Multi-target double-layer game optimization method and system for wind power participating in frequency modulation
By constructing a multi-objective, two-level game optimization model and a hybrid strategy game optimization theory, the problem of unfair distribution of frequency regulation benefits and interests in multi-energy coordinated frequency regulation is solved, and fair incentives and efficient frequency regulation of multi-energy systems are achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-17
- Publication Date
- 2026-03-13
AI Technical Summary
Existing optimization methods struggle to balance frequency regulation benefits with the fair distribution of interests among multiple stakeholders in multi-energy coordinated frequency regulation, and traditional generating units are unable to meet the frequency regulation demands of high proportions of renewable energy.
A multi-objective, two-layer game optimization method for wind power participation in frequency regulation is constructed. The method adopts hybrid strategy game optimization theory, obtains frequency regulation power allocation scheme through multi-objective optimization model and two-layer game model, and determines the optimal solution by combining fuzzy evaluation factors.
It achieves fairness and incentive in multi-energy coordinated frequency regulation, quickly and accurately determines the optimal frequency regulation power allocation scheme, and takes into account frequency regulation benefits, response speed and control accuracy.
Smart Images
Figure CN121663546A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of power grid dispatching technology, and in particular to a multi-objective two-level game optimization method and system for wind power participation in frequency regulation. Background Technology
[0002] With a high proportion of renewable energy connected to the grid, the power system is increasingly characterized by the coexistence of multiple power sources, including wind, solar, hydro, and thermal power. Automatic generation control (AGC), as a core means of secondary frequency regulation, is crucial for ensuring grid frequency stability. When deviations occur in system frequency or tie-line power, frequency regulation commands must be distributed to participating units according to a specific mechanism. Traditionally, hydropower and thermal power have undertaken the main frequency regulation tasks; however, with the continuous expansion of new energy installed capacity, traditional units alone are insufficient to meet frequency regulation demands, necessitating the active participation of new energy sources such as wind and solar power in AGC.
[0003] Multi-energy coordinated frequency regulation is becoming the future direction of frequency control, and how to balance the benefits and performance of frequency regulation has become a key issue in the coordinated operation of the power source and grid. To incentivize the participation of multiple stakeholders, the two detailed rules clarify the performance indicators and compensation mechanisms for various power sources participating in AGC.
[0004] However, while existing optimization methods have proven effective in improving system performance, they often focus on single-objective optimization and rarely consider the fair distribution of benefits among multiple stakeholders. Therefore, there is an urgent need to construct a new mechanism applicable to the collaborative optimization of multi-objective and multi-type frequency regulation resources and the reasonable sharing of benefits. Summary of the Invention
[0005] The purpose of this invention is to overcome the shortcomings of the existing technology and provide a multi-objective, two-layer game optimization method and system for wind power participation in frequency regulation, which is applicable to multi-objective, multi-type frequency regulation resource collaborative optimization and reasonable benefit sharing.
[0006] The objective of this invention can be achieved through the following technical solutions: A multi-objective, two-level game optimization method for wind power participation in frequency regulation includes: Obtain the unit frequency regulation revenue, average frequency regulation speed, average frequency regulation accuracy, and output of wind turbines, photovoltaic units, hydropower units, thermal power units, and energy storage power stations, and input them into a pre-constructed multi-objective optimization model with frequency regulation revenue, frequency regulation speed, frequency regulation accuracy, and frequency control performance as optimization objectives; Construct a two-layer game model based on the multi-objective optimization model; The two-layer game model is solved using hybrid strategy game optimization theory to obtain multiple frequency regulation power allocation schemes, forming a Pareto solution set. Evaluation factors are set according to the actual needs of the current power grid, and the comprehensive score of each solution in the Pareto solution set is calculated. The solution with the highest score is selected as the optimal frequency regulation power allocation scheme that best meets the current preferences.
[0007] Furthermore, the expression for the multi-objective optimization model is: In the formula, To optimize the goal of maximizing frequency modulation benefits, To maximize the optimization objective of frequency modulation speed, The optimization objective is to minimize the frequency modulation accuracy. To minimize the optimization objective of frequency modulation performance, c w,i c P,i c H,i c T,i c E,i v represents the unit frequency regulation revenue for the i-th wind turbine, photovoltaic turbine, hydropower turbine, thermal power turbine, and energy storage power station; w,i v P,i v H,i v T,i v E,i Let q be the average frequency regulation speed of the i-th wind turbine, photovoltaic turbine, hydropower turbine, thermal power turbine, and energy storage power station; w,i q P,i q H,i q T,i q E,i P represents the average frequency regulation accuracy of the i-th wind turbine, photovoltaic power unit, hydropower unit, thermal power unit, and energy storage power station. w,i P p,i P H,i P T,i P E,i For the output of the i-th wind turbine, photovoltaic unit, hydropower unit, thermal power unit, and energy storage power station participating in AGC; N w N p N H N T N E The total number of wind turbines, photovoltaic units, hydropower units, thermal power units, and energy storage power stations participating in AGC frequency regulation is β1, β2, β3, and β4, which are the weighting coefficients corresponding to the frequency deviation of the power grid system; α1, α2, α3, and α4 are the frequency deviation zoning values.
[0008] Furthermore, the constraints of the multi-objective optimization model include system frequency regulation power constraints, unit output constraints, system frequency regulation speed constraints, and system frequency regulation accuracy constraints.
[0009] Furthermore, the expression for the system frequency modulation power constraint is: In the formula, This refers to the frequency modulation power of AGC.
[0010] Furthermore, the output constraints of each unit can be expressed as: In the formula, R w,i R p,i R H,i R T,i R E,i Let represent the available frequency regulation capacity of the i-th wind turbine, photovoltaic power unit, hydropower unit, thermal power unit, and energy storage power station.
[0011] Furthermore, the system frequency modulation speed constraint can be expressed as: In the formula, v s,ave,min This is the lower limit of the system's average frequency modulation speed.
[0012] Furthermore, the method also adjusts the frequency modulation time scale, and the corresponding expression is: In the formula, m w,i,t m p,i,t m H,i,t m T,i,t m E,i,t Let ΔP be the variable output flag of the i-th wind turbine, photovoltaic unit, hydropower unit, thermal power unit, and energy storage power station at time t. w,i,t ΔP p,i,t ΔP H,i,t ΔP T,i,t ΔP E,i,t Let represent the tolerable range of output variation for the i-th wind turbine, photovoltaic power unit, hydropower unit, thermal power unit, and energy storage power station.
[0013] Furthermore, the upper-level model of the two-layer game model uses frequency regulation speed, frequency regulation accuracy, and frequency regulation revenue as objective functions to determine the frequency regulation performance indicators and the frequency regulation power of each wind turbine, photovoltaic unit, hydropower unit, thermal power unit, and energy storage power station; the lower-level model of the two-layer game model allocates the frequency regulation revenue based on the output of the upper-level model.
[0014] Furthermore, the setting of evaluation factors based on the actual needs of the current power grid includes: The optimization objective of frequency modulation revenue is divided into three intervals: large, medium, and small, and the corresponding fuzzy evaluation values a1, a2, and a3 are determined. The optimization objective of frequency modulation speed is divided into three intervals: large, medium, and small, and the corresponding fuzzy evaluation values b1, b2, and b3 are determined. The optimization target for frequency modulation accuracy is divided into three intervals: large, medium, and small, and the corresponding fuzzy evaluation values c1, c2, and c3 are determined. The optimization objective of frequency modulation performance is divided into three intervals: large, medium, and small, and the corresponding fuzzy evaluation values d1, d2, and d3 are determined. The comprehensive score for each solution in the Pareto solution set includes: Based on each fuzzy evaluation value, the fuzzy membership degree corresponding to each objective function of the multi-objective optimization model is determined, thereby calculating the comprehensive score of each solution in the Pareto solution set; The formula for calculating the comprehensive score is as follows: In the formula, Let be the overall score of the i-th solution in the Pareto solution set. , and These represent the fuzzy membership degrees of the objective function at low, medium, and high return levels, respectively.
[0015] The present invention also provides a multi-objective two-layer game optimization system for wind power participation in frequency regulation, including a memory and a processor. The memory stores a computer program, and the processor calls the computer program to execute the steps of the method described above.
[0016] Compared with the prior art, the present invention has the following advantages: (1) The power system is showing a new pattern of coordinated operation of multiple power sources such as wind power, photovoltaic power, hydropower, thermal power, and energy storage power stations, which requires the development of an efficient multi-energy joint frequency regulation mechanism. This invention constructs a multi-objective two-layer game optimization model of wind power-photovoltaic-hydropower-thermal power-energy storage power station that takes into account frequency regulation benefits, response speed, and control accuracy. In the upper-layer model, frequency regulation benefits, frequency regulation speed, and frequency regulation accuracy are regarded as three game subjects respectively. A hierarchical hybrid strategy game framework is designed, and the multi-objective optimization model is solved by combining the model prediction algorithm. By introducing adjustable evaluation factors, the preferences of the dispatching side for different objectives are flexibly reflected, thereby determining the overall optimal benefits of the system and the frequency regulation power allocation scheme of each type of power source. In the lower-layer model, a cooperative alliance is formed with five types of power units as participants, and the benefits are allocated based on the output results of the upper layer. This method not only considers the frequency regulation output of each unit, but also integrates its response speed and control accuracy, making the allocation results fairer and more incentive-oriented.
[0017] (2) In response to the problem that it is difficult to achieve the optimality of multiple optimization objectives simultaneously in the process of solving multi-objective optimization models, this invention proposes to use hybrid strategy game optimization theory to deal with the problem. Each objective function is regarded as a participant in the game, and all possible trade-offs constitute the Pareto optimal solution set. The fuzzy evaluation method is used to determine the fuzzy evaluation value of each objective function and set the fuzzy membership degree of each objective function. Then, the comprehensive score that measures the satisfaction of each Pareto solution is obtained by fusion. The solution with the highest score is the optimal frequency modulation power allocation scheme that best meets the current preference, thereby realizing fast and accurate multi-objective optimization. Attached Figure Description
[0018] Figure 1 This is a flowchart illustrating a multi-objective, two-layer game optimization method for wind power participation in frequency regulation provided in an embodiment of the present invention. Detailed Implementation
[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.
[0020] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.
[0021] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.
[0022] Example 1 like Figure 1 As shown, this embodiment provides a multi-objective, two-layer game optimization method for wind power participation in frequency regulation, including: S1: Obtain the unit frequency regulation revenue, average frequency regulation speed, average frequency regulation accuracy, and output of wind turbines, photovoltaic units, hydropower units, thermal power units, and energy storage power stations, and input them into a pre-constructed multi-objective optimization model with frequency regulation revenue, frequency regulation speed, frequency regulation accuracy, and frequency control performance as optimization objectives; S2: Construct a two-level game model based on a multi-objective optimization model; S3: The two-level game model is solved using hybrid strategy game optimization theory to obtain multiple frequency regulation power allocation schemes, forming a Pareto solution set; evaluation factors are set according to the actual needs of the current power grid, the comprehensive score of each solution in the Pareto solution set is calculated, and the solution with the highest score is selected as the optimal frequency regulation power allocation scheme that best meets the current preferences.
[0023] The specific process includes: Step S1 constructs an optimization model with the frequency regulation revenue, frequency regulation speed, frequency regulation accuracy, and frequency control performance of wind power, photovoltaic power, hydropower, thermal power units, and energy storage power stations as optimization objectives, which can be expressed as: (1) In the formula: c w,i c P,i c H,i c T,i c E,i v represents the unit frequency regulation revenue for the i-th wind power, photovoltaic, hydropower, thermal power unit, and energy storage power station; w,i v P,i v H,i v T,i v E,i Let q be the average frequency regulation speed of the i-th wind power, photovoltaic, hydropower, thermal power unit, and energy storage power station; w,i q P,i q H,i q T,i q E,i P represents the average frequency regulation accuracy of the i-th wind power, photovoltaic, hydropower, thermal power unit, and energy storage power station; w,i P p,i P H,i P T,i P E,i To contribute power to the i-th wind power, photovoltaic, hydropower, thermal power unit, or energy storage power station unit participating in AGC; N w N p N H N T N E This represents the total number of wind power, photovoltaic, hydropower, thermal power units, and energy storage power station units participating in AGC frequency regulation. β1, β2, β3, and β4 are the weighting coefficients corresponding to the frequency deviation of the power grid system; α1, α2, α3, and α4 are the frequency deviation zone values.
[0024] The constraints include system frequency regulation power constraints, unit output constraints, system frequency regulation speed constraints, and system frequency regulation accuracy constraints.
[0025] The system frequency modulation power constraint can be expressed as: (2) The output constraints of each unit can be expressed as: (3) In the formula R w,i R p,i R H,i R T,i R E,i This represents the available frequency regulation capacity for the i-th wind power, photovoltaic, hydropower, thermal power unit, and energy storage power station unit.
[0026] The system frequency modulation speed constraint can be expressed as: (4) In the formula v s,ave,min This is the lower limit of the system's average frequency modulation speed.
[0027] The system frequency modulation accuracy constraint can be expressed as: (5) Other constraints are as follows: The unit's ramp-up work constraint can be expressed as: (6) In the formula K w,i K p,i K H,i K T,i K E,i Let Δt be the ramp rate limit for the i-th wind power, photovoltaic, hydropower, thermal power unit, and energy storage power station unit, and let Δt represent the dispatch interval.
[0028] Preferably, considering that wind power, photovoltaic power, hydropower, thermal power units, and energy storage power stations participate in frequency regulation, their time scales differ. For example, if the frequency regulation scheduling time period of thermal power follows the energy storage device, the frequency regulation response performance of thermal power will deteriorate. Therefore, the frequency regulation time scale must be flexibly adjusted, which can be expressed as: (7) In the formula m w,i,t m p,i,t m H,i,t m T,i,t m E,i,t Let ΔP be the variable output flag of the i-th wind power, photovoltaic, hydropower, thermal power unit, and energy storage power station unit at time t. w,i,t ΔP p,i,t ΔP H,i,t ΔP T,i,t ΔP E,i,t Let represent the tolerable range of output variation for the i-th wind power, photovoltaic, hydropower, thermal power unit, and energy storage power station unit.
[0029] The two-layer game model established in step S2 is as follows: Upper-level model: Input the unit frequency regulation revenue, average frequency regulation speed and average frequency regulation accuracy parameters of each energy unit, optimize and solve three objective functions through model prediction algorithm, namely system frequency regulation speed, system frequency regulation accuracy and system frequency regulation revenue, and output the system frequency regulation performance index and the frequency regulation power of each unit.
[0030] Lower-level model: Using data obtained from the upper-level model (including system frequency regulation revenue and the frequency regulation power of each unit), the frequency regulation revenue allocation factor is determined. This process fully considers the frequency regulation power, average frequency regulation speed, and average frequency regulation accuracy of each energy unit, achieving a reasonable allocation of frequency regulation revenue.
[0031] Considering the time scale issues related to the participation of wind power, photovoltaic, hydropower, thermal power units, and energy storage power station units in frequency regulation, as well as the time deviation between issuing control plans and real-time execution commands, the traditional real-time frequency regulation scheduling mode has gradually evolved into a rolling optimization strategy that takes into account the coupling characteristics of multiple time periods. The model prediction algorithm performs rolling optimization over N scheduling cycles, finally executing the scheduling plan [t, t+Δt]. This optimization process covers multiple consecutive time periods, where only the scheduling scheme of the first time period is actually executed, while the plans for subsequent time periods are mainly used for guidance and reference to enhance the foresight and robustness of decision-making.
[0032] In step S3, considering that it is difficult for the three objective functions to simultaneously reach their optimal state, hybrid strategy game optimization theory can effectively handle this type of multi-objective optimization problem. Each objective function is regarded as a "participant" in the game, and all its possible trade-offs constitute the Pareto optimal solution set, which is the "strategy set" of the corresponding participant. The evaluation table of the strategy set can be represented as shown in Table 1.
[0033] Table 1 The mathematical model of hierarchical mixed strategy game can be expressed as: (8) (9) (10) Each objective function is stratified according to its membership degree, a i b i c i These are the evaluation values determined by decision-makers based on preference information. Each non-dominated solution can be categorized into 3×3 membership degrees, thereby evaluating the merits of each strategy.
[0034] The evaluation index for each solution scheme in the corresponding Pareto solution set can be defined as: (11) That is, the decision-maker bases the decision on the objective function value obj. iThe magnitude of obj is used to measure the satisfaction with each Pareto solution. i The larger the value, the higher the satisfaction level. Among them, u s,i u m,i and u l,i f represents the fuzzy membership degree of the i-th objective function at low, medium, and high return levels, respectively; i,max f i,min and f i,mean These correspond to the maximum, minimum, and average values of the objective function in the Pareto optimal solution set, respectively.
[0035] The following is a specific implementation example: Step 1: Problem Initialization and Parameter Input Input Data: Collect key parameters for all wind, solar, hydro, thermal power units, and energy storage power stations participating in frequency regulation, including: Unit Frequency Regulation Revenue (c): The reward received for providing 1MW of frequency regulation power. Average Frequency Regulation Speed (v): The speed at which the unit responds to commands. Average Frequency Regulation Accuracy (q): The degree of deviation between the unit's output and the command. Available Frequency Regulation Capacity (R): The maximum power that the unit can participate in frequency regulation.
[0036] Setting Objectives: Four optimization objectives are defined: F1 (Maximize Frequency Regulation Benefits): Highest overall system frequency regulation benefits. F2 (Maximize Frequency Regulation Speed): Fastest overall system frequency regulation speed. F3 (Minimize Frequency Regulation Accuracy): Minimize overall system frequency regulation deviation. F4 (Frequency Regulation Performance): Optimal power quality for the power system.
[0037] Step Two: Multi-Objective Optimization Solution Process: 1. The algorithm randomly generates a set of possible frequency modulation power allocation schemes (population). 2. For each scheme, calculate the values of the three objective functions F1, F2, F3, and F4. 3. Perform non-dominated sorting to find the high-quality solutions (Pareto optimal solution set) that cannot be surpassed by other schemes in all three objectives. This solution set includes various trade-offs; for example, some schemes have high returns but slow speed, while others are fast but have slightly lower returns. Output: Obtain a Pareto optimal solution set, where each solution contains a set of frequency modulation power allocation values for each unit (P... w,i , P p,i , P H,i , P T,i ,P E,i The corresponding total system revenue F1, average system frequency modulation speed F2 and accuracy F3, and system frequency modulation performance F4.
[0038] Step 3: Scheme optimization based on hierarchical hybrid strategy game: Decision intervention: Decision-makers (such as the power grid dispatch center) set evaluation factors based on the actual needs of the current power grid (e.g., whether they value economic efficiency or rapid response more).
[0039] Optimization Mechanism: A formula is used to calculate the comprehensive score of each solution in the Pareto set. The solution with the highest score is the optimal frequency regulation power allocation scheme that best suits the current preferences. Output: A specific set of frequency regulation power allocation instructions is finally determined and issued to each generating unit, specifying the total frequency regulation benefits obtained by the system.
[0040] Example 2 This embodiment provides a multi-objective two-layer game optimization system for wind power participation in frequency regulation, including a memory and a processor. The memory stores a computer program, and the processor calls the computer program to execute the steps of the method as described in Embodiment 1.
[0041] The computer program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This computer program code can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the computer program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The computer program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.
[0042] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.
Claims
1. A multi-objective, two-level game optimization method for wind power participation in frequency regulation, characterized in that, include: Obtain the unit frequency regulation revenue, average frequency regulation speed, average frequency regulation accuracy, and output of wind turbines, photovoltaic units, hydropower units, thermal power units, and energy storage power stations, and input them into a pre-constructed multi-objective optimization model with frequency regulation revenue, frequency regulation speed, frequency regulation accuracy, and frequency control performance as optimization objectives; Construct a two-layer game model based on the multi-objective optimization model; The two-layer game model is solved using hybrid strategy game optimization theory to obtain multiple frequency regulation power allocation schemes, forming a Pareto solution set. Evaluation factors are set according to the actual needs of the current power grid, and the comprehensive score of each solution in the Pareto solution set is calculated. The solution with the highest score is selected as the optimal frequency regulation power allocation scheme that best meets the current preferences.
2. The multi-objective two-layer game optimization method for wind power participation in frequency regulation according to claim 1, characterized in that, The expression for the multi-objective optimization model is: In the formula, To optimize the goal of maximizing frequency modulation benefits, To maximize the optimization objective of frequency modulation speed, The optimization objective is to minimize the frequency modulation accuracy. To minimize the optimization objective of frequency modulation performance, c w,i c P,i c H,i c T,i c E,i v represents the unit frequency regulation revenue for the i-th wind turbine, photovoltaic turbine, hydropower turbine, thermal power turbine, and energy storage power station; w,i v P,i v H,i v T,i v E,i Let q be the average frequency regulation speed of the i-th wind turbine, photovoltaic turbine, hydropower turbine, thermal power turbine, and energy storage power station; w,i q P,i q H,i q T,i q E,i P represents the average frequency regulation accuracy of the i-th wind turbine, photovoltaic power unit, hydropower unit, thermal power unit, and energy storage power station. w,i P p,i P H,i P T,i P E,i For the output of the i-th wind turbine, photovoltaic unit, hydropower unit, thermal power unit, and energy storage power station participating in AGC; N w N p N H N T N E The total number of wind turbines, photovoltaic units, hydropower units, thermal power units, and energy storage power stations participating in AGC frequency regulation is β1, β2, β3, and β4, which are the weighting coefficients corresponding to the frequency deviation of the power grid system; α1, α2, α3, and α4 are the frequency deviation zoning values.
3. The multi-objective two-layer game optimization method for wind power participation in frequency regulation according to claim 2, characterized in that, The constraints of the multi-objective optimization model include system frequency regulation power constraints, unit output constraints, system frequency regulation speed constraints, and system frequency regulation accuracy constraints.
4. The multi-objective two-layer game optimization method for wind power participation in frequency regulation according to claim 3, characterized in that, The expression for the system frequency modulation power constraint is: In the formula, This refers to the frequency modulation power of AGC.
5. The multi-objective two-layer game optimization method for wind power participation in frequency regulation according to claim 3, characterized in that, The output constraints of each unit can be expressed as follows: In the formula, R w,i R p,i R H,i R T,i R E,i Let represent the available frequency regulation capacity of the i-th wind turbine, photovoltaic power unit, hydropower unit, thermal power unit, and energy storage power station.
6. The multi-objective two-level game optimization method for wind power participation in frequency regulation according to claim 3, characterized in that, The system frequency modulation speed constraint can be expressed as: In the formula, v s,ave,min This is the lower limit of the system's average frequency modulation speed.
7. The multi-objective two-layer game optimization method for wind power participation in frequency regulation according to claim 1, characterized in that, The method also adjusts the frequency modulation time scale, and the corresponding expression is: In the formula, m w,i,t m p,i,t m H,i,t m T,i,t m E,i,t Let ΔP be the variable output flag of the i-th wind turbine, photovoltaic unit, hydropower unit, thermal power unit, and energy storage power station at time t. w,i,t ΔP p,i,t ΔP H,i,t ΔP T,i,t ΔP E,i,t Let represent the tolerable range of output variation for the i-th wind turbine, photovoltaic power unit, hydropower unit, thermal power unit, and energy storage power station.
8. A multi-objective two-level game optimization method for wind power participation in frequency regulation according to claim 1, characterized in that, The upper-level model of the two-layer game model uses frequency regulation speed, frequency regulation accuracy, and frequency regulation revenue as objective functions to determine the frequency regulation performance indicators and the frequency regulation power of each wind turbine, photovoltaic unit, hydropower unit, thermal power unit, and energy storage power station; the lower-level model of the two-layer game model allocates the frequency regulation revenue based on the output of the upper-level model.
9. A multi-objective, two-layer game optimization method for wind power participation in frequency regulation according to claim 8, characterized in that, The evaluation factors set based on the actual needs of the current power grid include: The optimization objective of frequency modulation revenue is divided into three intervals: large, medium, and small, and the corresponding fuzzy evaluation values a1, a2, and a3 are determined. The optimization objective of frequency modulation speed is divided into three intervals: large, medium, and small, and the corresponding fuzzy evaluation values b1, b2, and b3 are determined. The optimization target for frequency modulation accuracy is divided into three intervals: large, medium, and small, and the corresponding fuzzy evaluation values c1, c2, and c3 are determined. The optimization objective of frequency modulation performance is divided into three intervals: large, medium, and small, and the corresponding fuzzy evaluation values d1, d2, and d3 are determined. The comprehensive score for each solution in the Pareto solution set includes: Based on each fuzzy evaluation value, the fuzzy membership degree corresponding to each objective function of the multi-objective optimization model is determined, thereby calculating the comprehensive score of each solution in the Pareto solution set; The formula for calculating the comprehensive score is as follows: In the formula, Let be the overall score of the i-th solution in the Pareto solution set. , and These represent the fuzzy membership degrees of the objective function at low, medium, and high return levels, respectively.
10. A multi-objective, two-layer game optimization system for wind power participation in frequency regulation, characterized in that, It includes a memory and a processor, the memory storing a computer program, the processor invoking the computer program to perform the steps of the method as described in any one of claims 1 to 9.