Regional power grid reactive compensation optimization method and system based on genetic algorithm

Abstract

The invention discloses a regional power grid reactive power compensation optimization method and system based on a genetics algorithm, and relates to the field of reactive power compensation capacity optimization configuration, and the method comprises the steps: S1, obtaining application scenes of a plurality of power systems, the operation data of a past power grid, and the configuration information of the power grid, the plurality of application scenes of the power system correspond to a plurality of past power grid operation data respectively; s2, constructing a power model according to the power grid configuration information, and substituting past power grid operation data into the power model for simulation to obtain past reactive compensation nodes; s3, obtaining current power grid operation data, and substituting the data into the power model for simulation to obtain a current reactive compensation node; and S4, iterating the previous reactive power compensation node and the current reactive power compensation node by adopting a genetics algorithm to obtain a target reactive power compensation node, and calculating the reactive power compensation capacity of each compensation point to form a power grid reactive power compensation optimization strategy. According to the method and system provided by the invention, the compensation node is optimized to obtain a more accurate compensation position, so that the stability of the power grid is improved.

Description

Technical Field

[0001] The present invention relates to the field of reactive power compensation in power grids, and particularly to an optimization method and system for reactive power compensation in regional power grids based on genetic algorithms. Background Art

[0002] The power system is an important part of modern society, providing electrical energy for various devices and facilities. However, in the operation of the power system, there is not only active power but also reactive power.

[0003] Reactive power cannot be directly utilized by actual devices like active power, but it is crucial for the normal operation of the power system. Reactive power is mainly used to establish and maintain electromagnetic fields. For example, transformers and motors need to establish magnetic fields during operation, which requires consuming reactive power. If the reactive power is insufficient, problems such as voltage drop and reduced power quality will occur, and even the stability of power supply will be affected.

[0004] The reactive power in the power system is mainly generated by inductive components (such as transformers, motors, etc.) and capacitive components (such as capacitors, cables, etc.). Inductive components absorb reactive power when establishing magnetic fields, while capacitive components generate reactive power.

[0005] When there are more inductive loads in the power system, the demand for reactive power will increase. If there is not enough reactive power to meet these demands, a reactive power deficit will occur.

[0006] To solve the problem of insufficient reactive power, reactive power compensation is required. The main purpose of reactive power compensation is to improve the power factor, reduce equipment and line losses, optimize the performance of the power grid, and ensure power supply quality and power grid safety.

[0007] Through reactive power compensation, the required reactive power can be provided to the power system, thereby maintaining voltage stability, improving power quality, and at the same time reducing the burden on power plants and power supply equipment, and improving the efficiency and stability of the power system.

[0008] Appropriate node selection can effectively maintain the voltage stability of the power grid. By performing reactive power compensation at key nodes, voltage fluctuations and voltage drops can be reduced, ensuring that the voltages of each node are within the specified range. For example, performing reactive power compensation at the hub substation or heavily loaded nodes in the regional power grid can quickly respond to load changes, provide timely reactive power support, and avoid voltage drops caused by insufficient reactive power.

[0009] Therefore, there is an urgent need for an optimization method and system for reactive power compensation in regional power grids based on genetic algorithms to optimize the problem of reactive power compensation nodes in regional power grids. Summary of the Invention

[0010] To solve the above problems, the present application provides an optimization method for reactive power compensation of regional power grids based on genetic algorithms, including the following steps:

[0011] S1. Obtain the application scenarios, past power grid operation data, and power grid configuration information of several power systems, and the application scenarios of the several power systems respectively correspond to several past power grid operation data;

[0012] S2. Construct a power model according to the power grid configuration information, and substitute the past power grid operation data into the power model for simulation to obtain past reactive power compensation nodes;

[0013] S3. Obtain the current power grid operation data and substitute it into the power model for simulation to obtain current reactive power compensation nodes;

[0014] S4. Use the genetic algorithm to iterate the past reactive power compensation nodes and the current reactive power compensation nodes to obtain target reactive power compensation nodes and calculate the reactive power compensation capacity of each compensation point to form an optimization strategy for reactive power compensation of the power grid.

[0015] Preferably, the specific content of constructing a power model according to the power grid configuration information in S2 and substituting the past power grid operation data into the power model for simulation to obtain past reactive power compensation nodes is as follows:

[0016] The power grid configuration information includes the power grid topology structure, power grid equipment parameters, power grid load characteristics, and equipment operation data;

[0017] Construct a power model according to the power grid configuration information, and divide the power model according to the application scenario to obtain several power model groups;

[0018] Substitute several past power grid operation data into several power model groups for simulation according to the corresponding application scenarios. The past power grid operation data includes operation parameters, past reactive power compensation nodes, and past compensation configurations;

[0019] Summarize the simulation situations under the same scenario to obtain several past reactive power compensation nodes.

[0020] Preferably, the specific content of obtaining the current power grid operation data and substituting it into the power model for simulation to obtain current reactive power compensation nodes is as follows:

[0021] Obtain the current power grid operation data and the current power grid application scenario, substitute the current power grid operation data into the power model of the corresponding scenario for simulation to obtain the operation state of the power grid and perform power flow calculation to determine the voltage and power distribution of each node in the power grid;

[0022] Obtain the change of reactive power voltage sensitivity under the current application scenario according to the operation state of the power grid and the voltage and power distribution of each node in the power grid;

[0023] The primary sensitivity region is obtained according to the change of the reactive power voltage sensitivity in the current application scenario, and the reactive power voltage sensitivity of each node in the primary sensitivity region is calculated to obtain the sensitivity index of each node;

[0024] A sensitivity threshold is preset, and the nodes with sensitivity indexes higher than the sensitivity threshold are screened to obtain the primary reactive power compensation nodes;

[0025] A reactive power balance analysis threshold is preset, and the reactive power balance analysis indexes of each node in the current application scenario are determined by using the reactive power balance analysis method. The nodes with reactive power balance analysis indexes lower than the reactive power balance analysis threshold are screened to obtain the secondary reactive power compensation nodes, and the nodes with reactive power balance analysis indexes higher than the reactive power balance analysis threshold are screened to obtain the reactive power surplus nodes;

[0026] A voltage stability threshold is preset, and the voltage stability values of each node in the current application scenario are determined by using the voltage stability analysis method. The nodes with reactive power voltage stability values lower than the voltage stability threshold are screened to obtain the tertiary reactive power compensation nodes;

[0027] The current reactive power compensation nodes include primary reactive power compensation nodes, secondary reactive power compensation nodes and tertiary reactive power compensation nodes.

[0028] Preferably, the specific content of using the genetic algorithm to iterate the past reactive power compensation nodes and the current reactive power compensation nodes to obtain the target reactive power compensation nodes and calculate the reactive power compensation capacity of each compensation point to form the grid reactive power compensation optimization strategy is as follows:

[0029] S401. Merge several past reactive power compensation nodes and primary reactive power compensation nodes in the same application scenario and randomly generate a primary original population;

[0030] S402. Merge several past reactive power compensation nodes and secondary reactive power compensation nodes in the same application scenario and randomly generate a secondary original population;

[0031] S403. Merge several past reactive power compensation nodes and tertiary reactive power compensation nodes in the same application scenario and randomly generate a tertiary original population;

[0032] S404. Set the fitness function, and respectively perform a primary screening on the primary original population, secondary original population and tertiary original population according to the fitness function to obtain a primary sub-population, secondary sub-population and tertiary sub-population;

[0033] S405. Randomly cross the primary sub-population, secondary sub-population and tertiary sub-population to obtain a primary cross population, secondary cross population and tertiary cross population;

[0034] S406. Set the second fitness function, and perform secondary screening on the primary crossover population, secondary crossover population, and tertiary crossover population according to the second fitness function to obtain the primary optimized population, secondary optimized population, and tertiary optimized population;

[0035] S407. Set the iteration threshold, and repeat steps S401 - S406 until the iteration threshold is reached, and then output the target reactive power compensation node.

[0036] Preferably, the specific content of setting the first fitness function and performing primary screening on the primary original population, secondary original population, and tertiary original population respectively according to the first fitness function to obtain the primary sub - population, secondary sub - population, and tertiary sub - population is as follows:

[0037] The expression of the first fitness function is:

[0038]

[0039] Where, Q 总 is the total compensation amount, K 总 is the total investment cost, Q i电机 is the motor compensation amount of the i - th reactive power compensation node, q i电机 is the margin of the motor compensation amount of the i - th reactive power compensation node, Q i变压器 is the transformer compensation amount of the i - th reactive power compensation node, q i变压器 is the margin of the transformer compensation amount of the i - th reactive power compensation node, K i电机 is the motor investment cost of the i - th reactive power compensation node, K i变压器 is the transformer investment cost of the i - th reactive power compensation node;

[0040] Sort the primary original population, secondary original population, and tertiary original population from high to low according to Q 总 to obtain the order of Q 总 ;

[0041] Sort the primary original population, secondary original population, and tertiary original population from low to high according to K 总 to obtain the order of K 总 ;

[0042] Preset the target Q 总 percentage and the target K 总 percentage, and screen and fuse the primary original population, secondary original population, and tertiary original population in the order of Q 总 percentage and the target K 总 percentage in the order of Q 总 , K 总 respectively to obtain the primary sub - population, secondary sub - population, and tertiary sub - population.

[0043] Preferably, a second fitness function is set, and the specific content of performing secondary screening on the primary crossover population, secondary crossover population, and tertiary crossover population according to the second fitness function to obtain the primary optimized population, secondary optimized population, and tertiary optimized population is as follows:

[0044] The expression of the second fitness function is:

[0045]

[0046] Among them, there is a preset origin position, L is the total distance, X j is the abscissa of the jth reactive power surplus node, and X i is the abscissa of the reactive power compensation node closest to the jth reactive power surplus node;

[0047] Sort the primary crossover population, secondary crossover population, and tertiary crossover population from high to low according to L to obtain the L order;

[0048] There is a preset target L percentage, and the primary optimized population, secondary optimized population, and tertiary optimized population are obtained by screening the L order according to the target L percentage.

[0049] This application also mentions a regional power grid reactive power compensation optimization system based on the genetic algorithm, including:

[0050] Data acquisition module: Acquire the application scenarios, past power grid operation data, and power grid configuration information of several power systems. Each of the application scenarios of the several power systems corresponds to several past power grid operation data;

[0051] Model establishment module: Construct a power model according to the power grid configuration information, and substitute the past power grid operation data into the power model for simulation to obtain the past reactive power compensation nodes;

[0052] Power grid simulation module: Acquire the current power grid operation data and substitute it into the power model for simulation to obtain the current reactive power compensation nodes;

[0053] Strategy generation module: Use the genetic algorithm to iterate the past reactive power compensation nodes and the current reactive power compensation nodes to obtain the target reactive power compensation nodes and calculate the reactive power compensation capacity of each compensation point to form a power grid reactive power compensation optimization strategy.

[0054] An electronic device includes a memory and a processor. When the processor calls the computer program stored in the memory, the content of the regional power grid reactive power compensation optimization method based on the genetic algorithm is implemented.

[0055] A storage medium stores computer-executable instructions. When the computer-executable instructions are loaded and executed by a processor, the content of the regional power grid reactive power compensation optimization method based on the genetic algorithm is implemented.

[0056] In summary, for the reactive power compensation optimization method and system of a regional power grid based on a genetic algorithm according to the present invention, compared with traditional technologies, the present invention uses genetic algorithms and system simulations to screen compensation nodes, and can obtain more accurate compensation positions and scheduling strategies, thereby optimizing the reactive power compensation of the regional power grid.

[0057] The following further describes the technical solutions of the present invention in detail through the accompanying drawings and embodiments. Description of the Drawings

[0058] Figure 1 is a flowchart of a reactive power compensation optimization method for a regional power grid based on a genetic algorithm according to the present invention;

[0059] Figure 2 is a module diagram of a reactive power compensation optimization system for a regional power grid based on a genetic algorithm according to the present invention. Detailed Embodiments

[0060] The following further illustrates the technical solutions of the present invention through the accompanying drawings and embodiments. It should be noted that: unless otherwise specifically stated, the relative arrangements, numerical expressions, and values of the components and steps described in these embodiments do not limit the scope of the present application.

[0061] The following description of at least one exemplary embodiment is merely illustrative and in no way limits the present application or its application or use.

[0062] Technologies, systems, and devices known to those of ordinary skill in the relevant field may not be discussed in detail, but where appropriate, the technologies, systems, and devices should be regarded as part of the specification.

[0063] In all the examples shown and discussed here, any specific value should be construed as merely exemplary and not as a limitation. Therefore, other examples of the exemplary embodiments may have different values.

[0064] Unless otherwise defined, the technical terms or scientific terms used in the present invention should have the ordinary meaning understood by those of ordinary skill in the field to which the present invention belongs.

[0065] As Figure 1 shown, the present invention provides a reactive power compensation optimization method for a regional power grid based on a genetic algorithm. This method optimally configures the reactive power compensation devices in the power grid by simulating the process of biological evolution to achieve the dual goals of economy and power quality. The specific steps are as follows:

[0066] S1. Obtain the application scenarios, past grid operation data, and grid configuration information of several power systems. The application scenarios of several said power systems respectively correspond to several past grid operation data;

[0067] S2. Construct a power model according to the grid configuration information, and substitute the past grid operation data into the power model for simulation to obtain past reactive power compensation nodes;

[0068] Preferably, the specific content of constructing a power model according to the grid configuration information in S2 and substituting the past grid operation data into the power model for simulation to obtain past reactive power compensation nodes is as follows:

[0069] The said grid configuration information includes grid topology structure, grid equipment parameters, grid load characteristics, and equipment operation data;

[0070] Construct a power model according to the grid configuration information, and divide the power model according to the application scenario to obtain several power model groups;

[0071] Substitute several past grid operation data into several power model groups for simulation according to the corresponding application scenarios. The said past grid operation data includes operation parameters, past reactive power compensation nodes, and past compensation configurations;

[0072] Summarize the simulation situations under the same scenario to obtain several past reactive power compensation nodes.

[0073] S3. Obtain the current grid operation data and substitute it into the power model for simulation to obtain the current reactive power compensation nodes;

[0074] Preferably, the specific content of obtaining the current grid operation data and substituting it into the power model for simulation to obtain the current reactive power compensation nodes is as follows:

[0075] Obtain the current grid operation data and the current grid application scenario, substitute the current grid operation data into the power model of the corresponding scenario for simulation to obtain the operation state of the grid and perform power flow calculation to determine the voltage and power distribution of each node in the grid;

[0076] Obtain the change of reactive power voltage sensitivity under the current application scenario according to the operation state of the grid and the voltage and power distribution of each node in the grid. Reactive power voltage sensitivity analysis and reactive power compensation configuration optimization strategy are indispensable technical means in modern power systems. It not only helps to improve the operation efficiency and stability of the grid, but also can effectively cope with the challenges brought by new energy grid connection;

[0077] Obtain the primary sensitivity region according to the change of reactive power voltage sensitivity under the current application scenario, perform reactive power voltage sensitivity calculation on each node in the primary sensitivity region, and obtain the sensitivity index of each node;

[0078] A sensitivity threshold is preset, and nodes with sensitivity indexes higher than the sensitivity threshold are screened to obtain primary reactive power compensation nodes;

[0079] A reactive power balance analysis threshold is preset, and the reactive power balance analysis indexes of each node in the current application scenario are determined by using the reactive power balance analysis method. Nodes with reactive power balance analysis indexes lower than the reactive power balance analysis threshold are screened to obtain secondary reactive power compensation nodes, and nodes with reactive power balance analysis indexes higher than the reactive power balance analysis threshold are screened to obtain reactive power surplus nodes;

[0080] A voltage stability threshold is preset, and the voltage stability values of each node in the current application scenario are determined by using the voltage stability analysis method. Nodes with reactive voltage stability values lower than the voltage stability threshold are screened to obtain tertiary reactive power compensation nodes;

[0081] The current reactive power compensation nodes include primary reactive power compensation nodes, secondary reactive power compensation nodes and tertiary reactive power compensation nodes.

[0082] S4. Use the genetic algorithm to iterate the past reactive power compensation nodes and the current reactive power compensation nodes to obtain the target reactive power compensation nodes and calculate the reactive power compensation capacity of each compensation point to form an optimized strategy for grid reactive power compensation. The method for optimizing the reactive power compensation capacity of regional power grids based on the genetic algorithm is an effective power system management tool. It can not only improve the economy and reliability of the power grid, but also cope with the increasing complexity and dynamic changes in modern power grids.

[0083] Preferably, the specific content of using the genetic algorithm to iterate the past reactive power compensation nodes and the current reactive power compensation nodes to obtain the target reactive power compensation nodes and calculate the reactive power compensation capacity of each compensation point to form an optimized strategy for grid reactive power compensation is as follows:

[0084] S401. Integrate several past reactive power compensation nodes and primary reactive power compensation nodes under the same application scenario and randomly generate a primary original population;

[0085] S402. Integrate several past reactive power compensation nodes and secondary reactive power compensation nodes under the same application scenario and randomly generate a secondary original population;

[0086] S403. Integrate several past reactive power compensation nodes and tertiary reactive power compensation nodes under the same application scenario and randomly generate a tertiary original population;

[0087] S404. Set the fitness function, and respectively perform a first screening on the primary original population, the secondary original population and the tertiary original population according to the fitness function to obtain a primary sub-population, a secondary sub-population and a tertiary sub-population;

[0088] S405. Randomly cross the primary sub-population, secondary sub-population, and tertiary sub-population to obtain the primary cross-population, secondary cross-population, and tertiary cross-population;

[0089] S406. Set the second fitness function, and perform a secondary screening on the primary cross-population, secondary cross-population, and tertiary cross-population according to the second fitness function to obtain the primary optimized population, secondary optimized population, and tertiary optimized population;

[0090] S407. Set the iteration threshold, and repeat steps S401 - S406 until the iteration threshold is reached, and then output the target reactive power compensation node.

[0091] Preferably, the specific content of setting the first fitness function and performing a primary screening on the primary original population, secondary original population, and tertiary original population respectively according to the first fitness function to obtain the primary sub-population, secondary sub-population, and tertiary sub-population is as follows:

[0092] The expression of the first fitness function is:

[0093]

[0094] Among them, Q 总 is the total compensation amount, K 总 is the total investment cost, Q i电机 is the motor compensation amount of the i-th reactive power compensation node, q i电机 is the margin of the motor compensation amount of the i-th reactive power compensation node, Q i变压器 is the transformer compensation amount of the i-th reactive power compensation node, q i变压器 is the margin of the transformer compensation amount of the i-th reactive power compensation node, K i电机 is the motor investment cost of the i-th reactive power compensation node, K i变压器 is the transformer investment cost of the i-th reactive power compensation node;

[0095] Sort the primary original population, secondary original population, and tertiary original population from high to low according to Q 总 to obtain the Q 总 order;

[0096] Sort the primary original population, secondary original population, and tertiary original population from low to high according to K 总 to obtain the K 总 order;

[0097] Preset a target Q 总 percentage and a target K 总 percentage, and according to the target Q 总 percentage and the target K 总 percentage, for the Q 总 order, K 总The first-generation original population, second-generation original population, and third-generation original population in the order are screened and each is fused to obtain the first-generation sub-population, second-generation sub-population, and third-generation sub-population respectively.

[0098] Preferably, a second fitness function is set, and the specific content of performing a secondary screening on the first crossover population, second crossover population, and third crossover population according to the second fitness function to obtain the first optimized population, second optimized population, and third optimized population is as follows:

[0099] The expression of the second fitness function is:

[0100]

[0101] Among them, there is a preset origin position, L is the total distance, X j is the abscissa of the jth reactive power surplus node, and X i is the abscissa of the reactive power compensation node closest to the jth reactive power surplus node;

[0102] Sort the first crossover population, second crossover population, and third crossover population from high to low according to L to obtain the L order;

[0103] There is a preset target L percentage, and the first optimized population, second optimized population, and third optimized population are screened according to the target L percentage from the L order.

[0104] This application also mentions a regional power grid reactive power compensation optimization system based on a genetic algorithm, including:

[0105] Data acquisition module: Acquire the application scenarios of several power systems, past power grid operation data, and power grid configuration information, and several application scenarios of the power systems respectively correspond to several past power grid operation data;

[0106] Model establishment module: Construct a power model according to the power grid configuration information, and bring the past power grid operation data into the power model for simulation to obtain past reactive power compensation nodes;

[0107] Power grid simulation module: Acquire the current power grid operation data and bring it into the power model for simulation to obtain the current reactive power compensation nodes;

[0108] Strategy generation module: Use a genetic algorithm to iterate the past reactive power compensation nodes and the current reactive power compensation nodes to obtain target reactive power compensation nodes and calculate the reactive power compensation capacity of each compensation point to form a regional power grid reactive power compensation optimization strategy.

[0109] An electronic device includes a memory and a processor. When the processor calls the computer program stored in the memory, it implements the content of the regional power grid reactive power compensation optimization method based on a genetic algorithm.

[0110] A storage medium stores computer-executable instructions, which, when loaded and executed by a processor, implement the content of the reactive power compensation optimization method for regional power grids based on genetic algorithms.

[0111] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that they can still modify or equivalently replace the technical solutions of the present invention, and these modifications or equivalent replacements cannot make the modified technical solutions deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A reactive power compensation optimization method for regional power grids based on genetic algorithms, characterized in that, It includes the following steps: S1. Obtain the application scenarios, past grid operation data, and grid configuration information of several power systems. The application scenarios of several power systems respectively correspond to several past grid operation data; S2. Construct a power model according to the grid configuration information, and substitute the past grid operation data into the power model for simulation to obtain past reactive power compensation nodes; S3. Obtain the current grid operation data and substitute it into the power model for simulation to obtain current reactive power compensation nodes; S4. Use the genetic algorithm to iterate the past reactive power compensation nodes and the current reactive power compensation nodes to obtain target reactive power compensation nodes and calculate the reactive power compensation capacity of each compensation point to form a grid reactive power compensation optimization strategy.

2. The reactive power compensation optimization method for regional power grids based on genetic algorithms according to claim 1, wherein The specific content of constructing a power model according to the grid configuration information in S2 and substituting the past grid operation data into the power model for simulation to obtain past reactive power compensation nodes is as follows: The grid configuration information includes grid topology structure, grid equipment parameters, grid load characteristics, and equipment operation data; Construct a power model according to the grid configuration information, and divide the power model according to the application scenario to obtain several power model groups; Substitute several past grid operation data into several power model groups for simulation according to the corresponding application scenarios. The past grid operation data includes operation parameters, past reactive power compensation nodes, and past compensation configurations; Summarize the simulation situations under the same scenario to obtain several past reactive power compensation nodes.

3. The regional power grid reactive power compensation optimization method based on genetic algorithm according to claim 2, characterized in that The specific content of obtaining the current grid operation data and substituting it into the power model for simulation to obtain current reactive power compensation nodes is as follows: Obtain the current grid operation data and the current grid application scenario, substitute the current grid operation data into the power model of the corresponding scenario for simulation to obtain the operation state of the grid and perform a power flow calculation to determine the voltage and power distribution of each node in the grid; Obtain the change in reactive power voltage sensitivity under the current application scenario according to the operation state of the grid and the voltage and power distribution of each node in the grid; Obtain a primary sensitivity region according to the change in reactive power voltage sensitivity under the current application scenario, perform reactive power voltage sensitivity calculations on each node in the primary sensitivity region, and obtain the sensitivity index of each node; Preset a sensitivity threshold, and screen the nodes with sensitivity indexes higher than the sensitivity threshold to obtain primary reactive power compensation nodes; Preset a reactive power balance analysis threshold, use the reactive power balance analysis method to determine the reactive power balance analysis indexes of each node under the current application scenario, screen the nodes with reactive power balance analysis indexes lower than the reactive power balance analysis threshold to obtain secondary reactive power compensation nodes, and screen the nodes with reactive power balance analysis indexes higher than the reactive power balance analysis threshold to obtain reactive power surplus nodes; Preset a voltage stability threshold, use the voltage stability analysis method to determine the voltage stability values of each node under the current application scenario, and screen the nodes with reactive power voltage stability values lower than the voltage stability threshold to obtain tertiary reactive power compensation nodes; The current reactive power compensation nodes include primary reactive power compensation nodes, secondary reactive power compensation nodes, and tertiary reactive power compensation nodes.

4. A reactive power compensation optimization method for regional power grids based on genetic algorithm according to claim 3, characterized in that, The specific content of using the genetic algorithm to iterate over past reactive power compensation nodes and current reactive power compensation nodes to obtain target reactive power compensation nodes and calculate the reactive power compensation capacity of each compensation point to form an optimal strategy for grid reactive power compensation is as follows: S401. Integrate several past reactive power compensation nodes and primary reactive power compensation nodes under the same application scenario and randomly generate a primary original population; S402. Integrate several past reactive power compensation nodes and secondary reactive power compensation nodes under the same application scenario and randomly generate a secondary original population; S403. Integrate several past reactive power compensation nodes and tertiary reactive power compensation nodes under the same application scenario and randomly generate a tertiary original population; S404. Set a fitness function, and respectively perform a primary screening on the primary original population, secondary original population, and tertiary original population according to the fitness function to obtain a primary sub-population, secondary sub-population, and tertiary sub-population; S405. Randomly cross the primary sub-population, secondary sub-population, and tertiary sub-population to obtain a primary cross population, secondary cross population, and tertiary cross population; S406. Set a second fitness function, and respectively perform a secondary screening on the primary cross population, secondary cross population, and tertiary cross population according to the second fitness function to obtain a primary optimal population, secondary optimal population, and tertiary optimal population; S407. Set an iteration threshold, and repeat steps S401 - S406 until the iteration threshold is reached and output the target reactive power compensation nodes.

5. The optimization method for reactive power compensation of regional power grids based on genetic algorithm according to claim 4, characterized in that, The specific content of setting a first fitness function and respectively performing a primary screening on the primary original population, secondary original population, and tertiary original population according to the first fitness function to obtain a primary sub-population, secondary sub-population, and tertiary sub-population is as follows: The expression of the first fitness function is: Among them, Q 总 is the total compensation amount, K 总 is the total investment cost, Q i电机 is the motor compensation amount of the i-th reactive power compensation node, q i电机 is the margin of the motor compensation amount of the i-th reactive power compensation node, Q i变压器 is the transformer compensation amount of the i-th reactive power compensation node, q i变压器 is the margin of the transformer compensation amount of the i-th reactive power compensation node, K i电机 is the motor investment cost of the i-th reactive power compensation node, K i变压器 is the transformer investment cost of the i-th reactive power compensation node; According to Q 总 Sort the primary original population, secondary original population, and tertiary original population from high to low to obtain Q 总 Order; According to K 总 Sort the primary original population, secondary original population, and tertiary original population from low to high by K 总 Order; Preset with target Q 总 Percentage and target K 总 Percentage, according to target Q 总 Percentage and target K 总 Percentage for Q 总 Order, K 总 Screen the primary original population, secondary original population, and tertiary original population in the order and fuse them respectively to obtain the primary subpopulation, secondary subpopulation, and tertiary subpopulation.

6. The method for optimizing reactive power compensation of a regional power grid based on a genetic algorithm according to claim 5, characterized in that The specific content of setting a second fitness function and respectively performing a secondary screening on the primary cross population, secondary cross population, and tertiary cross population according to the second fitness function to obtain a primary optimal population, secondary optimal population, and tertiary optimal population is as follows: The expression of the second fitness function is: Among them, there is a preset origin position, L is the total distance, and X j is the abscissa of the j-th reactive power surplus node, and X i is the abscissa of the reactive power compensation node closest to the j-th reactive power surplus node; Sort the primary cross population, secondary cross population, and tertiary cross population from high to low according to L to obtain the L order; Preset a target L percentage, and screen the L order according to the target L percentage to obtain a primary optimal population, secondary optimal population, and tertiary optimal population.

7. A reactive power compensation optimization system for regional power grids based on genetic algorithms, characterized in that, It includes: Data acquisition module: Obtain the application scenarios of several power systems, past grid operation data, and grid configuration information. The application scenarios of several power systems respectively correspond to several past grid operation data; Model establishment module: Construct a power model according to the grid configuration information, and substitute the past grid operation data into the power model for simulation to obtain past reactive power compensation nodes; Grid simulation module: Obtain the current grid operation data and substitute it into the power model for simulation to obtain the current reactive power compensation nodes; Strategy generation module: Use the genetic algorithm to iterate over past reactive power compensation nodes and current reactive power compensation nodes to obtain target reactive power compensation nodes and calculate the reactive power compensation capacity of each compensation point to form an optimal strategy for grid reactive power compensation.

8. An electronic device, characterized in that, It includes a memory and a processor. A computer program is stored in the memory. When the processor calls the computer program in the memory, it realizes the content of the method for optimizing reactive power compensation of regional power grids based on genetic algorithms as described in any one of claims 1 to 6.

9. A storage medium, characterized in that, Computer-executable instructions are stored in the storage medium. When the computer-executable instructions are loaded and executed by a processor, it realizes the content of the method for optimizing reactive power compensation of regional power grids based on genetic algorithms as described in any one of claims 1 to 6.

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