Virtual power plant capacity configuration optimization method and system considering accurate capacity market

Abstract

The invention provides a virtual power plant capacity configuration optimization method and system considering an accurate capacity market, and the method comprises the steps: building a virtual power plant model according to the related parameters of a virtual power plant; according to the preprocessed historical demand data and price data of the capacity market, establishing a capacity market model; based on the virtual power plant model and the capacity market model, taking maximization of the total revenue of the virtual power plant in the capacity market as a target, considering system reliability, and establishing an optimal configuration model; the optimal configuration model is solved, a global optimal solution is output, and an optimal configuration scheme of the virtual power plant in the capacity market is obtained; by implementing the method provided by the invention, the virtual power plant can participate in the power capacity market more flexibly, the economic benefit of the virtual power plant can be improved, the stability and reliability of the power system can be enhanced, and powerful support is provided for healthy development of the power market.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system optimization configuration, and specifically relates to a virtual power plant capacity configuration optimization method and system considering a precise capacity market. Background Art

[0002] With the transformation of the global energy structure and the deepening of the electricity market reform, the power system is facing unprecedented challenges and opportunities. Against this background, Virtual Power Plant (VPP) has emerged as a new concept for optimizing the configuration of the power system and has become the focus of attention in the power industry. Virtual power plants integrate a variety of distributed energy resources such as distributed generation (DG), energy storage system (ESS) and controllable load (CL) to form a coordinated power supply network, which can flexibly participate in the operation of the power market, especially playing an important role in the power capacity market.

[0003] The power capacity market is an important part of the power market, and its main function is to ensure the reliability and stability of the power system. In the capacity market, power suppliers obtain economic compensation by providing a certain amount of power capacity to meet the predicted peak demand. Virtual power plants can provide efficient and reliable power capacity services in the capacity market by optimizing the allocation of their internal resources, thereby obtaining economic benefits.

[0004] However, existing virtual power plant configuration methods have some limitations. First, these methods often do not fully take into account the complexity and diversity of resources within virtual power plants. For example, different types of distributed generation units (such as solar photovoltaic, wind power, etc.) have different power generation characteristics and environmental dependencies, and the charging and discharging efficiency and capacity limitations of energy storage systems also have an impact on power supply. Secondly, existing methods are also insufficient in effectively combining with capacity market rules, and fail to make full use of market mechanisms to optimize resource allocation, resulting in low resource allocation efficiency, and system reliability and economy fail to achieve optimality. In addition, with the increasingly fierce competition in the electricity market and frequent fluctuations in electricity prices, higher requirements are placed on the operation strategies of virtual power plants.

[0005] In summary, how to maximize the economic benefits of virtual power plants while ensuring system reliability has become an urgent problem to be solved in the field of power system optimization configuration. Summary of the invention

[0006] In view of this, the object of the present invention is to provide a method and system for optimizing the capacity configuration of a virtual power plant taking into account the precise capacity market, so as to solve the above-mentioned technical problems.

[0007] The technical solution of the present invention is: on the one hand, the present invention provides a virtual power plant capacity configuration optimization method considering the precise capacity market, comprising:

[0008] According to the relevant parameters of the virtual power plant, a virtual power plant model is constructed, wherein the relevant parameters of the virtual power plant include: distributed power generation information in the virtual power plant, the rated capacity E of the energy storage system ESS , charge and discharge efficiency, initial state of charge SOC and controllable load characteristic information;

[0009] Establish a capacity market model based on the pre-processed historical demand data and price data of the capacity market;

[0010] Based on the virtual power plant model and the capacity market model, an optimization configuration model is established with the goal of maximizing the total revenue of the virtual power plant in the capacity market and taking system reliability into consideration;

[0011] Solving the optimization configuration model, outputting a global optimal solution, and obtaining an optimal configuration plan for the virtual power plant in the capacity market;

[0012] Based on the optimal configuration plan, a comprehensive evaluation of the virtual power plant capacity configuration is conducted.

[0013] Preferably, according to the relevant parameters of the virtual power plant, a virtual power plant model is constructed, including:

[0014] Initialize a virtual power plant model based on the collected relevant parameter data of the virtual power plant, where the virtual power plant model includes determining distributed generation, energy storage system and controllable load;

[0015] The output power of the distributed generation is obtained by modeling according to the generation characteristics and environmental factors of the generation system;

[0016] The charge state change model of the energy storage system is:

[0017]

[0018] Among them, P ESS (t) is the charge and discharge power of the energy storage system at time t, charging is positive / discharging is negative, Δt is the time interval, E ESS is the rated capacity of the energy storage system;

[0019] According to the load characteristics and user acceptance, the upper and lower limits are set to obtain the adjustable power P of the controllable load. CL (t).

[0020] Preferably, the method for preprocessing the capacity market historical demand data and price data comprises:

[0021] Clean the collected historical demand and price data of the capacity market and remove obviously erroneous data points through data screening algorithms;

[0022] The data with obviously erroneous data points removed are then normalized to map the data to a specific interval to eliminate the dimensional effect and reduce noise interference.

[0023] Preferably, a capacity market model is established based on the pre-processed capacity market historical demand data and price data, including:

[0024] Based on historical data and load growth trends, the time series analysis method is used to predict the demand D(t) of the capacity market;

[0025] Establish a pricing mechanism model for the capacity market: Assume that the capacity price C(t) is related to the market supply and demand balance, and establish a relationship curve based on supply and demand:

[0026]

[0027] Where a and b are price coefficients, and S(t) is the capacity provided by the virtual power plant at time t.

[0028] Preferably, based on the virtual power plant model and the capacity market model, with the goal of maximizing the total revenue of the virtual power plant in the capacity market, taking reliability into consideration, an optimization configuration model is established, wherein the total revenue of the virtual power plant in the capacity market includes capacity revenue and ancillary service revenue;

[0029]

[0030] Where T is the optimization period, R AS (t) is the ancillary service revenue, C op (t) is the operating cost of the virtual power plant;

[0031] Constraints:

[0032] Power balance constraints:

[0033] P DG (t)+P ESS (t)+P CL (t) = S(t)

[0034] Among them, P DG (t): output power of distributed generation at time t, P ESS (t): The charging and discharging power of the energy storage system at time t, charging is positive and discharging is negative, P CL (t): power of controllable load at time t;

[0035] Distributed generation power limits:

[0036]

[0037] The minimum output power of distributed generation at time t; The maximum output power of distributed generation at time t;

[0038] Energy storage system power and state of charge limitations:

[0039]

[0040] SOC min ≤SOC(t)≤SOC max

[0041] The minimum output power of the energy storage system at time t;

[0042] Controllable load power limit:

[0043]

[0044] and are the minimum power and maximum power of the controllable load at time t respectively;

[0045] System reliability constraints: Constraints are established by establishing reliability evaluation indicators.

[0046] Preferably, a particle swarm optimization algorithm is used to solve the optimization configuration model, output a global optimal solution, and obtain the optimal configuration scheme of the virtual power plant in the capacity market, including:

[0047] The particle swarm optimization algorithm is started, and each particle generates a configuration scheme of the virtual power plant according to its initial position;

[0048] Calculate the objective function value corresponding to each particle, that is, the total revenue of the virtual power plant in the capacity market, and check whether the constraints are met;

[0049] Update the individual optimal position and global optimal position of the particle according to the objective function value, and continuously iterate through the speed and position update formula;

[0050] When the iteration termination condition is met, the global optimal solution is output, that is, the optimal configuration plan of the virtual power plant in the capacity market.

[0051] Preferably, each particle represents a configuration scheme of the virtual power plant, and the optimal solution is searched by continuously updating the speed and position of the particle. The particle speed update formula is:

[0052] v i (t+1)=wv i (t)+c1r1(pbest,i -x i (t))+c2r2(g best -x i (t))

[0053] Among them, v i (t) is the velocity of particle i at time t, w is the inertia weight, c1 and c2 are learning factors, r1 and r2 are random numbers between [0,1], p best,i is the individual optimal position of particle i, g best is the global optimal position, x i (t) is the position of particle i at time; the particle position update formula is:

[0054] x i (t+1)=x i (t)+v i (t+1)

[0055] When the iteration termination condition is met, the global optimal solution is output, that is, the optimal configuration plan of the virtual power plant in the capacity market;

[0056] Comprehensively evaluate the capacity configuration of the virtual power plant based on the optimal configuration scheme, where the optimal configuration scheme includes the power settings of distributed generation, energy storage and controllable loads;

[0057] Compare the optimal configuration scheme with the system reliability index to verify whether it meets the reliability requirements; if not, analyze the reasons and propose improvement measures;

[0058] Analyze the revenue growth and cost reduction ratio of virtual power plants in the capacity market, and evaluate the impact on market supply and demand balance and price stability.

[0059] In a second aspect, the present invention further provides a virtual power plant capacity configuration optimization system considering the precise capacity market, comprising:

[0060] Data collection and preprocessing module, used to collect virtual power plant parameters, capacity market data, and preprocess the capacity market data;

[0061] A virtual power plant model building module initializes the virtual power plant model according to the collected virtual power plant parameters;

[0062] The capacity market model construction module initializes the parameters of the capacity market model in detail according to the preprocessed capacity market data;

[0063] The optimization configuration module aims to maximize the total revenue of the virtual power plant in the capacity market, taking system reliability into consideration;

[0064] A solution module solves the optimization configuration model through an optimization algorithm, outputs a global optimal solution, and obtains an optimal configuration plan for the virtual power plant in the capacity market;

[0065] The result output and evaluation module comprehensively evaluates the capacity configuration of the virtual power plant based on the optimal configuration solution;

[0066] Preferably, the solution module includes:

[0067] Particle swarm optimization algorithm module: Start the particle swarm optimization algorithm, and each particle generates a configuration scheme of the virtual power plant according to its initial position;

[0068] Calculation module: calculates the objective function value corresponding to each particle, that is, the total revenue of the virtual power plant in the capacity market, and checks whether the constraints are met;

[0069] Update iteration module: Update the individual optimal position and global optimal position of the particle according to the objective function value, and continuously iterate through the speed and position update formula; when the iteration termination condition is met, output the global optimal solution and obtain the optimal configuration plan of the virtual power plant in the capacity market.

[0070] The present invention provides a virtual power plant capacity configuration optimization method and system considering the precise capacity market. The method constructs a comprehensive virtual power plant model that can accurately reflect the dynamic characteristics and operating limitations of distributed power generation, energy storage systems and controllable loads. At the same time, an accurate capacity market model is also established, including a demand forecasting model and a price mechanism model to predict market demand changes and price fluctuations. On this basis, an optimization configuration model is constructed, and the optimization configuration model is used to maximize the total revenue of the virtual power plant in the capacity market, while considering the reliability constraints of the system.

[0071] The present invention realizes efficient allocation of virtual power plant resources through precise modeling and intelligent optimization algorithms to improve its competitiveness and economic benefits in the capacity market while ensuring the reliability and economy of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0073] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0074] Figure 1A schematic flow chart of a method for optimizing capacity configuration of a virtual power plant taking into account a precise capacity market provided by the present invention. DETAILED DESCRIPTION

[0075] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present invention. Instead, they are merely examples of systems consistent with some aspects of the present invention as detailed in the appended claims.

[0076] In order to maximize the economic benefits of virtual power plants and achieve optimal configuration of power systems while ensuring system reliability, this implementation scheme first provides a virtual power plant capacity configuration optimization method considering the precise capacity market, such as Figure 1 As shown, including:

[0077] S1: Construct a virtual power plant model based on the relevant parameters of the virtual power plant, where the relevant parameters of the virtual power plant include: distributed power generation information in the virtual power plant, the rated capacity E of the energy storage system ESS , charge and discharge efficiency, initial state of charge SOC and controllable load characteristic information;

[0078] For distributed generation in virtual power plants (such as photovoltaic cells STC , η T etc.), collect various technical parameters, such as power, temperature coefficient, reference temperature, etc. under standard test conditions of photovoltaic cells, as well as other power generation types (such as wind speed-power curve parameters of wind power generation, etc.).

[0079] The rated capacity E of the energy storage system ESS , charging and discharging efficiency and other key parameters, while recording the initial state of charge (SOC) of the energy storage system.

[0080] In-depth analysis of controllable load characteristics, collection of characteristic parameters, including load type (such as industrial load, commercial load, residential load and other types of load characteristics), load curves at different time periods, user acceptance of load regulation and other information.

[0081] Collect historical demand data from the capacity market, covering demand data from different seasons and time periods (such as working days, holidays, peak hours, off-peak hours, etc.) to fully reflect the changing patterns of demand. At the same time, obtain price data, including historical transaction prices, price fluctuation ranges, and other information. Clean the collected data, remove obviously erroneous data points (such as demand values ​​or price values ​​that exceed the normal range) through data screening algorithms, and then normalize and map the data to a specific interval (such as the [0,1] interval) to eliminate dimensional effects, facilitate subsequent calculations and analysis, and reduce noise interference.

[0082] S2: Establish a capacity market model based on the pre-processed capacity market historical demand data and price data;

[0083] S3: Based on the virtual power plant model and the capacity market model, with the goal of maximizing the total revenue of the virtual power plant in the capacity market and considering system reliability, an optimization configuration model is established;

[0084] Initialize the virtual power plant model based on the collected data, including determining the initial state of distributed generation, energy storage and controllable load. Initialize the parameters of the capacity market model, such as price coefficients a and b, and parameters of the demand forecasting model.

[0085] Virtual power plant model construction: A virtual power plant contains a variety of distributed energy resources, such as distributed generation (DG), energy storage system (ESS) and controllable load (CL).

[0086] The output power of distributed generation can be modeled based on its generation characteristics and environmental factors, such as for a solar photovoltaic system:

[0087]

[0088] Among them, P STC is the power under standard test conditions, G(t) is the actual light intensity, G STC is the standard light intensity, η T is the temperature coefficient, T(t) is the photovoltaic cell temperature, T ref is the reference temperature.

[0089] The state of charge (SOC) change model of the energy storage system is:

[0090]

[0091] Among them, P ESS (t) is the charge and discharge power of the energy storage system at time t (charging is positive, discharging is negative), Δt is the time interval, E ESS is the rated capacity of the energy storage system.

[0092] Adjustable power P of controllable loadCL (t) Upper and lower limits can be set based on load characteristics and user acceptance.

[0093] Capacity market model establishment: Demand forecasting of the capacity market is key. Based on historical data and load growth trends, time series analysis and other methods are used to predict the demand D(t) of the capacity market. For example, using the autoregressive moving average model (ARMA):

[0094]

[0095] in, and θ j are model parameters, p and q are model orders, and ε(t) is a white noise sequence.

[0096] Modeling the price mechanism of the capacity market: Assuming that the capacity price C(t) is related to the market supply and demand balance, according to the supply and demand relationship curve:

[0097]

[0098] Among them, a and b are price coefficients, and S(t) is the capacity provided by the virtual power plant at that moment.

[0099] S3: Based on the virtual power plant model and the capacity market model, with the goal of maximizing the total revenue of the virtual power plant in the capacity market and considering system reliability, an optimization configuration model is established;

[0100] Optimization configuration model establishment: For the optimization algorithm, set the parameters of the particle swarm optimization algorithm, such as the number of particles, inertia weight w, learning factors c1 and c2.

[0101] Objective function: The goal is to maximize the total revenue of the virtual power plant in the capacity market, while taking system reliability into consideration. The total revenue includes capacity revenue and possible ancillary service revenue.

[0102]

[0103] Where T is the optimization period, R AS (t) is the ancillary service revenue, C op (t) is the operating cost of the virtual power plant, including the operation and maintenance cost of distributed energy and the charging and discharging loss cost of energy storage.

[0104] Constraints:

[0105] Power balance constraints:

[0106] P DG (t)+P ESS (t)+P CL (t) = S(t)

[0107] PDG (t): output power of distributed generation at time t, P ESS (t): The charging and discharging power of the energy storage system at time t, charging is positive and discharging is negative, P CL (t): The power of the controllable load at time t.

[0108] Distributed generation power limits:

[0109]

[0110] The minimum output power of distributed generation at time t; The maximum output power of distributed generation at time t.

[0111] Energy storage system power and state of charge limitations:

[0112]

[0113] SOC min ≤SOC(t)≤SOC max

[0114] The minimum output power of the energy storage system at time t.

[0115] Controllable load power limit:

[0116]

[0117] and are the minimum power and maximum power of the controllable load at time t respectively.

[0118] System reliability constraints: For example, to ensure that the power supply is not interrupted under a certain confidence level, this can be constrained by establishing reliability evaluation indicators such as LOLP (Loss of Load Probability):

[0119] LOLP≤LOLP max

[0120] S4: Solve the optimization configuration model, output the global optimal solution, and obtain the optimal configuration plan of the virtual power plant in the capacity market;

[0121] In order to solve the optimization configuration model, the present invention adopts intelligent optimization algorithms such as particle swarm optimization (PSO) or genetic algorithm (GA). These algorithms can efficiently search for the optimal solution and quickly find a satisfactory solution even in the face of complex constraints and nonlinear objective functions.

[0122] (1) Start the particle swarm optimization algorithm, and each particle generates a configuration plan for the virtual power plant based on its initial position.

[0123] (2) Calculate the objective function value corresponding to each particle, that is, the total revenue of the virtual power plant in the capacity market, and check whether the constraints are met.

[0124] (3) Update the individual optimal position and global optimal position of the particle according to the objective function value, and continuously iterate through the speed and position update formula.

[0125] (4) When the iteration termination conditions are met (such as reaching the maximum number of iterations or the objective function value converges), the global optimal solution is output, that is, the optimal configuration plan of the virtual power plant in the capacity market.

[0126] The particle swarm optimization algorithm (PSO) is used to solve the above optimization configuration model. Taking the particle swarm optimization algorithm as an example, each particle represents a configuration scheme of the virtual power plant, and the optimal solution is searched by continuously updating the speed and position of the particle. The particle speed update formula is:

[0127] v i (t+1)=wv i (t)+c1r1(p best,i -x i (t))+c2r2(g best -x i (t))

[0128] Among them, v i (t) is the velocity of particle i at time t, w is the inertia weight, c1 and c2 are learning factors, r1 and r2 are random numbers between [0,1], p best,i is the individual optimal position of particle i, g best is the global optimal position, x i (t) is the position of particle i at time. The particle position update formula is:

[0129] x i (t+1)=x i (t)+v i (t+1)

[0130] When the iteration termination conditions are met (such as reaching the maximum number of iterations, such as 100-500 iterations; or the objective function value converges, that is, the change in the objective function value for multiple consecutive iterations is less than the set threshold), the global optimal solution is output, that is, the optimal configuration plan for the virtual power plant in the capacity market.

[0131] S5: Based on the optimal configuration plan, conduct a comprehensive evaluation of the virtual power plant capacity configuration.

[0132] Output the optimal configuration plan, including information such as power settings of distributed generation, energy storage and controllable loads. Calculate system reliability indicators such as LOLP and compare them with the set reliability requirements (such as) to verify whether the reliability requirements are met. If not, analyze the reasons (such as insufficient energy storage capacity in the configuration plan, poor stability of distributed generation, etc.) and propose improvement measures (such as increasing energy storage capacity, optimizing distributed generation combinations, etc.).

[0133] An in-depth analysis of the revenue of virtual power plants in the capacity market is conducted, which is compared with traditional configuration methods. The advantages of the method of the present invention are reflected in terms of revenue growth rate, cost reduction ratio, etc., while the impact on market supply and demand balance and price stability is evaluated.

[0134] By implementing the method of the present invention, virtual power plants can participate in the electricity capacity market more flexibly, which can not only improve their economic benefits, but also enhance the stability and reliability of the power system, and provide strong support for the healthy development of the power market.

[0135] In a second aspect, the present invention provides a virtual power plant capacity configuration optimization system considering an accurate capacity market, comprising:

[0136] Data collection and preprocessing module, used to collect virtual power plant parameters, capacity market data, and preprocess the capacity market data;

[0137] A virtual power plant model building module initializes the virtual power plant model according to the collected virtual power plant parameters;

[0138] The capacity market model construction module initializes the parameters of the capacity market model in detail according to the preprocessed capacity market data;

[0139] The optimization configuration module aims to maximize the total revenue of the virtual power plant in the capacity market, taking into account the reliability of the power system;

[0140] A solution module solves the optimization configuration model through an optimization algorithm, outputs a global optimal solution, and obtains an optimal configuration plan for the virtual power plant in the capacity market;

[0141] The result output and evaluation module comprehensively evaluates the capacity configuration of the virtual power plant based on the optimal configuration solution;

[0142] The solution module includes:

[0143] Particle swarm optimization algorithm module: Start the particle swarm optimization algorithm, and each particle generates a configuration scheme of the virtual power plant according to its initial position;

[0144] Calculation module: calculates the objective function value corresponding to each particle, that is, the total revenue of the virtual power plant in the capacity market, and checks whether the constraints are met;

[0145] Update iteration module: Update the individual optimal position and global optimal position of the particle according to the objective function value, and continuously iterate through the speed and position update formula; when the iteration termination condition is met, output the global optimal solution and obtain the optimal configuration plan of the virtual power plant in the capacity market.

[0146] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These changes and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A virtual power plant capacity configuration optimization method considering the precise capacity market, characterized in that: include: According to the relevant parameters of the virtual power plant, a virtual power plant model is constructed, wherein the relevant parameters of the virtual power plant include: distributed power generation information in the virtual power plant, the rated capacity E of the energy storage system ESS , charge and discharge efficiency, initial state of charge SOC and controllable load characteristic information; Establish a capacity market model based on the pre-processed historical demand data and price data of the capacity market; Based on the virtual power plant model and the capacity market model, an optimization configuration model is established with the goal of maximizing the total revenue of the virtual power plant in the capacity market and considering the reliability of the power system; Solving the optimization configuration model, outputting a global optimal solution, and obtaining an optimal configuration plan for the virtual power plant in the capacity market; Based on the optimal configuration plan, a comprehensive evaluation of the virtual power plant capacity configuration is conducted.

2. A virtual power plant capacity configuration optimization method considering the precise capacity market according to claim 1, characterized in that: According to the relevant parameters of the virtual power plant, a virtual power plant model is constructed, including: Initialize a virtual power plant model based on the collected relevant parameter data of the virtual power plant, where the virtual power plant model includes determining distributed generation, energy storage system and controllable load; The output power of the distributed generation is obtained by modeling according to the generation characteristics and environmental factors of the generation system; The charge state change model of the energy storage system is: Among them, P ESS (t) is the charge and discharge power of the energy storage system at time t, charging is positive / discharging is negative, Δt is the time interval, E ESS is the rated capacity of the energy storage system; According to the load characteristics and user acceptance, the upper and lower limits are set to obtain the adjustable power P of the controllable load. CL (t).

3. The method for optimizing capacity configuration of a virtual power plant considering an accurate capacity market according to claim 1, characterized in that: The method for preprocessing the capacity market historical demand data and price data comprises: Clean the collected historical demand and price data of the capacity market and remove obviously erroneous data points through data screening algorithms; The data with obviously erroneous data points removed are then normalized to map the data to a specific interval to eliminate the dimensional effect and reduce noise interference.

4. The method for optimizing capacity configuration of a virtual power plant considering an accurate capacity market according to claim 1, characterized in that: Based on the pre-processed historical demand data and price data of the capacity market, a capacity market model is established, including: Based on historical data and load growth trends, the time series analysis method is used to predict the demand D(t) of the capacity market; Establish a pricing mechanism model for the capacity market: Assume that the capacity price C(t) is related to the market supply and demand balance, and establish a relationship curve based on supply and demand: Where a and b are price coefficients, and S(t) is the capacity provided by the virtual power plant at time t.

5. The virtual power plant capacity configuration optimization method considering the precise capacity market according to claim 1 is characterized in that: Based on the virtual power plant model and the capacity market model, an optimization configuration model is established with the goal of maximizing the total revenue of the virtual power plant in the capacity market and taking reliability into consideration, wherein the total revenue of the virtual power plant in the capacity market includes capacity revenue and ancillary service revenue; Where T is the optimization period, R AS (t) is the ancillary service revenue, C op (t) is the operating cost of the virtual power plant; Constraints: Power balance constraints: P DG (t)+P ESS (t)+P CL (t)=S(t) Among them, P DG (t): output power of distributed generation at time t, P ESS (t): The charging and discharging power of the energy storage system at time t, charging is positive and discharging is negative, P CL (t): power of controllable load at time t; Distributed generation power limits: The minimum output power of distributed generation at time t; The maximum output power of distributed generation at time t; Energy storage system power and state of charge limitations: SOC min ≤SOC(t)≤SOC max The minimum output power of the energy storage system at time t; Controllable load power limit: and are the minimum power and maximum power of the controllable load at time t respectively; System reliability constraints: Constraints are established by establishing reliability evaluation indicators.

6. The virtual power plant capacity configuration optimization method considering the precise capacity market according to claim 1 is characterized in that: The particle swarm optimization algorithm is used to solve the optimization configuration model, output the global optimal solution, and obtain the optimal configuration plan of the virtual power plant in the capacity market, including: The particle swarm optimization algorithm is started, and each particle generates a configuration scheme of the virtual power plant according to its initial position; Calculate the objective function value corresponding to each particle, that is, the total revenue of the virtual power plant in the capacity market, and check whether the constraints are met; Update the individual optimal position and global optimal position of the particle according to the objective function value, and continuously iterate through the speed and position update formula; When the iteration termination condition is met, the global optimal solution is output, that is, the optimal configuration plan of the virtual power plant in the capacity market.

7. A virtual power plant capacity configuration optimization method considering the precise capacity market according to claim 6, characterized in that: Each particle represents a configuration scheme of the virtual power plant. The optimal solution is searched by continuously updating the speed and position of the particle. The particle speed update formula is: v i (t+1)=wv i (t)+c1r1(p best,i -x i (t))+c2r2(g best -x i (t)) Among them, v i (t) is the velocity of particle i at time t, w is the inertia weight, c1 and c2 are learning factors, r1 and r2 are random numbers between [0,1], p best,i is the individual optimal position of particle i, g best is the global optimal position, x i (t) is the position of particle i at time; the particle position update formula is: x i (t+1)=x i (t)+v i (t+1) When the iteration termination condition is met, the global optimal solution is output, that is, the optimal configuration plan of the virtual power plant in the capacity market; Comprehensively evaluate the capacity configuration of the virtual power plant based on the optimal configuration scheme, where the optimal configuration scheme includes the power settings of distributed generation, energy storage and controllable loads; Compare the optimal configuration scheme with the reliability index of the power system to verify whether the reliability requirements are met; if not, analyze the reasons and propose improvement measures; Analyze the revenue growth and cost reduction ratio of virtual power plants in the capacity market, and evaluate the impact on market supply and demand balance and price stability.

8. A virtual power plant capacity configuration optimization system considering the precise capacity market, characterized in that: include: Data collection and preprocessing module, used to collect virtual power plant parameters, capacity market data, and preprocess the capacity market data; A virtual power plant model building module initializes the virtual power plant model according to the collected virtual power plant parameters; The capacity market model construction module initializes the parameters of the capacity market model in detail according to the preprocessed capacity market data; The optimization configuration module aims to maximize the total revenue of the virtual power plant in the capacity market, taking into account the reliability of the power system; A solution module solves the optimization configuration model through an optimization algorithm, outputs a global optimal solution, and obtains an optimal configuration plan for the virtual power plant in the capacity market; The result output and evaluation module conducts a comprehensive evaluation of the capacity configuration of the virtual power plant based on the optimal configuration plan.

9. A virtual power plant capacity configuration optimization system considering the precise capacity market according to claim 8, characterized in that: The solution module includes: Particle swarm optimization algorithm module: Start the particle swarm optimization algorithm, and each particle generates a configuration scheme of the virtual power plant according to its initial position; Calculation module: calculates the objective function value corresponding to each particle, that is, the total revenue of the virtual power plant in the capacity market, and checks whether the constraints are met; Update iteration module: Update the individual optimal position and global optimal position of the particle according to the objective function value, and continuously iterate through the speed and position update formula; when the iteration termination condition is met, output the global optimal solution and obtain the optimal configuration plan of the virtual power plant in the capacity market.

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