Centralized regulation and control resource supporting capability assessment method considering electricity market
By constructing an assessment scheme that couples technology, market and commercial willingness, the problem of unquantified commercial willingness of resources in existing assessment methods is solved, and accurate assessment of the availability of regulatory resources in the power market environment is achieved, providing accurate availability assessment.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-08
- Publication Date
- 2026-03-10
AI Technical Summary
Existing assessment methods lack deep integration with electricity market mechanisms and fail to quantify the commercial willingness of resources, resulting in a discrepancy between assessment results and the actual value of resources in the real market, and failing to accurately reflect their availability in the electricity market environment.
A comprehensive evaluation scheme is constructed that couples technology, market and business willingness. Through Monte Carlo simulation and regression analysis, the probability of winning the bid and business willingness of regulatory resources are quantified. Combined with a multi-timescale market clearing model, the overall effective support capacity of regulatory resources is calculated.
It achieves a precise mapping of regulation resources from static technical capabilities to dynamic market support capabilities, helps power grid dispatching agencies identify truly reliable regulation resources, solves the problem of fragmented technical representation and market disconnect in traditional assessment methods, and provides accurate resource availability assessment.
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Figure CN121638795A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of power system analysis and operation technology, and more specifically, to a method for assessing the centralized control resource support capacity of the power market. Background Technology
[0002] The power system is undergoing a profound transformation from a traditional centralized, planned model to a market-oriented and low-carbon one. Against this backdrop, accurately assessing the grid's support capacity for centralized control resources such as generating units and energy storage power stations is crucial for ensuring the safe and stable operation of the system and promoting the integration of new energy sources.
[0003] Currently, assessment methods for the support capacity of centralized regulation resources mainly focus on the physical and technical levels. Existing research and practice typically construct assessment index systems based on the static technical parameters of resources, such as rated power, ramp rate, and start-up / shutdown time, and use these to determine whether resources can meet the system's technical requirements for power balance, frequency regulation, and reserve capacity. These methods can reflect the theoretical support potential of resources to a certain extent. However, with the continuous improvement of the electricity market system, the actual deployment and output of resources are determined by market clearing results, and whether their technical capabilities can be transformed into effective support for the power grid strongly depends on their performance in the market environment. Existing assessment methods have significant shortcomings: First, existing methods generally lack deep integration with electricity market mechanisms. They mostly assume that the technical capabilities of resources can be unconditionally mobilized by the dispatch center, ignoring the fundamental fact that resources must win bids through market competition to secure their output. A resource with superior technical performance may fail to win a bid due to uncompetitive pricing, leaving its technical capabilities idle at critical moments. Conversely, a resource with ordinary technical parameters may frequently win bids due to flexible bidding strategies, providing significant actual support. This disconnect between technology and the market leads to a significant discrepancy between existing assessment results and the actual value of resources in the real market.
[0004] Secondly, existing assessment systems struggle to quantify the commercial willingness of resources to participate in the market. Resource pricing behavior is influenced not only by physical costs but also by subjective factors such as operational strategies, market expectations, and risk appetite. Most existing studies treat resources as passive price takers or assume they bid at marginal cost, failing to effectively capture the strategic behaviors that resource owners may exhibit based on commercial considerations (such as holding capacity or raising bids). This neglect of commercial willingness prevents assessment systems from reflecting the true availability of resources under specific market conditions. Summary of the Invention
[0005] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a method for assessing the centralized regulation and control resource support capabilities of the electricity market. By constructing a comprehensive assessment scheme that couples technology, market, and business intentions, the method addresses the problems of traditional assessment methods, such as the dispersed and static nature of technological representation, disconnect from actual market operation, and failure to consider the business strategies of resource owners, which result in assessment results that cannot accurately reflect the true availability of resources in the electricity market environment.
[0006] To achieve the above objectives, the present invention provides the following technical solution: The proposed method for assessing the support capacity of centralized regulation resources in the electricity market includes the following steps: constructing a set of power system operation scenarios that include uncertainties in system load and renewable energy output; conducting electricity market clearing simulations based on the set of scenarios to determine the winning probability of regulation resources under different market types; and calculating the overall effective support capacity of the regulation resources based on the upper limit of the technical capabilities of the regulation resources, the winning probability, and the commercial willingness coefficient.
[0007] In a preferred embodiment, constructing a set of power system operation scenarios that includes uncertainties in system load and renewable energy output includes: obtaining a parameter set of the distribution of predicted uncertainties in system load and renewable energy output based on the statistical characteristics of historical power system operation data; performing Monte Carlo simulation based on the parameter set to generate multiple system load-renewable energy output scenarios, thus constituting the set of power system operation scenarios.
[0008] In a preferred embodiment, the predictions of system load and renewable energy output are obtained by superimposing prediction error terms on their respective baseline curves. The system load prediction baseline curve is composed of a base load component, a meteorologically sensitive load component, a special event load component, and a random error term. The wind power output prediction baseline curve is characterized by a function determined based on wind speed, air density, and the wind turbine's wind-catching area. The photovoltaic output prediction baseline curve is characterized by a function determined based on solar radiation intensity, temperature, and the effective area of the photovoltaic panel. The prediction uncertainty distribution parameter set includes the expected value and variance of the load prediction error term and the wind power and photovoltaic output prediction error terms.
[0009] In a preferred embodiment, the step of performing electricity market clearing simulation based on the scenario set to determine the winning probability of regulatory resources under different market types includes: for each operating scenario in the scenario set, simulating and generating bids from each market participant; inputting the bids into a multi-timescale market clearing model for sequential clearing to obtain the winning capacity of the regulatory resources in the day-ahead energy market, frequency regulation ancillary service market, and spinning reserve market; and based on the statistical results of the winning capacity in all scenarios, calculating the ratio of the average winning capacity of the regulatory resources in each market to its corresponding upper limit of technical capability, as the winning probability in each market.
[0010] In a preferred embodiment, the simulation of generating bids for each market participant includes: using historical market clearing and bid data, and obtaining strategic coefficients reflecting their bidding strategies through regression analysis; and calculating and generating their bid curves based on the strategic coefficients and current scenario information using a preset bid prediction function.
[0011] In a preferred embodiment, the price prediction function is a linear combination of the unit's marginal cost, market tension index, and market competition level index; wherein the coefficients of the linear combination are strategic pricing coefficients obtained through regression of historical data.
[0012] In a preferred embodiment, the multi-timescale market clearing model includes clearing models for the day-ahead energy market, the frequency regulation ancillary services market, and the spinning reserve market. The day-ahead energy market clearing model uses social welfare maximization as its objective function and includes system power balance constraints, network security constraints, and unit operation constraints. The frequency regulation ancillary services market clearing model uses the minimization of system frequency regulation capacity procurement costs as its objective function and includes system frequency regulation demand constraints, unit frequency regulation capacity constraints, and frequency regulation performance standard constraints. The spinning reserve market clearing model uses the minimization of system spinning reserve capacity procurement costs as its objective function and includes system reserve demand constraints, unit reserve capacity constraints, and response time constraints.
[0013] In a preferred embodiment, the upper limit of technical capability includes maximum peak capacity, maximum frequency regulation capacity, and maximum reserve capacity; the upper limit of technical capability is calculated based on the rated power, energy capacity, ramp rate, start-stop time static technical parameters of the regulation resource, and taking into account its continuous operation constraints and response requirements.
[0014] In a preferred embodiment, the step of calculating the overall effective support capacity of the regulated resources based on the upper limit of the technical capabilities of the regulated resources, the probability of winning the bid, and the commercial willingness coefficient includes: obtaining the commercial willingness coefficient obtained by analyzing the regression of the historical bidding behavior of the regulated resources; multiplying the upper limit of the technical capabilities of the resources in the corresponding market, the probability of winning the bid, and the commercial willingness coefficient, and then weighting and summing them to obtain the overall effective support capacity of the resources.
[0015] In a preferred embodiment, the formula for calculating the business willingness coefficient is as follows:
[0016] In the formula, The strategic bidding coefficient for the resource. The initial business intention is derived by retrospectively inferring from historical strategic pricing coefficients; As a dynamic willingness adjustment factor, This indicates the expected value of the market clearing price. This represents the average operating cost of the resource.
[0017] This invention considers the technical effects and advantages of a method for assessing the centralized regulation and control resource support capacity of the electricity market: This invention constructs a comprehensive evaluation scheme that couples technology, market, and commercial willingness. Based on historical power system data and market clearing mechanisms, it uses Monte Carlo simulation and regression analysis to quantify market winning probabilities and commercial willingness respectively, and integrates these three factors using a multiplicative fusion model. This achieves a precise mapping of centralized control resources from static technical capabilities to dynamic market support capabilities. Furthermore, by simulating competition and bidding processes in a real market environment, this method helps power grid dispatching agencies identify truly reliable control resources under given market rules and commercial behavior. It effectively solves the core problems of traditional evaluation methods, such as fragmented and static technical representations, disconnect from actual market operation, and failure to consider resource owners' commercial strategies, which lead to evaluation results that cannot accurately reflect the true availability of resources in the power market environment. Attached Figure Description
[0018] Figure 1 A schematic diagram of the process for assessing the centralized regulation and control resource support capacity of the power market, provided in an embodiment of the present invention. Detailed Implementation
[0019] 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. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.
[0020] Example 1, Figure 1 This invention presents a method for assessing the centralized regulation and control resource support capacity of the electricity market, comprising the following steps: S1. Construct a set of power system operation scenarios that include uncertainties in system load and renewable energy output.
[0021] This step aims to generate a large number of future system states that take into account uncertainty, providing a basis for market simulation; the specific implementation method is as follows: Obtaining the prediction baseline curve and uncertainty distribution parameter set: First, based on historical data, construct the prediction baseline curves for system load, wind power, and photovoltaic output; The system load forecast baseline curve is composed of the superposition of the base load component, the meteorological sensitive component, the special event component, and the random error term. Its expression is: , in, This represents the load forecast value for time period t. This represents the base load component over time period t. This represents the meteorological sensitive component for time period t. This represents the special event component in time period t. This represents the random error term for time period t; The wind power output prediction baseline curve is characterized by a power conversion function determined by wind speed, air density, and the wind turbine's catcher area. Its expression is: The baseline curve for wind power output prediction is shown in the following formula: , , In the formula, The wind speed represents time t. The density of air at time t. This indicates the wind-catching area of the wind turbine. The prediction error term for wind power output follows a normal distribution. Indicates the cut-in wind speed. Indicates the cut-out wind speed. Indicates the rated wind speed; The photovoltaic power output prediction baseline curve is characterized by a function determined by solar radiation intensity, temperature, and the effective area of the photovoltaic panel, and its expression is: , , In the formula, This represents the intensity of solar radiation at time t. The temperature at time t, Indicates the effective area of the photovoltaic panel. The prediction error term for photovoltaic output follows a normal distribution. By statistically analyzing historical prediction errors, the statistical distribution of the aforementioned error terms is obtained (usually assumed to follow a normal distribution), and key parameters such as their expected value (usually 0) and variance are acquired, forming a set of prediction uncertainty distribution parameters. The parameter set for the uncertainty distribution of system load forecasting is given by the following formula: , in, This is the expected value of the load forecast. The variance of the load forecast; The parameter set for the uncertainty distribution of new energy output prediction is given by the following formula: , In the formula, The prediction error term for wind power output follows a normal distribution. This is the prediction error term for photovoltaic power output. The variance of the prediction error term for wind power output. The variance of the prediction error term for photovoltaic power output; Scene sets generated based on Monte Carlo simulation: The obtained set of prediction uncertainty distribution parameters is used as input to drive Monte Carlo simulation; In each simulation, the load and renewable energy prediction error terms for each time period t are independently and randomly sampled from their respective distributions. The sampled error values are then superimposed onto the corresponding prediction baseline curve to form a set of specific and definite load-renewable energy output operation scenarios. Repeat the above process N times (e.g., N=2000) to generate a set of power system operation scenarios containing N possible future system operating states, which is used to cover prediction uncertainties.
[0022] S2. Simulate the power market clearing process based on scenario sets to determine the probability of winning bids for regulatory resources under different market types.
[0023] This step aims to simulate the competitive outcomes of target regulatory resources under various market environments and quantify their market monetization capabilities. Simulated market participant quotes: For each running scenario generated by S1, the bidding behavior of all market participants (including the target resource and its competitors) needs to be simulated. By using historical market clearing and pricing data, regression analysis is used to fit the strategic coefficients of each unit to reflect its pricing strategy. Based on the strategic coefficients obtained from the fitting and information from the current simulation scenario (such as system net load and competitor status), a bid is generated using a pre-defined bid prediction function. This function is a linear combination of unit marginal cost, market tension index, and market competition level index, for example: , , , , In the formula, This indicates the price quoted by generator set g during time period t. , , This indicates the cost characteristic parameters of the unit. This indicates the planned output of unit g during time period t. As an indicator of market tension, This indicates the maximum available capacity of all units except unit g. This represents the uncertainty in the system net load forecast for time period t. As an indicator of the degree of market competition, , , For unit g, the strategic bidding coefficient; Multi-timescale market clearing sequence: The generated bids are input into a multi-timescale market clearing model for simulated clearing; the model sequence includes: Day-ahead energy market clearing model: with social welfare maximization as the objective function, constraints include system power balance, network security and unit operation constraints; Frequency regulation ancillary services market clearing model: with the objective function of minimizing the procurement cost of system frequency regulation capacity, and constraints including system frequency regulation demand, unit frequency regulation capacity and frequency regulation performance standards; Spinning reserve market clearing model: The objective function is to minimize the procurement cost of system spinning reserve capacity, and the constraints include system reserve demand, unit reserve capacity and response time. By solving the clearing model above in sequence, we can obtain the winning bid power of the target control resources in the day-ahead energy market, the winning bid capacity in the frequency regulation market, and the winning bid capacity in the spinning reserve market. The objective function of the day-ahead energy market clearing model is given by the following formula: , in, This indicates the electricity purchase price for load d during time period t; This represents the electrical power consumption of load d during time period t; This represents the electricity generation price quoted by unit g during time period t; This represents the power generation of unit g during time period t; D represents the total time period of the short-term dispatch cycle; G represents the total number of loads in the system; and G represents the total number of generators in the system. The objective function of the frequency modulation ancillary services market clearing model is as follows: , in, This indicates the frequency regulation capacity quotation for unit g during time period t; This indicates the frequency regulation capacity clearing result of unit g in time period t; The objective function of the rotating reserve market model is given by the following formula: , in, This indicates the quoted price for the spinning reserve capacity of unit g during time period t; This indicates the clearing result of the spinning reserve capacity of unit g in time period t; Calculate the probability of winning the bid in each market: Perform statistical calculations after all N scenario simulations are completed; The ratio of the average winning bid capacity of resources in the energy market, frequency regulation market, and reserve market to the corresponding upper limit of technical capability is calculated as the winning probability in that market. For example, the probability of winning a bid in the energy market is calculated using the following formula: , The probability of winning a bid in the FM market is calculated using the following formula: , The probability of winning a bid in the rotating spare parts market is calculated using the following formula: , In the formula, This represents the winning bid power of resources in the energy market during time period t in scenario k; This represents the winning bid capacity of resources in the frequency modulation market during time period t in scenario k; This represents the winning bid capacity of resources in the rotating reserve market during time period t in scenario k. , , These represent the maximum peak resource capacity, maximum frequency regulation resource capacity, and maximum spinning reserve resource capacity, respectively; N represents the total number of scenarios; and T represents the total number of time periods in the evaluation cycle.
[0024] S3. The overall effective support capability for computing and regulating resources.
[0025] This step integrates physical limits, market competition outcomes, and business motivations to calculate the final effective capability index; Determine the upper limit of technical capabilities: Based on the static technical parameters of the target control resources (such as rated power, energy capacity, ramp rate, start-up and shutdown time, etc.), and considering constraints such as continuous operation and rapid response, calculate its upper limit of technical capabilities in each market, including: maximum peak capacity, maximum frequency regulation capacity, and maximum reserve capacity; the constraints such as continuous operation and rapid response include: available energy, required continuous peak time, bidirectional capacity limit, ramp rate, performance coefficient, operating status / reliability, ramp response requirements for spinning reserve, and start-up time requirements for non-spinning reserve, etc. The maximum peak capability is calculated using the following formula: , In the formula, To contribute the most technical expertise, Indicates available energy; This indicates a requirement for sustained peak time; The maximum frequency modulation capability is calculated using the following formula: , In the formula, This indicates a capacity limitation that takes into account bidirectional adjustment; Indicates the frequency modulation response time requirement; This represents the frequency modulation performance coefficient, taking into account adjustment accuracy and response delay. For the upward climbing rate, The downward slope rate; The maximum reserve capacity is calculated using the following formula: , In the formula, Indicates the current operating power; and These represent the response times of spinning reserve and non-spinning reserve, respectively. This indicates availability over a given time period, taking into account startup time and reliability. Determine the business willingness coefficient: By analyzing the historical bidding behavior of the target resource itself, the strategic bidding coefficient is derived through regression analysis, thereby deriving its initial business willingness to participate in a specific market; Calculate the dynamic willingness adjustment factor by combining expectations of future market clearing prices and average resource operating costs; The final business willingness coefficient is determined by both the initial willingness and the dynamic adjustment factor, as shown in the following formula: , In the formula, The strategic bidding coefficient for the resource. The initial business intention is derived by retrospectively inferring from historical strategic pricing coefficients; As a dynamic willingness adjustment factor, This indicates the expected value of the market clearing price. This represents the average operating cost of the resource; Calculate and aggregate effective support capabilities: For each market type (energy, frequency regulation, reserve), multiply the corresponding upper limit of technical capabilities, probability of winning the bid, and commercial willingness coefficient to obtain the effective support capability component for that market; for example: Effective peak support capacity: , Effective frequency modulation support capability: , Effective backup support capacity: , In the formula, This represents the coefficient of commercial willingness to participate in the energy market; This represents the commercial willingness coefficient for resources to participate in the FM market; This represents the commercial willingness coefficient for resources to participate in the reserve market; , , These represent the maximum peak resource capacity, the maximum frequency regulation resource capacity, and the maximum spinning reserve resource capacity, respectively. , , These represent the probabilities of winning bids in the energy market, the frequency regulation market, and the spinning reserve market, respectively. Finally, weights ω1, ω2, and ω3 (satisfying ω1+ω2+ω3=1) are set according to system scheduling requirements or market value, and the effective support capacity components of each market are weighted and summed to obtain the final overall effective support capacity. It should be noted that the weighting can be determined based on the system's priority in supporting each market. For example, in a system with a high proportion of renewable energy and large fluctuations, the weights of frequency regulation and reserve can be increased accordingly. Alternatively, it can be determined based on the average value ratio of various ancillary services to electrical energy in historical market data. For example, one possible weighting setting is: 0.6 for the energy market, 0.25 for the frequency regulation market, and 0.15 for the reserve market, with the weights summing to 1. The overall effective support capability integrates technical feasibility, market competitiveness, and economic drivers, providing grid dispatching agencies with quantitative and practical core decision-making basis for accurately perceiving the potential of controllable resources in the market environment and for power generators or energy storage investors to optimize their operation strategies.
[0026] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.
[0027] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.
[0028] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.
[0029] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.
[0030] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.
[0031] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.
Claims
1. A centralized regulation resource support capability evaluation method considering power market, characterized in that, The method comprises the following steps: constructing a power system operation scenario set containing system load and new energy output uncertainty; performing power market clearing simulation based on the scenario set to determine the winning probability of the regulation resource in different market types; calculating the overall effective support capacity of the regulation resource according to the upper limit of the technical capacity of the regulation resource, the winning probability and the business willingness coefficient. 2.The centralized regulation resource support capability evaluation method considering power market according to claim 1, wherein, The construction of the power system operation scenario set containing system load and new energy output uncertainty comprises: obtaining a parameter set of system load and new energy output prediction uncertainty distribution according to the statistical characteristics of historical operation data of the power system; generating a plurality of system load-new energy output scenarios based on Monte Carlo simulation based on the parameter set to constitute the power system operation scenario set. 3.The centralized regulation resource support capability evaluation method considering power market according to claim 2, wherein, The prediction of the system load and the new energy output is based on the superposition of the respective reference curves and the prediction error items; The system load prediction reference curve is composed of the superposition of the basic load component, the meteorological sensitive load component, the special event load component and the random error item; The wind power output prediction reference curve is represented by a function determined based on wind speed, air density and wind turbine catch area; The photovoltaic output prediction reference curve is represented by a function determined based on solar radiation intensity, temperature and photovoltaic panel effective area; The prediction uncertainty distribution parameter set includes the expected value and variance of the load prediction error item and the wind power and photovoltaic output prediction error item.
4. The method of claim 1, wherein, The power market clearing simulation based on the scenario set to determine the winning probability of the regulation resource in different market types comprises: For each operation scenario in the scenario set, simulate to generate the quotes of each market participant; input the quotes into a multi-time scale market clearing model for sequential clearing to obtain the winning capacity of the regulation resource in the day-ahead energy market, the frequency regulation auxiliary service market and the spinning reserve market; based on the statistical results of the winning capacity in all scenarios, calculate the ratio of the average winning capacity of the regulation resource in each market to the corresponding upper limit of the technical capacity as the winning probability of each market.
5. The method for centralized regulation resource support capability assessment considering electricity market according to claim 4, characterized in that, The simulation to generate the quotes of each market participant comprises: use historical market clearing and quote data to fit the strategic coefficients reflecting the quoting strategy through regression analysis; based on the strategic coefficients and the current scenario information, calculate the quote curve through a preset quote prediction function. 6.The method for centralized regulation resource support capability evaluation considering power market according to claim 5, wherein, The quote prediction function is a linear combination of the marginal cost of the unit, the market tension index and the market competition index; wherein the coefficients of the linear combination are the strategic quoting coefficients obtained by regression of historical data. 7.The method for centralized regulation resource support capability evaluation considering power market according to claim 4, wherein, The multi-time scale market clearing model comprises the clearing models of the day-ahead energy market, the frequency regulation auxiliary service market and the spinning reserve market; The day-ahead energy market clearing model takes the maximization of social welfare as the objective function and includes system power balance constraints, network security constraints and unit operation constraints; The frequency regulation auxiliary service market clearing model takes the minimization of system frequency regulation capacity procurement cost as the objective function and includes system frequency regulation demand constraints, unit frequency regulation capacity constraints and frequency regulation performance standard constraints; The spinning reserve market clearing model takes the minimum system spinning reserve capacity procurement cost as the objective function, and contains system reserve demand constraints, unit reserve capacity constraints and response time constraints. 8.The method for centralized regulation resource support capability evaluation considering power market according to claim 1, wherein, The upper limit of technical capability includes maximum peak capacity, maximum frequency modulation capacity and maximum reserve capacity; The upper limit of technical capability is calculated based on the rated power, energy capacity, ramp rate, start-stop time static technical parameters of the regulation resource, and considering its continuous operation constraints and response requirements. 9.The method for centralized regulation resource support capability evaluation considering power market according to claim 1, wherein, The overall effective support capability of the regulation resource is calculated according to the upper limit of technical capability of the regulation resource, the bid-winning probability and the commercial willingness coefficient, including: Obtaining the commercial willingness coefficient obtained by analyzing the historical bidding behavior regression of the regulation resource; Multiplying the upper limit of technical capability of the resource in the corresponding market, the bid-winning probability and the commercial willingness coefficient, and weighted sum to obtain the overall effective support capability of the resource. 10.The method for centralized regulation resource support capability evaluation considering power market according to claim 1, wherein, The commercial willingness coefficient calculation formula is as follows: wherein, is the strategic bidding coefficient of the resource, is the initial commercial willingness based on the historical strategic bidding coefficient backstepping; is the dynamic willingness adjustment factor, represents the expected value of the market clearing price, represents the average operating cost of the resource.