A novel multi-agent multi-objective dispatching optimization analysis method for power distribution systems

Abstract

The application discloses a novel multi-subject and multi-target scheduling optimization analysis method for a power distribution system, and comprises the following optimization analysis steps: S1, constructing a multi-target function of a power distribution system optimization scheduling model; S2, constructing a target function constraint condition; S3, adopting an improved non-dominated sorting genetic algorithm III to perform optimization solving, and obtaining a Pareto solution set; S4, adopting an analytic hierarchy process to combine real-time data to construct a weight calculation model, and determining the weight of each target; and S5, adopting a TOPSIS method based on the target weight to determine the optimal solution in the Pareto solution set, and taking the optimal solution as an optimal scheduling scheme; the application can quickly obtain a scheduling strategy, balances economy, safety and environmental protection at the same time, and effectively improves scheduling efficiency.

Description

Technical Field

[0001] This invention belongs to the field of novel power distribution system scheduling optimization analysis technology, specifically involving a novel multi-subject, multi-objective scheduling optimization analysis method for power distribution systems. Background Technology

[0002] With the rapid advancement of the integrated development of power generation, grid, load, and energy storage, microgrids and virtual power plants, as important components of new power distribution systems, are becoming increasingly diverse in form. The flexible and varied operating strategies of multiple entities increase the complexity of power distribution network planning, and the randomness and uncertainty of power output further affect the stability of power distribution system operation. All of these factors make the dispatch characteristics of new power distribution systems increasingly complex, making it difficult for traditional power distribution system dispatch operation and management models to adapt, resulting in low dispatch efficiency and limiting the efficiency of distributed power generation, load, and energy storage resource allocation.

[0003] Therefore, in order to solve the above problems, it is necessary to develop a new multi-subject, multi-objective scheduling optimization analysis method for power distribution systems. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a novel multi-subject, multi-objective scheduling optimization analysis method for power distribution systems, which can quickly derive scheduling strategies while balancing economy, safety and environmental protection, and effectively improve scheduling efficiency.

[0005] The objective of this invention is achieved as follows: a novel multi-subject, multi-objective scheduling optimization analysis method for power distribution systems, comprising the following optimization analysis steps:

[0006] S1. Construct a multi-objective function for the power distribution system optimization scheduling model, including economic objectives, safety objectives, and environmental objectives;

[0007] S2. Construct the objective function constraints, including equality constraints and inequality constraints. Among them, equality constraints include power balance constraints, and inequality constraints include equipment capacity constraints, node voltage constraints, and electricity market transaction constraints.

[0008] S3. An improved non-dominated sorting genetic algorithm III is used for optimization. Through the elite retention strategy, high-quality individuals are retained and directly enter the next generation. At the same time, adaptive crossover and mutation operators are introduced to dynamically adjust the operator parameters according to the population evolution level, which accelerates the convergence speed. The local search algorithm hill climbing method is combined to perform a fine search of the neighborhood of the optimal solution to obtain the Pareto solution set.

[0009] S4. Use the analytic hierarchy process (AHP) combined with real-time data to construct a weight calculation model and determine the weight of each objective.

[0010] S5. Based on the target weight, the TOPSIS method is used to calculate the distance between all solutions in the Pareto solution set output in step S3 and their positive and negative ideal solutions. The positive and negative ideal values ​​are compared according to the sorting rules to determine the optimal solution in the Pareto solution set output in step S3, which is then used as the optimal scheduling scheme.

[0011] Furthermore, the economic objective in step S1 aims to minimize the total operating cost of the system, specifically expressed as follows: In the formula, This indicates the cost of purchasing electricity from the upper-level power grid. This represents the cost of load shedding. This indicates the production cost of the power plant. This represents the depreciation cost of an energy storage power station. This indicates the cost of network loss.

[0012] Furthermore, the safety objective in step S1 is to minimize the voltage deviation and the risk of power flow exceeding limits, specifically expressed as follows: ;in, Indicates the degree of voltage deviation, where, Indicates the number of system nodes. Represents a node Voltage; This indicates the risk of branch current exceeding the limit, where, Indicates a branch The trend value that transcends limits, Representing branch roads The probability density function and severity function of power flow exceeding limits; whereby the severity function refers to the degree of severity of power flow exceeding limits caused by distributed generation access to the distribution network, and adopts an offset combined with a utility theory preference-type function as the severity function, expressed as: , In the formula, This indicates the active power offset. Indicates a branch The baseline power flow value.

[0013] Furthermore, the environmental protection objective in step S1 aims to minimize carbon emission costs, specifically expressed as follows: In the formula, Indicates the carbon emission cost coefficient. Indicates the boundary busbar. This represents a collection of microgrids in a distribution network. Indicates the marginal emission factor. This indicates the active power output of the unit.

[0014] Furthermore, the power balance constraint in step S2 is expressed as: In the formula, , Representing nodes respectively The injected active and reactive power, , These represent the active and reactive power outputs of the distributed power source, respectively. , These represent active and reactive loads, respectively. , Representing nodes respectively , voltage amplitude, , , Representing branch conductance, susceptance, and node, respectively. , The difference in phase angle between voltages, Indicates the number of branches.

[0015] Furthermore, the equipment capacity constraint in step S2 includes upper and lower limit constraints on generator output and line transmission capacity constraints, wherein the upper and lower limit constraints on generator output are expressed as follows: In the formula, , These respectively represent the generator at The effort exerted and the effort exerted at any given moment, , These represent the maximum and minimum active power output of the generator, respectively. , These represent the maximum and minimum reactive power output of the generator, respectively; the line transmission capacity constraint is expressed as: In the formula, , They represent the lines respectively. Maximum active and reactive power transmission capacity.

[0016] Furthermore, the system node voltage constraint in step S2 is expressed as: In the formula, , Representing nodes respectively The maximum and minimum values ​​of voltage amplitude.

[0017] Furthermore, the electricity market transaction constraints in step S2 are expressed as follows: In the formula, , These represent the maximum and current power purchase capacity under the day-ahead contracts between the distribution network and the electricity market, respectively. , These represent the real-time maximum power purchase and maximum power sale of the distribution network in the electricity market, respectively. express The state variable for the distribution network to purchase electricity from the electricity market in real time during the dispatch period: a value of 1 indicates that the distribution network purchases electricity from the electricity market in real time, and a value of 0 indicates that the distribution network does not purchase electricity from the electricity market in real time. express The state variable for the distribution network to sell electricity to the electricity market in real time during the dispatch period is 1, which means that the distribution network sells electricity to the electricity market in real time, and 0 means that the distribution network does not sell electricity to the electricity market in real time.

[0018] Furthermore, in step S3, an elite retention strategy is adopted to store the top 10% of the best individuals in the population based on their fitness values. For the remaining individuals, a roulette wheel selection method is used for selection, specifically including the following steps: ① The probability of a selected individual is: In the formula, Indicates the number of chromosomes in the population. Represents the individual fitness value; ② Randomly generates one A random number, if Then it means the first Each individual is selected, and the process is repeated sequentially. Next, obtain containing A population of individuals.

[0019] Furthermore, in step S3, the population is decomposed, and crossover and mutation operators with fixed and adaptive probabilities are used respectively, and the population generated in each generation is interacted; wherein, the adaptive crossover probability formula of the adaptive crossover operator is expressed as: In the formula: and Let represent the maximum and minimum crossover probabilities, respectively. Indicates adaptive parameters, and Let these represent the current and maximum iteration counts, respectively; the adaptive mutation probability formula of the adaptive mutation operator is expressed as: In the formula, and Let each represent the maximum and minimum mutation probabilities. Indicates adaptive parameters, and These represent the current and maximum number of iterations, respectively.

[0020] Furthermore, the local search algorithm in step S3 specifically employs a hill-climbing method to perform a fine search of the neighborhood of the optimal solution, which includes the following steps: ① Given a neighborhood containing Chromosomes of each node ② Set the local search probability ③ Randomly select a node Based on local search probability Randomly reinserted chromosomes In the process, new chromosomes were obtained. ④ Use evaluation functions to evaluate and ,like Then output New chromosome; otherwise, return to step ③.

[0021] Furthermore, in step S4, the weights of each objective are determined using the Analytic Hierarchy Process (AHP), specifically an improved AHP incorporating a three-level scaling method, which includes the following steps: ① Using the three-level scaling method Construct a judgment matrix , where the matrix medium elements The meaning is as follows: ② The importance of each objective is determined based on expert opinions, and a judgment matrix is ​​then obtained. ③ Determine the transfer matrix When the matrix satisfy At that time, matrix It is an antisymmetric matrix and has transitivity, therefore it is a transitive matrix; ④ Introducing the transitive matrix Optimal matrix ; where, when the transfer matrix With matrix satisfy: At that time, matrix For matrix The optimal matrix; ⑤ Introducing the matrix Consistency matrix ; where, when the matrix For matrix The optimal matrix, and satisfying At that time, matrix It is a matrix Consistency matrix , ⑥ Obtain the matrix The largest eigenvector is obtained and normalized to obtain the weights of each target.

[0022] Furthermore, the optimal solution selection using the TOPSIS method in step S5 specifically includes the following steps: ① Constructing a sample matrix And perform normalization processing. ②Establish a weighted sample matrix ,in Indicate the target weight; ③ Determine the positive and negative ideal solutions, where the positive ideal solution is represented as... The negative ideal solution is represented as ④ Calculate the Euclidean distance between each sample and the positive and negative ideal solutions, expressed as: , ⑤ Calculate the closeness of each sample to the ideal solution. .

[0023] Due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0024] (1) By constructing multi-objective functions and constraints, and using the improved non-dominated sorting genetic algorithm III to solve multi-objective optimization, an elite retention strategy is adopted to retain high-quality individuals to directly enter the next generation, thereby improving the quality of the population. Adaptive crossover and mutation operators are introduced, and the operator parameters are dynamically adjusted according to the degree of population evolution to accelerate the convergence speed. The hill climbing method of the local search algorithm is combined to perform a fine search of the neighborhood of the optimal solution, thereby improving the accuracy of the solution and effectively improving the solution efficiency of multi-subject, multi-objective, and multi-constraint problems.

[0025] (2) By using the analytic hierarchy process combined with the TOPSIS method, the distance between all solutions in the Pareto solution set and their positive and negative ideal solutions is calculated based on the target weight. The positive and negative ideal values ​​are compared according to the sorting rules to determine the optimal solution in the Pareto solution set output in step S3. This optimal solution is then used as the optimal scheduling scheme to achieve rapid acquisition of the Pareto optimal solution and improve the optimal scheduling scheme in a comprehensive and accurate manner. Attached Figure Description

[0026] Figure 1 This is a flowchart of the present invention.

[0027] Figure 2 This is a flowchart of step S3 in this invention. Detailed Implementation

[0028] The technical solution of the present invention will be further described in detail below through embodiments and in conjunction with the accompanying drawings.

[0029] like Figure 1 , Figure 2 As shown, a novel multi-subject, multi-objective scheduling optimization analysis method for power distribution systems includes the following optimization analysis steps:

[0030] S1. Construct a multi-objective function for the power distribution system optimization scheduling model, including economic objectives, safety objectives, and environmental objectives.

[0031] Preferably, the economic objective in step S1 is to minimize the total operating cost of the system, specifically expressed as: In the formula, This indicates the cost of purchasing electricity from the upper-level power grid. This represents the cost of load shedding. This indicates the production cost of the power plant. This represents the depreciation cost of an energy storage power station. This indicates the cost of network loss.

[0032] Among them, the cost of purchasing electricity from the upper-level power grid , , These represent the busbars respectively. The unit price of electricity purchased from the superior power grid and the active power exchanged. Represents the set of boundary buses; load shedding cost , This indicates the unit price for load outages in the distribution network. Indicates the decrease in active power load; power plant production costs , , , They represent generator sets respectively. Production cost constant coefficient Indicates generator Those who have made contributions Represents a set of controllable power generation units; depreciation cost of energy storage power stations. , It represents a collection of energy storage devices. This represents a constant coefficient indicating the degradation cost of energy storage batteries. Indicates the charging power of the energy storage battery. Indicates the discharge power of the energy storage battery; network loss cost. , , They represent Active power loss cost per unit time and line active power loss.

[0033] Preferably, the safety objective in step S1 is to minimize the voltage deviation and the risk of power flow exceeding limits, specifically expressed as follows: ;in, Indicates the degree of voltage deviation, where, Indicates the number of system nodes. Represents a node Voltage; This indicates the risk of branch current exceeding the limit, where, Indicates a branch The trend value that transcends limits, Representing branch roads The probability density function and severity function of power flow exceeding limits; whereby the severity function refers to the degree of severity of power flow exceeding limits caused by distributed generation access to the distribution network, and adopts an offset combined with a utility theory preference-type function as the severity function, expressed as: , In the formula, This indicates the active power offset. Indicates a branch The baseline power flow value.

[0034] Preferably, the environmental protection objective in step S1 is to minimize carbon emission costs, specifically expressed as follows: In the formula, Indicates the carbon emission cost coefficient. Indicates the boundary busbar. This represents a collection of microgrids in a distribution network. Indicates the marginal emission factor. This indicates the active power output of the unit.

[0035] S2. Construct the objective function constraints, including equality constraints and inequality constraints. The equality constraints include power balance constraints, and the inequality constraints include equipment capacity constraints, node voltage constraints, and electricity market transaction constraints.

[0036] Preferably, the power balance constraint in step S2 is expressed as: In the formula, , Representing nodes respectively The injected active and reactive power, , These represent the active and reactive power outputs of the distributed power source, respectively. , These represent active and reactive loads, respectively. , Representing nodes respectively , voltage amplitude, , , Representing branch conductance, susceptance, and node, respectively. , The difference in phase angle between voltages, Indicates the number of branches.

[0037] Preferably, the equipment capacity constraint in step S2 includes upper and lower limit constraints on generator output and line transmission capacity constraints, wherein the upper and lower limit constraints on generator output are expressed as follows: In the formula, , These respectively represent the generator at The effort exerted and the effort exerted at any given moment, , These represent the maximum and minimum active power output of the generator, respectively. , These represent the maximum and minimum reactive power output of the generator, respectively; the line transmission capacity constraint is expressed as: In the formula, , They represent the lines respectively. Maximum active and reactive power transmission capacity.

[0038] Preferably, the system node voltage constraint in step S2 is expressed as: In the formula, , Representing nodes respectively The maximum and minimum values ​​of voltage amplitude.

[0039] Preferably, the electricity market transaction constraints in step S2 are expressed as follows: In the formula, , These represent the maximum and current power purchase capacity under the day-ahead contracts between the distribution network and the electricity market, respectively. , These represent the real-time maximum power purchase and maximum power sale of the distribution network in the electricity market, respectively. express The state variable for the distribution network to purchase electricity from the electricity market in real time during the dispatch period: a value of 1 indicates that the distribution network purchases electricity from the electricity market in real time, and a value of 0 indicates that the distribution network does not purchase electricity from the electricity market in real time. express The state variable for the distribution network to sell electricity to the electricity market in real time during the dispatch period is 1, which means that the distribution network sells electricity to the electricity market in real time, and 0 means that the distribution network does not sell electricity to the electricity market in real time.

[0040] Preferably, in step S2, a penalty function is used to handle inequality constraints, transforming the constraints into penalty terms of the objective function, and the constraint satisfaction and objective optimization are balanced by dynamically adjusting the penalty factor; the Lagrange multiplier method is used to handle equality constraints, integrating them into the objective function to reduce the dimensionality of optimization variables.

[0041] S3. A multi-objective optimization algorithm is used for optimization. An improved non-dominated sorting genetic algorithm III (NSGA-Ⅲ) is used for optimization. An elite retention strategy is adopted to retain high-quality individuals directly into the next generation (to improve the quality of the population). At the same time, adaptive crossover and mutation operators are introduced to dynamically adjust the operator parameters according to the degree of population evolution, thereby accelerating the convergence speed. The hill-climbing local search algorithm is combined to perform a fine search of the neighborhood of the optimal solution to obtain the Pareto solution set.

[0042] Preferably, step S3 employs an elite retention strategy, storing the top 10% of the population's best individuals based on fitness values. For the remaining population, a roulette wheel selection method is used for selection, specifically including the following steps: ① The probability of a selected individual is: In the formula, Indicates the number of chromosomes in the population. Represents the individual fitness value; ② Randomly generates one A random number, if Then it means the first Each individual is selected, and the process is repeated sequentially. Next, obtain containing A population of individuals.

[0043] Preferably, in step S3, the population is decomposed, and crossover and mutation operators with fixed and adaptive probabilities are used respectively, and the population generated in each generation is interacted; wherein, the adaptive crossover probability formula of the adaptive crossover operator is expressed as: In the formula: and Let represent the maximum and minimum crossover probabilities, respectively. Indicates adaptive parameters, and Let these represent the current and maximum iteration counts, respectively; the adaptive mutation probability formula of the adaptive mutation operator is expressed as: In the formula, and Let each represent the maximum and minimum mutation probabilities. Indicates adaptive parameters, and These represent the current and maximum number of iterations, respectively.

[0044] Preferably, the local search algorithm in step S3 specifically employs a hill-climbing method to perform a fine search of the neighborhood of the optimal solution, which specifically includes the following steps: ① Given a neighborhood containing Chromosomes of each node ② Set the local search probability ③ Randomly select a node Based on local search probability Randomly reinserted chromosomes In the process, new chromosomes were obtained. ④ Use evaluation functions to evaluate and ,like Then output New chromosome; otherwise, return to step ③.

[0045] S4. Use the analytic hierarchy process (AHP) combined with real-time data to construct a weight calculation model and determine the weight of each objective.

[0046] Preferably, in step S4, the weights of each objective are determined using the Analytic Hierarchy Process (AHP), specifically an improved AHP with a three-level scaling method, which includes the following steps: ① Using the three-level scaling method Construct a judgment matrix , where the matrix medium elements The meaning is as follows: ② The importance of each objective is determined based on expert opinions, and a judgment matrix is ​​then obtained. ③ Determine the transfer matrix When the matrix satisfy At that time, matrix It is an antisymmetric matrix and has transitivity, therefore it is a transitive matrix; ④ Introducing the transitive matrix Optimal matrix ; where, when the transfer matrix With matrix satisfy: At that time, matrix For matrix The optimal matrix; ⑤ Introducing the matrix Consistency matrix ; where, when the matrix For matrix The optimal matrix, and satisfying At that time, matrix It is a matrix Consistency matrix , ⑥ Obtain the matrix The largest eigenvector is obtained and normalized to obtain the weights of each target.

[0047] S5. Based on the target weight, the TOPSIS method is used to calculate the distance between all solutions in the Pareto solution set output in step S3 and their positive and negative ideal solutions. The positive and negative ideal values ​​are compared according to the sorting rules to find the solution that is closest to the positive ideal value and far away from the negative ideal value. The optimal solution in the Pareto solution set output in step S3 is determined and used as the optimal scheduling scheme.

[0048] Preferably, step S5, which uses the TOPSIS method to select the optimal solution, specifically includes the following steps: ① Constructing a sample matrix And perform normalization processing. ②Establish a weighted sample matrix ,in Indicate the target weight; ③ Determine the positive and negative ideal solutions, where the positive ideal solution is represented as... The negative ideal solution is represented as ④ Calculate the Euclidean distance between each sample and the positive and negative ideal solutions, expressed as: , ⑤ Calculate the closeness of each sample to the ideal solution. Sort the results, and the higher the proximity value, the better the solution.

[0049] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of the claims of the present invention.

Claims

1. A novel multi-subject, multi-objective scheduling optimization analysis method for power distribution systems, characterized in that: The following optimization analysis steps are included: S1. Construct a multi-objective function for the power distribution system optimization scheduling model, including economic objectives, safety objectives, and environmental objectives; S2. Construct the objective function constraints, including equality constraints and inequality constraints. Among them, equality constraints include power balance constraints, and inequality constraints include equipment capacity constraints, node voltage constraints, and electricity market transaction constraints. S3. An improved non-dominated sorting genetic algorithm III is used for optimization. Through the elite retention strategy, high-quality individuals are retained and directly enter the next generation. At the same time, adaptive crossover and mutation operators are introduced to dynamically adjust the operator parameters according to the population evolution level, which accelerates the convergence speed. The local search algorithm hill climbing method is combined to perform a fine search of the neighborhood of the optimal solution to obtain the Pareto solution set. S4. Use the analytic hierarchy process (AHP) combined with real-time data to construct a weight calculation model and determine the weight of each objective. S5. Based on the target weight, the TOPSIS method is used to calculate the distance between all solutions in the Pareto solution set output in step S3 and their positive and negative ideal solutions. The positive and negative ideal values ​​are compared according to the sorting rules to determine the optimal solution in the Pareto solution set output in step S3, which is then used as the optimal scheduling scheme.

2. The novel multi-subject, multi-objective scheduling optimization analysis method for power distribution systems according to claim 1, characterized in that: The economic objective in step S1 is to minimize the total operating cost of the system, specifically expressed as follows: In the formula, This indicates the cost of purchasing electricity from the upper-level power grid. This represents the cost of load shedding. This indicates the production cost of the power plant. This represents the depreciation cost of an energy storage power station. This represents the network loss cost; the safety objective in step S1 is to minimize the voltage deviation and the risk of power flow exceeding limits, specifically expressed as: ;in, Indicates the degree of voltage deviation, where, Indicates the number of system nodes. Represents a node Voltage; This indicates the risk of branch current exceeding the limit, where, Indicates a branch The trend value that transcends limits, Representing branch roads The probability density function and severity function of power flow exceeding limits; whereby the severity function refers to the degree of severity of power flow exceeding limits caused by distributed generation access to the distribution network, and adopts an offset combined with a utility theory preference-type function as the severity function, expressed as: , In the formula, This indicates the active power offset. Indicates a branch The baseline current value; the environmental protection objective in step S1 is to minimize carbon emission costs, specifically expressed as: In the formula, Indicates the carbon emission cost coefficient. Indicates the boundary busbar. This represents a collection of microgrids in a distribution network. Indicates the marginal emission factor. This indicates the active power output of the unit.

3. The novel multi-subject, multi-objective scheduling optimization analysis method for power distribution systems according to claim 1, characterized in that: The power balance constraint in step S2 is expressed as follows: In the formula, , Representing nodes respectively The injected active and reactive power, , These represent the active and reactive power outputs of the distributed power source, respectively. , These represent active and reactive loads, respectively. , Representing nodes respectively , voltage amplitude, , , Representing branch conductance, susceptance, and node, respectively. , The difference in phase angle between voltages, Indicates the number of branches.

4. The novel multi-subject, multi-objective scheduling optimization analysis method for power distribution systems according to claim 1, characterized in that: The equipment capacity constraints in step S2 include upper and lower limits of generator output and line transmission capacity constraints, wherein the upper and lower limits of generator output are expressed as follows: In the formula, , These respectively represent the generator at The effort exerted and the effort exerted at any given moment, , These represent the maximum and minimum active power output of the generator, respectively. , These represent the maximum and minimum reactive power output of the generator, respectively; the line transmission capacity constraint is expressed as: In the formula, , They represent the lines respectively. The maximum active and reactive power transmission capacity; the system node voltage constraint in step S2 is expressed as: In the formula, , Representing nodes respectively The maximum and minimum values ​​of voltage amplitude; the electricity market transaction constraints in step S2 are expressed as: In the formula, , These represent the maximum and current power purchase capacity under the day-ahead contracts between the distribution network and the electricity market, respectively. , These represent the real-time maximum power purchase and maximum power sale of the distribution network in the electricity market, respectively. express The state variable for the distribution network to purchase electricity from the electricity market in real time during the dispatch period: a value of 1 indicates that the distribution network purchases electricity from the electricity market in real time, and a value of 0 indicates that the distribution network does not purchase electricity from the electricity market in real time. express The state variable for the distribution network to sell electricity to the electricity market in real time during the dispatch period is 1, which means that the distribution network sells electricity to the electricity market in real time, and 0 means that the distribution network does not sell electricity to the electricity market in real time.

5. The novel multi-subject, multi-objective scheduling optimization analysis method for power distribution systems according to claim 1, characterized in that: In step S3, an elite retention strategy is adopted, storing the top 10% of the best individuals in the population based on their fitness values. For the remaining individuals, a roulette wheel selection method is used, specifically including the following steps: ① The probability of a selected individual is: In the formula, Indicates the number of chromosomes in the population. Represents the individual fitness value; ② Randomly generates one A random number, if Then it means the first Each individual is selected, and the process is repeated sequentially. Next, obtain containing A population of individuals.

6. The novel multi-subject, multi-objective scheduling optimization analysis method for power distribution systems according to claim 1, characterized in that: In step S3, the population is decomposed, and crossover and mutation operators with fixed and adaptive probabilities are used respectively, and the population generated in each generation is interacted; wherein, the adaptive crossover probability formula of the adaptive crossover operator is expressed as: In the formula: and Let represent the maximum and minimum crossover probabilities, respectively. Indicates adaptive parameters, and Let these represent the current and maximum iteration counts, respectively; the adaptive mutation probability formula of the adaptive mutation operator is expressed as: In the formula, and Let each represent the maximum and minimum mutation probabilities. Indicates adaptive parameters, and These represent the current and maximum number of iterations, respectively.

7. The novel multi-subject, multi-objective scheduling optimization analysis method for power distribution systems according to claim 1, characterized in that: The local search algorithm in step S3 specifically employs a hill-climbing method to perform a fine-grained search of the neighborhood of the optimal solution, including the following steps: ① Given a neighborhood containing Chromosomes of each node ② Set the local search probability ③ Randomly select a node Based on local search probability Randomly reinserted chromosomes In the process, new chromosomes were obtained. ④ Use evaluation functions to evaluate and ,like Then output New chromosome; otherwise, return to step ③.

8. The novel multi-subject, multi-objective scheduling optimization analysis method for power distribution systems according to claim 1, characterized in that: In step S4, the weights of each objective are determined using the Analytic Hierarchy Process (AHP). Specifically, an improved AHP with a three-level scaling method is adopted, which includes the following steps: ① Using the three-level scaling method Construct a judgment matrix , where the matrix Middle elements The meaning is as follows: ② The importance of each objective is determined based on expert opinions, and a judgment matrix is ​​then obtained. ③ Determine the transfer matrix When the matrix satisfy At that time, matrix It is an antisymmetric matrix and has transitivity, therefore it is a transitive matrix; ④ Introducing the transitive matrix Optimal matrix ; where, when the transfer matrix With matrix satisfy: At that time, matrix For matrix The optimal matrix; ⑤ Introducing the matrix Consistency matrix ; where, when the matrix For matrix The optimal matrix, and satisfying At that time, matrix It is a matrix Consistency matrix , ⑥ Obtain the matrix The largest eigenvector is obtained and normalized to obtain the weights of each target.

9. A novel multi-subject, multi-objective scheduling optimization analysis method for power distribution systems according to claim 1, characterized in that: Step S5, which uses the TOPSIS method to select the optimal solution, specifically includes the following steps: ① Constructing a sample matrix And perform normalization processing. ②Establish a weighted sample matrix ,in Indicate the target weight; ③ Determine the positive and negative ideal solutions, where the positive ideal solution is represented as... The negative ideal solution is represented as ④ Calculate the Euclidean distance between each sample and the positive and negative ideal solutions, expressed as: , ⑤ Calculate the closeness of each sample to the ideal solution. .

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