Market power prevention and control supervision mechanism parameter optimization method and system for electricity market

Abstract

The invention discloses a market power prevention and control supervision mechanism parameter optimization method for a power market. The method comprises the steps of obtaining data information of a target power system and a target power market; predicting typical system marginal data of the power market in the next period; setting a market power mitigation simulation scene and determining simulation parameters to be optimized; setting parameters of a genetic algorithm; spot simulation and market power supervision are carried out on the set market power relieving simulation scene according to parameters in each individual in the population, and the corresponding unit excess income is calculated; and performing iterative calculation according to a simulation result to obtain a final optimized simulation parameter, and completing parameter optimization of the market power prevention and control supervision mechanism for the electricity market. The invention also discloses a system for realizing the market power prevention and control supervision mechanism parameter optimization method for the electricity market. According to the method, the market power relieving model is constructed, and the heritage algorithm is adopted to optimize the market power prevention and control supervision mechanism parameters, so that the reliability is higher, and the accuracy is better.

Description

Technical Field

[0001] The present invention belongs to the field of electrical automation, and specifically relates to a method and system for optimizing parameters of a market power prevention and control regulatory mechanism for an electric power market. Background Art

[0002] With the development of economy and technology and the improvement of people's living standards, electricity has become an indispensable secondary energy source in people's production and life, bringing endless convenience to people's production and life. Therefore, ensuring the stable and reliable supply of electricity has become one of the most important tasks of the power system.

[0003] At present, the power market in the power system has developed rapidly, and it has also greatly promoted the stable, reliable and efficient operation of the power system. However, at this stage, the development of the power market is not perfect. In today's power market, if there is collusion among power generation companies, multiple companies may be able to influence market prices through coordinated actions, thereby forming market power. Therefore, it is of great significance to prevent and control the market power in the power market.

[0004] At present, market power prevention and control and supervision solutions often adopt the solution of manually setting the parameters of the supervision mechanism. However, this solution is obviously time-consuming and labor-intensive, and has poor reliability and accuracy. Summary of the invention

[0005] One of the purposes of the present invention is to provide a method for optimizing parameters of a market power prevention and control regulatory mechanism for an electricity market with high reliability and good accuracy.

[0006] A second objective of the present invention is to provide a system for implementing the method for optimizing parameters of the market power prevention and control regulatory mechanism for the electricity market.

[0007] The method for optimizing parameters of the market power prevention and control supervision mechanism for the power market provided by the present invention comprises the following steps:

[0008] S1. Obtain data information of the target power system and target power market;

[0009] S2. Based on the data information obtained in step S1, predict the typical system marginal data of the next cycle of the power market;

[0010] S3. According to the data obtained in step S2, set a market power mitigation simulation scenario and determine the simulation parameters to be optimized;

[0011] S4. According to the simulation parameters determined in step S3, set the parameters of the genetic algorithm;

[0012] S5. Based on the parameter values ​​set in step S4, spot simulation and market power supervision are performed on the set market power mitigation simulation scenario according to the parameters of each individual in the population, and the corresponding excess revenue of the unit is calculated;

[0013] S6. Perform iterative calculation of step S5 according to the simulation result obtained in step S5 to obtain the final optimized simulation parameters, thereby completing the parameter optimization of the market power prevention and control regulatory mechanism for the electricity market.

[0014] Step S2 predicts typical system marginal data of the next cycle of the power market based on the data information obtained in step S1, and specifically includes the following steps:

[0015] According to the data information obtained in step S1, predict the typical system marginal data of the next cycle of the power market;

[0016] The marginal data include: the load data D of each node m at each time t m,t , each fixed unit i f Output data at each time t and unit quotation (C i,t,k ,price i,t,k );C i,t,k is the electricity reported by unit i in the kth period of time period t, price i,t,k Declare the electricity price for unit i in the kth segment of time period t.

[0017] Step S3, according to the data obtained in step S2, sets a market power mitigation simulation scenario and determines the simulation parameters to be optimized, which specifically includes the following steps:

[0018] A. Establishment of a basic model for provincial power spot market simulation:

[0019] According to the actual situation of the power system, a generator set operation model, a node load model and a section flow model are established, and finally a spot market clearing model is established with SCUC+SCED+LMP as the optimization algorithm and gurobi as the optimization tool; the optimization goal of the spot market clearing model is to minimize the system power generation cost, and the constraints of the spot market clearing model include system load balance constraints, unit climbing constraints, unit start-stop constraints, positive and negative reserve constraints of the power system, rotating reserve constraints and section flow constraints; the marginal data of the spot market clearing model is the marginal data obtained in step S2; the generator set includes thermal power units, hydropower units, wind power units, photovoltaic units and energy storage units;

[0020] B. Based on the established provincial power spot market simulation basic model, consider the market power mitigation solution:

[0021] Assessing the market oligopoly structure:

[0022] The HHI value of the overall structure on the power generation side is calculated using the following formula:

[0023]

[0024] Where N is the total number of power generation groups; S j is the market share of the jth power generation group;

[0025] If HHI>1800, the market is judged to be highly oligopolistic;

[0026] If HHI≤1800, the market share of power generation groups is ranked from large to small, and the total market share of the top four power generation groups is calculated and judged: if the total market share is greater than 65%, the market is judged to be a high oligopoly structure; if the total market share is less than or equal to 65%, the market is judged to be a non-high oligopoly structure; the judgment of the market oligopoly structure type is mainly used to preliminarily judge the potential size of the use of market power;

[0027] The residual supply index RSI is used to evaluate the market power potential of each generator: if the residual supply index RSI of the jth generator exceeds the set critical value ρ RSI , then the j-th generator is relieved: the generation capacity of the j-th generator is divided into critical capacity and regulated capacity, and the declared capacity of the j-th generator is guaranteed to be no less than the critical capacity; the calculation formula of the residual supply index RSI of the j-th generator is Where S total is the total declared generating capacity of the system, S j is the declared capacity of the jth generator, Load p is the load forecast value;

[0028] The behavior and impact test system is used to evaluate the bidding behavior of power producers: First, the behavior test is performed to determine whether the unit's bid exceeds the RefPrice i , and then conduct an impact test to determine whether the electricity price after clearing exceeds RefPrice i ; Acceptance relief for power generators that fail the assessment: Replace the unit quotations corresponding to the power generators that fail the assessment with the unit reference price RefPrice i RefPrice i The value of is 1.1 times the sum of variable cost and operation and maintenance cost; finally, the power generation plan of the market entity on that day is formed and used as the execution basis of market settlement;

[0029] Set the market clearing price threshold P that triggers the ex post regulatory mechanism REF; After the market is cleared on the previous day, the triggering conditions of the regulatory procedures on that day are judged: if the average price of the nodes cleared for the whole day is higher than P REF If the trigger multiple is greater than , the supervision procedure for the day will be initiated;

[0030] If the daily regulatory procedure is triggered, the price impact contribution calculation will be carried out for all power generators: First, all unit quotations are replaced with the unit reference price RefPrice i , re-clear the market and calculate the average electricity price of the replaced time-sharing node Then, compare the actual average electricity price P of the time-sharing nodes on that day t AVE , calculate the price impact contribution rate ω of the jth power generation group j for The total excess revenue ΔR on the power generation side is calculated as And calculate the compensation amount ΔR of the jth power generation group j ΔR j =μ×ΔR×ω j ; Finally, compensation is made according to the proportion of electricity traded by each market-oriented user; among them, G t is the settlement quantity of the electricity market in period t, μ is the penalty multiple;

[0031] C. Determine the simulation parameters to be optimized:

[0032] Select the critical value ρ RSI , trigger multiple γ, penalty multiple μ and market clearing price threshold P REF , as the simulation parameter to be optimized.

[0033] The step S4 of setting the parameters of the genetic algorithm according to the simulation parameters determined in step S3 specifically includes the following steps:

[0034] Set the upper limit of population iterations and the number of individuals in the population;

[0035] Set the variable range of each optimization parameter;

[0036] Within the set variable range, several groups of optimization parameters are randomly generated as the optimization parameters for each individual in the initialization population.

[0037] Step S5, based on the parameter values ​​set in step S4, performs spot simulation and market power supervision on the set market power mitigation simulation scenario according to the parameters in each individual in the population, and calculates the corresponding unit excess profit, which specifically includes the following steps:

[0038] For each individual in the current population, establish a market power mitigation simulation scenario according to the content of step S3;

[0039] According to the optimization parameters of each individual, set the parameter data of the corresponding market power mitigation simulation scenario;

[0040] Market power mitigation simulation is carried out to obtain the excess profit of the unit under the corresponding optimization parameters.

[0041] Step S6, performing the iterative calculation of step S5 according to the simulation result obtained in step S5 to obtain the final optimized simulation parameters, specifically includes the following steps:

[0042] Determine the number of iterations of step S5:

[0043] If the number of iterations reaches the set value, the parameters of the individual corresponding to the minimum excess profit are selected as the final optimized simulation parameters; the simulation parameters correspond to the optimization results of the parameters of the market power prevention and control regulatory mechanism for the power market;

[0044] If the number of falls and the number of times do not reach the set value, the following genetic operations will be performed:

[0045] Dynamically adjust the number of next-generation populations according to the optimization effect of each iteration: if the optimization effect of this iteration is greater than Y%, keep the population size unchanged; if the optimization effect of this iteration is X% to Y%, set the number of next-generation populations to T1 times the number of this population; if the optimization effect of this iteration is less than Y%, set the number of next-generation populations to T2 times the number of this population; where Y>X, T2>T1;

[0046] Finally, half of the individuals with smaller excess returns are selected and retained to the next generation; genetic operations are performed through the set crossover probability and mutation probability to generate the remaining half of the individuals; finally, the updated population is used as the current population, and the next round of iterative calculation of step S5 is performed.

[0047] The present invention also provides a system for implementing the method for optimizing parameters of the market power prevention and control regulatory mechanism for the power market, including a data acquisition module, a data prediction module, a model building module, a parameter setting module, a genetic optimization module and a parameter optimization module; the data acquisition module, the data prediction module, the model building module, the parameter setting module, the genetic optimization module and the parameter optimization module are connected in series in sequence; the data acquisition module is used to acquire data information of the target power system and the target power market, and upload the data information to the data prediction module; the data prediction module is used to predict the typical system marginal data of the next cycle of the power market according to the received data information and the acquired data information, and upload the data information to the model building module; the model building module is used to set the system marginal data according to the received data information and the acquired data A market power mitigation simulation scenario is set, and simulation parameters to be optimized are determined, and the data information is uploaded to the parameter setting module; the parameter setting module is used to set the parameters of the genetic algorithm according to the received data information and the determined simulation parameters, and upload the data information to the genetic optimization module; the genetic optimization module is used to perform spot simulation and market power supervision on the set market power mitigation simulation scenario according to the parameters of each individual in the population based on the received data information and the set parameter values, and calculate the corresponding unit excess profit, and upload the data information to the parameter optimization module; the parameter optimization module is used to perform iterative calculations of the genetic optimization module according to the received data information and the obtained simulation results, to obtain the final optimized simulation parameters, and complete the parameter optimization of the market power prevention and control supervision mechanism for the power market.

[0048] The method and system for optimizing parameters of the market power prevention and control regulatory mechanism for the power market provided by the present invention construct a corresponding market power mitigation model based on the acquired power market data, and use a legacy algorithm to optimize the parameters of the market power prevention and control regulatory mechanism for the power market. Therefore, the present invention avoids the drawbacks brought about by artificial parameter setting, and has higher reliability and better accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 The figure is a schematic diagram of the method flow of the present invention.

[0050] Figure 2 A scatter diagram of excess returns calculated according to an embodiment of the method of the present invention.

[0051] Figure 3 Schematic diagram of the functional modules of the system of the present invention. DETAILED DESCRIPTION

[0052] like Figure 1 The method flow diagram of the method of the present invention is shown as follows: The method for optimizing parameters of the market power prevention and control supervision mechanism for the power market disclosed in the present invention comprises the following steps:

[0053] S1. Obtain data information of the target power system and target power market;

[0054] S2. According to the data information obtained in step S1, predict the typical system marginal data of the next cycle of the power market; specifically, the steps include:

[0055] According to the data information obtained in step S1, predict the typical system marginal data of the next cycle of the power market;

[0056] The marginal data include: the load data D of each node m at each time t m,t , each fixed unit i f Output data at each time t and unit quotation (C i,t,k ,price i,t,k );C i,t,k is the electricity reported by unit i in the kth period of time period t, price i,t,k Declare the electricity price for unit i in the kth segment of time period t;

[0057] S3. According to the data obtained in step S2, a market power mitigation simulation scenario is set and simulation parameters to be optimized are determined; specifically, the following steps are included:

[0058] A. Establishment of a basic model for provincial power spot market simulation:

[0059] According to the actual situation of the power system, a generator set operation model, a node load model and a section flow model are established, and finally a spot market clearing model is established with SCUC+SCED+LMP as the optimization algorithm and gurobi as the optimization tool; the optimization goal of the spot market clearing model is to minimize the system power generation cost, and the constraints of the spot market clearing model include system load balance constraints, unit climbing constraints, unit start-stop constraints, positive and negative reserve constraints of the power system, rotating reserve constraints and section flow constraints; the marginal data of the spot market clearing model is the marginal data obtained in step S2; the generator set includes thermal power units, hydropower units, wind power units, photovoltaic units and energy storage units;

[0060] B. Based on the established provincial power spot market simulation basic model, consider the market power mitigation solution:

[0061] Assessing the market oligopoly structure:

[0062] The HHI value of the overall structure on the power generation side is calculated using the following formula:

[0063]

[0064] Where N is the total number of power generation groups; S jis the market share of the jth power generation group;

[0065] If HHI>1800, the market is judged to be highly oligopolistic;

[0066] If HHI≤1800, the market share of power generation groups is ranked from large to small, and the total market share of the top four power generation groups is calculated and judged: if the total market share is greater than 65%, the market is judged to be a high oligopoly structure; if the total market share is less than or equal to 65%, the market is judged to be a non-high oligopoly structure; the judgment of the market oligopoly structure type is mainly used to preliminarily judge the potential size of the use of market power;

[0067] The residual supply index RSI is used to evaluate the market power potential of each generator: if the residual supply index RSI of the jth generator exceeds the set critical value ρ RSI , then the j-th generator is relieved: the generation capacity of the j-th generator is divided into critical capacity and regulated capacity, and the declared capacity of the j-th generator is guaranteed to be no less than the critical capacity; the calculation formula of the residual supply index RSI of the j-th generator is Where S total is the total declared generating capacity of the system, S j is the declared capacity of the jth generator, Load p is the load forecast value; the smaller the RSI, the greater the consequences of economic retention by the generator and the greater the potential for abuse of market power; the critical capacity is set to ensure that the RSI value exceeds the critical value ρ RSI The minimum declared quantity of power generators;

[0068] The behavior and impact test system is used to evaluate the bidding behavior of power generators. The bidding behavior of power generators is evaluated by first conducting a behavior test to determine whether the unit's bid exceeds the RefPrice i , and then conduct an impact test to determine whether the electricity price after clearing exceeds RefPrice i ; Acceptance relief for power generators that fail the assessment: Replace the unit quotations corresponding to the power generators that fail the assessment with the unit reference price RefPrice i RefPrice i The value of is 1.1 times the sum of variable cost and operation and maintenance cost; finally, the power generation plan of the market entity on that day is formed and used as the execution basis of market settlement;

[0069] Set the market clearing price threshold P that triggers the ex post regulatory mechanism REF ; After the market is cleared on the previous day, the triggering conditions of the regulatory procedures on that day are judged: if the average price of the nodes cleared for the whole day is higher than P REFIf the trigger multiple is greater than , the supervision procedure for the day will be initiated;

[0070] If the daily regulatory procedure is triggered, the price impact contribution calculation will be carried out for all power generators: First, all unit quotations are replaced with the unit reference price RefPrice i , re-clear the market and calculate the average electricity price of the replaced time-sharing node Then, compare the actual average electricity price P of the time-sharing nodes on that day t AVE , calculate the price impact contribution rate ω of the jth power generation group j for The total excess revenue ΔR on the power generation side is calculated as And calculate the compensation amount ΔR of the jth power generation group j ΔR j =μ×ΔR×ω j ; Finally, compensation is made according to the proportion of electricity traded by each market-oriented user; among them, G t is the settlement quantity of the electricity market in period t, μ is the penalty multiple;

[0071] C. Determine the simulation parameters to be optimized:

[0072] Select the critical value ρ RSI , trigger multiple γ, penalty multiple μ and market clearing price threshold P REF , as the simulation parameter to be optimized;

[0073] S4. According to the simulation parameters determined in step S3, the parameters of the genetic algorithm are set; specifically comprising the following steps:

[0074] Set the upper limit of population iterations and the number of individuals in the population;

[0075] Set the variable range of each optimization parameter;

[0076] Randomly generate several groups of optimization parameters within the set variable range as the optimization parameters of each individual in the initialization population;

[0077] S5. Based on the parameter values ​​set in step S4, spot simulation and market power supervision are performed on the set market power mitigation simulation scenario according to the parameters of each individual in the population, and the corresponding excess profit of the unit is calculated; specifically, the steps include:

[0078] For each individual in the current population, establish a market power mitigation simulation scenario according to the content of step S3;

[0079] According to the optimization parameters of each individual, set the parameter data of the corresponding market power mitigation simulation scenario;

[0080] Conduct market power mitigation simulation to obtain the excess revenue of the unit under the corresponding optimization parameters;

[0081] S6. Perform iterative calculation of step S5 according to the simulation result obtained in step S5 to obtain the final optimized simulation parameters, and complete the parameter optimization of the market power prevention and control supervision mechanism for the power market; specifically comprising the following steps:

[0082] Determine the number of iterations of step S5:

[0083] If the number of iterations reaches the set value, the parameters of the individual corresponding to the minimum excess profit are selected as the final optimized simulation parameters; the simulation parameters correspond to the optimization results of the parameters of the market power prevention and control regulatory mechanism for the power market;

[0084] If the number of falls and the number of times do not reach the set value, the following genetic operations will be performed:

[0085] Dynamically adjust the number of next-generation populations according to the optimization effect of each iteration: if the optimization effect of this iteration is greater than Y%, keep the population size unchanged; if the optimization effect of this iteration is X% to Y%, set the number of next-generation populations to T1 times the number of this population; if the optimization effect of this iteration is less than Y%, set the number of next-generation populations to T2 times the number of this population; where Y>X, T2>T1; in specific implementation, the preferred value of Y is 5, the preferred value of X is 1, the preferred value of T1 is 1.5, and the preferred value of T2 is 2;

[0086] Finally, half of the individuals with smaller excess returns are selected and retained to the next generation; genetic operations are performed through the set crossover probability (preferably 0.8) and mutation probability (preferably 0.2) to generate the remaining half of the individuals; finally, the updated population is used as the current population, and the next round of iterative calculation of step S5 is performed.

[0087] The method of the present invention is further described below in conjunction with an embodiment:

[0088] A simulation scenario is set up based on the real 96-period power system data of a certain region on a certain day in summer. The reference price of each unit is 1.1 times the variable cost + operation and maintenance cost, where the variable cost is the fuel cost, and the coal price is considered to be 1,200 yuan / ton.

[0089] The parameters that need to be optimized include the critical surplus index, trigger multiple, penalty multiple, and clearing price threshold. The parameter ranges are 1-1.25, 1-1.5, 1-2, and 400-480 yuan / MWh. Set the number of individuals in the population n to 50 and the number of population iterations t to 25;

[0090] The method of the present invention is used to perform simulation optimization, and the excess return scatter plot under each population iteration is obtained as follows: Figure 2 As shown;

[0091] After the 10th generation of the population, the lowest excess return in the population basically does not change, from the lowest 19.38 yuan / MWh in the initial population to 16.65 yuan / MWh. At this time, the corresponding optimal parameters are the critical surplus supply index, trigger multiple, penalty multiple, and clearing price threshold = 405.94 yuan / MWh. The parameter optimization method proposed in this application can be completed by setting the relevant parameters of market power control in the current period to the optimal parameters of the above simulation.

[0092] like Figure 3 The figure shows a schematic diagram of the functional modules of the system of the present invention: the system disclosed in the present invention for realizing the parameter optimization method of the market power prevention and control regulatory mechanism for the power market comprises a data acquisition module, a data prediction module, a model building module, a parameter setting module, a genetic optimization module and a parameter optimization module; the data acquisition module, the data prediction module, the model building module, the parameter setting module, the genetic optimization module and the parameter optimization module are connected in series in sequence; the data acquisition module is used to acquire data information of the target power system and the target power market, and upload the data information to the data prediction module; the data prediction module is used to predict the typical system marginal data of the next cycle of the power market according to the received data information and the acquired data information, and upload the data information to the model building module; the model building module is used to, according to the received data information, According to the obtained data, a market power mitigation simulation scenario is set, and the simulation parameters to be optimized are determined, and the data information is uploaded to the parameter setting module; the parameter setting module is used to set the parameters of the genetic algorithm according to the received data information and the determined simulation parameters, and upload the data information to the genetic optimization module; the genetic optimization module is used to perform spot simulation and market power supervision on the set market power mitigation simulation scenario according to the parameters of each individual in the population based on the received data information and the set parameter values, and calculate the corresponding unit excess profit, and upload the data information to the parameter optimization module; the parameter optimization module is used to perform iterative calculations of the genetic optimization module according to the received data information and the obtained simulation results, to obtain the final optimized simulation parameters, and complete the parameter optimization of the market power prevention and control supervision mechanism for the power market.

Claims

1. A method for optimizing parameters of a market power prevention and control regulatory mechanism for an electricity market, comprising the following steps: S1. Obtain data information of the target power system and target power market; S2. Based on the data information obtained in step S1, predict the typical system marginal data of the next cycle of the power market; S3. According to the data obtained in step S2, set a market power mitigation simulation scenario and determine the simulation parameters to be optimized; S4. According to the simulation parameters determined in step S3, set the parameters of the genetic algorithm; S5. Based on the parameter values ​​set in step S4, spot simulation and market power supervision are performed on the set market power mitigation simulation scenario according to the parameters of each individual in the population, and the corresponding excess revenue of the unit is calculated; S6. Perform iterative calculation of step S5 according to the simulation result obtained in step S5 to obtain the final optimized simulation parameters, thereby completing the parameter optimization of the market power prevention and control regulatory mechanism for the electricity market.

2. The method for optimizing parameters of the market power prevention and control supervision mechanism for the power market according to claim 1 is characterized in that Step S2 predicts typical system marginal data of the next cycle of the power market based on the data information obtained in step S1, and specifically includes the following steps: According to the data information obtained in step S1, predict the typical system marginal data of the next cycle of the power market; The marginal data include: the load data D of each node m at each time t m,t , each fixed unit i f Output data at each time t and unit quotation (C i,t,k ,price i,t,k );C i,t,k is the electricity reported by unit i in the kth period of time period t, price i,t,k Declare the electricity price for unit i in the kth segment of time period t.

3. The method for optimizing parameters of the market power prevention and control supervision mechanism for the power market according to claim 2 is characterized in that Step S3, according to the data obtained in step S2, sets a market power mitigation simulation scenario and determines the simulation parameters to be optimized, which specifically includes the following steps: A. Establishment of a basic model for provincial power spot market simulation: According to the actual situation of the power system, a generator set operation model, a node load model and a section flow model are established, and finally a spot market clearing model is established with SCUC+SCED+LMP as the optimization algorithm and gurobi as the optimization tool; the optimization goal of the spot market clearing model is to minimize the system power generation cost, and the constraints of the spot market clearing model include system load balance constraints, unit climbing constraints, unit start-stop constraints, positive and negative reserve constraints of the power system, rotating reserve constraints and section flow constraints; the marginal data of the spot market clearing model is the marginal data obtained in step S2; the generator set includes thermal power units, hydropower units, wind power units, photovoltaic units and energy storage units; B. Based on the established provincial power spot market simulation basic model, consider the market power mitigation solution: Assessing the market oligopoly structure: The HHI value of the overall structure on the power generation side is calculated using the following formula: Where N is the total number of power generation groups; S j is the market share of the jth power generation group; If HHI>1800, the market is judged to be highly oligopolistic; If HHI≤1800, the market share of power generation groups is ranked from large to small, and the total market share of the top four power generation groups is calculated and judged: if the total market share is greater than 65%, the market is judged to have a high oligopoly structure; if the total market share is less than or equal to 65%, the market is judged to have a non-high oligopoly structure; The residual supply index RSI is used to evaluate the market power potential of each generator: if the residual supply index RSI of the jth generator exceeds the set critical value ρ RSI , then the j-th generator is subject to acceptance relief: the generation capacity of the j-th generator is divided into critical capacity and regulated capacity, and the declared capacity of the j-th generator is guaranteed to be no less than the critical capacity; the calculation formula of the residual supply index RSI of the j-th generator is Where S total is the total declared generating capacity of the system, S j is the declared capacity of the jth generator, Load p is the load forecast value; The behavior and impact test system is used to evaluate the bidding behavior of power generators: First, the behavior test is performed to determine whether the unit quotation exceeds the RefPrice i , and then conduct an impact test to determine whether the electricity price after clearing exceeds RefPrice i ; Acceptance relief for power generators that fail the assessment: Replace the unit quotations corresponding to the power generators that fail the assessment with the unit reference price RefPrice i RefPrice i The value of is 1.1 times the sum of variable cost and operation and maintenance cost; finally, the power generation plan of the market entity on that day is formed and used as the execution basis of market settlement; Set the market clearing price threshold P that triggers the ex post regulatory mechanism REF ; After the day-ahead market is cleared, the triggering conditions of the regulatory procedures for the day are judged: if the average price of the nodes cleared for the entire day is higher than P REF If the trigger multiple is greater than , the supervision procedure for the day will be initiated; If the daily regulatory procedure is triggered, the price impact contribution calculation will be carried out for all power generators: First, all unit quotations are replaced with the unit reference price RefPrice i , re-clear the market and calculate the average electricity price of the replaced time-sharing node Then, compare the actual average electricity price P of the time-sharing nodes on that day t AVE , calculate the price impact contribution rate ω of the jth power generation group j for The total excess revenue ΔR on the power generation side is calculated as And calculate the compensation amount ΔR of the jth power generation group j ΔR j =μ×ΔR×ω j ; Finally, compensation is made according to the proportion of electricity traded by each market-oriented user; among them, G t is the settlement quantity of the electricity market in period t, μ is the penalty multiple; C. Determine the simulation parameters to be optimized: Select the critical value ρ RSI , trigger multiple γ, penalty multiple μ and market clearing price threshold P REF , as the simulation parameter to be optimized.

4. The method for optimizing parameters of the market power prevention and control supervision mechanism for the power market according to claim 3 is characterized in that The step S4 of setting the parameters of the genetic algorithm according to the simulation parameters determined in step S3 specifically includes the following steps: Set the upper limit of population iterations and the number of individuals in the population; Set the variable range of each optimization parameter; Within the set variable range, several groups of optimization parameters are randomly generated as the optimization parameters for each individual in the initialization population.

5. The method for optimizing parameters of the market power prevention and control supervision mechanism for the power market according to claim 4 is characterized in that Step S5, based on the parameter values ​​set in step S4, performs spot simulation and market power supervision on the set market power mitigation simulation scenario according to the parameters in each individual in the population, and calculates the corresponding unit excess profit, which specifically includes the following steps: For each individual in the current population, establish a market power mitigation simulation scenario according to the content of step S3; According to the optimization parameters of each individual, set the parameter data of the corresponding market power mitigation simulation scenario; Market power mitigation simulation is carried out to obtain the excess profit of the unit under the corresponding optimization parameters.

6. The method for optimizing parameters of the market power prevention and control supervision mechanism for the power market according to claim 5 is characterized in that Step S6, performing the iterative calculation of step S5 according to the simulation result obtained in step S5 to obtain the final optimized simulation parameters, specifically includes the following steps: Determine the number of iterations of step S5: If the number of iterations reaches the set value, the parameters of the individual corresponding to the minimum excess profit are selected as the final optimized simulation parameters; the simulation parameters correspond to the optimization results of the parameters of the market power prevention and control regulatory mechanism for the power market; If the number of falls and the number of times do not reach the set value, the following genetic operations will be performed: Dynamically adjust the number of next-generation populations according to the optimization effect of each iteration: if the optimization effect of this iteration is greater than Y%, keep the population size unchanged; if the optimization effect of this iteration is X% to Y%, set the number of next-generation populations to T1 times the number of this population; if the optimization effect of this iteration is less than Y%, set the number of next-generation populations to T2 times the number of this population; where Y>X, T2>T1; Finally, half of the individuals with smaller excess returns are selected and retained to the next generation; genetic operations are performed through the set crossover probability and mutation probability to generate the remaining half of the individuals; finally, the updated population is used as the current population, and the next round of iterative calculation of step S5 is performed.

7. A system for implementing the method for optimizing parameters of a market power prevention and control regulatory mechanism for a power market as claimed in any one of claims 1 to 6, characterized in that It includes a data acquisition module, a data prediction module, a model building module, a parameter setting module, a genetic optimization module and a parameter optimization module; the data acquisition module, the data prediction module, the model building module, the parameter setting module, the genetic optimization module and the parameter optimization module are connected in series in sequence; the data acquisition module is used to obtain data information of the target power system and the target power market, and upload the data information to the data prediction module; The data prediction module is used to predict the typical system marginal data of the next cycle of the power market based on the received data information and upload the data information to the model establishment module; The model building module is used to set the market power mitigation simulation scenario based on the received data information and the obtained data, and determine the simulation parameters to be optimized, and upload the data information to the parameter setting module; The parameter setting module is used to set the parameters of the genetic algorithm according to the received data information and the determined simulation parameters, and upload the data information to the genetic optimization module; The genetic optimization module is used to perform spot simulation and market power supervision on the set market power mitigation simulation scenario according to the received data information and the set parameter values, and calculate the corresponding unit excess profit, and upload the data information to the parameter optimization module; The parameter optimization module is used to perform iterative calculations of the genetic optimization module according to the received data information and the obtained simulation results, obtain the final optimized simulation parameters, and complete the parameter optimization of the market power prevention and control regulatory mechanism for the electricity market.

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