Site selection and sizing optimization configuration method and device for phase modifier

Abstract

The invention discloses a phase modifier locating and sizing optimal configuration method and device, and the method comprises the steps: obtaining system parameters of a power system in a target region, so as to build a power system simulation model; establishing a phase modifier locating and sizing dual-objective optimization model considering the short-circuit ratio and the economic index; inputting a position parameter and capacity configuration scheme set capable of newly building and / or transforming a thermal power generating unit into a phase modifier into the power system simulation model so as to solve the dual-objective optimization model, and outputting an optimal solution set meeting set constraint conditions; and evaluating each solution in the optimal solution set so as to decide the position and capacity configuration scheme of the phase modifier from the optimal solution set. According to the method, under the condition of comprehensively considering the economic cost, the resource configuration is optimized, the short-circuit ratio level of the power system is effectively improved, and the stability of the power system and the consumption capability of new energy are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system optimization, and in particular to a method and device for optimizing the location and capacity of phase regulators. Background Art

[0002] With the continuous expansion of the scale of new energy grid connection in traditional energy areas, the grid structure and operating characteristics have changed significantly, bringing new challenges to the stability and safety of the power system. The short-circuit ratio index of wind farms in some areas has approached the minimum requirements of safety regulations. Phase regulators, as a dynamic reactive power compensation device, can provide reactive power support and improve the short-circuit capacity and voltage stability of the system.

[0003] At present, the most common methods for selecting and sizing phase shifters in the prior art are the successive addition method and the multi-objective optimization method. The successive addition method gradually increases the number and capacity of phase shifters in an iterative manner, and selects the node with the lowest short-circuit ratio or its associated nodes for configuration to improve the short-circuit ratio of the system; however, this method only considers the one-time investment cost of the equipment, and does not fully consider the operation and maintenance costs, making it difficult to conduct accurate input-output benefit analysis and evaluate the comprehensive economic benefits; and this method has the problems of low optimization efficiency and difficulty in obtaining the global optimal solution. Other multi-objective optimization methods balance system performance by optimizing two or more objective functions at the same time, such as short-circuit ratio and economy, system stability, etc. The algorithms usually used to solve the multi-objective optimization model are weighted summation method, particle swarm optimization, etc. Although these methods can balance multiple performance indicators and generate optimal solutions between different objectives, the applicability of these methods is still insufficient, and there are problems such as high computational complexity and slow algorithm convergence speed. In addition, these methods at this stage usually only focus on the site selection and sizing of new phase shifters, without considering new construction and reuse and transformation at the same time, resulting in resource waste and increased investment. Summary of the invention

[0004] In order to address the deficiencies in the prior art, the present invention provides a method and device for optimizing the siting and sizing of phase shifters. By comprehensively considering two configuration modes of phase shifters, namely, new construction and renovation, the siting and sizing optimization of phase shifters can be achieved, so as to avoid the problems of low efficiency in the evaluation and solution of the prior art, ignoring the economic advantages of reusing and renovating thermal power units into phase shifters, and difficulty in comprehensively balancing the optimal solution of stability and economy. The method can optimize resource allocation while comprehensively considering economic costs, effectively improve the short-circuit ratio of the power system, and improve the stability of the power system and the ability to absorb new energy.

[0005] The present invention adopts the following technical solution.

[0006] In a first aspect, the present invention provides a method for optimizing the location and capacity of a phase shifter, the method comprising:

[0007] Obtain system parameters of the power system in the target area to build a power system simulation model;

[0008] A dual-objective optimization model for phase shifter location and capacity determination considering short-circuit ratio and economic performance is established;

[0009] Inputting a set of location parameters and capacity configuration schemes of newly built and / or modified thermal power units into phase-shifting units into the power system simulation model to solve the dual-objective optimization model and output an optimal solution set that meets the set constraints;

[0010] Each solution in the optimal solution set is evaluated respectively to determine the location and capacity configuration of the phase regulator from the optimal solution set.

[0011] Optionally, the system parameters include power injected into the grid node, bus voltage, branch flow, line impedance, generator output and load data.

[0012] Optionally, the expression of the first objective function of the dual-objective optimization model for phase shifter site selection and capacity determination is as follows:

[0013]

[0014] x={x1,x2},x1∈C,x2∈Q

[0015] Where f1(x) represents the first objective function that aims to maximize the minimum short-circuit ratio value of all nodes; x represents the decision variables, including location x1 and capacity x2; C and Q are the set of all locations and all capacities in the deployable solution set, respectively; SCR_U i is the short-circuit ratio of node i, and P represents the total number of nodes in the power system.

[0016] Optionally, the expression of the second objective function of the dual-objective optimization model for phase shifter site selection and capacity determination is as follows:

[0017] f2(x)=min(C O +C y1 +C y2 -EC)

[0018] Where f2(x) represents the second objective function with the minimization of comprehensive cost as the optimization goal; C O is the total cost of installing a phase regulator, which includes the installation cost of the new phase regulator and the cost of converting the retired thermal power units into phase regulators; C y1 is the total operation and maintenance cost of the phase regulator; C y2 is the total cost of electricity consumption of the phase regulator; EC is the total economic compensation of the phase regulator.

[0019] Optionally, the constraints include node short-circuit ratio constraint, phase-shifter deployment quantity constraint, power flow active power balance constraint, power flow reactive power balance constraint, generator active output limit constraint, generator reactive output limit constraint, node voltage limit constraint, line transmission capacity limit constraint and / or node fault current limit constraint.

[0020] Optionally, the expressions of the node short-circuit ratio constraints are as follows:

[0021]

[0022] Where g1(x) is the node short circuit ratio constraint; SCR_U i is the voltage-based short-circuit ratio index in node i; i = 1, 2…P; P represents the total number of nodes in the power system; U Ni represents the nominal voltage of node i, E eqi

[0023] Represents the voltage vector when node i is not connected to the new energy unit; Indicates the number of new energy units connected to node i

[0024] The voltage vector change before and after; Represents the voltage vector after node i is connected to the new energy unit.

[0025] Optionally, the algorithm used to solve the dual-objective optimization model is an improved NSGA-II algorithm;

[0026] The improved NSGA-II algorithm includes: initializing the population according to a set of solutions for deployable locations and capacities of the phase regulator, and iteratively optimizing the population in combination with linear ranking-based parent selection, guided crossover, and non-uniform mutation to coordinate the conflicting dual objective functions in the model, thereby obtaining the Pareto optimal solution set for the location and capacity configuration of the phase regulator.

[0027] Optionally, the method for initializing the population is: generating an initial population by using a good point set initialization method, that is, generating an initial population uniformly distributed in a multidimensional search space through mathematical construction; wherein each individual in the population represents a possible phase shifter configuration scheme, including the deployment location and configuration capacity of the phase shifter.

[0028] Optionally, the step of selecting a parent based on linear ranking includes:

[0029] Each individual in the population is linearly sorted according to its corresponding non-dominated level and crowding distance; the lower the non-dominated level, the higher the priority; within the same level, the larger the crowding distance, the higher the priority;

[0030] The sorted individuals are numbered j=1, 2, ..., N in sequence; j=1 is the optimal individual, and N is the number of individuals in the population;

[0031] Assign a selection probability p to each individual based on its ranking number j , to obtain the cumulative probability interval of each individual choice;

[0032] Randomly generate N random numbers, and extract the individuals corresponding to the cumulative probability interval that each random number falls into from the population, and use the extracted individuals as parent individuals.

[0033] Optionally, the selection probability p j The calculation formula is as follows:

[0034]

[0035] In the formula, p j represents the probability of individual j being selected, η - and η + are the minimum and maximum selection parameters in linear sorting, respectively, satisfying η - +η + =2, and 0≤η - ≤1.

[0036] Optionally, the guided crossover step includes:

[0037] Randomly select a set number of crossover individuals from the population as parents for pairing;

[0038] The update rate of the corresponding offspring is calculated through the gene difference value and commonality value of each paired parent individual to generate the corresponding offspring individual; the expressions are as follows:

[0039] v pa = k·cp ab +h1·r1·(p a -zp ab )+h2·r2·(p b -zp ab )

[0040] v pb = k·cp ab +h1·r3·(p b -zp ab )+h2·r4·(p a -zp ab )

[0041] c a =p a +v pa

[0042] c b =p b +v pb

[0043] In the formula, v pa and v pb The two parent individuals p are paired a and p b The update speed of the offspring; p a and p b are two paired parent individuals; a, b∈N1 and a≠b; N1 is the set crossover number; k, h1 and h2 are three set constants, r1, r2, r3, r4 are all random numbers in the range [0,1]; cp ab and cp ab The two parent individuals p are paired a and p b Genetic differences and commonality values; c a is the parent individual p a The generated offspring; c b Parent individual p b The generated offspring.

[0044] Optionally, the calculation formulas for the gene difference value and commonality value of each paired parent individual are as follows:

[0045] cp ab =p a -p b

[0046]

[0047] Optionally, the step of non-uniform mutation includes:

[0048] Randomly select a set number of mutation individuals from the population as mutation individuals;

[0049] Based on the genes of each of the mutant individuals, a random value of the mutation direction and a random value of the mutation amplitude of each mutant individual are randomly generated and substituted into the following formula for mutation:

[0050]

[0051] In the formula, x k′ is the value of the kth decision variable after the current individual mutation, x k is the kth decision variable value of the current individual; k∈x, x represents the decision variables of the dual-objective optimization model, including position x1 and capacity x2; U k is the upper bound of the kth decision variable, L k is the lower bound of the kth decision variable; z is U k -x k or x k -L k ; r and r1 are random numbers in the range of (0,1), r is the random value of the current individual variation direction, r1 is the random value of the current individual variation amplitude; w is the parameter that controls the attenuation rate of the variation amplitude; t is the current iteration number, and T is the maximum iteration number.

[0052] Optionally, the algorithm adopted when evaluating each solution in the optimal solution set is a TOPSIS algorithm that approximates the ideal solution, so as to output a solution with the best evaluation value as the optimal solution for the location and capacity configuration of the phase regulator.

[0053] In a second aspect, the present invention provides a phase shifter site selection and capacity optimization configuration device, which runs the steps of any method described in the first aspect of the present invention, and the device comprises:

[0054] An acquisition module is used to acquire system parameters of the power system in the target area to build a power system simulation model;

[0055] Establish a module for establishing a dual-objective optimization model for phase shifter location and capacity determination considering short-circuit ratio and economic performance indicators;

[0056] A solution module, used for inputting a set of location parameters and capacity configuration schemes of newly built and / or modified thermal power units into the phase-converting units into the power system simulation model, so as to solve the dual-objective optimization model and output an optimal solution set that satisfies the set constraints;

[0057] The decision module is used to evaluate each solution in the optimal solution set respectively to determine the location and capacity configuration scheme of the phase regulator from the optimal solution set.

[0058] In a third aspect, the present invention provides a terminal, including a processor and a storage medium;

[0059] The storage medium is used to store instructions;

[0060] The processor is used to operate according to the instructions to execute the steps of any one of the methods described in the first aspect of the present invention.

[0061] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any one of the methods described in the first aspect of the present invention.

[0062] The beneficial effects of the present invention are as follows:

[0063] 1. From the perspective of improving the short-circuit ratio index, the present invention comprehensively considers the actual situation of the need to build new phase-shifting units and transform thermal power units into phase-shifting units in the context of large-scale development of new energy power generation in areas dominated by traditional energy, and establishes a dual-objective optimization model for phase-shifting unit site selection and sizing considering stability and economic indicators to optimize the site selection and sizing scheme of the phase-shifting unit, effectively improves the short-circuit ratio level of the power system, thereby enhancing the stability of the system, providing guarantee for large-scale grid connection of new energy, and improving the economy and stability of the power system.

[0064] 2. The present invention solves the optimization problem of the dual-objective optimization model through the improved NSGA-II algorithm. By improving the algorithm links such as population initialization, parent selection, crossover and mutation operations, the convergence speed of the algorithm and the diversity of solutions are significantly improved, and the overall optimization effect is enhanced; a uniformly distributed initial population is generated through a good point set, which improves the coverage of the search space and the diversity of the population; the parent selection method based on linear ranking flexibly adjusts the selection parameters, maintains high diversity in the early stage, and increases the selection of excellent individuals in the later stage, which accelerates the convergence speed; guided crossover is combined with the idea of ​​particle swarm algorithm to guide the offspring to evolve in a better direction and enhance the search ability; non-uniform mutation realizes the effective combination of early large-scale search and late fine search through the variation amplitude associated with the iterative algebra, prevents the algorithm from falling into the local optimum, and improves the accuracy and quality of the final solution.

[0065] 3. In summary, the present invention can effectively improve the short-circuit ratio level of the power system while comprehensively considering the economic cost, and provide an optimized phase-shifting phase location and capacity deployment plan, thereby improving the stability of the power system and the absorption capacity of new energy. Through a systematic, comprehensive, scientific and complete optimization solution process, combined with the improved NSGA-II algorithm, the present invention not only solves the dual-objective optimization problem of phase-shifting phase site selection and sizing, but also significantly improves the efficiency and quality of the optimization results, ensuring its practicality and reliability in practical applications. These improvements are not only applicable to the phase-shifting phase site selection and sizing problems, but can also be widely used in other multi-objective optimization scenarios, with high versatility and application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 It is a flow chart of the method for optimizing the location and capacity of a phase regulator in an embodiment of the present invention;

[0067] Figure 2 It is a schematic diagram of the composition of the dual-objective optimization model for phase shifter site selection and capacity determination in an embodiment of the present invention;

[0068] Figure 3 It is a schematic diagram of the process of solving the dual-objective optimization of phase condenser site selection and capacity determination in an embodiment of the present invention;

[0069] Figure 4This is an example diagram of the target value distribution change of the short-circuit ratio in the optimization iteration in the embodiment of the present invention;

[0070] Figure 5 This is an example diagram of target value distribution changes of economic indicators in optimization iterations in an embodiment of the present invention;

[0071] Figure 6 This is an example diagram of the change of short circuit ratio and economic index distribution in the optimization iteration in the embodiment of the present invention;

[0072] Figure 7 It is a structural principle block diagram of a phase regulator site selection, capacity determination and optimization configuration device in an embodiment of the present invention. DETAILED DESCRIPTION

[0073] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The embodiments described in the present invention are only part of the embodiments of the present invention, not all of the embodiments. Based on the spirit of the present invention, other embodiments obtained by ordinary technicians in this field without creative work are all within the scope of protection of the present invention.

[0074] Embodiment 1:

[0075] Reference Figure 1 The embodiment of the present invention provides a method for optimizing the location and capacity of a phase shifter, which specifically includes the following steps:

[0076] Step 1: Obtain system parameters of the power system in the target area to build a power system simulation model;

[0077] In this embodiment, an improved model based on the 39bus New England system (IEEE-39) is used for the experiment. In the actual power system, a position can be newly built or renovated with multiple capacities of phase regulators, but for the convenience of optimization and solution, it is agreed that only one capacity is selected at each position. For example, in the optimization problem, a 600Mvar phase regulator is regarded as a combination of two 300Mvar phase regulators. The power system topology information is input, and the simulation model is established with parameters such as the location where the thermal power unit can be newly built or renovated as a phase regulator. The power system simulation model is established, and the phase regulator and experimental wind turbine are preset to verify the power grid model.

[0078] Specifically, the system parameters obtained include the power injected into the grid node, bus voltage, branch flow, line impedance, generator output and load data, etc. The obtained grid data is processed to establish the grid topology. In the actual grid simulation process, the accurate establishment of the grid topology is crucial. Grid operation data including bus data, branch data, generator data and load data are collected, and a complete grid topology is formed through analysis and arrangement of these data. Then, these data are used to construct the bus matrix, branch matrix and power generation matrix to ensure that the grid simulation model can accurately reflect the current operation status of the grid. Then, parameter setting and initialization are performed. The data processing work in this process needs to ensure the accuracy and consistency of the data.

[0079] After building the simulation model of the real power grid, the degree of fit between the simulation model and the actual power grid is quantitatively analyzed. Specifically, the historical operation data or actual test data of the power grid, including key parameters such as bus voltage, branch current, generator output and load level, are used as the input of the simulation model. Under the same input conditions, the simulation model is run to obtain the simulation output results.

[0080] Then, the simulation results are compared with the operating data of the actual power grid to calculate the error indicators of key parameters. Indicators such as the mean absolute percentage error (MAPE) of the bus voltage are calculated, and the error threshold is set. When the calculated error indicator exceeds the preset threshold, it is considered that the simulation model has a large deviation and needs to be adjusted. In order to reduce the error, the relevant parameters of the simulation model are adjusted, such as the resistance of the line to match the actual measured line characteristics, the transformation ratio and short-circuit impedance of the transformer to correct the influence of the transformer on the voltage and power flow, the generator model parameters such as the active and reactive output limits are adjusted to improve the control effect of the voltage and frequency, and the load model parameters such as the active and reactive power and power factor of the load are adjusted according to the actual load characteristics.

[0081] The sensitivity analysis method is used to determine the parameters that have the greatest impact on the error, and these parameters are fine-tuned first. After adjustment, the simulation model is rerun, and the error calculation and comparison process is repeated to iterate the simulation model optimization.

[0082] The iterative adjustment process continues until the error indicators of all key parameters are within the allowable range. Usually, the accuracy requirements can be met after several iterations. The final output is a calibrated simulation model, whose simulation results are highly consistent with the actual power grid operation data, and the error indicators are within the preset threshold. In this way, a simulation model suitable for the research problem in the target area is constructed, including its parameters, voltage matrix, etc., which can accurately reflect the operating characteristics of the actual power grid and provide a reliable simulation basis for the subsequent phase-shifting phase site selection and capacity optimization.

[0083] The simulation model of this embodiment is established through PyPower. The deployment position and deployment capacity of the phase regulator are preset in the simulation model, and a simulation environment for the deployment configuration of the phase regulator is built, thereby providing a simulation data source and calculation analysis tools for studying the optimization of phase regulator deployment.

[0084] Step 2: Establish a dual-objective optimization model for phase shifter location and capacity determination considering short-circuit ratio and economic performance indicators;

[0085] Reference Figure 2 ,This embodiment aims at the problem of phase shifter location and capacity optimization in the power system. It is necessary to establish a power system optimization model. From the perspective of improving the stability of the power system, a dual-objective optimization model for the phase shifter location and capacity optimization problem is constructed, including three main parts: decision variables, constraints and optimization objectives;

[0086] As an embodiment of the present invention, the dual-objective optimization model for phase shifter site selection and capacity determination established in step 2 takes the phase shifter deployment location and capacity as discrete decision variables, takes the short-circuit ratio and economic index as optimization targets, and takes the deployment quantity and the critical value of the short-circuit ratio as constraints. Its expressions are as follows:

[0087] (1) Decision variables:

[0088] x={x1,x2},x1∈C,x2∈Q

[0089] Where x represents the decision variable, including location x1 and capacity x2; C and Q are the set of all locations and all capacities in the deployable solution set, respectively.

[0090] (2) Objective function:

[0091]

[0092] f2(x)=min(C O +C y1 +C y2 -EC)

[0093]

[0094] C y1 =C OM ×C O

[0095] C y2 =τ×C e ×λ loss ×Q unit ×∑N SC,i

[0096] EC=Y×Q×τ

[0097] Where f1(x) represents the first objective function that aims to maximize the minimum short-circuit ratio value among all nodes; SCR_U i is the short circuit ratio SCR_U of node i i is the voltage-based short-circuit ratio index in node i; i = 1, 2…P; P represents the total number of nodes in the power system; U Ni represents the nominal voltage of node i, E eqi Represents the voltage vector when node i is not connected to the new energy unit; Indicates the number of new energy units connected to node i

[0098] The voltage vector change before and after; Represents the voltage vector after node i is connected to the new energy unit;

[0099] f2(x) represents the second objective function with the minimization of comprehensive cost as the optimization goal, where f2(x) comprehensively measures the economic performance of building or transforming retired thermal power units into phase-shifting units based on installation cost, operation and maintenance cost, loss cost and economic compensation; C O is the total cost of installing a phase regulator, which includes the installation cost of a new phase regulator and the cost of converting retired thermal power units into phase regulators; i * is the node location where the condenser can be deployed. The subscript add indicates that a condenser is added, and the subscript rem indicates that a retired thermal power unit is transformed into a condenser. add,i is node i * The capacity of the new phase regulator, c add,i is node i * The price per unit capacity (Va) of a newly built condenser; Cap rem,i is node i * The capacity of converting retired thermal power units into phase-shifting units, c add,i is to make node i * The price per unit capacity (lack) when the retired thermal power unit is transformed into a phase-shifting unit. Generally speaking, the larger the deployed capacity, the lower the price per unit capacity of the phase-shifting unit. y1 is the total operation and maintenance cost of the phase regulator, C OM is the operation and maintenance cost coefficient of the condenser; C y2 is the total electricity consumption cost of the phase regulator, τ is the online time of the phase regulator (hours / year), C e is the annual average electricity cost (10,000 yuan / MWh), λ loss is the power consumption coefficient of the condenser, Q unit is the unit capacity (lack), ∑N SC,i represents the sum of the capacities of all deployed condensers; EC is the total economic compensation of the condenser, Y is the compensation unit price, Q is the deployed capacity of the condenser, and τ is the online time of the condenser.

[0100] (3) Constraints:

[0101] In this embodiment, the node short circuit ratio constraint and the phase regulator deployment quantity constraint are as follows:

[0102]

[0103] g2(x)=Num add +Num rem ≤Num dep

[0104] Where g1(x) is the node short circuit ratio constraint; SCR_U i is the node short-circuit ratio indicator based on voltage calculation; g2(x) is the phase regulator deployment quantity constraint, Num add is the number of newly built condensers, Num rem is the number of retired thermal power units converted into phase regulators, Num dep is the total number of deployments allowed.

[0105] Furthermore, the constraints in the embodiment of the present invention also include:

[0106]

[0107] Where: g3(x) is the power flow active power balance constraint, g4(x) is the power flow reactive power balance constraint, g5(x) is the generator active output limit constraint, g6(x) is the generator reactive output limit constraint, g7(x) is the node voltage limit constraint, g8(x) is the line transmission capacity limit constraint, where P is the set of all nodes, E is the set of nodes where the generator is located, and P G,i (x) is the active output of the i-th generator under decision x, Q G,i (x) is the reactive power output of the i-th generator under decision x, P L,i is the active load of the ith node, Q L,i is the reactive load of the ith node, P loss is the network active power loss, Q loss is the network reactive power loss; where V i (x) is the voltage amplitude of the ith bus, S ij (x) is the complex power of the line, Z is the set of all lines, is the thermal stability limit of the line or transformer; g9(x), g 10 (x) is the node fault current limit constraint and the key node short circuit ratio constraint, where I fault,i (x) is the short-circuit current of busbar i under fault condition, is the maximum breaking current allowed by the device corresponding to node i, Pcritical For critical busbars that need to meet higher or special short-circuit ratio requirements, SCR min It is used to limit the critical nodes or specific fault scenarios to be greater than a certain higher threshold. The optimization problem is to solve how to select decision variables in the feasible domain to minimize the value of the optimization objective function.

[0108] In summary, the steps for establishing the dual-objective optimization model for phase shifter site selection and capacity determination in step 2 of this embodiment are summarized as follows:

[0109] Step 2.1: Determine the decision variables, where x1 is the discrete variable of the optional deployment location of the condenser, and x2 is the capacity configuration that can be selected if the condenser is deployed at each location;

[0110] Step 2.2: Determine the optimization target, and use the voltage-based short-circuit ratio index as the first optimization target f1 of the dual-objective optimization model, and expect to maximize the minimum short-circuit ratio value in all nodes, and take the comprehensive consideration of the economic efficiency of phase-shifting deployment as the second optimization target f2;

[0111] Step 2.3: Determine the constraints. In the constraint setting of the phase-shifting phase position and capacity optimization model, set two constraints: the minimum short-circuit ratio constraint g1 and the maximum deployment quantity constraint g2.

[0112] Step 3: inputting a set of location parameters and capacity configuration schemes of newly built and / or modified thermal power units into phase-converting units into the power system simulation model to solve the dual-objective optimization model and output an optimal solution set that meets the set constraints;

[0113] Those skilled in the art can use traditional non-dominated sorting genetic algorithm, conjugate gradient, quadratic programming and other algorithms to solve the model, but in order to obtain better technical effects, the embodiment of the present invention uses an improved NSGA-II algorithm to solve the dual-objective optimization model.

[0114] It should be noted that in dual-objective optimization problems, the objective functions are usually conflicting, and it is impossible to ensure that a single solution optimizes each objective at the same time; the Pareto optimal solution (non-dominated solution) means that no other solution can improve other objective values ​​without degrading any objective value; in order to improve the stability of the power grid and comprehensively consider the economic cost, the two optimization objectives of short-circuit ratio and economic index are considered in the optimization objectives of this model. The present invention solves this problem by adopting an improved second-generation non-dominated sorting genetic algorithm (NSGA-II algorithm) to generate a Pareto optimal solution set for the position and capacity configuration of the phase regulator, so that the optimization process can find a reasonable balance point between multiple conflicting objectives; each Pareto frontier solution represents a different compromise solution, providing the option of optimizing other objectives without compromising a certain objective, so that the optimization process can flexibly respond to conflicts between different optimization objectives according to actual needs; ultimately effectively improve the practicality and reliability of the optimization results, while achieving power grid stability, it also ensures the minimization of economic costs.

[0115] This embodiment uses an improved second-generation non-dominated sorting genetic algorithm to solve the optimization problem, so as to effectively solve the dual-objective optimization problem of phase shifter site selection and capacity determination. Compared with the traditional NSGA-II algorithm, the present invention has made adaptive improvements in multiple links such as population initialization, parent selection, crossover and mutation operations, improving the algorithm convergence speed, the diversity of optimization solutions, and optimizing the overall effect. The specific implementation steps of the optimization algorithm and its improved contents are described in detail below.

[0116] Step 3.1, initialize the population:

[0117] The initial population is generated by initializing the optimal point set. Each individual in the population represents a possible phase-shifting configuration scheme, including the deployment location and configuration capacity of the phase-shifting.

[0118] In the initial stage of the optimization algorithm, the initialization of the population has an important impact on the subsequent search process; the traditional NSGA-II algorithm usually adopts a random initialization method, however, this method may cause the initial population to be unevenly distributed in the search space, affecting the global search ability of the algorithm; to solve this problem, the present invention introduces a good point set initialization method, which generates an initial solution that is more evenly distributed in the multidimensional search space through mathematical construction, thereby improving the diversity and coverage of the initial population.

[0119] The specific method of generating the optimal point set is to use a specific mathematical formula to generate a uniformly distributed point set. For example, assuming that the population size is N and the decision variable dimension is d, the following formula is used to construct each individual x in the initial population: i ={x i1 ,x i2 ,...,x id}:

[0120]

[0121] In the formula, a j and b j are the lower and upper limits of the jth decision variable, respectively, and k is the parameter for constructing the optimal point set. Usually, the smallest prime number that satisfies k-3≥2d is selected. The initial population generated by the above method can be evenly distributed in the entire search space, reducing the blindness of the search process and improving the global search capability and convergence speed of the algorithm.

[0122] In addition, the PyPower calculation model is also initialized to ensure that the power model matches the initialization results of the decision variables, and is calculated in combination with the power flow calculation and short-circuit analysis models built by PyPower. Power flow calculation is to obtain key indicators such as bus voltage, line flow, generator output power, network loss, etc. by performing steady-state analysis on the nodes, branches and power generation units of the entire network, reflecting the actual operating status and characteristics of the power grid under this scheme. After completing the power flow calculation, the short-circuit analysis will evaluate the fault current and related electrical quantity changes when a three-phase fault occurs at a specific node based on the current system configuration and parameters, and obtain the dynamic response degree of the power grid under the fault state of the scheme, thereby evaluating the short-circuit ratio level and system anti-disturbance capability of the node or area.

[0123] Step 3.2, fitness evaluation:

[0124] After the population is initialized, the fitness of each individual needs to be evaluated; the fitness evaluation process includes objective function calculation and constraint verification. Using functions such as power flow calculation and short-circuit analysis, the objective function value corresponding to the current decision variable is comprehensively evaluated, including obtaining key parameters such as node voltage, power flow distribution and short-circuit ratio through power flow calculation, and evaluating the objective function value of power grid stability; the economic objective function value is calculated by combining the construction cost of new equipment, operation and maintenance cost and network loss cost. That is, according to the dual objective function in the optimization model, the value of each individual under the short-circuit ratio optimization target f1 and the economic optimization target f2 is calculated respectively. Constraint verification is to check whether each individual meets all constraints, including power flow balance, equipment capacity limit, short-circuit ratio lower limit, etc. For individuals that do not meet the constraints, their fitness can be reduced through a penalty mechanism, or directly determined as an infeasible solution.

[0125] The accuracy of fitness evaluation directly affects the quality of optimization results. Therefore, in practical applications, it is necessary to ensure that the power system simulation model pypower can accurately reflect the system operation status and the short-circuit analysis results are reliable.

[0126] Step 3.3, non-dominated sorting:

[0127] The evaluated population is sorted by non-dominance, and individuals are divided into different levels according to their dominance relationship. According to the non-dominance sorting rule, the population is divided into different levels: p and q represent two different individuals in the population, f m (p) and f m (q) represents the function value of individual p and individual q on the mth optimization target, m represents the number of the optimization target, where m∈{1,2} means there are two optimization targets. If individual p dominates q, it must satisfy:

[0128]

[0129] It means that for all optimization objectives m, individual p performs no worse than individual q on objective m. That is, p is at least as good as or better than q on every objective.

[0130]

[0131] It means that there is at least one optimization goal m such that individual p performs better than individual q on this goal. That is, p is significantly better than q on at least one goal.

[0132] During the sorting process, first identify the individuals that are not dominated by any individual and mark them as the first level (Rank = 1); then remove these individuals from the population and continue to look for solutions that are not dominated by the remaining individuals to form the second level (Rank = 2); and so on until all individuals are stratified.

[0133] Step 3.4, crowding distance calculation:

[0134] In order to maintain the diversity of the population within the same non-dominated level, the crowding distance of each individual needs to be calculated. The main purpose of calculating crowding is to measure the diversity of the population in the multi-objective optimization process, thereby providing an important reference for selecting the next generation of populations. i It is determined by calculating the distance between an individual and its neighbors in the target space:

[0135]

[0136] Where, d i represents the crowding distance of individual i, M represents the number of optimization targets, and f m (i+1) is the target value of the adjacent individual after individual i on the mth target, f m (i-1) The target value of the adjacent individual before individual i on the mth target, f m max is the maximum value on the mth target, f mmin is the minimum value on the mth target. The larger the crowding distance, the sparser the position of the individual in the target space, and the algorithm is more inclined to retain such individuals to ensure the diversity of the population as a whole in the target space.

[0137] Step 3.5: Parent selection based on linear ranking:

[0138] The traditional NSGA-II algorithm usually adopts the tournament selection method, randomly selecting several individuals to compete and selecting individuals with higher fitness as parents. However, tournament selection may lead to unstable selection pressure in some cases, affecting the convergence speed and diversity of the algorithm.

[0139] To this end, the present invention improves the selection method based on linear ranking. First, in terms of sorting and numbering, the population is sorted according to the non-dominated level and crowding distance. The lower the level (i.e., the stronger the non-dominated), the higher the priority; within the same level, the larger the crowding distance, the higher the priority. The sorted individuals are numbered i=1, 2, ..., N in sequence, where i=1 is the best individual. In the selection probability allocation, a selection probability p is assigned to each individual. i , the formula is as follows:

[0140]

[0141] Among them, p j represents the probability of individual j being selected, η - and η + are the minimum and maximum selection parameters in linear sorting, respectively (the probability of a good individual being selected relative to the probability of an inferior individual in the selection process), satisfying η - +η + =2, and 0≤η - ≤1; In this embodiment, the initial period η is set - and η + They are 0.5 and 1.5 respectively, the number of iterations is 200, and the values ​​at the last iteration based on algebraic linear adjustment are 0.2 and 1.8.

[0142] At the same time, when performing the selection operation, N random numbers are randomly generated, and the cumulative probability (the cumulative probability interval selected by each individual in the population, ensuring that the sum is 1) is matched with the random number, and the generated random number is matched with the cumulative probability array to determine the individual corresponding to the cumulative probability interval in which the random number falls, and the individuals corresponding to the cumulative probability interval in which each random number falls are extracted from the population, and the extracted individuals are used as parent individuals; this method can maintain a high diversity in the early stage of the algorithm, and gradually increase the selection parameters as the iteration proceeds to accelerate convergence.

[0143] Step 3.6, guided crossover:

[0144] Crossover operation is an important step in the genetic algorithm for generating new individuals. Traditional crossover methods such as polynomial crossover (SBX) can maintain population diversity to a certain extent, but may converge slowly during the optimization process. The present invention introduces the guided crossover method, combined with the idea of ​​particle swarm optimization, and guides the offspring to evolve in a better direction by introducing the genetic differences and commonalities between the parents. The specific steps are:

[0145] Randomly select a set number of crossover individuals from the population as parents for pairing;

[0146] Calculate the gene difference and commonality values ​​of each pair of parent individuals:

[0147] cp ab =p a -p b

[0148]

[0149] In the formula, p a and p b are two paired parent individuals; a, b∈N1 and a≠b; cp ab and cp ab The two parent individuals p are paired a and p b The genetic differences and commonality values ​​of

[0150] The update rate of the corresponding offspring is calculated by the gene difference value and commonality value of each paired parent individual:

[0151] v pa = k·cp ab +h1·r1·(p a -zp ab )+h2·r2·(p b -zp ab )

[0152] v pb = k·cp ab +h1·r3·(p b -zp ab )+h2·r4·(p a -zp ab )

[0153] In the formula, v pa and v pb The two parent individuals p are paired a and p bThe update speed of the offspring; k, h1 and h2 are three set constants, r1, r2, r3 and r4 are all random numbers in the range of [0,1].

[0154] Finally, generate the corresponding offspring individuals:

[0155] c a =p a +v pa

[0156] c b =p b +v pb

[0157] In the formula, c a is the parent individual p a The generated offspring; c b Parent individual p b The generated offspring.

[0158] The generated new individual c a and c b In the gene space, it tends to move towards a better direction, while maintaining a certain degree of randomness to maintain population diversity. Finally, boundary processing is performed. For the generated offspring individuals, if a gene value exceeds its definition domain, it is restricted to the boundary value to ensure that all offspring individuals are within the feasible domain.

[0159] Through the guided crossover method, offspring individuals can evolve towards a better solution space while retaining the excellent genes of their parents, thereby accelerating the convergence of the algorithm while maintaining the diversity of solutions.

[0160] Step 3.7, non-uniform mutation:

[0161] The mutation operation is used to increase the diversity of the population and prevent the algorithm from falling into the local optimum. The traditional polynomial mutation has the same mutation amplitude in different iteration stages, which may not meet the needs of different search stages. Therefore, this step adopts a non-uniform mutation method, so that the mutation amplitude is associated with the current iteration generation, achieving the effect of large-scale search in the early stage and fine search in the later stage. The steps include:

[0162] Randomly select a set number of mutant individuals from the population as mutant individuals;

[0163] Based on the genes of each of the mutant individuals, a random value of the mutation direction and a random value of the mutation amplitude of each mutant individual are randomly generated and substituted into the following formula for mutation:

[0164]

[0165] In the formula, x k′is the value of the kth decision variable after the current individual mutation, x k is the kth decision variable value of the current individual; k∈x, x represents the decision variables of the dual-objective optimization model, including location x1 and capacity x2; U k is the upper bound of the kth decision variable, L k is the lower bound of the kth decision variable; z is U k -x k or x k -L k ; r and r1 are random numbers in the range of (0,1), r is the random value of the current individual mutation direction, r1 is the random value of the current individual mutation amplitude; w is the parameter that controls the attenuation rate of the mutation amplitude (usually takes a value of 2 to 5); t is the current iteration generation, and T is the maximum iteration generation.

[0166] The specific implementation process requires random selection of mutation sites, that is, for each offspring individual, randomly select one or more gene sites for mutation; then calculate the mutation amplitude, that is, according to the above formula, calculate the mutation amplitude Δ(t,z) of each selected gene; and update the gene value, that is, adjust the gene value according to the mutation amplitude to ensure that the mutated gene value is still within its definition domain. Through the non-uniform mutation method, the algorithm has a greater exploration ability in the early stage of optimization and can search the solution space extensively; while in the later stage of optimization, the mutation amplitude gradually decreases, which enhances the ability to search for the local optimal solution in detail and improves the accuracy and quality of the final solution.

[0167] Step 3.8: Merge to form the next generation population

[0168] After completing the crossover and mutation operations of the parent and child generations, the two are merged to form a temporary population of size 2N; then, the temporary population is non-dominated sorted and the crowding distance is calculated, and the temporary population is non-dominated sorted and divided into several non-dominated levels.

[0169] In the selection process, individuals are selected from low to high non-dominated levels to enter the next generation population; when the number of individuals in a certain level exceeds the number of remaining selectable individuals, individuals are selected from large to small according to the crowding distance until the next generation population is filled. In this way, it is ensured that the next generation population has good diversity distribution and excellent solution quality while maintaining non-dominance.

[0170] Step 3.9, repeat steps 3.2 to 3.8 for iterative optimization, and output the optimal solution set in combination with the termination condition;

[0171] After each iteration of the optimization algorithm, it is necessary to determine whether the termination condition is met. Typical termination conditions include reaching the preset maximum iteration number T or the Pareto front solution set has not changed significantly in several consecutive generations, indicating that the algorithm has basically converged. Once the termination condition is met, the algorithm stops iterating and outputs the final Pareto front solution set. These solution sets represent different compromises between the two goals of short-circuit ratio optimization and economic optimization, for decision makers to select and evaluate according to actual needs.

[0172] To summarize the above optimization algorithm steps, several improvements have been made, including improving the initialization of the good point set population during initialization, using a mathematical construction method to generate a uniformly distributed initial population, improving the coverage and diversity of the population in the search space, reducing the blindness caused by random initialization, and accelerating the global search ability of the algorithm; improving the selection to a parent selection based on linear ranking, using the linear ranking probability allocation and cumulative probability matching method, and flexibly adjusting the selection parameters, so that the algorithm maintains high diversity in the early stage, and increases the selection of excellent individuals in the later stage, improving the convergence speed and the quality of the solution; improving the crossover to a guided crossover, integrating the idea of ​​particle swarm differentiation, introducing parent differences and common points, guiding the offspring to move towards a better direction, enhancing the offspring's search ability and optimization effect, while maintaining the diversity of the population; improving the mutation to a non-uniform mutation, associating the mutation amplitude with the iterative algebra, realizing the mutation strategy of large-scale search in the early stage and fine search in the later stage, preventing the algorithm from falling into the local optimum, and improving the accuracy and optimization effect of the final solution.

[0173] Through the above improvements, the optimization algorithm proposed by the present invention can obtain a high-quality Pareto front solution set in the problem of site selection and capacity determination of power system phase shifters with fewer iterations, taking into account the stability and economy of the power grid, and significantly improving the efficiency and quality, practicality and reliability of the optimization results. These improvements are not only applicable to the problem of site selection and capacity determination of phase shifters, but can also be widely used in other multi-objective optimization scenarios, with high versatility and application value.

[0174] Step 4: Evaluate each solution in the optimal solution set to determine the location and capacity configuration of the phase regulator from the optimal solution set.

[0175] A person skilled in the art may use algorithms such as fuzzy comprehensive evaluation method to evaluate the model solution. However, in order to obtain a better technical effect, the embodiment of the present invention uses the approximate ideal solution TOPSIS algorithm to evaluate each solution in the optimal solution set respectively, and outputs the solution with the best evaluation value as the optimal solution for the location and capacity configuration of the phase regulator.

[0176] Specifically, the process of deciding the location and capacity deployment plan of the phase shifter from the Pareto optimal solution set in step 4 is as follows: first, determine the weight of each target (such as short-circuit ratio 0.6 and economy 0.4) based on expert experience, and then construct a decision matrix, a standardized decision matrix, and a weighted standardized decision matrix to determine the ideal solution and the negative ideal solution. Based on the distance to the ideal solution, calculate the relative proximity of each plan to make a plan decision and give suggestions on the location and capacity deployment plan of the phase shifter.

[0177] The expression for calculating the relative closeness of each solution based on the distance between the positive and negative ideal solutions is as follows:

[0178]

[0179] In the formula, S l + represents the distance between solution l and the positive ideal solution, S l - represents the distance between solution l and the negative ideal solution, C l The closer the value is to 1, the better the solution is.

[0180] The location and capacity deployment plan of the phase regulator is decided from the Pareto optimal solution set using the approximate ideal solution algorithm, and recommendations on the location and capacity deployment plan of the phase regulator are given.

[0181] The following experiment is conducted using an improved IEEE-39 node to verify the effectiveness of the phase shifter site selection and capacity optimization configuration method provided by the present invention.

[0182] For example, the experiment is set up to deploy 5 phase regulators at most, with a population size of 100, running until the maximum number of iterations is 200, and using the program of this method to run optimization and decision-making; the deployment decision is to transform the 50Mvar phase regulator at nodes 32, 36, and 38.

[0183] The distribution of target values ​​changes during the optimization iteration process as follows Figure 4 As shown in the figure, the horizontal axis represents the number of population iterations, and the vertical axis represents the value of the short-circuit ratio index. The larger the expected short-circuit ratio value, the better. The distribution of economic indicators changes during the optimization iteration process as shown in the figure. Figure 5 As shown in the figure, the horizontal axis represents the number of population iterations, and the vertical axis represents the value of the economic indicator. It is expected that the smaller the economic indicator, the better. As the number of iterations increases, the short-circuit ratio of all individuals in each generation of the population and the optimal, worst, and average values ​​of the economic indicators are effectively improved. It can be seen that the optimization task can improve the indicators well.

[0184] The target values ​​of short-circuit ratio and economic indicators are as follows: Figure 6 As shown, points of different colors represent different numbers of iterations, and the number of iterations corresponding to each color is Figure 4 and 5The colors in are the same; Figure 6 The horizontal axis is the economic indicator, and the vertical axis is the negative value of the short-circuit ratio indicator. It is expected that both the horizontal and vertical axes will approach the negative direction of the coordinate axis. It can be seen that with the iteration, the target value of the decision variable approaches the negative direction in the two-dimensional graph, the Pareto front is updated well, and the best non-dominated front is gradually found.

[0185] At the same time, the present invention is also compared with other prior art methods:

[0186] Under the research problem, the method of the present invention shows certain advantages, especially in terms of optimization efficiency. Among them, for example, a comparative experiment is conducted with the configuration method in the prior art "Optimal Configuration Strategy for Phase Modulators to Improve Transient Voltage Safety in Power Grids with a High Proportion of New Energy Sending Ends". Under the same simulation and optimization model conditions, the short-circuit ratio index of the present invention and the prior art method are 5.14 and 4.94 respectively, and the economic index is 1.35 and 2.25 respectively. The time consumption of the solution is also significantly superior to that of the present invention, indicating that the method of the present invention has certain advantages in terms of solution effect and efficiency under the current problem background, and meets the target expectation.

[0187] In summary, the present invention considers both the construction of new thermal power units and the transformation of old thermal power units into phase regulators, builds a power system simulation model, establishes a dual-objective optimization model for phase regulator site selection and capacity determination that comprehensively considers short-circuit ratio and economic indicators, and uses an improved second-generation non-dominated sorting genetic algorithm to solve the problem, thereby effectively optimizing the location and capacity deployment of phase regulators and improving the stability and economy of the power system.

[0188] At the method level, in view of the problem that the existing technology fails to comprehensively consider the relationship between the phase-shifting unit configuration requirements and the actual operating status of the power system, the present invention flexibly incorporates two configuration methods of phase-shifting units, namely, newly built phase-shifting units and reusing and transforming retired thermal power units, into the optimization model; the comprehensive optimization scheme not only fully utilizes the resources of retired thermal power units and reduces the investment cost of newly built phase-shifting units, but also effectively improves the reactive power compensation capability and overall economy of the system; by establishing a detailed power system simulation model, accurate reflection of various parameters and operating status during the optimization process is ensured, providing a reliable foundation for subsequent optimization.

[0189] At the algorithm level, the present invention has made a number of adaptive improvements to the traditional NSGA-II algorithm, including good point set population initialization, linear ranking-based parent selection, guided crossover, and non-uniform mutation. These improvements significantly improve the convergence speed of the algorithm and the diversity of the optimization solution, and enhance the overall optimization effect; the method of initializing the good point set population generates a uniformly distributed initial population through mathematical construction, which improves the coverage of the search space and the diversity of the population; the method of linear ranking-based parent selection flexibly adjusts the selection parameters, maintains high diversity in the early stage, and increases the selection of excellent individuals in the later stage, which accelerates the convergence speed; guided crossover combines the idea of ​​particle swarm algorithm to guide the offspring to evolve in a better direction and enhance the search ability; non-uniform mutation realizes the effective combination of early large-scale search and late fine search through the variation amplitude associated with the iterative algebra, prevents the algorithm from falling into the local optimum, and improves the accuracy and quality of the final solution.

[0190] Ultimately, the present invention can effectively improve the short-circuit ratio level of the power system while comprehensively considering the economic cost, and provide an optimized phase-shifting phase location and capacity deployment plan, thereby improving the stability of the power system and the ability to absorb new energy. Through a systematic, comprehensive, scientific and complete optimization solution process, combined with the improved NSGA-II algorithm, the present invention not only solves the dual-objective optimization problem of phase-shifting phase site selection and sizing, but also significantly improves the efficiency and quality of the optimization results, ensuring its practicality and reliability in practical applications. These improvements are not only applicable to the phase-shifting phase site selection and sizing problems, but can also be widely used in other multi-objective optimization scenarios, with high versatility and application value.

[0191] Embodiment 2:

[0192] like Figure 7 As shown, the present invention provides a phase shifter site selection and capacity optimization configuration device, the device is used to implement the steps of the method in the above embodiment 1, and the device specifically includes:

[0193] An acquisition module is used to acquire system parameters of the power system in the target area to build a power system simulation model;

[0194] Establish a module for establishing a dual-objective optimization model for phase shifter location and capacity determination considering short-circuit ratio and economic performance indicators;

[0195] A solution module, used for inputting a set of location parameters and capacity configuration schemes of newly built and / or modified thermal power units into the phase-converting units into the power system simulation model, so as to solve the dual-objective optimization model and output an optimal solution set that satisfies the set constraints;

[0196] The decision module is used to evaluate each solution in the optimal solution set respectively to determine the location and capacity configuration scheme of the phase regulator from the optimal solution set.

[0197] The phase regulator site selection and capacity optimization configuration device provided in the embodiment of the present invention and the phase regulator site selection and capacity optimization configuration method provided in Example 1 are based on the same technical concept, and can produce the beneficial effects as described in Example 1. For the contents not described in detail in this embodiment, please refer to Example 1.

[0198] Embodiment three:

[0199] A terminal provided by an embodiment of the present invention includes a processor and a storage medium;

[0200] The storage medium is used to store instructions;

[0201] The processor is used to operate according to the instruction to execute the steps of the method according to any one of the first embodiments.

[0202] Embodiment 4:

[0203] An embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the steps of any method described in Embodiment 1 are implemented.

[0204] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0205] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples of computer-readable storage media (a non-exhaustive list) include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium is not to be interpreted as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0206] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0207] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be customized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0208] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for optimizing the location and capacity of a phase-shifting machine, characterized in that: include: Obtain system parameters of the power system in the target area to build a power system simulation model; A dual-objective optimization model for phase shifter location and capacity determination considering short-circuit ratio and economic performance is established; Inputting a set of location parameters and capacity configuration schemes of newly built and / or modified thermal power units into phase-shifting units into the power system simulation model to solve the dual-objective optimization model and output an optimal solution set that meets the set constraints; Each solution in the optimal solution set is evaluated respectively to determine the location and capacity configuration of the phase regulator from the optimal solution set.

2. The method for optimizing the location and capacity of a phase condenser according to claim 1 is characterized in that: The system parameters include power injected into the grid node, bus voltage, branch power flow, line impedance, generator output and load data.

3. The method for optimizing the location and capacity of a phase condenser according to claim 1, characterized in that: The expression of the first objective function of the dual-objective optimization model for phase shifter location and capacity is as follows: x={x1,x2},x1∈C,x2∈Q Where f1(x) represents the first objective function that aims to maximize the minimum short-circuit ratio value of all nodes; x represents the decision variables, including location x1 and capacity x2; C and Q are the set of all locations and all capacities in the deployable solution set, respectively; SCR_U i is the short-circuit ratio of node i, and P represents the total number of nodes in the power system.

4. The method for optimizing the location and capacity of a phase condenser according to claim 3 is characterized in that: The expression of the second objective function of the dual-objective optimization model for phase shifter location and capacity is as follows: f2(x)=min(C O +C y1 +C y2 -EC) Where f2(x) represents the second objective function with the minimization of comprehensive cost as the optimization goal; C O is the total cost of installing a phase regulator, which includes the installation cost of the new phase regulator and the cost of converting the retired thermal power units into phase regulators; C y1 is the total operation and maintenance cost of the phase regulator; C y2 is the total cost of electricity consumption of the phase regulator; EC is the total economic compensation of the phase regulator.

5. The method for optimizing the location and capacity of a phase condenser according to claim 1, characterized in that: The constraints include node short circuit ratio constraints, phase regulator deployment quantity constraints, power flow active power balance constraints, power flow reactive power balance constraints, generator active output limit constraints, generator reactive output limit constraints, node voltage limit constraints, line transmission capacity limit constraints and / or node fault current limit constraints.

6. The method for optimizing the location and capacity of a phase condenser according to claim 5 is characterized in that: The expressions of the node short-circuit ratio constraints are as follows: Where g1(x) is the node short circuit ratio constraint; SCR_U i is the voltage-based short-circuit ratio index in node i; i = 1, 2…P; P represents the total number of nodes in the power system; U Ni represents the nominal voltage of node i, E eqi Represents the voltage vector when node i is not connected to the new energy unit; Represents the voltage vector change before and after the node i is connected to the new energy unit; Represents the voltage vector after node i is connected to the new energy unit.

7. The method for optimizing the location and capacity of a phase condenser according to claim 1, characterized in that: The algorithm used to solve the dual-objective optimization model is the improved NSGA-II algorithm; The improved NSGA-II algorithm includes: initializing the population according to a set of solutions for deployable locations and capacities of the phase regulator, and iteratively optimizing the population in combination with linear ranking-based parent selection, guided crossover, and non-uniform mutation to coordinate the conflicting dual objective functions in the model, thereby obtaining the Pareto optimal solution set for the location and capacity configuration of the phase regulator.

8. The method for optimizing the location and capacity of a phase condenser according to claim 7 is characterized in that: The method for initializing the population is: generating an initial population by using a good point set initialization method, that is, generating an initial population evenly distributed in a multidimensional search space by mathematical construction; wherein each individual in the population represents a possible phase regulator configuration scheme, including the deployment position and configuration capacity of the phase regulator.

9. The method for optimizing the location and capacity of a phase condenser according to claim 7, characterized in that: The step of parent selection based on linear ranking includes: Each individual in the population is linearly sorted according to its corresponding non-dominated level and crowding distance; the lower the non-dominated level, the higher the priority; within the same level, the larger the crowding distance, the higher the priority; The sorted individuals are numbered j=1, 2, ..., N in sequence; j=1 is the optimal individual, and N is the number of individuals in the population; Assign a selection probability p to each individual based on its ranking number j , to obtain the cumulative probability interval of each individual choice; Randomly generate N random numbers, and extract the individuals corresponding to the cumulative probability interval that each random number falls into from the population, and use the extracted individuals as parent individuals.

10. The method for optimizing the location and capacity of a phase condenser according to claim 9, characterized in that: in, The selection probability p j The calculation formula is as follows: In the formula, p j represents the probability of individual j being selected, η - and η + are the minimum and maximum selection parameters in linear sorting, respectively, satisfying η - +η + =2, and 0≤η - ≤1.

11. The method for optimizing the location and capacity of a phase condenser according to claim 7, characterized in that: The steps of the guided crossover include: Randomly select a set number of crossover individuals from the population as parents for pairing; The update rate of the corresponding offspring is calculated through the gene difference value and commonality value of each paired parent individual to generate the corresponding offspring individual; the expressions are as follows: in pa =k·cp ab +h1·r1·(p a -zp ab )+h2·r2·(p b -zp ab ) in pb =k·cp ab +h1·r3·(p b -zp ab )+h2·r4·(p a -zp ab ) c a =p a +v pa c b =p b +v pb In the formula, v pa and v pb The two parent individuals p are paired a and p b The update speed of the offspring; p a and p b are two paired parent individuals; a, b∈N1 and a≠b; N1 is the set crossover number; k, h1 and h2 are three set constants, r1, r2, r3, r4 are all random numbers in the range [0,1]; cp ab and cp ab The two parent individuals p are paired a and p b Genetic differences and commonality values; c a is the parent individual p a The generated offspring; c b Parent individual p b The generated offspring.

12. The method for optimizing the location and capacity of a phase condenser according to claim 11, characterized in that: The calculation formulas for the gene difference value and commonality value of each paired parent individual are as follows:

13. The method for optimizing the location and capacity of a phase condenser according to claim 7, characterized in that: The steps of the non-uniform variation include: Randomly select a set number of mutant individuals from the population as mutant individuals; Based on the genes of each of the mutant individuals, a random value of the mutation direction and a random value of the mutation amplitude of each mutant individual are randomly generated and substituted into the following formula for mutation: In the formula, x k′ is the value of the kth decision variable after the current individual mutation, x k is the kth decision variable value of the current individual; k∈x, x represents the decision variables of the dual-objective optimization model, including location x1 and capacity x2; U k is the upper bound of the kth decision variable, L k is the lower bound of the kth decision variable; z is U k -x k or x k -L k ; r and r1 are random numbers in the range of (0,1), r is the random value of the current individual variation direction, r1 is the random value of the current individual variation amplitude; w is the parameter that controls the attenuation rate of the variation amplitude; t is the current iteration number, and T is the maximum iteration number.

14. The method for optimizing the location and capacity of a phase condenser according to claim 1, characterized in that: The algorithm used when evaluating each solution in the optimal solution set is the TOPSIS algorithm that approximates the ideal solution, and the solution with the best evaluation value is output as the optimal solution for the location and capacity configuration of the phase regulator.

15. A device for optimizing the location and capacity of a phase condenser, which runs the method for optimizing the location and capacity of a phase condenser according to any one of claims 1 to 14, characterized in that: The device includes: An acquisition module is used to acquire system parameters of the power system in the target area to build a power system simulation model; Establish a module for establishing a dual-objective optimization model for phase shifter location and capacity determination considering short-circuit ratio and economic performance indicators; A solution module, used for inputting a set of location parameters and capacity configuration schemes of newly built and / or modified thermal power units into phase-shifting units into the power system simulation model, so as to solve the dual-objective optimization model and output an optimal solution set that satisfies set constraints; The decision module is used to evaluate each solution in the optimal solution set respectively to determine the location and capacity configuration scheme of the phase regulator from the optimal solution set.

16. A terminal comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1-15.

17. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 15 are implemented.

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