A capacity market suitability evaluation method

Abstract

The present application relates to the technical field of power market and power system operation management, and particularly relates to a capacity market adaptability evaluation method, which is based on the operation characteristics and demands of the capacity market in the power system, combines multi-dimensional evaluation theory, and realizes quantitative evaluation of the adaptability of the capacity resources by constructing a comprehensive index system including four dimensions of technology, economy, environment and society. The method adopts data acquisition and normalization technology to model key indicators such as capacity adequacy rate, regulation flexibility, carbon emission intensity and market price fluctuation sensitivity, combines weight analysis and comprehensive evaluation model, scientifically calculates the adaptability level of various resources, supports the planning, optimization and policy making of the capacity market, improves the efficiency of market operation and the rationality of resource allocation, overcomes the shortcomings of traditional power market in data processing and evaluation index construction, and provides a theoretical basis and decision support for long-term stable operation of the power system.

Description

Technical Field

[0001] The present invention relates to the technical field of power market and power system operation management, and in particular to a capacity market adaptability evaluation method. Background Art

[0002] With the profound transformation of the global energy mix and the continued rise in the proportion of renewable energy, power systems are facing unprecedented challenges. Due to the inherent intermittent and volatile nature of renewable energy sources like wind and solar, traditional power markets are struggling to cope with high penetrations of renewable energy and meet the stringent requirements for grid stability. Against this backdrop, capacity markets, as a mechanism designed to balance electricity supply and demand and ensure reliable grid operation, are gaining increasing global attention.

[0003] However, the implementation of capacity markets is not straightforward. Numerous factors influence their applicability and effectiveness. Significant differences in power resource distribution, market structure, and policy orientations exist across regions, leading to distinct variations in the performance of capacity markets in different regions. Ensuring sufficient backup capacity to mitigate potential power shortages during peak demand periods is a pressing issue.

[0004] Crucially, there is currently a lack of systematic, scientific evaluation criteria for assessing a region's suitability for a capacity market and quantifying the degree of alignment between electricity supply and demand. While existing research has explored the theoretical framework and implementation plans for capacity markets, the lack of evaluation criteria in practice often leads to significant uncertainty and regional disparity in assessing the suitability of capacity markets.

[0005] Therefore, developing a comprehensive, regionally adaptable capacity market adaptability evaluation index method has become a key issue that needs to be addressed. This method needs to comprehensively consider key factors such as the energy structure, load demand characteristics, and market mechanisms of each region, providing a scientific basis for the introduction of capacity markets and ensuring that it is consistent with local power resource characteristics and market development conditions, thereby effectively improving the stability and operational efficiency of the power market.

[0006] In summary, facing the transformation of the global energy mix and the increasing proportion of renewable energy, how to scientifically and systematically evaluate the adaptability of capacity markets in different regions has become a research challenge that urgently needs to be overcome. This paper addresses this issue by proposing a novel capacity market adaptability evaluation index method. This method aims to provide a quantitative basis for the introduction of capacity markets in regional power markets, ensuring that the construction of capacity markets truly meets local electricity demand and market development conditions. Summary of the Invention

[0007] The present invention provides a capacity market adaptability evaluation method, which can effectively solve the problems in the background technology.

[0008] In order to achieve the above object, the technical solution adopted by the present invention is:

[0009] A capacity market adaptability evaluation method includes the following steps:

[0010] Collect historical and real-time data related to the capacity market based on the power system operation data platform and market transaction database;

[0011] Based on the operational characteristics of the capacity market, an evaluation index model is constructed from four characteristic dimensions: technical, economic, environmental and social.

[0012] The analytic hierarchy process is used to calculate the indicator weight of each characteristic dimension in the evaluation indicator model;

[0013] Adjusting the weights of indicators in the evaluation indicator model based on the degree of volatility of the market environment;

[0014] A sensitivity analysis is conducted on the robustness of the evaluation index model to verify its adaptability in different market environments.

[0015] Furthermore, the collection of historical and real-time data related to the capacity market includes multiple information data sets on power demand, resource capacity, market price, power generation output, and new energy penetration;

[0016] The collected multiple information data sets are cleaned and outliers, missing values ​​and duplicate values ​​are removed. The cleaned data are normalized and dimensional differences are eliminated.

[0017] Furthermore, the outliers are identified and processed by the 3σ detection method, specifically using the following formula:

[0018] x i -μ>3σ;

[0019] The missing values ​​are filled by the mean filling method, specifically using the following formula:

[0020]

[0021] in:

[0022]

[0023] The linear normalization method is used to eliminate the dimensional differences of different indicators. The specific formula is as follows:

[0024]

[0025] Among them, x i represents valid data points (i.e., non-missing values); μ represents the mean of the data set, which is used to fill missing values; n represents the number of valid data points (i.e., the number of samples after removing missing values); x j Indicates the missing values ​​that need to be filled, and x j =NaN; σ represents the standard deviation of the data set, which is used to measure the volatility of the beam data; min(x) represents the minimum value in the data set; max(x) represents the maximum value in the data set; x′ i+ Represents the normalized data of positive indicators; x′ i- Represents the normalized data of negative indicators.

[0026] Furthermore, the random noise and fluctuation in the data set are reduced by time series smoothing method, and the moving average formula is as follows:

[0027]

[0028] Among them, SMA t represents the moving average of time t, n represents the window size of the moving average, x t-i Represents the data point of the time series at time ti;

[0029] The covariance matrix is ​​used to analyze the correlation between features and to select key features to optimize the data dimension. The following formula is used:

[0030]

[0031] C·v=λv;

[0032] Among them, C represents the covariance matrix, which is used to measure the correlation between each feature; λ is the eigenvalue, which indicates the variance of the feature; ν is the principal component corresponding to the eigenvector.

[0033] Furthermore, the adequacy, response speed, and adjustment flexibility of the evaluation index model are analyzed from a technical perspective to evaluate the adaptability of the evaluation index model to load changes;

[0034] Analyze the cost-effectiveness and market performance of the evaluation index model from an economic perspective, and evaluate the economic feasibility of the evaluation index model;

[0035] Analyze the carbon emission level and the fairness of inter-regional capacity distribution of the evaluation index model from an environmental perspective, and evaluate the green and sustainable development of the evaluation index model;

[0036] Based on the social perspective, the user's participation and potential in demand response are analyzed, and the degree of adaptability of the evaluation index model to social needs is evaluated.

[0037] Furthermore, the adequacy of the evaluation index model is measured by the capacity factor, specifically using the following formula:

[0038]

[0039] The response speed of the evaluation index model is measured by the climbing rate, and the specific formula is as follows:

[0040]

[0041] The adjustment flexibility of the evaluation index model is measured by a reliability index based on the downtime ratio, specifically using the following formula:

[0042]

[0043] The adaptability of the evaluation index model to load changes is measured by comprehensive response capability, specifically using the following formula:

[0044] CRP=w1·R ramp +w2·CF+w3·AFR;

[0045] The cost-effectiveness of the evaluation index model is measured by comprehensive benefit efficiency, specifically using the following formula:

[0046]

[0047] The market performance of the evaluation index model is measured by the return sensitivity fluctuation, specifically using the following formula:

[0048]

[0049] The carbon emission level of the evaluation index model is measured by weighted carbon emission intensity, specifically using the following formula:

[0050]

[0051] The fairness of the inter-region capacity distribution of the evaluation index model is measured by the fairness index, which is specifically measured using the following formula:

[0052]

[0053] The degree of adaptability of the evaluation index model to social needs is measured by user response potential, specifically using the following formula:

[0054]

[0055] Among them, CRP represents comprehensive response capability; R rampRamp rate; CF capacity factor, which is the ratio of actual power generation to maximum power generation capacity; AFR represents a reliability index based on downtime ratio; T outage T represents the downtime, which is the total duration of resource downtime due to planned maintenance or failure during the evaluation period, in hours; total represents the total duration of the evaluation cycle; w1, w2, w3 represent the indicator weights; ERE represents the comprehensive benefit efficiency; R t represents the total revenue in year t; C t represents the operating cost in year t; r represents the discount rate; P t represents the actual power supply; T represents the evaluation period; REV represents the revenue sensitivity fluctuation, which is used to measure the impact of market prices on revenue; σ R represents the standard deviation of returns; represents the average market price; β represents the profit sensitivity coefficient related to market price fluctuations; WCI represents weighted carbon emission intensity, which is calculated based on the weighted proportion of power generation resources; P i represents the power generation of the i-th resource; P total Indicates the total power generation of the system; EI i represents the unit carbon emission intensity of the i-th resource; FI represents the fairness index; URP represents the user response potential, which is used to comprehensively evaluate the potential of users to participate in demand response; E i,DR represents the actual electricity consumption of user i in the demand response event; E i,baseline represents the baseline electricity consumption of user i; N represents the total number of users participating in demand response.

[0056] Furthermore, the use of the analytic hierarchy process to calculate the indicator weights of each feature dimension in the evaluation indicator model includes the following steps:

[0057] Construct the judgment matrix A and use the eigenvalue and eigenvector to calculate the initial weight W of each indicator. The specific formula is as follows:

[0058]

[0059] A·W=λ max W;

[0060] The rationality of the judgment matrix A is evaluated through consistency test, specifically using the following formula:

[0061]

[0062] If CR < 0.1, the judgment matrix A passes the consistency test; where a ij represents the relative importance of feature dimensions i and j; W represents the initial weight vector; CI represents the consistency index; CR represents the consistency ratio; RI represents the random consistency index.

[0063] Furthermore, the calculated indicator weights are allocated to specific indicators under each feature dimension, and the weighted calculation of each indicator in the evaluation indicator model is evaluated. Specifically, the following formula is used:

[0064]

[0065] Among them, w i represents the weight calculated by the hierarchical analysis method; X i Indicates the normalized indicator value.

[0066] Furthermore, the adaptability of the evaluation index model is optimized by adjusting the weights of key indicators in the evaluation index model, specifically using the following formula:

[0067]

[0068] By calculating the most sensitive indicators that have the greatest impact on overall adaptability and making key adjustments, the following formula is used:

[0069]

[0070] Among them, w′ i represents the weight of the adjusted indicator i; S represents the comprehensive adaptability score, which is the evaluation result of the capacity market adaptability; S i Indicates the sensitivity to indicator i, which is the degree of influence of the weight change of indicator i on the total adaptability score; Δw i Indicates the weight change of indicator i.

[0071] Furthermore, the adaptability of the evaluation index model in different market environments is verified by calculating the robustness of the evaluation index model, specifically using the following formula:

[0072]

[0073] Wherein, R represents the robustness of the evaluation index model, which is the adaptive stability of the evaluation index model in different situations; S base represents the adaptability score in the baseline scenario.

[0074] The beneficial effects of the present invention are:

[0075] By collecting multiple information data sets such as electricity demand, resource capacity, market prices, power generation output, and new energy penetration, the comprehensiveness and diversity of the model data are ensured; the data is cleaned and normalized to effectively remove outliers, missing values, and duplicate values, eliminate dimensional differences, and improve the accuracy and reliability of the data.

[0076] An evaluation index model is constructed from four characteristic dimensions: technical, economic, environmental and social, covering multiple key aspects of the capacity market; the hierarchical analysis method is further used to calculate the indicator weights of each characteristic dimension, and dynamic adjustments are made according to the degree of market environment fluctuation, thereby enhancing the adaptability and robustness of the model.

[0077] A sensitivity analysis was conducted on the evaluation index model, key sensitive indicators were identified, and key adjustments were made to improve the model's adaptability to different market environments. Further, through computational robustness, the adaptability and stability of the model under different scenarios were verified, providing strong support for the dynamic adjustment and optimization of the capacity market.

[0078] This invention overcomes the deficiencies of traditional power markets in data processing and evaluation index construction, especially in response to the challenges posed by the intermittency and volatility of renewable energy, and provides a more accurate, comprehensive and reliable capacity market adaptability evaluation method, which contributes to the healthy development of the power market and the optimal allocation of resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0080] Figure 1 Flowchart of the capacity market adaptability evaluation method in the present invention. DETAILED DESCRIPTION

[0081] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0082] It should be noted that when an element is referred to as being "fixed to" another element, it may be directly attached to the other element or there may be an intermediate element. When an element is referred to as being "connected to" another element, it may be directly connected to the other element or there may be an intermediate element. The terms "vertical," "horizontal," "left," "right," and similar expressions used herein are for illustrative purposes only and do not represent the only implementation methods.

[0083] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this invention pertains. The terms used in this specification of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0084] Faced with the profound transformation of the global energy mix and the continued rise in the proportion of renewable energy, traditional electricity markets appear unable to cope with the high penetration of renewable energy, given the intermittent and volatile nature of renewable energy sources like wind and solar. Consequently, capacity markets, as a mechanism for balancing electricity supply and demand and ensuring reliable grid operation, have garnered widespread attention.

[0085] The present invention discloses a capacity market adaptability evaluation method, such as Figure 1 As shown, the following steps are included:

[0086] Based on the power system operation data platform and market transaction database, historical and real-time data related to the capacity market are collected; based on the operating characteristics of the capacity market, an evaluation index model is constructed from four characteristic dimensions: technical, economic, environmental, and social; the hierarchical analysis method is used to calculate the indicator weights of each characteristic dimension in the evaluation index model; based on the degree of volatility of the market environment, the indicator weights in the evaluation index model are adjusted; and a sensitivity analysis is conducted on the robustness of the evaluation index model to verify its adaptability under different market environments.

[0087] The disclosed capacity market adaptability evaluation method addresses current issues such as a lack of evaluation standards, incomplete and inaccurate data, uncertainty in adaptability assessments, and regional disparities by comprehensively collecting and processing data, constructing a scientific evaluation index model, rationally calculating and adjusting index weights, and conducting robust sensitivity analysis. This method provides a quantitative basis for the introduction of capacity markets in regional power markets, ensuring that capacity market development truly meets local power demand and market development conditions, thereby effectively improving the stability and operational efficiency of power markets.

[0088] By integrating resources from the power system operation data platform and market transaction database, the collected data covers multiple key information sets, including power demand, resource capacity, market prices, power generation output, and renewable energy penetration, providing comprehensive data support for subsequent analysis. Real-time data collection can promptly reflect market changes, providing a basis for rapid adjustment of evaluation indicator models and enhancing the timeliness of evaluation.

[0089] Furthermore, by comprehensively constructing an evaluation index model based on four characteristic dimensions, the singleness and incompleteness of the evaluation criteria in the traditional electricity market are resolved; the evaluation index model can comprehensively reflect the overall performance and potential problems of the capacity market, providing policymakers and market participants with a more comprehensive decision-making basis.

[0090] The analytic hierarchy process can ensure that the weight distribution of each characteristic dimension and specific indicator is reasonable and scientific, reflecting their importance in the capacity market; adjusting the weight according to the volatility of the market environment can ensure that the evaluation indicator model remains sensitive and accurate under different market conditions; it solves the subjectivity and static nature of weight distribution in the traditional electricity market and improves the flexibility and adaptability of the evaluation.

[0091] Sensitivity analysis can evaluate the stability and reliability of the evaluation index model under different market environments, ensure that the evaluation index model can remain accurate and effective under different market conditions, and provide strong guarantees for the long-term operation of the capacity market; enhance the ability of traditional electricity markets to deal with uncertainty and regional differences, and improve the accuracy and reliability of evaluation.

[0092] Among them, historical data and real-time data related to the capacity market are collected, including multiple information data sets such as electricity demand, resource capacity, market price, power generation output, and new energy penetration; the collected multiple information data sets are cleaned and outliers, missing values, and duplicate values ​​are removed, and the cleaned data are normalized to eliminate dimensional differences.

[0093] Specifically, the outliers are identified and processed by the 3σ detection method to avoid abnormal data from misleading the evaluation index model. Specifically, the following formula is used:

[0094] x i -μ>3σ;

[0095] The missing values ​​are filled by the mean filling method, which helps to reduce the evaluation bias caused by missing data. The specific formula is as follows:

[0096]

[0097] in:

[0098]

[0099] The linear normalization method is used to eliminate the dimensional differences of different indicators and convert the data of different indicators to the same dimension to facilitate subsequent comparison and analysis. The specific formula is as follows:

[0100]

[0101] Among them, x irepresents valid data points (i.e., non-missing values); μ represents the mean of the data set, which is used to fill missing values; n represents the number of valid data points (i.e., the number of samples after removing missing values); x j Indicates the missing values ​​that need to be filled, and x j =NaN; σ represents the standard deviation of the data set, which is used to measure the volatility of the beam data; min(x) represents the minimum value in the data set; max(x) represents the maximum value in the data set; x′ i+ Represents the normalized data of positive indicators; x′ i- Represents the normalized data of negative indicators.

[0102] Data cleaning and normalization address shortcomings in traditional power market data processing, particularly addressing data fluctuations and anomalies caused by the intermittent and volatile nature of renewable energy, providing a more accurate and reliable data foundation. Furthermore, by eliminating dimensional differences, different indicators can be effectively compared and comprehensively analyzed, improving the accuracy and comprehensiveness of evaluations. Data cleaning and normalization also provide a cleaner and more organized dataset for subsequent analysis and modeling, helping to reduce the impact of noise and interference on evaluation results. This also provides more accurate data support for the dynamic adjustment and optimization of the capacity market.

[0103] Furthermore, the random noise and fluctuation in the data set are reduced by time series smoothing method, and the moving average formula is as follows:

[0104]

[0105] Among them, SMA t represents the moving average of time t, n represents the window size of the moving average, x t-i Represents the data point of the time series at time ti; through time series smoothing methods, especially moving average technology, it can significantly reduce the random noise and fluctuations in the data set, which helps to observe the underlying trends and patterns of the data more clearly.

[0106] The covariance matrix is ​​used to analyze the correlation between features and to select key features to optimize the data dimension. The following formula is used:

[0107]

[0108] C·v=λv;

[0109] Here, C represents the covariance matrix, which measures the correlation between features; λ represents the eigenvalue, indicating the variance of the feature; and ν represents the principal component corresponding to the eigenvector. Principal component analysis (PCA) is used to select key features to optimize data dimensionality. PCA uses the eigenvalues ​​and eigenvectors of the covariance matrix to project high-dimensional data into a lower-dimensional space while preserving the data's variance as much as possible. Specifically, the dimensionality reduction is achieved by calculating the covariance matrix, solving for the eigenvalues ​​and eigenvectors, and selecting the first k principal components for data projection.

[0110] During the implementation process, the adequacy, response speed and adjustment flexibility of the evaluation index model are analyzed from a technical perspective, and the adaptability of the evaluation index model to load changes is evaluated; the cost-effectiveness and market performance of the evaluation index model are analyzed from an economic perspective, and the economic feasibility of the evaluation index model is evaluated; the carbon emission level and the fairness of capacity distribution among regions of the evaluation index model are analyzed from an environmental perspective, and the green and sustainable development of the evaluation index model is evaluated; the degree of user participation and potential in demand response are analyzed from a social perspective, and the degree of adaptability of the evaluation index model to social needs is evaluated.

[0111] Specifically, the adequacy of the evaluation index model is measured by the capacity factor. This indicator reflects the utilization efficiency of capacity resources, that is, the comparison between the actual power generation capacity of the resources during the evaluation period and the theoretical maximum power generation capacity. It is an important evaluation criterion in the technical dimension. The specific formula is as follows:

[0112]

[0113] The response speed of the evaluation index model is measured by the ramp rate. It is one of the key indicators for measuring system flexibility and adaptability. It is used to evaluate the speed at which capacity resources respond to load changes, that is, the ability of the system to quickly transition from one state to another. The specific formula is as follows:

[0114]

[0115] The adjustment flexibility of the evaluation index model is measured by a reliability index based on downtime ratio. The reliability index based on downtime ratio is used to evaluate the total duration of system downtime due to planned maintenance or failure. The specific formula is as follows:

[0116]

[0117] The adaptability of the evaluation index model to load changes is measured by comprehensive response capability, which comprehensively considers the ramp rate, capacity factor and reliability index. The comprehensive index of the overall response capability of the power system is measured by weighted summation, reflecting the overall evaluation result of the technical dimension. The specific formula is as follows:

[0118] CRP=w1·R ramp +w2·CF+w3·AFR;

[0119] The cost-effectiveness of the evaluation index model is measured by comprehensive benefit efficiency, which is used to evaluate the economic feasibility of capacity resources, that is, the net benefit after considering the cost. The specific formula is as follows:

[0120]

[0121] The market performance of the evaluation index model is measured by the fluctuation of revenue sensitivity, that is, the impact of market prices on revenue. This indicator also reflects the economic stability of capacity resources in the market. The specific formula is as follows:

[0122]

[0123] The overall carbon emission intensity is calculated based on the weighted proportion of power generation resources. The carbon emission level of the evaluation index model is measured by weighted carbon emission intensity. This indicator is used to evaluate the environmental performance of capacity resources and is an important evaluation standard in the environmental dimension. The specific formula is as follows:

[0124]

[0125] The social dimension involves policy adaptability indicators such as user participation and regional resource distribution fairness. The fairness of the inter-regional capacity distribution of the evaluation index model is measured by the fairness index, which is specifically measured using the following formula:

[0126]

[0127] The degree of adaptability of the evaluation index model to social needs is measured by user response potential, specifically using the following formula:

[0128]

[0129] Among them, CRP represents comprehensive response capability; R ramp Ramp rate; CF capacity factor, which is the ratio of actual power generation to maximum power generation capacity; AFR represents a reliability index based on downtime ratio; T outage T represents the downtime, which is the total duration of resource downtime due to planned maintenance or failure during the evaluation period, in hours; total represents the total duration of the evaluation cycle; w1, w2, w3 represent the indicator weights; ERE represents the comprehensive benefit efficiency; R t represents the total revenue in year t; C t represents the operating cost in year t; r represents the discount rate; P trepresents the actual power supply; T represents the evaluation period; REV represents the revenue sensitivity fluctuation, which is used to measure the impact of market prices on revenue; σ R represents the standard deviation of returns; represents the average market price; β represents the profit sensitivity coefficient related to market price fluctuations; WCI represents weighted carbon emission intensity, which is calculated based on the weighted proportion of power generation resources; P i represents the power generation of the i-th resource; P total Indicates the total power generation of the system; EI i represents the unit carbon emission intensity of the i-th resource; FI represents the fairness index; URP represents the user response potential, which is used to comprehensively evaluate the potential of users to participate in demand response; E i,DR represents the actual electricity consumption of user i in the demand response event; E i,baseline represents the baseline electricity consumption of user i; N represents the total number of users participating in demand response.

[0130] During the implementation process, the analytic hierarchy process is used to calculate the indicator weights of each characteristic dimension in the evaluation indicator model, including the following steps:

[0131] Construct a judgment matrix A and use the eigenvalue and eigenvector to calculate the initial weight W of each indicator. The judgment matrix A is the core of the AHP method and is used to quantify the relative importance of different indicators. After clarifying the indicators, experts or decision makers assign a relative importance value a to each pair of indicators based on experience or data. ij , forming a judgment matrix A, specifically using the following formula:

[0132]

[0133] A·W=λ max W;

[0134] The initial weight W reflects the relative importance of each indicator in the comprehensive evaluation. The initial weight W is calculated using eigenvalues ​​and eigenvectors. First, the eigenvalues ​​and eigenvectors of the judgment matrix A are calculated. Then, the eigenvector corresponding to the largest eigenvalue is selected and normalized to obtain the initial weight vector W.

[0135] The rationality of the judgment matrix A is evaluated through consistency testing to ensure that the relative importance relationship between the indicators is consistent. First, the consistency index CI is calculated, then the random consistency index RI corresponding to the matrix order n is found or calculated, and finally the consistency ratio CR is calculated. The specific formula is as follows:

[0136]

[0137] If CR < 0.1, the judgment matrix A passes the consistency test, indicating that the relative importance relationship of the indicators in the judgment matrix A is consistent and can be used for subsequent weight allocation. ij represents the relative importance of feature dimensions i and j; W represents the initial weight vector; CI represents the consistency index; CR represents the consistency ratio; RI represents the random consistency index.

[0138] Furthermore, the calculated indicator weights are allocated to specific indicators under each feature dimension, and the weighted calculation of each indicator in the evaluation indicator model is evaluated. Specifically, the following formula is used:

[0139]

[0140] Among them, w i represents the weight calculated by the hierarchical analysis method; X i Represents the normalized index value. Weighted calculation is the core step of the comprehensive evaluation model. By assigning weights to each indicator and calculating their weighted values, the final comprehensive evaluation result is obtained.

[0141] During implementation, the adaptability of the evaluation index model is optimized by adjusting the weights of key indicators in the evaluation index model. Specifically, the following formula is used:

[0142]

[0143] By calculating the most sensitive indicators that have the greatest impact on overall adaptability and making key adjustments, the following formula is used:

[0144]

[0145] Among them, w′ i represents the weight of the adjusted indicator i; S represents the comprehensive adaptability score, which is the evaluation result of the capacity market adaptability; S i Indicates the sensitivity to indicator i, which is the degree of influence of the weight change of indicator i on the total adaptability score; Δw i Indicates the weight change of indicator i.

[0146] The adaptability of the evaluation index model in different market environments is verified by calculating the robustness of the evaluation index model, specifically using the following formula:

[0147]

[0148] Wherein, R represents the robustness of the evaluation index model, which is the adaptive stability of the evaluation index model in different situations; S base represents the adaptability score in the baseline scenario.

[0149] According to the changes in market environment, the weights or parameters in the model are adjusted in real time to ensure the accuracy and reliability of the evaluation results under different situations. Specifically, a dynamic weight adjustment method is adopted, combining with the degree of market fluctuation, by adjusting the weights of each key indicator, the adaptability of the model is optimized. When the penetration rate of new energy increases significantly, the weight of renewable energy related indicators can be appropriately increased; when the demand peak period comes, the weight of load regulation capacity can be enhanced. In addition, through sensitivity analysis, the impact of changes in different indicators on the overall adaptability score is evaluated, so as to identify the most sensitive indicators and focus on adjusting them. Finally, through robustness analysis, the stability and adaptability of the model under different market situations are tested, ensuring that the model can still provide effective decision support under the influence of various uncertain factors.

[0150] In this embodiment, after completing all the above steps, the final results are shown in the following table:

[0151]

[0152]

[0153] The above model is used to evaluate the United Kingdom, PJM and Jiangsu:

[0154] United Kingdom: score 9.2 (high proportion of new energy access and perfect market mechanism support).

[0155] PJM: score 9.0 (the most mature capacity market in the world, with a perfect policy framework).

[0156] Jiangsu: score 8.7 (the proportion of new energy access is steadily increasing, and the spot market is still in the development stage).

[0157] Those skilled in the art should understand that the present application is not limited to the above embodiments, and the above embodiments and descriptions in the specification are only to illustrate the principles of the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the claimed application. The scope of protection of the present application is defined by the appended claims and their equivalents.

Claims

1. A capacity market adaptability evaluation method, characterized in that: The following steps are involved: Collect historical and real-time data related to the capacity market based on the power system operation data platform and market transaction database; Based on the operational characteristics of the capacity market, an evaluation index model is constructed from four characteristic dimensions: technical, economic, environmental and social. The analytic hierarchy process is used to calculate the indicator weight of each characteristic dimension in the evaluation indicator model; Adjusting the weights of indicators in the evaluation indicator model based on the degree of volatility of the market environment; Conduct sensitivity analysis on the robustness of the evaluation indicator model to verify its adaptability in different market environments; Analyze the adequacy, response speed, and adjustment flexibility of the evaluation index model from a technical perspective, and evaluate the adaptability of the evaluation index model to load changes; Analyze the cost-effectiveness and market performance of the evaluation index model from an economic perspective, and evaluate the economic feasibility of the evaluation index model; Analyze the carbon emission level and the fairness of inter-regional capacity distribution of the evaluation index model from an environmental perspective, and evaluate the green and sustainable development of the evaluation index model; Analyze the user's participation and potential in demand response from a social perspective, and evaluate the degree to which the evaluation index model adapts to social needs; The adaptability of the evaluation index model is optimized by adjusting the weights of key indicators in the evaluation index model, specifically using the following formula: By calculating the most sensitive indicators that have the greatest impact on overall adaptability and making key adjustments, the following formula is used: Among them, w′ i represents the weight of the adjusted indicator i; S represents the comprehensive adaptability score, which is the evaluation result of the capacity market adaptability; S i Indicates the sensitivity to indicator i, which is the degree of influence of the weight change of indicator i on the total adaptability score; Δw i Indicates the weight change of indicator i, w i Represents the weight calculated by the hierarchical analysis method.

2. The capacity market adaptability evaluation method according to claim 1, characterized in that: The collected capacity market-related historical and real-time data includes multiple information data sets on power demand, resource capacity, market price, power generation output, and new energy penetration; The collected multiple information data sets are cleaned and outliers, missing values ​​and duplicate values ​​are removed. The cleaned data are normalized and dimensional differences are eliminated.

3. The capacity market adaptability evaluation method according to claim 2, characterized in that: The outliers are identified and processed by the 3σ detection method, specifically using the following formula: x i -μ>3σ; The missing values ​​are filled by the mean filling method, specifically using the following formula: in: The linear normalization method is used to eliminate the dimensional differences of different indicators. The specific formula is as follows: Among them, x i represents valid data points, i.e., non-missing values; μ represents the mean of the data set, which is used to fill missing values; n represents the number of valid data points, i.e., the number of samples after removing missing values; x j Indicates the missing values ​​that need to be filled, and x j =NaN; σ represents the standard deviation of the data set, which is used to measure the volatility of the beam data; min(x) represents the minimum value in the data set; max(x) represents the maximum value in the data set; x′ i+ Represents the normalized data of positive indicators; x′ i- Represents the normalized data of negative indicators.

4. The capacity market adaptability evaluation method according to claim 2, characterized in that: The time series smoothing method is used to reduce random noise and fluctuations in the data set. The moving average formula is as follows: Among them, SMA t represents the moving average of time t, n represents the window size of the moving average, x t-i Represents the data point of the time series at time ti; The covariance matrix is ​​used to analyze the correlation between features and to select key features to optimize the data dimension. The following formula is used: C·v=λv; Among them, C represents the covariance matrix, which is used to measure the correlation between each feature; λ is the eigenvalue, which represents the variance of the feature; ν is the principal component corresponding to the eigenvector; x i represents valid data points, i.e. non-missing values; μ represents the mean of the data set, which is used to fill missing values.

5. The capacity market adaptability evaluation method according to claim 1, characterized in that: The adequacy of the evaluation index model is measured by the capacity factor, specifically using the following formula: The response speed of the evaluation index model is measured by the climbing rate, and the specific formula is as follows: The adjustment flexibility of the evaluation index model is measured by a reliability index based on the downtime ratio, specifically using the following formula: The adaptability of the evaluation index model to load changes is measured by comprehensive response capability, specifically using the following formula: CRP=w1·R ramp +w2·CF+w3·AFR; The cost-effectiveness of the evaluation index model is measured by comprehensive benefit efficiency, specifically using the following formula: The market performance of the evaluation index model is measured by the return sensitivity fluctuation, specifically using the following formula: The carbon emission level of the evaluation index model is measured by weighted carbon emission intensity, specifically using the following formula: The fairness of the inter-region capacity distribution of the evaluation index model is measured by the fairness index, which is specifically measured using the following formula: The degree of adaptability of the evaluation index model to social needs is measured by user response potential, specifically using the following formula: Among them, CRP represents comprehensive response capability; R ramp Ramp rate; CF capacity factor, which is the ratio of actual power generation to maximum power generation capacity; AFR represents a reliability index based on downtime ratio; T outage T represents the downtime, which is the total duration of resource downtime due to planned maintenance or failure during the evaluation period, in hours; total represents the total duration of the evaluation cycle; w1, w2, w3 represent the indicator weights; ERE represents the comprehensive benefit efficiency; R t represents the total revenue in year t; C t represents the operating cost in year t; r represents the discount rate; P t represents the actual power supply; T represents the evaluation period; REV represents the revenue sensitivity fluctuation, which is used to measure the impact of market prices on revenue; σ R represents the standard deviation of returns; represents the average market price; β represents the profit sensitivity coefficient related to market price fluctuations; WCI represents weighted carbon emission intensity, which is calculated based on the weighted proportion of power generation resources; P i represents the power generation of the i-th resource; P total Indicates the total power generation of the system; EI i represents the unit carbon emission intensity of the i-th resource; FI represents the fairness index; URP represents the user response potential, which is used to comprehensively evaluate the potential of users to participate in demand response; E i,DR represents the actual electricity consumption of user i in the demand response event; E i,baseline represents the baseline electricity consumption of user i; N represents the total number of users participating in demand response.

6. The capacity market adaptability evaluation method according to claim 1, characterized in that: The method of using the hierarchical analysis method to calculate the index weight of each characteristic dimension in the evaluation index model includes the following steps: Construct the judgment matrix A and use the eigenvalue and eigenvector to calculate the initial weight W of each indicator. The specific formula is as follows: A·W=λ max ·W; The rationality of the judgment matrix A is evaluated through consistency test, specifically using the following formula: If CR < 0.1, the judgment matrix A passes the consistency test; where a ij represents the relative importance of feature dimensions i and j; W represents the initial weight vector; CI represents the consistency index; CR represents the consistency ratio; RI represents the random consistency index.

7. The capacity market adaptability evaluation method according to claim 6, characterized in that: The calculated indicator weights are allocated to specific indicators under each feature dimension, and the weighted calculation of each indicator in the evaluation indicator model is evaluated. Specifically, the following formula is used: Among them, w i represents the weight calculated by the hierarchical analysis method; X i represents the normalized index value, and S represents the comprehensive adaptability score.

8. The capacity market adaptability evaluation method according to claim 1, characterized in that: The adaptability of the evaluation index model in different market environments is verified by calculating the robustness of the evaluation index model, specifically using the following formula: Wherein, R represents the robustness of the evaluation index model, which is the adaptive stability of the evaluation index model in different situations; S base represents the adaptability score in the baseline scenario.

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