Capacity market adaptability evaluation method

Abstract

The invention relates to the technical field of electric power markets and electric power system operation management, in particular to a capacity market adaptability evaluation method which is based on operation characteristics and requirements of a capacity market in an electric power system and combines a multi-dimensional evaluation theory. By constructing a comprehensive index system including four dimensions of technicality, economy, environmental property and sociality, quantitative evaluation of capacity resource adaptability is realized. The method adopts a data acquisition and normalization technology, carries out modeling on key indexes such as capacity adequacy, adjustment flexibility, carbon emission intensity, market price fluctuation sensitivity and the like, combines a weight analysis and comprehensive evaluation model, scientifically calculates the adaptability level of various resources, supports planning, optimization and policy making of a capacity market, and improves the capacity market quality. The market operation efficiency and the rationality of resource allocation are improved, the defects of a traditional power market in the aspects of data processing and evaluation index construction are overcome, and a theoretical basis and decision support are provided for long-term stable operation of a 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 structure, the proportion of renewable energy continues to rise, and the power system is facing unprecedented challenges. Due to the inherent intermittent and volatile characteristics of renewable energy such as wind power and solar power, the traditional power market is unable to cope with the high proportion of renewable energy access and is unable to meet the stringent requirements of grid stability. In this context, the capacity market, as a mechanism aimed at balancing power supply and demand and ensuring the reliable operation of the power grid, is increasingly receiving widespread attention around the world.

[0003] However, the implementation of the capacity market is not achieved overnight, and its applicability and implementation effect are affected by many factors. There are significant differences in the distribution of power resources, market structure, and policy orientation in different regions, which leads to completely different performances of the capacity market in different regions. Especially during peak power demand periods, how to ensure sufficient spare capacity to cope with potential power shortage risks has become an urgent problem to be solved.

[0004] More importantly, there is a lack of a systematic and scientific evaluation standard for assessing whether a region is suitable for establishing a capacity market and quantifying the matching degree between the region's power supply and demand. Although existing studies have conducted in-depth discussions on the theoretical framework and implementation plan of the capacity market, at the practical level, due to the lack of evaluation standards, the adaptability assessment of the capacity market often carries great uncertainty and regional differences.

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

[0006] In summary, in the face of the transformation of the global energy structure and the increase in the proportion of renewable energy, how to scientifically and systematically evaluate the adaptability of capacity markets in different regions has become a research problem that needs to be solved urgently. This paper proposes a new capacity market adaptability evaluation index method to address this problem, aiming to provide a quantitative basis for the introduction of capacity markets in regional power markets, and ensure that the construction of capacity markets can truly meet local power 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 comprises the following steps:

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

[0011] Based on the operating 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] Based on the volatility of the market environment, adjust the indicator weights in the evaluation indicator model;

[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 data and real-time data related to the capacity market includes multiple information data sets such as 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 abnormal value is 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 for 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 the positive indicator; 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 correlation between features is analyzed through the covariance matrix, and key features are selected to optimize the data dimension. The specific formula is as follows:

[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, and the adaptability of the evaluation index model to load changes is evaluated;

[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 of the evaluation index model and the fairness of capacity distribution among regions from an environmental perspective, and evaluate the green and sustainable development of the evaluation index model;

[0036] The user's 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.

[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 downtime ratio, and the following formula is used specifically:

[0042]

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

[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, and the specific formula is as follows:

[0048]

[0049] The carbon emission level of the evaluation index model is measured by weighted carbon emission intensity, and the specific formula is as follows:

[0050]

[0051] The fairness of the inter-regional capacity distribution of the evaluation index model is measured by the fairness index, which is specifically formulated as follows:

[0052]

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

[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 revenue 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 by weighting the 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 benchmark electricity consumption of user i; N represents the total number of users participating in demand response.

[0056] Furthermore, the use of the hierarchical analysis method to calculate the indicator weights of each characteristic 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, and the following formula is used:

[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 using the following formula:

[0064]

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

[0066] Furthermore, by adjusting the weights of key indicators in the evaluation indicator model, the adaptability of the evaluation indicator model is optimized, 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 index 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 adaptability and 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 building 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 many 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 fluctuations, which enhances 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 in different scenarios were verified, providing strong support for the dynamic adjustment and optimization of the capacity market.

[0078] The present invention overcomes the deficiencies of traditional power markets in data processing and evaluation index construction, especially in response to the challenges brought by the intermittency and volatility of renewable energy, and provides a more accurate, comprehensive and reliable capacity market adaptability evaluation method, which is conducive 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 drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. 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 creative work.

[0080] Figure 1 The figure is a flow chart 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 described clearly and completely 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 on the other element or there may be a central element. When an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be a central element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are for illustrative purposes only and do not represent the only implementation method.

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

[0084] Faced with the profound transformation of the global energy structure and the continuous increase in the proportion of renewable energy, considering the intermittent and volatile nature of renewable energy such as wind and solar energy, the traditional electricity market seems unable to cope with the high proportion of renewable energy access. Therefore, the capacity market, as a mechanism to balance power supply and demand and ensure the reliable operation of the power grid, has received 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 data and real-time data related to the capacity market are collected; based on the operation 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 weights of each characteristic dimension in the evaluation index model; based on the volatility of the market environment, the indicator weights in the evaluation index model are adjusted; a sensitivity analysis is performed on the robustness of the evaluation index model to verify its adaptability under different market environments.

[0087] The capacity market adaptability evaluation method disclosed in the present invention solves the current problems of lack of evaluation standards, incomplete and inaccurate data, uncertainty in adaptability evaluation and regional differences by comprehensively collecting and processing data, building a scientific evaluation index model, reasonably calculating and adjusting index weights, and conducting robust sensitivity analysis. This method provides a quantitative basis for the introduction of capacity markets in power markets in various regions, ensuring that the construction of capacity markets can truly meet local power demand and market development conditions, thereby effectively improving the stability and operating efficiency of the power market.

[0088] Among them, by integrating the resources of the power system operation data platform and the market transaction database, it is ensured that the collected data covers multiple key information data sets such as power demand, resource capacity, market price, power generation output and new energy penetration, providing comprehensive data support for subsequent analysis. The collection of real-time data can reflect market changes in a timely manner, provide a basis for the rapid adjustment of the evaluation index model, and enhance the timeliness of the 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 solved; 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 its importance in the capacity market; adjusting the weight according to the volatility of the market environment can ensure that the evaluation indicator model maintains sensitivity and accuracy 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] Through sensitivity analysis, the stability and reliability of the evaluation index model in different market environments can be evaluated, ensuring that the evaluation index model can remain accurate and effective under different market conditions, providing strong guarantees for the long-term operation of the capacity market; enhancing the ability of traditional electricity markets to deal with uncertainties and regional differences, and improving the accuracy and reliability of the 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; the cleaned data are normalized and dimensional differences are eliminated.

[0093] Specifically, the outliers are identified and processed by the 3σ detection method to avoid misleading the evaluation index model by the abnormal data, and the following formula is specifically 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 for 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 the positive indicator; x′ i- Represents the normalized data of negative indicators.

[0102] Data cleaning and normalization solve the deficiencies of traditional power markets in data processing, especially for data fluctuations and anomalies caused by the intermittency and volatility of renewable energy, and provide a more accurate and reliable data basis; further, by eliminating dimensional differences, different indicators can be effectively compared and comprehensively analyzed, improving the accuracy and comprehensiveness of the evaluation. Data cleaning and normalization can also provide a cleaner and neater data set for subsequent analysis and modeling, helping to reduce the impact of noise and interference factors on the evaluation results. At the same time, 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 correlation between features is analyzed through the covariance matrix, and key features are selected to optimize the data dimension. The specific formula is as follows:

[0107]

[0108] C·v=λv;

[0109] 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. Through the principal component analysis (PCA) technology, key features are selected to optimize the data dimension. PCA uses the eigenvalues ​​and eigenvectors of the covariance matrix to project high-dimensional data into a low-dimensional space while retaining the variance information of the data as much as possible. Specifically, the data after dimensionality reduction is obtained by calculating the covariance matrix, solving 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 user's participation and potential in demand response are analyzed from a social perspective, and the 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, which reflects the utilization efficiency of capacity resources, that is, the comparison between the actual power generation capacity of resources within the evaluation period and the theoretical maximum power generation capacity, and is an important evaluation criterion of the technical dimension. The following formula is used:

[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, and measures the comprehensive index of the overall response capability of the power system 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 revenue efficiency, which is used to evaluate the economic feasibility of capacity resources, that is, the net revenue after considering the cost, and the specific formula is as follows:

[0120]

[0121] The market performance of the evaluation index model is measured by the revenue sensitivity fluctuation, that is, the impact of market price 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 fairness of regional resource distribution. The fairness of the inter-regional capacity distribution of the evaluation index model is measured by the fairness index, which is specifically formulated as follows:

[0126]

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

[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 revenue 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 by weighting the 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 benchmark 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 eigenvalues ​​and eigenvectors to calculate the initial weights 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, 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 the eigenvalues ​​and eigenvectors. First, the eigenvalues ​​and eigenvectors of the judgment matrix A are calculated. Then, the eigenvector corresponding to the maximum eigenvalue is selected and normalized to obtain the initial weight vector W.

[0135] The rationality of the judgment matrix A is evaluated through consistency test to ensure that the relative importance relationship between the indicators is consistent; first calculate the consistency index CI, then find or calculate the random consistency index RI corresponding to the matrix order n, and finally calculate the consistency ratio CR; 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 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 using the following formula:

[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 index and calculating its weighted value, the final comprehensive evaluation result is obtained.

[0141] During the implementation process, the adaptability of the evaluation index model is optimized by adjusting the weights of the key indicators in the evaluation index model, and the following formula is specifically 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 index 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] By calculating the robustness of the evaluation index model, the adaptability of the evaluation index model in different market environments is verified, and the following formula is specifically used:

[0147]

[0148] Wherein, R represents the robustness of the evaluation index model, which is the adaptability and 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 the market environment, the indicator weights or parameters in the model are adjusted in real time to ensure the accuracy and reliability of the evaluation results in different scenarios. Specifically, the dynamic weight adjustment method is adopted, and the weights of various key indicators are adjusted in combination with the degree of market fluctuations to optimize the adaptability of the model. When the penetration rate of new energy increases significantly, the weights of renewable energy-related indicators can be appropriately increased; when the peak demand 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 adjust them in a focused manner. Finally, through robustness analysis, the stability and adaptability of the model in different market scenarios are tested to ensure 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 following table is finally obtained:

[0151]

[0152]

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

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

[0155] PJM: Score 9.0 (the world’s most mature capacity market with a sound policy framework).

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

[0157] Those skilled in the art should understand that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A capacity market adaptability evaluation method, characterized in that: The following steps are involved: Based on the power system operation data platform and market transaction database, collect historical and real-time data related to the capacity market; 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 analytic hierarchy process is used to calculate the indicator weight of each characteristic dimension in the evaluation indicator model; Based on the volatility of the market environment, adjust the indicator weights in the evaluation indicator model; A sensitivity analysis is conducted on the robustness of the evaluation index model to verify its adaptability in different market environments.

2. The capacity market adaptability evaluation method according to claim 1, characterized in that: The historical data and real-time data related to the acquisition capacity market include multiple information data sets such as 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 abnormal value is 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 for 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 the positive indicator; 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 correlation between features is analyzed through the covariance matrix, and key features are selected to optimize the data dimension. The specific formula is as follows: 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 indicates the variance of the feature; ν is the principal component corresponding to the eigenvector.

5. The capacity market adaptability evaluation method according to claim 1, characterized in that: 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 of the evaluation index model and the fairness of capacity distribution among regions from an environmental perspective, and evaluate the green and sustainable development of the evaluation index model; The user's 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.

6. The capacity market adaptability evaluation method according to claim 5, characterized in that: The adequacy of the evaluation index model is measured by the capacity factor, and the specific formula is as follows: 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 downtime ratio, and the following formula is used specifically: The adaptability of the evaluation index model to load changes is measured by comprehensive response capability, and the following formula is used specifically: 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, and the specific formula is as follows: The carbon emission level of the evaluation index model is measured by weighted carbon emission intensity, and the specific formula is as follows: The fairness of the inter-regional capacity distribution of the evaluation index model is measured by the fairness index, which is specifically formulated as follows: The adaptability of the evaluation index model to social needs is measured by user response potential, and the following formula is used specifically: 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 revenue 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 by weighting the 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 benchmark electricity consumption of user i; N represents the total number of users participating in demand response.

7. 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, and the following formula is used: 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.

8. The capacity market adaptability evaluation method according to claim 7, 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. The specific formula is as follows: Among them, w i represents the weight calculated by the hierarchical analysis method; X i Represents the normalized index value.

9. The capacity market adaptability evaluation method according to claim 1, characterized in that: 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 index 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.

10. The capacity market adaptability evaluation method according to claim 9, characterized in that: By calculating the robustness of the evaluation index model, the adaptability of the evaluation index model in different market environments is verified, and the following formula is specifically used: Wherein, R represents the robustness of the evaluation index model, which is the adaptability and stability of the evaluation index model in different situations; S base Represents the adaptability score in the baseline scenario.

Images