Power distribution network reactive power scheduling scheme generation method and device, computer equipment, readable storage medium and program product

Abstract

The invention relates to a power distribution network reactive power scheduling scheme generation method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the steps of randomly generating a plurality of power distribution network reactive power scheduling schemes based on parameters of a power distribution network system, and establishing a fitness function; based on a fitness function, iteratively optimizing the plurality of schemes, each iteration process comprising: based on fitness values of the plurality of schemes of the last iteration, determining a target scheme of the current iteration; based on the target scheme and a piecewise nonlinear decreasing weight strategy, performing first optimization, second optimization and horizontal crossover operation on the plurality of schemes of the last iteration to obtain a plurality of schemes of the current iteration, and based on the fitness function, determining fitness values of the plurality of schemes of the current iteration; and when the number of iterations reaches a preset number of iterations, taking the optimal scheme of the last iteration as an optimized power distribution network reactive power scheduling scheme. The method can prevent from falling into a local optimal solution during optimization.

Description

Technical Field

[0001] The present application relates to the technical field of power systems, and particularly to a method, device, computer equipment, computer-readable storage medium, and computer program product for generating a reactive power scheduling scheme for a distribution network. Background Art

[0002] At present, renewable energy has become the key path to solve the energy problem. After a high proportion of renewable energy is connected to the power grid, it will significantly change the power flow distribution inside the power grid, resulting in certain changes in the reactive power flow, and then having varying degrees of impact on the voltage quality and posing challenges to the power grid stability. Existing research shows that the output of new energy has large fluctuations, which will increase the risks of voltage over-limit and network loss. Therefore, it is extremely necessary to carry out reactive power scheduling optimization to improve the system stability.

[0003] In the power distribution network, reactive power optimization aims to adjust the distribution of reactive power to reach a specific equilibrium state. Different from active power, reactive power cannot be directly measured, but it plays a key role in the power quality. Existing single-objective optimization schemes often can only optimize the reactive power scheduling of power distribution from one aspect of economy, security, or stability, and cannot optimize from the three directions of economy, security, and stability simultaneously. Summary of the Invention

[0004] Based on this, it is necessary to provide a method, device, computer equipment, computer-readable storage medium, and computer program product for generating a reactive power scheduling scheme for a distribution network that can optimize from the three directions of economy, security, and stability simultaneously for the above technical problems.

[0005] In a first aspect, the present application provides a method for generating a reactive power scheduling scheme for a distribution network, including:

[0006] Based on the parameters of the distribution network system, randomly generate multiple reactive power scheduling schemes for the distribution network, and establish an objective function and optimization constraints; establish a fitness function according to the objective function;

[0007] Based on the fitness function, iteratively optimize the multiple reactive power dispatch schemes of the distribution network. Each iteration process includes: determining the optimal reactive power dispatch scheme of the previous iteration based on the fitness values of the multiple reactive power dispatch schemes obtained in the previous iteration, and using the optimal reactive power dispatch scheme of the distribution network as the target scheme for the current iteration; based on the target scheme and the piecewise non-linear decreasing weight strategy, perform the first optimization and the second optimization on the multiple reactive power dispatch schemes obtained in the previous iteration in sequence to obtain multiple preliminary optimization schemes for the current iteration; perform a horizontal crossover operation on the multiple preliminary optimization schemes to obtain multiple reactive power dispatch schemes for the current iteration, and determine the fitness values of the multiple reactive power dispatch schemes for the current iteration based on the fitness function and the optimization constraints.

[0008] When the number of iterations reaches the preset number of iterations, stop the iterative optimization process. Determine the optimal reactive power dispatch scheme of the last iteration based on the fitness values of the multiple reactive power dispatch schemes obtained in the last iteration, and use the optimal reactive power dispatch scheme of the distribution network as the optimized reactive power dispatch scheme of the distribution network.

[0009] In one embodiment, the performing the first optimization and the second optimization on the multiple reactive power dispatch schemes obtained in the previous iteration in sequence based on the target scheme of the current iteration and the piecewise non-linear decreasing weight strategy to obtain multiple preliminary optimization schemes for the current iteration includes:

[0010] Determine the weight of the current iteration based on the piecewise non-linear decreasing weight strategy; determine the first optimization method and the second optimization method based on the target scheme and the weight of the current iteration; perform the first optimization on the multiple reactive power dispatch schemes obtained in the previous iteration in sequence based on the first optimization method to obtain multiple first optimization schemes for the current iteration; perform the second optimization on the multiple first optimization schemes for the current iteration based on the second optimization method to obtain multiple preliminary optimization schemes for the current iteration.

[0011] In one embodiment, the determining the weight of the current iteration based on the piecewise non-linear decreasing weight strategy includes:

[0012] Determine the maximum inertia weight of the current iteration and the minimum inertia weight of the current iteration based on the number of the current iteration and the preset number of iterations; determine the weight of the current iteration based on the number of the current iteration, the preset number of iterations, the maximum inertia weight of the current iteration, and the minimum inertia weight of the current iteration.

[0013] In one embodiment, the establishment of the objective function includes:

[0014] Based on the parameters of the distribution network system, establish the network loss function, voltage deviation function, and reactive power compensation device investment function of the distribution network; based on the network loss function, voltage deviation function, and reactive power compensation device investment function of the distribution network, establish an objective function, and the optimization objective of the objective function is to minimize the network loss function of the distribution network, minimize the voltage deviation function, and minimize the reactive power compensation device investment function.

[0015] In one embodiment, the establishment of the fitness function according to the objective function includes:

[0016] Establish a fitness function according to a preset network loss weight, a preset voltage deviation weight, a preset reactive power compensation weight, the network loss function of the distribution network, the voltage deviation function, and the reactive power compensation device investment function.

[0017] In one embodiment, the establishment of the optimization constraints includes:

[0018] Based on the parameters of the distribution network system, establish power flow constraints, node voltage constraints, and control variable constraints; based on the power flow constraints, node voltage constraints, and control variable constraints, establish optimization constraints.

[0019] In a second aspect, the present application also provides a device for generating a reactive power scheduling scheme for a distribution network, including:

[0020] An initialization module, configured to randomly generate multiple reactive power scheduling schemes for the distribution network based on the parameters of the distribution network system, and establish an objective function and optimization constraints; establish a fitness function according to the objective function;

[0021] An optimization module, configured to iteratively optimize the multiple reactive power scheduling schemes for the distribution network based on the fitness function. Each iteration process includes: determining the optimal reactive power scheduling scheme for the distribution network in the previous iteration based on the fitness values of the multiple reactive power scheduling schemes obtained in the previous iteration, and using the optimal reactive power scheduling scheme for the distribution network as the target scheme for the current iteration; based on the target scheme and the segmented non-linear decreasing weight strategy, performing first optimization and second optimization on the multiple reactive power scheduling schemes obtained in the previous iteration in sequence to obtain multiple preliminary optimization schemes for the current iteration; performing horizontal crossover operations on the multiple preliminary optimization schemes to obtain multiple reactive power scheduling schemes for the distribution network in the current iteration, and determining the fitness values of the multiple reactive power scheduling schemes for the distribution network in the current iteration based on the fitness function and optimization constraints;

[0022] A generation module, configured to stop the iterative optimization process when the number of iterations reaches a preset number of iterations, determine the optimal reactive power dispatch scheme for the distribution network in the last iteration based on the fitness values of multiple reactive power dispatch schemes for the distribution network obtained in the last iteration, and use the optimal reactive power dispatch scheme for the distribution network as the optimized reactive power dispatch scheme for the distribution network.

[0023] In a third aspect, the present application further provides a computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0024] Randomly generate multiple reactive power dispatch schemes for the distribution network based on the parameters of the distribution network system, and establish an objective function and optimization constraints; establish a fitness function according to the objective function;

[0025] Based on the fitness function, iteratively optimize the multiple reactive power dispatch schemes for the distribution network. Each iteration process includes: determining the optimal reactive power dispatch scheme for the distribution network in the previous iteration based on the fitness values of the multiple reactive power dispatch schemes for the distribution network obtained in the previous iteration, and using the optimal reactive power dispatch scheme for the distribution network as the target scheme for the current iteration; based on the target scheme and the piecewise non-linear decreasing weight strategy, perform the first optimization and the second optimization on the multiple reactive power dispatch schemes for the distribution network obtained in the previous iteration in sequence to obtain multiple preliminary optimization schemes for the current iteration; perform a horizontal crossover operation on the multiple preliminary optimization schemes to obtain multiple reactive power dispatch schemes for the distribution network in the current iteration, and determine the fitness values of the multiple reactive power dispatch schemes for the distribution network in the current iteration based on the fitness function and the optimization constraints;

[0026] When the number of iterations reaches a preset number of iterations, stop the iterative optimization process, determine the optimal reactive power dispatch scheme for the distribution network in the last iteration based on the fitness values of the multiple reactive power dispatch schemes for the distribution network obtained in the last iteration, and use the optimal reactive power dispatch scheme for the distribution network as the optimized reactive power dispatch scheme for the distribution network.

[0027] In a fourth aspect, the present application further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0028] Randomly generate multiple reactive power dispatch schemes for the distribution network based on the parameters of the distribution network system, and establish an objective function and optimization constraints; establish a fitness function according to the objective function;

[0029] Based on the fitness function, iteratively optimize the multiple reactive power scheduling schemes of the distribution network. Each iteration process includes: based on the fitness values of the multiple reactive power scheduling schemes obtained in the previous iteration, determine the optimal reactive power scheduling scheme of the previous iteration, and use the optimal reactive power scheduling scheme of the distribution network as the target scheme for the current iteration; based on the target scheme and the piecewise non-linear decreasing weight strategy, perform the first optimization and the second optimization on the multiple reactive power scheduling schemes obtained in the previous iteration in sequence to obtain multiple preliminary optimization schemes for the current iteration; perform a horizontal crossover operation on the multiple preliminary optimization schemes to obtain multiple reactive power scheduling schemes of the distribution network for the current iteration, and determine the fitness values of the multiple reactive power scheduling schemes of the distribution network for the current iteration based on the fitness function and the optimization constraints;

[0030] When the number of iterations reaches the preset number of iterations, stop the iterative optimization process. Based on the fitness values of the multiple reactive power scheduling schemes obtained in the last iteration, determine the optimal reactive power scheduling scheme of the last iteration, and use the optimal reactive power scheduling scheme of the distribution network as the optimized reactive power scheduling scheme of the distribution network.

[0031] In a fifth aspect, the present application also provides a computer program product, including a computer program, which when executed by a processor implements the following steps:

[0032] Based on the parameters of the distribution network system, randomly generate multiple reactive power scheduling schemes of the distribution network, and establish an objective function and optimization constraints; establish a fitness function according to the objective function;

[0033] Based on the fitness function, iteratively optimize the multiple reactive power scheduling schemes of the distribution network. Each iteration process includes: based on the fitness values of the multiple reactive power scheduling schemes obtained in the previous iteration, determine the optimal reactive power scheduling scheme of the previous iteration, and use the optimal reactive power scheduling scheme of the distribution network as the target scheme for the current iteration; based on the target scheme and the piecewise non-linear decreasing weight strategy, perform the first optimization and the second optimization on the multiple reactive power scheduling schemes obtained in the previous iteration in sequence to obtain multiple preliminary optimization schemes for the current iteration; perform a horizontal crossover operation on the multiple preliminary optimization schemes to obtain multiple reactive power scheduling schemes of the distribution network for the current iteration, and determine the fitness values of the multiple reactive power scheduling schemes of the distribution network for the current iteration based on the fitness function and the optimization constraints;

[0034] When the number of iterations reaches the preset number of iterations, stop the iterative optimization process. Based on the fitness values of the multiple reactive power scheduling schemes obtained in the last iteration, determine the optimal reactive power scheduling scheme of the last iteration, and use the optimal reactive power scheduling scheme of the distribution network as the optimized reactive power scheduling scheme of the distribution network.

[0035] The above method, device, computer equipment, computer-readable storage medium, and computer program product for generating a reactive power scheduling scheme of a distribution network are based on the parameters of the distribution network system, randomly generate multiple reactive power scheduling schemes of the distribution network, and establish an objective function and optimization constraints; establish a fitness function according to the objective function; based on the fitness function, iteratively optimize the multiple reactive power scheduling schemes of the distribution network. Each iteration process includes: based on the fitness values of the multiple reactive power scheduling schemes obtained in the previous iteration, determine the optimal reactive power scheduling scheme of the previous iteration, and use the optimal reactive power scheduling scheme as the target scheme of the current iteration; based on the target scheme and the piecewise non-linear decreasing weight strategy, perform the first optimization and the second optimization on the multiple reactive power scheduling schemes obtained in the previous iteration in sequence to obtain multiple preliminary optimization schemes of the current iteration; perform a horizontal crossover operation on the multiple preliminary optimization schemes to obtain multiple reactive power scheduling schemes of the current iteration, and determine the fitness values of the multiple reactive power scheduling schemes of the current iteration based on the fitness function and the optimization constraints; when the number of iterations reaches the preset number of iterations, stop the iterative optimization process, and based on the fitness values of the multiple reactive power scheduling schemes obtained in the last iteration, determine the optimal reactive power scheduling scheme of the last iteration, and use the optimal reactive power scheduling scheme as the optimized reactive power scheduling scheme of the distribution network. Through this method, it is possible to prevent falling into a local optimal solution during optimization, and it is also possible to optimize the reactive power scheduling scheme of the distribution network from three directions of economy, security, and stability at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required for use in the description of the embodiments of the present application or related technologies. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0037] Figure 1 It is a schematic flowchart of a method for generating a reactive power scheduling scheme of a distribution network in an embodiment;

[0038] Figure 2 It is a detailed flowchart of a method for generating a reactive power scheduling scheme of a distribution network in an embodiment;

[0039] Figure 3 It is a structural block diagram of a device for generating a reactive power scheduling scheme of a distribution network in an embodiment;

[0040] Figure 4 It is an internal structure diagram of a computer device in an embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0041] In order to make the objectives, technical solutions and advantages of the present application more clear and understandable, the present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0042] In one embodiment, as Figure 1 shown, a method for generating a reactive power scheduling scheme for a distribution network is provided. In this embodiment, an example is given where this method is applied to a terminal. It can be understood that this method can also be applied to a server, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0043] Step 102, based on the parameters of the distribution network system, randomly generate multiple reactive power scheduling schemes for the distribution network, and establish an objective function and optimization constraints; establish a fitness function according to the objective function.

[0044] Among them, the distribution network is a link in the power system that is directly connected to users and distributes electric energy to users. It consists of overhead lines, cables, poles, distribution transformers, disconnect switches, reactive power compensation capacitors, and some ancillary facilities, etc.

[0045] Among them, the parameters of the distribution network system are various data describing the operating characteristics and structure of the distribution network.

[0046] Optionally, an improved honey badger algorithm can be used to generate multiple reactive power scheduling schemes for the distribution network.

[0047] Exemplarily, taking the improved honey badger algorithm as an example, the formula for randomly generating multiple reactive power scheduling schemes for the distribution network during initialization is:

[0048]

[0049] Among them, is the i-th honey badger, that is, the i-th reactive power scheduling scheme for the distribution network, and are the upper and lower bounds, corresponding to the lower and upper limits of the compensation capacity of the distribution network nodes, is a random number from 0 to 1.

[0050] Step 104: Based on the fitness function, iteratively optimize the multiple reactive power dispatch schemes of the distribution network. Each iteration process includes: determining the optimal reactive power dispatch scheme of the previous iteration based on the fitness values of the multiple reactive power dispatch schemes obtained in the previous iteration, and using the optimal reactive power dispatch scheme of the distribution network as the target scheme for the current iteration; based on the target scheme and the piecewise non-linear decreasing weight strategy, sequentially perform the first optimization and the second optimization on the multiple reactive power dispatch schemes obtained in the previous iteration to obtain multiple preliminary optimization schemes for the current iteration; perform horizontal crossover operations on the multiple preliminary optimization schemes to obtain multiple reactive power dispatch schemes of the distribution network for the current iteration, and determine the fitness values of the multiple reactive power dispatch schemes of the distribution network for the current iteration based on the fitness function and the optimization constraints.

[0051] Among them, in the first iteration, after generating multiple reactive power dispatch schemes of the distribution network, calculate the fitness values of the multiple reactive power dispatch schemes according to the fitness function, and then determine the target scheme of the first iteration according to the fitness values.

[0052] Optionally, the iterative optimization can be obtained by improving an existing algorithm, such as the improved honey badger algorithm. In the honey badger algorithm, multiple reactive power dispatch schemes of the distribution network correspond to multiple honey badgers, the target scheme is the prey, the first optimization can be the improved digging optimization, and the second optimization can be the improved honey collection optimization.

[0053] Among them, horizontal crossover is the information exchange between two different parents to generate new offspring.

[0054] Optionally, the formula of the horizontal crossover algorithm can be:

[0055]

[0056] Among them, and are the i-th and j-th solutions in the population respectively; and are random numbers in the ranges from 0 to 1 and from -1 to 1 respectively; and are two new solutions generated after crossover.

[0057] Exemplarily, multiple reactive power scheduling schemes of the distribution network are regarded as multiple honey badgers; based on the fitness function, the multiple honey badgers are iteratively optimized. Each iteration process includes: determining the optimal honey badger of the previous iteration based on the fitness values of the multiple honey badgers obtained in the previous iteration, and taking the optimal honey badger as the prey of the current iteration; based on the prey and the piecewise non-linear decreasing weight strategy, successively performing improved digging optimization and improved honey collection optimization on the multiple honey badgers obtained in the previous iteration to obtain multiple preliminarily optimized honey badgers of the current iteration; performing horizontal crossover operation on the multiple preliminarily optimized honey badgers to obtain the multiple honey badgers of the current iteration, and determining the fitness values of the multiple honey badgers of the current iteration based on the fitness function and the optimization constraints.

[0058] Step 106, when the number of iterations reaches the preset number of iterations, stop the iterative optimization process, determine the optimal reactive power scheduling scheme of the distribution network in the last iteration based on the fitness values of the multiple reactive power scheduling schemes of the distribution network obtained in the last iteration, and take the optimal reactive power scheduling scheme of the distribution network as the optimized reactive power scheduling scheme of the distribution network.

[0059] Optionally, the preset number of iterations is the set total number of iterations, which can be 500 times.

[0060] Exemplarily, when the number of iterations reaches 500 times, stop the iterative optimization process, determine the optimal honey badger in the last iteration based on the fitness values of the multiple honey badgers obtained in the last iteration, and take the reactive power scheduling scheme of the distribution network corresponding to the optimal honey badger as the optimized reactive power scheduling scheme of the distribution network.

[0061] The above method, device, computer equipment, computer-readable storage medium and computer program product for generating a reactive power dispatch plan of a distribution network are based on the parameters of the distribution network system, randomly generate multiple reactive power dispatch plans of the distribution network, and establish an objective function and optimization constraints; establish a fitness function according to the objective function; based on the fitness function, iteratively optimize the multiple reactive power dispatch plans of the distribution network, and each iteration process includes: based on the fitness values of the multiple reactive power dispatch plans obtained in the previous iteration, determine the optimal reactive power dispatch plan of the previous iteration, and use the optimal reactive power dispatch plan as the target plan of the current iteration; based on the target plan and the piecewise non-linear decreasing weight strategy, perform the first optimization and the second optimization on the multiple reactive power dispatch plans obtained in the previous iteration in sequence to obtain multiple preliminary optimization plans of the current iteration; perform a horizontal crossover operation on the multiple preliminary optimization plans to obtain multiple reactive power dispatch plans of the current iteration, and determine the fitness values of the multiple reactive power dispatch plans of the current iteration based on the fitness function and the optimization constraints; when the number of iterations reaches the preset number of iterations, stop the iterative optimization process, and based on the fitness values of the multiple reactive power dispatch plans obtained in the last iteration, determine the optimal reactive power dispatch plan of the last iteration, and use the optimal reactive power dispatch plan as the optimized reactive power dispatch plan of the distribution network. Through this method, it is possible to prevent falling into a local optimal solution during optimization, and it is also possible to optimize the reactive power dispatch plan of the distribution network from three directions of economy, security and stability at the same time.

[0062] In an exemplary embodiment, the performing the first optimization and the second optimization on the multiple reactive power dispatch plans obtained in the previous iteration in sequence based on the target plan of the current iteration and the piecewise non-linear decreasing weight strategy to obtain multiple preliminary optimization plans of the current iteration includes:

[0063] Determine the weight of the current iteration based on the piecewise non-linear decreasing weight strategy; determine the first optimization method and the second optimization method based on the target plan and the weight of the current iteration; perform the first optimization on the multiple reactive power dispatch plans obtained in the previous iteration in sequence based on the first optimization method to obtain multiple first optimization plans of the current iteration; perform the second optimization on the multiple first optimization plans of the current iteration based on the second optimization method to obtain multiple preliminary optimization plans of the current iteration.

[0064] Optionally, the first optimization method may be an improved mining optimization strategy, and the second optimization method may be an improved honey collection optimization strategy.

[0065] Exemplarily, based on the piecewise non-linear decreasing weight strategy, determine the weight of the current iteration; based on the prey and the weight of the current iteration, determine the improved mining optimization strategy and the improved honey collection optimization strategy of the current iteration; the formula for the improved mining optimization strategy is:

[0066]

[0067] Wherein, is the position of the updated honey badger, is the position of the prey, is the weight of the current iteration, is the direction correction parameter, is the ability to obtain food, , can be 6, is the odor intensity, corresponding to the voltage deviation, is the distance between the i-th honey badger and the prey, corresponding to the node topology relationship. The odor intensity The calculation formula of is:

[0068]

[0069] Wherein, S is the perception intensity, represents the i-th honey badger, represents the i + 1-th honey badger. The formula for the improved honey collection optimization strategy is:

[0070]

[0071] Wherein, is the density factor, The calculation formula of is as follows:

[0072]

[0073] Wherein, C is a constant value, taking 2, is the preset maximum number of iterations, and t is the number of the current iteration. Based on the improved mining optimization strategy of the current iteration, optimize the multiple honey badgers obtained in the previous iteration in sequence to obtain multiple first optimized honey badgers of the current iteration; based on the improved honey collection optimization strategy of the current iteration, optimize the multiple first optimized honey badgers of the current iteration to obtain multiple preliminarily optimized honey badgers of the current iteration.

[0074] In this embodiment, through the improved mining optimization strategy and the improved honey collection optimization strategy, more accurate optimization results can be obtained.

[0075] In an exemplary embodiment, the determining the weight of the current iteration based on the piecewise non-linear decreasing weight strategy includes:

[0076] Determine the maximum inertia weight and the minimum inertia weight of the current iteration based on the number of the current iteration and the preset number of iterations; determine the weight of the current iteration based on the number of the current iteration, the preset number of iterations, the maximum inertia weight of the current iteration, and the minimum inertia weight of the current iteration.

[0077] Exemplarily, based on the number of the current iteration and the preset number of iterations, determine the maximum inertia weight and the minimum inertia weight of the current iteration. The formulas are as follows:

[0078]

[0079] Where and are the maximum and minimum values of the inertia weight, , and are the non-linear decreasing coefficients at different numbers of iterations. Determine the weight of the current iteration based on the number of the current iteration, the preset number of iterations, the maximum inertia weight of the current iteration, and the minimum inertia weight of the current iteration. That is, the calculation formula of the non-linear decreasing weight strategy is as follows:

[0080]

[0081] Where PDN is the weight of the current iteration, t is the number of the current iteration, is the preset number of iterations, , and are neighborhood parameters, and U(0,1) is a random number uniformly distributed in the interval [0, 1].

[0082] In this embodiment, the weight of each iteration is generated by the non-linear decreasing weight strategy, which can more efficiently optimize multiple reactive power dispatch schemes of the distribution network.

[0083] In an exemplary embodiment, the establishment of the objective function includes:

[0084] Based on the parameters of the distribution network system, establish the network loss function, voltage deviation function, and reactive power compensation device investment function of the distribution network; based on the network loss function, voltage deviation function, and reactive power compensation device investment function of the distribution network, establish the objective function. The optimization objective of the objective function is to minimize the network loss function of the distribution network, minimize the voltage deviation function, and minimize the reactive power compensation device investment function.

[0085] Exemplarily, based on the parameters of the distribution network system, establish the network loss function, voltage deviation function, and reactive power compensation device investment function of the distribution network; the network loss function is as follows:

[0086]

[0087] Among them, and are the voltage values of nodes i and j of the devices connected to the distribution network, is the conductance phase difference between the two nodes, is the number of branches, is the phase difference between nodes i and j. The voltage deviation function is as follows:

[0088]

[0089] Among them, n is the total number of nodes, is the expected voltage value of node j, is the maximum voltage deviation of node j. The investment function of the reactive power compensation device is as follows:

[0090]

[0091] Among them, is the number of reactive power compensation nodes, is the actual reactive power compensation capacity of node i, is the investment per unit capacity, taking 1. The optimization objective of the objective function is to minimize the network loss function of the distribution network, minimize the voltage deviation function, and minimize the investment function of the reactive power compensation device. Therefore, the objective function is:

[0092]

[0093] In this embodiment, by establishing the objective function, the reactive power scheduling scheme of the distribution network can be accurately optimized, so that the optimized scheme can achieve the objectives in terms of economy, safety, and stability.

[0094] In an exemplary embodiment, establishing the fitness function according to the objective function includes:

[0095] Establishing the fitness function according to the preset network loss weight, preset voltage deviation weight, preset reactive power compensation weight, the network loss function of the distribution network, the voltage deviation function, and the investment function of the reactive power compensation device.

[0096] Exemplarily, the fitness function is:

[0097]

[0098] Among them, , and are the preset network loss weight, preset voltage deviation weight, and preset reactive power compensation weight respectively.

[0099] In this embodiment, by establishing a fitness function, the advantages and disadvantages of multiple reactive power dispatching schemes obtained in each iteration can be effectively evaluated.

[0100] In an exemplary embodiment, the establishment of the optimization constraints includes:

[0101] Based on the parameters of the distribution network system, establish power flow constraints, node voltage constraints, and control variable constraints; based on the power flow constraints, the node voltage constraints, and the control variable constraints, establish optimization constraints.

[0102] Exemplarily, the formula for the power flow constraint is:

[0103]

[0104]

[0105] Where, and are the active and reactive powers of the generator respectively, and are the consumed active and reactive powers respectively, , are the conductance and susceptance between two nodes respectively. The formula for the node voltage constraint is:

[0106]

[0107] Where, is the node voltage, and represent the maximum and minimum values of the voltage. The formula for the control variable constraint is:

[0108]

[0109] Where, is the reactive power; is the capacity of the reactive power compensation device; is the number of distributed power sources. The power flow constraints, node voltage constraints, and control variable constraints together constitute the optimization constraints.

[0110] In this embodiment, by setting up optimization constraints, the safe and stable operation of the distribution network system can be ensured.

[0111] In an exemplary embodiment, as Figure 2 shown, a method for generating a reactive power dispatching scheme for a distribution network, based on the parameters of the distribution network system, establishes optimization constraints; the formula for the power flow constraint is:

[0112]

[0113]

[0114] Among them, and are the active and reactive powers of the generator respectively, and are the active and reactive powers consumed respectively, and are the conductance and susceptance between two nodes respectively. The formula for node voltage constraint is:

[0115]

[0116] Among them, is the node voltage, and represent the maximum and minimum voltage values. The formula for control variable constraint is:

[0117]

[0118] Among them, is the reactive power, is the capacity of reactive power compensation equipment, is the number of distributed power sources. The power flow constraint, node voltage constraint and control variable constraint together constitute the optimization constraint. Based on the parameters of the distribution network system, the network loss function, voltage deviation function and reactive power compensation device investment function of the distribution network are established; the network loss function is as follows:

[0119]

[0120] Among them, and are the voltage values of nodes i and j of the equipment connected to the distribution network, is the conductance phase difference between two nodes, is the number of branches, is the phase difference between nodes i and j. The voltage deviation function is as follows:

[0121]

[0122] Among them, n is the total number of nodes, is the expected voltage value of node j, is the maximum voltage deviation of node j. The reactive power compensation device investment function is as follows:

[0123]

[0124] Among them, is the number of reactive power compensation nodes, is the actual reactive power compensation capacity of node i, Taking the unit capacity investment as 1, the optimization objective of the objective function is to minimize the network loss function of the distribution network, minimize the voltage deviation function, and minimize the reactive power compensation device investment function. Therefore, the objective function is:

[0125]

[0126] . Based on the objective function, a fitness function is established, and the fitness function is:

[0127]

[0128] Among them, , and are the preset network loss weight, the preset voltage deviation weight, and the preset reactive power compensation weight respectively. Based on the parameters of the distribution network system, multiple reactive power scheduling schemes for the distribution network are randomly generated. Taking the improved honey badger algorithm as an example, each honey badger represents a reactive power scheduling scheme for the distribution network. The formula for randomly generating multiple honey badgers during initialization is:

[0129]

[0130] Among them, is the i-th honey badger, that is, the i-th reactive power scheduling scheme for the distribution network, and are the upper and lower bounds, is a random number from 0 to 1. Based on the fitness function, multiple honey badgers are iteratively optimized. Each iteration process includes: based on the fitness values of the multiple honey badgers obtained in the previous iteration, determining the optimal honey badger in the previous iteration and taking the optimal honey badger as the prey in the current iteration; based on the current iteration number and the preset iteration number, determining the maximum inertia weight in the current iteration and the minimum inertia weight in the current iteration. The formulas for determining the maximum inertia weight in the current iteration and the minimum inertia weight in the current iteration are as follows:

[0131]

[0132] Among them, and are the maximum and minimum values of the inertia weight, , and are the non-linear decreasing coefficients at different iteration numbers. Based on the current iteration number, the preset iteration number, the maximum inertia weight in the current iteration, and the minimum inertia weight in the current iteration, determine the weight in the current iteration, that is, the calculation formula of the non-linear decreasing weight strategy is as follows:

[0133]

[0134] where PDN is the weight of the current iteration, t is the number of the current iteration, is the preset number of iterations, 、 and are neighborhood parameters, U(0, 1) is a random number uniformly distributed in the interval [0, 1]. Based on the prey and the weight of the current iteration, the improved mining optimization strategy and the improved honey - gathering optimization strategy for the current iteration are determined; the formula for the improved mining optimization strategy is:

[0135]

[0136] where, is the position of the updated honey badger, is the position of the prey, is the weight of the current iteration, is the direction correction parameter, is the ability to obtain food, , can be 6, is the odor intensity, is the distance between the i - th honey badger and the prey. The odor intensity is calculated by the formula:

[0137]

[0138] where S is the perception intensity, represents the i - th honey badger, represents the (i + 1) - th honey badger. The formula for the improved honey - gathering optimization strategy is:

[0139]

[0140] where, is the density factor, is calculated as follows:

[0141]

[0142] where C is a constant value, taking 2, is the preset maximum number of iterations, and t is the number of the current iteration.

[0143] Based on the mining optimization strategy improved in the current iteration, multiple honey badgers obtained in the previous iteration are optimized one by one to obtain multiple first-optimized honey badgers in the current iteration; based on the nectar collection optimization strategy improved in the current iteration, multiple first-optimized honey badgers in the current iteration are optimized to obtain multiple preliminarily optimized honey badgers in the current iteration. A horizontal crossover operation is performed on the multiple preliminarily optimized honey badgers to obtain multiple honey badgers in the current iteration, and based on the fitness function and optimization constraints, the fitness values of the multiple honey badgers in the current iteration are determined; the formula of the horizontal crossover algorithm is:

[0144]

[0145] wherein, and are the i-th and j-th solutions in the population respectively; and are random numbers in the ranges from 0 to 1 and from -1 to 1 respectively; and are two new solutions generated after crossover. When the number of iterations reaches 500 times, the iterative optimization process is stopped, and based on the fitness values of the multiple honey badgers obtained in the last iteration, the optimal honey badger in the last iteration is determined, and the reactive power dispatch scheme of the distribution network corresponding to the optimal honey badger is used as the optimized reactive power dispatch scheme of the distribution network.

[0146] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are shown in sequence according to the arrows, these steps do not necessarily have to be executed in the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages, and these steps or stages do not necessarily have to be executed at the same time, but can be executed at different times, and the execution order of these steps or stages does not necessarily have to be sequential, but can be executed alternately or alternately with at least a part of the steps or stages in other steps or other steps.

[0147] In an exemplary embodiment, as Figure 3 shown, a device for generating a reactive power dispatch scheme of a distribution network is provided, including: an initialization module 301, an optimization module 302, and a generation module 303, wherein:

[0148] The initialization module is configured to randomly generate multiple reactive power dispatch schemes of the distribution network based on the parameters of the distribution network system, and establish an objective function and optimization constraints; establish a fitness function according to the objective function;

[0149] An optimization module, configured to iteratively optimize the multiple reactive power dispatch schemes of the distribution network based on the fitness function. Each iteration process includes: determining the optimal reactive power dispatch scheme of the previous iteration based on the fitness values of the multiple reactive power dispatch schemes obtained in the previous iteration, and using the optimal reactive power dispatch scheme of the distribution network as the target scheme for the current iteration; based on the target scheme and the piecewise non-linear decreasing weight strategy, performing first optimization and second optimization on the multiple reactive power dispatch schemes obtained in the previous iteration in sequence to obtain multiple preliminary optimization schemes for the current iteration; performing horizontal crossover operations on the multiple preliminary optimization schemes to obtain multiple reactive power dispatch schemes for the current iteration, and determining the fitness values of the multiple reactive power dispatch schemes for the current iteration based on the fitness function and the optimization constraints.

[0150] A generation module, configured to stop the iterative optimization process when the number of iterations reaches the preset number of iterations, determine the optimal reactive power dispatch scheme of the last iteration based on the fitness values of the multiple reactive power dispatch schemes obtained in the last iteration, and use the optimal reactive power dispatch scheme of the distribution network as the optimized reactive power dispatch scheme of the distribution network.

[0151] In one embodiment, the optimization module is further configured to:

[0152] Determine the weight of the current iteration based on the piecewise non-linear decreasing weight strategy; determine the first optimization method and the second optimization method based on the target scheme and the weight of the current iteration; perform first optimization on the multiple reactive power dispatch schemes obtained in the previous iteration in sequence based on the first optimization method to obtain multiple first optimization schemes for the current iteration; perform second optimization on the multiple first optimization schemes for the current iteration based on the second optimization method to obtain multiple preliminary optimization schemes for the current iteration.

[0153] In one embodiment, the optimization module is further configured to:

[0154] Determine the maximum inertia weight of the current iteration and the minimum inertia weight of the current iteration based on the number of the current iteration and the preset number of iterations; determine the weight of the current iteration based on the number of the current iteration, the preset number of iterations, the maximum inertia weight of the current iteration, and the minimum inertia weight of the current iteration.

[0155] In one embodiment, the initialization module is further configured to:

[0156] Based on the parameters of the distribution network system, establish the network loss function, voltage deviation function, and reactive power compensation device investment function of the distribution network; based on the network loss function, voltage deviation function, and reactive power compensation device investment function of the distribution network, establish an objective function, and the optimization objective of the objective function is to minimize the network loss function of the distribution network, minimize the voltage deviation function, and minimize the reactive power compensation device investment function.

[0157] In one embodiment, the initialization module is further configured to:

[0158] According to the preset network loss weight, preset voltage deviation weight, preset reactive power compensation weight, the network loss function of the distribution network, the voltage deviation function, and the reactive power compensation device investment function, establish a fitness function.

[0159] In one embodiment, the initialization module is further configured to:

[0160] Based on the parameters of the distribution network system, establish power flow constraints, node voltage constraints, and control variable constraints; based on the power flow constraints, node voltage constraints, and control variable constraints, establish optimization constraints.

[0161] Each module in the above-mentioned reactive power scheduling scheme generation device for distribution network can be implemented in whole or in part by software, hardware, and their combination. The above-mentioned modules can be embedded in the processor of the computer device in hardware form or be independent of it, or can be stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to the above-mentioned modules.

[0162] In an exemplary embodiment, a computer device is provided. The computer device can be a server, and its internal structure diagram can be as Figure 4 shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, memory, and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store the reactive power scheduling scheme for the distribution network. The input / output interface of the computer device is used for the processor to exchange information with external devices. The communication interface of the computer device is used to communicate with external terminals through a network connection. When the computer program is executed by the processor, it implements a method for generating a reactive power scheduling scheme for a distribution network.

[0163] Those skilled in the art can understand that Figure 4 the structure shown in Figure 4 is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have a different component layout.

[0164] In an exemplary embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0165] Based on the parameters of the distribution network system, randomly generate multiple reactive power scheduling schemes for the distribution network, and establish an objective function and optimization constraints; establish a fitness function according to the objective function;

[0166] Based on the fitness function, iteratively optimize the multiple reactive power scheduling schemes for the distribution network. Each iteration process includes: based on the fitness values of the multiple reactive power scheduling schemes obtained in the previous iteration, determine the optimal reactive power scheduling scheme for the distribution network in the previous iteration, and use the optimal reactive power scheduling scheme for the distribution network as the target scheme for the current iteration; based on the target scheme and the piecewise non-linear decreasing weight strategy, perform the first optimization and the second optimization on the multiple reactive power scheduling schemes obtained in the previous iteration in sequence to obtain multiple preliminary optimization schemes for the current iteration; perform a horizontal crossover operation on the multiple preliminary optimization schemes to obtain multiple reactive power scheduling schemes for the distribution network in the current iteration, and based on the fitness function and optimization constraints, determine the fitness values of the multiple reactive power scheduling schemes for the distribution network in the current iteration;

[0167] When the number of iterations reaches the preset number of iterations, stop the iterative optimization process, based on the fitness values of the multiple reactive power scheduling schemes for the distribution network obtained in the last iteration, determine the optimal reactive power scheduling scheme for the distribution network in the last iteration, and use the optimal reactive power scheduling scheme for the distribution network as the optimized reactive power scheduling scheme for the distribution network.

[0168] In an embodiment, when the processor executes the computer program, the following steps are also implemented:

[0169] Based on the piecewise non-linear decreasing weight strategy, determine the weight for the current iteration; based on the target scheme and the weight for the current iteration, determine the first optimization method and the second optimization method; based on the first optimization method, perform the first optimization on the multiple reactive power scheduling schemes for the distribution network obtained in the previous iteration in sequence to obtain multiple first optimization schemes for the current iteration; based on the second optimization method, perform the second optimization on the multiple first optimization schemes for the distribution network in the current iteration to obtain multiple preliminary optimization schemes for the current iteration.

[0170] In one embodiment, when the processor executes the computer program, the following steps are further implemented:

[0171] Based on the number of current iterations and the preset number of iterations, determine the maximum inertia weight of the current iteration and the minimum inertia weight of the current iteration; based on the number of current iterations, the preset number of iterations, the maximum inertia weight of the current iteration, and the minimum inertia weight of the current iteration, determine the weight of the current iteration.

[0172] In one embodiment, when the processor executes the computer program, the following steps are further implemented:

[0173] Based on the parameters of the distribution network system, establish a network loss function, a voltage deviation function, and a reactive power compensation device investment function of the distribution network; based on the network loss function, the voltage deviation function, and the reactive power compensation device investment function of the distribution network, establish an objective function, and the optimization objective of the objective function is to minimize the network loss function of the distribution network, minimize the voltage deviation function, and minimize the reactive power compensation device investment function.

[0174] In one embodiment, when the processor executes the computer program, the following steps are further implemented:

[0175] According to the preset network loss weight, the preset voltage deviation weight, the preset reactive power compensation weight, the network loss function of the distribution network, the voltage deviation function, and the reactive power compensation device investment function, establish a fitness function.

[0176] In one embodiment, when the processor executes the computer program, the following steps are further implemented:

[0177] Based on the parameters of the distribution network system, establish a power flow constraint, a node voltage constraint, and a control variable constraint; based on the power flow constraint, the node voltage constraint, and the control variable constraint, establish an optimization constraint.

[0178] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0179] Based on the parameters of the distribution network system, randomly generate multiple reactive power scheduling schemes for the distribution network, and establish an objective function and an optimization constraint; establish a fitness function according to the objective function;

[0180] Based on the fitness function, iteratively optimize the multiple reactive power dispatch schemes of the distribution network. Each iteration process includes: determining the optimal reactive power dispatch scheme of the previous iteration based on the fitness values of the multiple reactive power dispatch schemes obtained in the previous iteration, and using the optimal reactive power dispatch scheme of the distribution network as the target scheme for the current iteration; based on the target scheme and the piecewise non-linear decreasing weight strategy, performing the first optimization and the second optimization on the multiple reactive power dispatch schemes obtained in the previous iteration in sequence to obtain multiple preliminary optimization schemes for the current iteration; performing a horizontal crossover operation on the multiple preliminary optimization schemes to obtain multiple reactive power dispatch schemes of the distribution network for the current iteration, and determining the fitness values of the multiple reactive power dispatch schemes of the distribution network for the current iteration based on the fitness function and the optimization constraints.

[0181] When the number of iterations reaches the preset number of iterations, stop the iterative optimization process. Determine the optimal reactive power dispatch scheme of the last iteration based on the fitness values of the multiple reactive power dispatch schemes obtained in the last iteration, and use the optimal reactive power dispatch scheme of the distribution network as the optimized reactive power dispatch scheme of the distribution network.

[0182] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented:

[0183] Based on the piecewise non-linear decreasing weight strategy, determine the weight for the current iteration; based on the target scheme and the weight for the current iteration, determine the first optimization method and the second optimization method; based on the first optimization method, perform the first optimization on the multiple reactive power dispatch schemes obtained in the previous iteration in sequence to obtain multiple first optimization schemes for the current iteration; based on the second optimization method, perform the second optimization on the multiple first optimization schemes for the current iteration to obtain multiple preliminary optimization schemes for the current iteration.

[0184] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented:

[0185] Based on the number of the current iteration and the preset number of iterations, determine the maximum inertia weight for the current iteration and the minimum inertia weight for the current iteration; based on the number of the current iteration, the preset number of iterations, the maximum inertia weight for the current iteration, and the minimum inertia weight for the current iteration, determine the weight for the current iteration.

[0186] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented:

[0187] Based on the parameters of the distribution network system, establish the network loss function, voltage deviation function, and reactive power compensation device investment function of the distribution network; based on the network loss function, voltage deviation function, and reactive power compensation device investment function of the distribution network, establish an objective function, and the optimization objective of the objective function is to minimize the network loss function of the distribution network, minimize the voltage deviation function, and minimize the reactive power compensation device investment function.

[0188] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0189] According to the preset network loss weight, preset voltage deviation weight, preset reactive power compensation weight, the network loss function of the distribution network, the voltage deviation function, and the reactive power compensation device investment function, establish a fitness function.

[0190] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0191] Based on the parameters of the distribution network system, establish power flow constraints, node voltage constraints, and control variable constraints; based on the power flow constraints, node voltage constraints, and control variable constraints, establish optimization constraints.

[0192] In one embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0193] Based on the parameters of the distribution network system, randomly generate multiple reactive power scheduling schemes for the distribution network, and establish an objective function and optimization constraints; establish a fitness function according to the objective function;

[0194] Based on the fitness function, iteratively optimize the multiple reactive power scheduling schemes for the distribution network. Each iteration process includes: based on the fitness values of the multiple reactive power scheduling schemes obtained in the previous iteration, determine the optimal reactive power scheduling scheme for the distribution network in the previous iteration, and use the optimal reactive power scheduling scheme as the target scheme for the current iteration; based on the target scheme and the piecewise non-linear decreasing weight strategy, perform the first optimization and the second optimization on the multiple reactive power scheduling schemes obtained in the previous iteration in sequence to obtain multiple preliminary optimization schemes for the current iteration; perform a horizontal crossover operation on the multiple preliminary optimization schemes to obtain multiple reactive power scheduling schemes for the distribution network in the current iteration, and determine the fitness values of the multiple reactive power scheduling schemes for the distribution network in the current iteration based on the fitness function and optimization constraints;

[0195] When the number of iterations reaches the preset number of iterations, stop the iterative optimization process, determine the optimal reactive power dispatch scheme for the distribution network in the last iteration based on the fitness values of multiple reactive power dispatch schemes for the distribution network obtained in the last iteration, and use the optimal reactive power dispatch scheme for the distribution network as the optimized reactive power dispatch scheme for the distribution network.

[0196] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented:

[0197] Based on the piecewise non-linear decreasing weight strategy, determine the weight of the current iteration; based on the target scheme and the weight of the current iteration, determine the first optimization method and the second optimization method; based on the first optimization method, perform the first optimization on multiple reactive power dispatch schemes for the distribution network obtained in the previous iteration in sequence to obtain multiple first optimization schemes for the current iteration; based on the second optimization method, perform the second optimization on the multiple first optimization schemes for the current iteration to obtain multiple preliminary optimization schemes for the current iteration.

[0198] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented:

[0199] Based on the number of the current iteration and the preset number of iterations, determine the maximum inertia weight of the current iteration and the minimum inertia weight of the current iteration; based on the number of the current iteration, the preset number of iterations, the maximum inertia weight of the current iteration, and the minimum inertia weight of the current iteration, determine the weight of the current iteration.

[0200] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented:

[0201] Based on the parameters of the distribution network system, establish the network loss function, voltage deviation function, and reactive power compensation device investment function of the distribution network; based on the network loss function, voltage deviation function, and reactive power compensation device investment function of the distribution network, establish an objective function, and the optimization objective of the objective function is to minimize the network loss function of the distribution network, minimize the voltage deviation function, and minimize the reactive power compensation device investment function.

[0202] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented:

[0203] According to the preset network loss weight, preset voltage deviation weight, preset reactive power compensation weight, the network loss function of the distribution network, the voltage deviation function, and the reactive power compensation device investment function, establish a fitness function.

[0204] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented:

[0205] Based on the parameters of the distribution network system, power flow constraints, node voltage constraints, and control variable constraints are established; based on the power flow constraints, the node voltage constraints, and the control variable constraints, optimization constraints are established.

[0206] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, data processing logics based on quantum computing, artificial intelligence (AI) processors, etc., without limitation.

[0207] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered to be within the scope recorded in the present application.

[0208] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. A method for generating a reactive power scheduling scheme for a distribution network, characterized in that, The method includes: Based on the parameters of the distribution network system, randomly generate multiple reactive power scheduling schemes for the distribution network, and establish an objective function and optimization constraints; establish a fitness function according to the objective function; Based on the fitness function, iteratively optimize the multiple reactive power scheduling schemes for the distribution network. Each iteration process includes: based on the fitness values of the multiple reactive power scheduling schemes obtained in the previous iteration, determine the optimal reactive power scheduling scheme for the distribution network in the previous iteration, and use the optimal reactive power scheduling scheme for the distribution network as the target scheme for the current iteration; based on the target scheme and the piecewise non-linear decreasing weight strategy, perform the first optimization and the second optimization on the multiple reactive power scheduling schemes obtained in the previous iteration in sequence to obtain multiple preliminary optimization schemes for the current iteration; perform a horizontal crossover operation on the multiple preliminary optimization schemes to obtain multiple reactive power scheduling schemes for the distribution network in the current iteration, and determine the fitness values of the multiple reactive power scheduling schemes for the distribution network in the current iteration based on the fitness function and the optimization constraints; When the number of iterations reaches the preset number of iterations, stop the iterative optimization process, determine the optimal reactive power scheduling scheme for the distribution network in the last iteration based on the fitness values of the multiple reactive power scheduling schemes obtained in the last iteration, and use the optimal reactive power scheduling scheme for the distribution network as the optimized reactive power scheduling scheme for the distribution network.

2. The method according to claim 1, wherein The performing the first optimization and the second optimization on the multiple reactive power scheduling schemes obtained in the previous iteration in sequence based on the target scheme for the current iteration and the piecewise non-linear decreasing weight strategy to obtain multiple preliminary optimization schemes for the current iteration includes: Determine the weight for the current iteration based on the piecewise non-linear decreasing weight strategy; Determine the first optimization method and the second optimization method based on the target scheme and the weight for the current iteration; Based on the first optimization method, perform the first optimization on the multiple reactive power scheduling schemes obtained in the previous iteration in sequence to obtain multiple first optimization schemes for the current iteration; Based on the second optimization method, perform the second optimization on the multiple first optimization schemes for the current iteration to obtain multiple preliminary optimization schemes for the current iteration.

3. The method according to claim 2, wherein The determining the weight for the current iteration based on the piecewise non-linear decreasing weight strategy includes: Determine the maximum inertia weight for the current iteration and the minimum inertia weight for the current iteration based on the number of the current iteration and the preset number of iterations; Determine the weight for the current iteration based on the number of the current iteration, the preset number of iterations, the maximum inertia weight for the current iteration, and the minimum inertia weight for the current iteration.

4. The method according to claim 1, characterized in that, The establishment of the objective function includes: Based on the parameters of the distribution network system, establish a network loss function, a voltage deviation function, and a reactive power compensation device investment function for the distribution network; Based on the network loss function, the voltage deviation function, and the reactive power compensation device investment function for the distribution network, establish an objective function, and the optimization objective of the objective function is to minimize the network loss function for the distribution network, minimize the voltage deviation function, and minimize the reactive power compensation device investment function.

5. The method according to claim 4, characterized in that, The establishing a fitness function according to the objective function includes: An fitness function is established according to a preset network loss weight, a preset voltage deviation weight, a preset reactive power compensation weight, a network loss function of the distribution network, the voltage deviation function, and the investment function of the reactive power compensation device.

6. The method according to claim 1, wherein The establishment of the optimization constraints includes: Based on the parameters of the distribution network system, power flow constraints, node voltage constraints, and control variable constraints are established; Based on the power flow constraints, the node voltage constraints, and the control variable constraints, optimization constraints are established.

7. A device for generating a reactive power dispatch scheme for a distribution network, characterized in that, The device includes: An initialization module, configured to randomly generate multiple distribution network reactive power scheduling schemes based on the parameters of the distribution network system, and establish an objective function and optimization constraints; establish an fitness function according to the objective function; An optimization module, configured to iteratively optimize the multiple distribution network reactive power scheduling schemes based on the fitness function. Each iteration process includes: determining the optimal distribution network reactive power scheduling scheme of the previous iteration based on the fitness values of the multiple distribution network reactive power scheduling schemes obtained in the previous iteration, and using the optimal distribution network reactive power scheduling scheme as the target scheme for the current iteration; performing first optimization and second optimization on the multiple distribution network reactive power scheduling schemes obtained in the previous iteration in sequence based on the target scheme and the piecewise non-linear decreasing weight strategy to obtain multiple preliminary optimization schemes for the current iteration; performing horizontal crossover operations on the multiple preliminary optimization schemes to obtain the multiple distribution network reactive power scheduling schemes for the current iteration, and determining the fitness values of the multiple distribution network reactive power scheduling schemes for the current iteration based on the fitness function and the optimization constraints; A generation module, configured to stop the iterative optimization process when the number of iterations reaches a preset number of iterations, determine the optimal distribution network reactive power scheduling scheme of the last iteration based on the fitness values of the multiple distribution network reactive power scheduling schemes obtained in the last iteration, and use the optimal distribution network reactive power scheduling scheme as the optimized distribution network reactive power scheduling scheme.

8. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 6 are implemented.

Images