construct a photovoltaic carrying capacity safety indicator comment set based on the safety indicator, calculate the membership degree of each safety indicator to the photovoltaic carrying capacity safety indicator comment set, and then obtain the fuzzy relationship matrix; The fourth calculation subunit is used to perform fuzzy synthesis on the safety index evaluation weight and the fuzzy relationship matrix, obtain the bearing capacity safety index evaluation membership vector, and calculate the bearing capacity safety index fuzzy score; The fifth calculation subunit is used to obtain the safety index loss function based on the fuzzy score of the bearing capacity safety index as the third optimization objective function.

9. The photovoltaic carrying capacity optimization system according to claim 8, characterized in that: The first iteration module includes: a multi-objective particle swarm optimization unit and a particle position selection unit; The multi-objective particle swarm optimization unit is used to normalize all optimization objective functions, use the multi-objective particle swarm algorithm to optimize all optimization objective functions, and store the non-dominated solutions in all optimization objective functions in each iteration in the Pareto frontier optimal solution set; The particle position selection unit is used to divide the dimension corresponding to each optimization objective function in the Pareto frontier optimal solution space into N parts through the grid method, and select the global optimal solution position of the particle by the roulette method; wherein N is an integer greater than or equal to 2.

10. The photovoltaic carrying capacity optimization system according to any one of claims 6 to 9, characterized in that: The second iteration module includes: a first correction unit, a second correction unit and a third correction unit; The first correction unit is used to calculate the weight score of the i-th particle in the iteration process based on the indicator weights between the optimization objective functions, and then obtain the influencing parameters of the optimization objective correction item; The second correction unit is used to calculate the influence weight of the penalty function by using an exponential decay method; The third correction unit is used to combine the influence weight of the penalty function and the influence parameter of the optimization target correction term, and adopts the roulette-weight gradual strengthening combination method to correct the probability of particle selection.

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