A renewable energy reliability confidence capacity evaluation method and related device

Abstract

A kind of renewable energy reliability confidence capacity comprehensive evaluation method and related device, comprising the following steps: establishing power system reliability evaluation model, obtains power system reliability index;On the basis of power system reliability index, construct renewable energy reliability confidence capacity evaluation model;On the basis of renewable energy reliability confidence capacity evaluation model, using the AHP-EWM subjective and objective weighting method based on game theory to establish renewable energy confidence capacity index system and calculate weight, obtain the final result of reliability confidence capacity.The method has good feasibility, accuracy, can provide decision support for the planning and operation of new power system.

Description

Technical Field

[0001] This invention belongs to the field of renewable energy reliability confidence capacity assessment technology, and specifically relates to a renewable energy reliability confidence capacity assessment method and related apparatus. Background Technology

[0002] With the continuous increase in the proportion of renewable energy integration, reliability confidence capacity, as an important decision-making reference for measuring the contribution of power sources to the power system, has received widespread attention in power system planning and operation. Scientific and reasonable assessment of the reliability confidence capacity of renewable energy is of great significance.

[0003] Currently, numerous scholars have conducted research on power system reliability assessment. Some technologies have proposed nonparametric importance stratified sampling and extended cross-entropy methods for power system reliability assessment, considering multi-state discrete and continuous random variables, to improve the efficiency of reliability assessment. However, these technologies only focus on how to quickly and efficiently assess power system reliability without addressing the application of the assessment results. In response, some technologies, based on power system reliability assessment, introduce the reliability confidence capacity index to quantitatively describe the system's ability to reliably supply power, and use the EENS index to calculate the reliability confidence capacity of photovoltaic power generation systems. Thus, it can be seen that the above technologies select different reliability indices to calculate the reliability source confidence capacity of renewable energy. However, they do not explain in detail the reasons for choosing these indices. Furthermore, although different reliability indices describe different aspects of system reliability, they are still interconnected and influence each other. Therefore, it is necessary to conduct a comprehensive assessment of the reliability confidence capacity of renewable energy while considering various reliability indices.

[0004] Currently, research on comprehensive evaluation methods has yielded numerous results. Existing technologies utilize the Analytic Hierarchy Process (AHP) to reasonably assign weights to each indicator, thereby constructing a comprehensive evaluation indicator system. Because the AHP method can reasonably determine the ranking of attribute weights based on the actual decision-making problem and the expert's own knowledge and experience, it avoids situations where attribute weights contradict the actual importance of the attributes. However, the evaluation results have a strong degree of subjectivity and arbitrariness, resulting in poor objectivity. To address this, some technologies have proposed a subjective and objective weighting method based on AHP-EWM to improve the accuracy of evaluation results. However, the above technologies only consider the importance between indicators, without considering the influence of each indicator on other indicators or the impact of the degree of influence on the weight. To address this, some technologies have constructed an evaluation model based on EWM-AHP-DEMATEL, making the determination of each indicator's weight more comprehensive and objective. However, when using the DEMATEL method to determine the weights, some technologies effectively combine the weights of each indicator with centrality, but do not consider the influence of causation on the weight. Other technologies use a distance formula to combine centrality and causation to calculate the final weight. While this method considers the impact of centrality and causality on the weights, the positive and negative values ​​of causality represent different meanings. Using a distance formula would inevitably affect the accuracy of the final weight calculation. Therefore, it is necessary to calculate the final weights by reasonably considering the weights of centrality and causality. Summary of the Invention

[0005] The purpose of this invention is to provide a comprehensive evaluation method and related apparatus for the reliability confidence capacity of renewable energy, so as to solve the problem that the reliability of the system described by different reliability indicators is different, resulting in large differences in the calculation results of reliability confidence capacity.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] In a first aspect, the present invention provides a method for assessing the reliability confidence capacity of renewable energy sources, comprising:

[0008] Establish a power system reliability assessment model and obtain power system reliability indicators;

[0009] Based on power system reliability indicators, a renewable energy reliability confidence capacity assessment model is constructed;

[0010] Based on the renewable energy reliability confidence capacity assessment model, a renewable energy confidence capacity index system is established and weights are calculated using the game theory-based AHP-EWM subjective and objective weighting method to obtain the final result of reliability confidence capacity.

[0011] Optionally, establish a power system reliability assessment model:

[0012] Step 1: Describe wind speed using the Weibull distribution. The expression for wind speed is:

[0013]

[0014] In the formula: v is the wind speed; k is the shape parameter;

[0015] The relationship between the output power of a wind turbine unit and wind speed is as follows:

[0016]

[0017] In the formula: PR is the rated output power; vCI, vCO, and vR are the cut-in, cut-out, and rated wind speeds, respectively;

[0018] Step 2: Treat the component as a two-state unit that continuously transitions between operation and failure; describe the duration of the component in each state using the operating time t1 and the repair time t2, respectively; the timing state distribution of each component can be obtained through sampling as follows:

[0019]

[0020]

[0021] In the formula, λ and μ are the failure rate and repair rate of the component, respectively; γ is a random number that follows a uniform distribution in [0,1].

[0022] If the system simulation period is N, x is an arbitrary reliability index, and the index obtained by sampling the i-th year is xi, then the sample mean is:

[0023]

[0024] Its standard deviation is:

[0025]

[0026] Referring to the criteria for judging the convergence of reliability indices, the variance coefficient test of reliability index x is used:

[0027]

[0028] Step 3: To comprehensively evaluate the reliability of wind farm aggregation systems of different types and topologies, basic reliability indicators are selected as the reliability evaluation indicators for wind farm aggregation systems. The reliability of the wind farm aggregation system is described from three aspects: power outage probability, power outage frequency, and expected power loss. The calculation formulas for the reliability indicators are shown below:

[0029] Power shortage time probability

[0030]

[0031] In the formula: LOLP is the probability of insufficient power for a certain period of time; pk is the probability of the system experiencing a power outage of capacity Ok; tk is the duration of the system experiencing a power outage of capacity Ok.

[0032] Expected time of power shortage

[0033]

[0034] Where: LOLE represents the expected duration of power shortage; m represents the number of time periods in a year; n σ Let σ be the number of days in the σ-th time period; For the σth time period, the th Peak load of the day; C σ Let σ represent the system's installed capacity during the σ-th time period; For the σth time period, the 1st time period The probability that the outage capacity is greater than or equal to the reserve capacity; this indicator can determine the probability that the outage capacity of the power system is greater than or equal to the reserve capacity.

[0035] Expected low battery time

[0036]

[0037] In the formula: EENS represents the expected power deficiency; For the σth time period, the 1st time period The probability that the service capacity is greater than or equal to X during the Kth hour of the day; m is the number of time periods in a year; n σ LσζK represents the number of days in the σ-th time period; LσζK represents the number of days in the σ-th time period. The hourly load of the Kth hour of the day; this indicator represents the expected reduction in power supply to users due to forced shutdown of generating units, and comprehensively expresses the number of outages, average duration, and average outage amount.

[0038] Optionally, the calculation steps for power system reliability indices based on the non-sequential Monte Carlo method are as follows:

[0039] Step 1: Input the failure rate and repair rate of each component in the power system;

[0040] Step 2: Use the component state duration sampling method to oversample the state of each group of components in the power system to form a state transition sequence of different components;

[0041] Step 3: Use the state transition sequence formed by sampling in Step 2 to correct the annual output sequence of various units in the system;

[0042] Step 4: Enter the annual load data;

[0043] Step 5: Calculate the power system reliability index; the formula for calculating the reliability index is as follows:

[0044]

[0045] In the formula: N is the number of simulations; LOLPε, LOLEε, and EENSε are the probability of power shortage time, the expected power shortage time, and the expected power shortage value of the power system in the ε-th calculation, respectively.

[0046] Step 6: Determine whether the set years for power system reliability assessment have been reached. If yes, output the reliability index; otherwise, return to step 3.

[0047] Optionally, based on power system reliability indicators, a renewable energy reliability confidence capacity assessment model can be constructed:

[0048] Step 1: Based on the different installed capacity of renewable energy, obtain the corresponding reliability indicators, and then draw the R-Cw curve of renewable energy;

[0049] Step 2: Replace the renewable energy units with the capacity of the conventional units in the system. Based on the different capacities of the conventional units, obtain the corresponding reliability indicators and plot the R-Cr curve of the conventional units replacing the renewable energy units.

[0050] Step 3: When the capacity of the renewable energy unit is CN0, first find the reliability index R0 corresponding to the capacity CN0 on the R-CN curve, and then find the corresponding capacity Cr0 on the R-Cr curve based on this value. The Cr0 value is the reliability confidence capacity.

[0051] The formula for calculating reliability confidence capacity is:

[0052] R(C r +C w C load )=R(C r +C equ C load )

[0053] In the formula, Cr represents the capacity of a conventional unit; Cw represents the installed capacity of wind power; and Cequ represents the equivalent unit capacity.

[0054] Optionally, the steps for establishing a renewable energy confidence capacity index system and calculating weights using the game theory-based AHP-EWM subjective and objective weighting method include:

[0055] Construct a comprehensive weighted influence model based on DEMATEL;

[0056] A game theory combinatorial weighting mathematical model based on AHP-EWM is constructed to calculate the final result of the reliability confidence capacity.

[0057] Optionally, construct a comprehensive weighted influence model based on DEMATEL:

[0058] Step 1: Construct the direct influence matrix

[0059] The relationships between various influencing factors were scored, and the relationships between the indicators were measured using a fuzzy language method with a 1-5 scale. The interrelationships were determined by experts, resulting in the direct influence matrix M.

[0060]

[0061] In the formula: a ij Let i represent the degree of direct influence of index i on j; i = 1, 2, 3, ..., n; j = 1, 2, 3, ..., n; when i = j, a ij =0;

[0062] Step 2: Construct the normative influence matrix

[0063] The direct influence matrix M is normalized, and the maximum value is used as the standard to obtain the normalized influence matrix N;

[0064] N = K × M

[0065]

[0066] In the formula, i = 1, 2, 3, ..., n; j = 1, 2, 3, ..., n;

[0067] Step 3: Construct a comprehensive influence matrix

[0068] The comprehensive influence matrix T is a matrix that adds the indirect influence of each indicator to the standard influence matrix N, which represents the direct influence between each indicator. Its calculation formula is as follows:

[0069] T = N(EN) -1

[0070] In the formula, E is the identity matrix;

[0071] Step 4: Calculate centrality and causality.

[0072] Cause R i It is divided into two categories: causal elements and outcome elements. When the causal degree is >0, it is a causal element, indicating that the element influences other factors; the larger the value, the stronger the influence. When the causal degree is <0, it is an outcome element, indicating that the element is influenced by other factors; the smaller the value, the stronger the influence. The calculation formula is as follows:

[0073]

[0074]

[0075] M i =D i +G i

[0076] R i =D i -G i

[0077] In the formula, t ij The element in the i-th row and j-th column of the comprehensive influence matrix T; D i G represents the combined impact of each indicator on other indicators. i This represents the combined impact of other indicators on each column of indicators;

[0078] Step 5: Determine the overall impact weight ω of the indicator effect The calculation formula is:

[0079] ω effect,i =αM i +βR i

[0080] 1 = α + β

[0081] In the formula, α and β are the weights of centrality and causality, respectively, and α and β are each set to 0.5.

[0082] Optionally, a game-theoretic combinatorial weighting mathematical model based on AHP-EWM can be constructed to calculate the final result of the reliability confidence capacity:

[0083] Step 1: Subjective Weighting Method Based on AHP

[0084] AHP: A decision analysis method that combines qualitative and quantitative approaches to solve complex multi-objective problems;

[0085] Combining quantitative and qualitative analysis, and using the decision-maker's experience to determine the relative importance of each indicator, the calculation steps are as follows:

[0086] (1) Establish a hierarchical model of reliability indicators

[0087] (2) Construct the judgment matrix

[0088] Based on the numerical table corresponding to the indicator evaluation level, scores are assigned, the importance of each reliability indicator is compared pairwise, and a judgment matrix is ​​constructed based on the scores.

[0089]

[0090] (3) Normalization process: The eigenvector values ​​of the judgment matrix are determined by the largest eigenvalue.

[0091] Arrange the required weights according to the magnitude of the calculated values;

[0092] Calculate the geometric mean of the elements in each row of the judgment matrix:

[0093]

[0094] In the formula, To determine the geometric mean of the elements in each row of a matrix; a ij To determine the elements in the matrix; m is the order of the matrix to be determined;

[0095] The obtained geometric mean is normalized to obtain the relative weight average.

[0096]

[0097] Based on this, the improved DEMATEL-AHP weights are calculated as follows:

[0098]

[0099] (4) Consistency check

[0100]

[0101] In the formula, RI = α; λmax is the largest eigenvalue of the judgment matrix; RI is the average random consistency index value, which is obtained by looking up a table based on the order of the judgment matrix. If CR < 0.1, it means that the consistency test is satisfied; otherwise, the judgment will continue until the consistency test meets the requirements.

[0102] Step 2: Objective Weighting Method Based on EWM

[0103] An objective weighting method based on EWM[29,30] is used for correction to improve calculation accuracy; the calculation steps are as follows:

[0104] (1) Dimensionless data

[0105] For attributes that are non-numerical, non-regular fuzzy numbers, or difficult to quantify, they need to be dimensionless and converted into numerical data.

[0106] (2) Establish an evaluation index matrix

[0107]

[0108] (3) Evaluation matrix normalization, the calculation formula is:

[0109]

[0110] In the formula: i = 1, 2, ..., m; j = 1, 2, ..., m.

[0111] (4) Calculate the index entropy value

[0112]

[0113] (5) Calculate the index weights

[0114]

[0115] Step 3: Combinatorial weighting method based on game theory

[0116] The specific steps for combining and assigning weights are as follows:

[0117] (1) The DEMATEL-AHP-EWM method is used to assign weights to each reliability index, and the set of weight vectors ω k ={ω1,ω2,…,ω m}(k=1,2,…,LL), where The set of weights determined by the k-th weighting method; m is the number of reliability indicators; LL is the number of methods for determining weights; assuming Let be the coefficients of the linear combination. Then any linear combination of these vectors is:

[0118]

[0119] (2) Based on the idea of ​​the game theory aggregation model, the L linear combination coefficients α1, α2, ..., in the above formula are... Optimize to and Minimizing the deviation is the objective, and we can obtain The optimal weights are determined, and the objective function is thus defined as follows:

[0120]

[0121] (3) According to the properties of matrix differentiation, the linear equation system with the same first-order derivative condition as the above equation is:

[0122]

[0123] (4) Normalize the optimal combination coefficients obtained from the above formula:

[0124]

[0125] (5) Comprehensive weighting based on game theory combinatorial weighting for:

[0126]

[0127] Secondly, the present invention provides a renewable energy reliability confidence capacity assessment system, comprising:

[0128] The power system reliability index acquisition module is used to establish a power system reliability assessment model and obtain power system reliability indices.

[0129] The assessment model building module is used to construct a renewable energy reliability confidence capacity assessment model based on power system reliability indicators.

[0130] The reliability confidence capacity acquisition module is used to establish a renewable energy confidence capacity index system and calculate weights based on the renewable energy reliability confidence capacity assessment model using the game theory-based AHP-EWM subjective and objective weighting method, so as to obtain the final result of reliability confidence capacity.

[0131] Thirdly, the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the steps of a renewable energy reliability confidence capacity assessment method.

[0132] Fourthly, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of a method for assessing the reliability confidence capacity of renewable energy.

[0133] Compared with the prior art, the present invention has the following technical effects:

[0134] The present invention proposes a comprehensive evaluation method for the reliability confidence capacity of renewable energy, which can fully take into account the differences in the meaning of various reliability indicators and the different reliability confidence caps calculated by various reliability indicators, and can provide accurate decision support for the planning and operation of new power systems.

[0135] The present invention proposes an improved DEMATEL method, which can construct an index system to calculate the comprehensive influence weight based on both centrality and causality. This method not only solves the problem of considering only centrality, but also avoids the unreasonableness of using distance formula to deal with the positive and negative of causality in a general way. It further clarifies the relationship between centrality and causality, making the evaluation results more accurate. Attached Figure Description

[0136] Figure 1 This is a flowchart of the present invention.

[0137] Figure 2 This is a flowchart of the power system reliability assessment process of the present invention.

[0138] Figure 3This is the R-Cw curve of renewable energy according to the present invention.

[0139] Figure 4 This is the R-Cr curve diagram of the conventional unit of the present invention.

[0140] Figure 5 This is a hierarchical model diagram of the reliability index of the present invention.

[0141] Figure 6 This is a reliability confidence capacity diagram corresponding to different reliability indices.

[0142] Figure 7 This is a graph showing the comprehensive evaluation results of the renewable energy reliability confidence capacity of this invention. Detailed Implementation

[0143] The present invention will be described in detail below with reference to the accompanying drawings and preferred embodiments. The purpose and effects of the present invention will become clearer. It should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0144] A comprehensive assessment method for the reliability confidence capacity of renewable energy sources includes:

[0145] Construct a confidence capacity assessment model for renewable energy reliability;

[0146] The overall impact weight was calculated using an improved DEMATEL method.

[0147] A renewable energy confidence capacity index system was established and weights were calculated using the game theory-based AHP-EWM subjective and objective weighting method, and then the final result of reliability confidence capacity was calculated.

[0148] Furthermore, the steps for establishing a power system reliability assessment model using the non-sequential Monte Carlo method are as follows:

[0149] Step 1: Describe wind speed using the Weibull distribution. The expression for wind speed is:

[0150]

[0151] In the formula: v is the wind speed; k is the shape parameter.

[0152] The relationship between the output power of a wind turbine unit and wind speed is as follows:

[0153]

[0154] In the formula: PR is the rated output power; vCI, vCO, and vR are the cut-in, cut-out, and rated wind speeds, respectively.

[0155] Step 2: Treat the component as a two-state unit that continuously transitions between operation and failure. Describe the duration of the component in each state using the operating time t1 and the repair time t2, respectively. It is generally assumed that both operating and repair times follow an exponential distribution. The timing state distribution of each component can be obtained through sampling as follows:

[0156]

[0157]

[0158] In the formula, λ and μ are the failure rate and repair rate of the component, respectively; γ is a random number that follows a uniform distribution in [0,1].

[0159] If the system simulation period is N, x is an arbitrary reliability index, and the index obtained by sampling the i-th year is xi, then the sample mean is:

[0160]

[0161] Its standard deviation is:

[0162]

[0163] The selection of reliability metrics affects the convergence of the Monte Carlo algorithm. Therefore, referring to the criteria for judging the convergence of reliability metrics, the variance coefficient of reliability metric x is used for testing:

[0164]

[0165] Since the Monte Carlo method is a fluctuating convergence process, and the variance coefficient β reflects the magnitude of the error between the sample mean and the theoretical mean, the smaller the error, the higher the calculation accuracy and the greater the reference value. Therefore, when calculating the reliability confidence capacity of renewable energy, the impact of the calculation accuracy of each reliability index should be considered first.

[0166] Step 3: To comprehensively evaluate the reliability of wind farm aggregation systems of different types and topologies, basic reliability indicators are selected as the reliability evaluation indicators for wind farm aggregation systems. The reliability of the wind farm aggregation system is described from three aspects: power outage probability, power outage frequency, and expected power loss. The calculation formulas for the reliability indicators are shown below:

[0167] Power shortage time probability

[0168]

[0169] In the formula: LOLP is the probability of insufficient power for a given time; pk is the probability of the system experiencing a capacity outage of 0k; tk is the duration of the system experiencing a capacity outage of 0k. When the unit capacity does not meet the load demand, this indicator can determine the probability of power outage time in the power system, but it does not consider the magnitude of the outage.

[0170] Expected time of power shortage

[0171]

[0172] Where: LOLE represents the expected duration of power shortage; m represents the number of time periods in a year; n σ Let σ be the number of days in the σ-th time period; For the σth time period, the th Peak load of the day; C σ Let σ represent the system's installed capacity during the σ-th time period; For the σth time period, the 1st time period The probability that the outage capacity is greater than or equal to the reserve capacity. This indicator can determine the probability that the outage capacity of the power system is greater than or equal to the reserve capacity.

[0173] Expected low battery time

[0174]

[0175] In the formula: EENS represents the expected power deficiency; For the σth time period, the 1st time period The probability that the service capacity is greater than or equal to X during the Kth hour of the day; m is the number of time periods in a year; n σ LσζK represents the number of days in the σ-th time period; LσζK represents the number of days in the σ-th time period. The hourly load of the Kth hour of the day. This indicator represents the expected reduction in power supply to users due to forced shutdowns of generating units, and comprehensively expresses the number of outages, average duration, and average outage amount.

[0176] Step 4: The calculation steps for power system reliability indices based on the non-sequential Monte Carlo method are as follows, and the flowchart is shown in Figure 1:

[0177] Step 1: Input the failure rate and repair rate of each component in the power system;

[0178] Step 2: The state of each group of components in the power system is oversampled using the component state duration sampling method, thereby forming the state transition sequence of different component components;

[0179] Step 3: Use the state transition sequence formed by sampling in Step 2 to correct the annual output sequence of various units in the system;

[0180] Step 4: Enter the annual load data;

[0181] Step 5: Calculate the power system reliability index. The formula for calculating the reliability index is as follows:

[0182]

[0183] In the formula: N is the number of simulations; LOLPε, LOLEε, and EENSε are the probability of power shortage time, the expected power shortage time, and the expected power shortage value of the power system in the εth calculation, respectively.

[0184] Step 6: Determine whether the set years for power system reliability assessment have been reached. If yes, output the reliability index; otherwise, return to step 3.

[0185] Furthermore, the steps for establishing a renewable energy reliability confidence capacity assessment model include:

[0186] Based on the aforementioned power system reliability assessment model, this section constructs a renewable energy reliability confidence capacity assessment model, the calculation steps of which are as follows:

[0187] Step 1: Based on the different installed capacity of renewable energy, corresponding reliability indicators can be obtained, thereby plotting the R-Cw curve of renewable energy, such as... Figure 2 As shown.

[0188] Step 2: Replace the renewable energy units with the capacity of the conventional units within the system. Based on the different capacities of the conventional units, corresponding reliability indicators can be obtained. Plot the R-Cr curve for the replacement of renewable energy units with conventional units, such as... Figure 3 As shown.

[0189] Step 3: When the capacity of the renewable energy unit is CN0, first find the reliability index R0 corresponding to the capacity CN0 on the R-CN curve, and then find the corresponding capacity Cr0 on the R-Cr curve based on this value. The Cr0 value is the reliability confidence capacity.

[0190] The formula for calculating reliability confidence capacity is:

[0191] R(C r +C w C load )=R(C r +C equ C load )

[0192] This section uses the non-sequential Monte Carlo method to assess the system's reliability and calculates the reliability confidence capacity corresponding to different reliability indices based on the assessment results. The calculation results are as follows: Figure 5 As shown.

[0193] Furthermore, the steps for establishing a renewable energy confidence capacity index system and calculating weights using the game theory-based AHP-EWM subjective and objective weighting method, and then calculating the final result of the reliability confidence capacity, are as follows:

[0194] Step 1: Construct a comprehensive weighted influence model based on Dematel

[0195] Step 1: Construct the direct influence matrix

[0196] The relationships between various influencing factors were scored using a 1-5 scale with fuzzy language (none, small, average, large, very large) to measure the relationships between indicators. Experts confirmed the interrelationships, resulting in the direct influence matrix M.

[0197]

[0198] In the formula: aij represents the degree of direct influence of index i on j; i = 1, 2, 3, ..., n; j = 1, 2, 3, ..., n; when i = j, aij = 0.

[0199] Step 2: Construct the normative influence matrix

[0200] The direct influence matrix M is normalized, and the maximum value is used as the standard to obtain the normalized influence matrix N.

[0201] N = K × M

[0202]

[0203] In the formula, i = 1, 2, 3, ..., n; j = 1, 2, 3, ..., n.

[0204] Step 3: Construct a comprehensive influence matrix

[0205] The comprehensive influence matrix T is a matrix that adds the indirect influence of each indicator to the standard influence matrix N, which represents the direct influence between each indicator. Its calculation formula is as follows:

[0206] T = N(EN) -1

[0207] In the formula, E is the identity matrix.

[0208] Step 4: Calculate centrality and causality.

[0209] The centrality Mi of each indicator reflects the position of the element in the system; the larger the value, the more important the factor. Causality Ri is divided into two categories: causal factors and outcome factors. When causality Ri > 0, it is a causal factor, indicating that the element influences other factors; the larger the value, the stronger the influence. When causality Ri < 0, it is an outcome factor, indicating that the element is influenced by other factors; the smaller the value, the stronger the influence. The calculation formula is as follows:

[0210]

[0211]

[0212] M i =D i +G i

[0213] R i =D i -G i

[0214] In the formula, tij is the element in the i-th row and j-th column of the comprehensive influence matrix T; Di is the comprehensive influence value of each row indicator on other indicators; Gi is the comprehensive influence value of each column indicator on other indicators.

[0215] Step 5: Determine the overall impact weight ωeffect of the indicator. The calculation formula is as follows:

[0216] ω effect,i =αM i +βR i

[0217] 1 = α + β

[0218] In the formula, α and β are the weights of centrality and causality, respectively. Considering that centrality and causality express different meanings, this section sets α and β to 0.5. These values ​​can be determined using expert consultation based on the focus of the research question.

[0219] Step 2: Construct a game-theoretic combinatorial weighting mathematical model based on AHP-EWM

[0220] Step 1: Subjective Weighting Method Based on AHP

[0221] AHP is a decision analysis method that combines qualitative and quantitative approaches to solve complex multi-objective problems.

[0222] This method combines quantitative and qualitative analysis, using the decision-maker's experience to determine the relative importance of each indicator. The calculation steps are as follows:

[0223] (1) Establish a hierarchical model of reliability indicators, such as Figure 4 As shown.

[0224] (2) Construct the judgment matrix

[0225] Experts assigned scores based on the numerical table corresponding to the indicator evaluation levels, compared the importance of each reliability indicator pairwise, and constructed a judgment matrix based on the scores. The numerical table corresponding to the indicator evaluation levels is shown in Table 1.

[0226]

[0227] Table 1. Values ​​Corresponding to Indicator Evaluation Levels

[0228] Tab 1 Corresponding Values ​​of Index Evaluation Levels

[0229]

[0230] (3) Normalization process: The eigenvector values ​​of the judgment matrix are determined by the largest eigenvalue.

[0231] Arrange the required weights according to the magnitude of the calculated values.

[0232] Calculate the geometric mean of the elements in each row of the judgment matrix:

[0233]

[0234] In the formula, To determine the geometric mean of the elements in each row of a matrix; a ij This is used to determine the elements in the matrix; m is the order of the matrix.

[0235] The obtained geometric mean is normalized to obtain the relative weight average.

[0236]

[0237] Based on this, the improved DEMATEL-AHP weights can be calculated as follows:

[0238]

[0239] (4) Consistency check

[0240]

[0241] In the formula, RI = α; λmax is the largest eigenvalue of the judgment matrix; RI is the average random consistency index value, which is obtained by looking up the value in a table based on the order of the judgment matrix, as shown in Table 2. If CR < 0.1, the consistency test is satisfied; otherwise, the judgment will continue until the consistency test meets the requirements.

[0242] Table 2. Reference Table for Consistency Index RI Values

[0243] Tab 2 Reference Table for Consistency Index RIValues

[0244]

[0245]

[0246] Step 2: Objective Weighting Method Based on EWM

[0247] Considering that the subjective weighting method based on AHP is heavily influenced by subjective factors and has limited quantitative data, making it less convincing, this section, based on the aforementioned objective weighting method based on EWM[29,30], is used to improve the calculation accuracy. The calculation steps are as follows:

[0248] (1) Dimensionless data

[0249] For attributes that are non-numerical, non-regular fuzzy, or difficult to quantify, they need to be dimensionless and converted into numerical data.

[0250] (2) Establish an evaluation index matrix

[0251]

[0252] (3) Evaluation matrix normalization, the calculation formula is:

[0253]

[0254] In the formula: i = 1, 2, ..., m; j = 1, 2, ..., m.

[0255] (4) Calculate the index entropy value

[0256]

[0257] (5) Calculate the index weights

[0258]

[0259] Step 3: Combinatorial weighting method based on game theory

[0260] The game theory-based combined weighting method aims at Nash equilibrium, coordinating the conflict between subjective and objective weights to find their consistency and compromise. It is an integrated process of mutual comparison and coordination. This method reduces subjective arbitrariness while fully considering the influence of objective data, thus improving the scientific rationality of weighting to a certain extent. The specific steps of its combined weighting are as follows:

[0261] (1) The DEMATEL-AHP-EWM method is used to assign weights to each reliability index, and the set of weight vectors ω k ={ω1,ω2,...,ω m}(k=1,2,…,LL), where Let α be the set of weights determined by the k-th weighting method; m be the number of reliability indicators; and LL be the number of methods for determining the weights. Assume α = {α1, α2, ..., α...} LL Let} be the coefficients of the linear combination. Then any linear combination of these vectors is:

[0262]

[0263] (2) Based on the idea of ​​the game theory aggregation model, the L linear combination coefficients α1, α2, ..., α3 in the above formula are... LL Optimize to and Minimizing the deviation is the objective, and we can obtain The optimal weights are determined, and the objective function is thus defined as follows:

[0264]

[0265] (3) According to the properties of matrix differentiation, the linear equation system with the same first-order derivative condition as the above equation is:

[0266]

[0267] (4) Normalize the optimal combination coefficients obtained from the above formula:

[0268]

[0269] (5) Comprehensive weighting based on game theory combinatorial weighting for:

[0270]

[0271] Based on the aforementioned calculation of the comprehensive weights, the renewable energy reliability confidence capacity results are as follows: Figure 6 As shown.

[0272] In another embodiment of the present invention, a renewable energy reliability confidence capacity assessment system is provided, which can be used to implement the above-mentioned renewable energy reliability confidence capacity assessment method. Specifically, the renewable energy reliability confidence capacity assessment system includes:

[0273] The power system reliability index acquisition module is used to establish a power system reliability assessment model and obtain power system reliability indices.

[0274] The assessment model building module is used to construct a renewable energy reliability confidence capacity assessment model based on power system reliability indicators.

[0275] The reliability confidence capacity acquisition module is used to establish a renewable energy confidence capacity index system and calculate weights based on the renewable energy reliability confidence capacity assessment model using the game theory-based AHP-EWM subjective and objective weighting method, so as to obtain the final result of reliability confidence capacity.

[0276] The module division in this embodiment of the invention is illustrative and represents only one logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional modules in the various embodiments of the invention can be integrated into a single processor, exist as separate physical entities, or be integrated into a single module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0277] In another embodiment of the present invention, a computer device is provided, comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the computer storage medium to achieve a corresponding method flow or corresponding function. The processor described in this embodiment of the present invention can be used in the operation of a renewable energy reliability confidence capacity assessment method.

[0278] In another embodiment of the present invention, a storage medium is provided, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by a processor, which can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the renewable energy reliability confidence capacity assessment method in the above embodiments.

[0279] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0280] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0281] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0282] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0283] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for assessing the reliability confidence capacity of renewable energy sources, characterized in that, include: Establish a power system reliability assessment model and obtain power system reliability indicators; Based on power system reliability indicators, a renewable energy reliability confidence capacity assessment model is constructed; Based on the renewable energy reliability confidence capacity assessment model, a renewable energy confidence capacity index system is established and weights are calculated using the game theory-based AHP-EWM subjective and objective weighting method to obtain the final result of reliability confidence capacity. The steps for establishing a renewable energy confidence capacity index system and calculating weights using the game theory-based AHP-EWM subjective and objective weighting method are as follows: Construct a comprehensive weighted influence model based on DEMATEL; A game-theoretic combinatorial weighting mathematical model based on AHP-EWM is constructed to calculate the final result of the reliability confidence capacity. Construct a comprehensive weighted impact model based on DEMATEL: Step 1: Construct the direct influence matrix The relationships between various influencing factors were scored, and the relationships between the indicators were measured using a fuzzy language method with a 1-5 scale. The interrelationships were determined by experts, resulting in the direct influence matrix M. In the formula: a ij As an indicator i right j The degree of direct impact; i =1, 2, 3, ..., n ; j =1, 2, 3, ..., n ; when i = j hour, a ij =0; Step 2: Construct the normative influence matrix Direct Influence Matrix M After normalization, and using the maximum value as the standard, the normalized influence matrix is ​​obtained. N ; In the formula, i =1, 2, 3, ..., n ; j =1, 2, 3, ..., n ; Step 3: Construct a comprehensive influence matrix The comprehensive influence matrix T is a matrix that adds the indirect influence of each indicator to the standard influence matrix N, which represents the direct influence between each indicator. Its calculation formula is as follows: In the formula, E It is the identity matrix; Step 4: Calculate centrality and causality. Cause degree R i Factors are categorized into two types: causal factors and outcome factors. When the causal degree is greater than 0, it is a causal factor, indicating that the factor influences other factors; the higher the value, the stronger the influence. When the causal degree is less than 0, it is an outcome factor, indicating that the factor is influenced by other factors; the lower the value, the stronger the influence. The calculation formula is as follows: In the formula, t ij For the comprehensive influence matrix T, the th i OK j Column elements; D i This represents the combined impact of each indicator on other indicators. G i This represents the combined impact of other indicators on each column of indicators; Step 5: Determine the overall impact weight of the indicators ω effect The calculation formula is: In the formula, α , β These are the weights for centrality and causality, respectively. α , β The values ​​are 0.5 respectively.

2. The renewable energy reliability confidence capacity assessment method according to claim 1, characterized in that, Establish a power system reliability assessment model: Step 1: Describe wind speed using the Weibull distribution. The expression for wind speed is: In the formula: v is the wind speed; k is the shape parameter; The relationship between the output power of a wind turbine unit and wind speed is as follows: In the formula: PR is the rated output power; vCI, vCO, and vR are the cut-in, cut-out, and rated wind speeds, respectively; Step 2: Treat the component as a two-state unit that continuously transitions between operation and failure; describe the duration of the component in each state using the operating time t1 and the repair time t2, respectively; the timing state distribution of each component can be obtained through sampling as follows: In the formula, λ and μ are the failure rate and repair rate of the component, respectively; γ is a random number that follows a uniform distribution in [0,1]. If the system simulation period is N, x is an arbitrary reliability index, and the index obtained by sampling the i-th year is xi, then the sample mean is: Its standard deviation is: Referring to the criteria for judging the convergence of reliability indices, the variance coefficient test of reliability index x is used: Step 3: To comprehensively evaluate the reliability of wind farm aggregation systems of different types and topologies, basic reliability indicators are selected as the reliability evaluation indicators for wind farm aggregation systems. The reliability of the wind farm aggregation system is described from three aspects: power outage probability, power outage frequency, and expected power loss. The calculation formulas for the reliability indicators are shown below: Power shortage time probability In the formula: LOLP is the probability of insufficient power for a certain period of time; pk is the probability of system outage; tk is the duration of system outage. Expected time of power shortage Where: LOLE represents the expected duration of power shortage; m represents the number of time periods in a year; For the first The number of days in a time period; For the first Within the time period, the first Peak load of the day; For the first The system's installed capacity during a given time period; For the first The first time period The probability that the outage capacity is greater than or equal to the reserve capacity; this indicator can determine the probability that the outage capacity of the power system is greater than or equal to the reserve capacity. Expected low battery time In the formula: EENS represents the expected power deficiency; For the first Time period The probability that the shutdown capacity in the Kth hour of a day is greater than or equal to X; m is the number of time periods in a year; For the first The number of days in the time period; LσζK is the number of days in the time period. Time period The hourly load of the Kth hour of the day; this indicator represents the expected reduction in power supply to users due to forced shutdown of generating units, and comprehensively expresses the number of outages, average duration, and average outage amount.

3. The renewable energy reliability confidence capacity assessment method according to claim 2, characterized in that, The calculation steps for power system reliability indices based on the non-sequential Monte Carlo method are as follows: Step 1: Input the failure rate and repair rate of each component in the power system; Step 2: Use the component state duration sampling method to oversample the state of each group of components in the power system to form a state transition sequence of different components; Step 3: Use the state transition sequence formed by sampling in Step 2 to correct the annual output sequence of various units in the system; Step 4: Enter the annual load data; Step 5: Calculate the power system reliability index; the formula for calculating the reliability index is as follows: In the formula: N is the number of simulations; LOLPɛ, LOLEɛ, and EENSɛ are the probability of power shortage time, the expected power shortage time, and the expected power shortage value of the power system in the ɛth calculation, respectively; Step 6: Determine whether the set years for power system reliability assessment have been reached. If yes, output the reliability index; otherwise, return to step 3.

4. The renewable energy reliability confidence capacity assessment method according to claim 1, characterized in that, Based on power system reliability indicators, a renewable energy reliability confidence capacity assessment model is constructed: Step 1: Based on the different installed capacity of renewable energy, obtain the corresponding reliability indicators, and then draw the R-Cw curve of renewable energy; Step 2: Replace the renewable energy units with the capacity of the conventional units in the system. Based on the different capacities of the conventional units, obtain the corresponding reliability indicators and plot the R-Cr curve of the conventional units replacing the renewable energy units. Step 3: When the capacity of the renewable energy unit is CN0, first find the reliability index R0 corresponding to the capacity CN0 on the R-CN curve, and then find the corresponding capacity Cr0 on the R-Cr curve based on this value. The Cr0 value is the reliability confidence capacity. The formula for calculating reliability confidence capacity is: In the formula, Cr represents the capacity of a conventional unit; Cw represents the installed capacity of wind power; and Cequ represents the equivalent unit capacity.

5. A method for assessing the reliability confidence capacity of renewable energy according to claim 1, characterized in that, A game-theoretic combinatorial weighting mathematical model based on AHP-EWM is constructed to calculate the final result of the reliability confidence capacity: Step 1: Subjective Weighting Method Based on AHP AHP: A decision analysis method that combines qualitative and quantitative approaches to solve complex multi-objective problems; Combining quantitative and qualitative analysis, and using the decision-maker's experience to determine the relative importance of each indicator, the calculation steps are as follows: (1) Establish a hierarchical model of reliability indicators (2) Construct the judgment matrix Based on the numerical table corresponding to the indicator evaluation level, scores are assigned, the importance of each reliability indicator is compared pairwise, and a judgment matrix is ​​constructed based on the scores. (3) Normalization process: The eigenvector values ​​of the judgment matrix are determined by the largest eigenvalue. Arrange the required weights according to the magnitude of the calculated values; Calculate the geometric mean of the elements in each row of the judgment matrix: In the formula, To determine the geometric mean of the elements in each row of a matrix; To determine the elements in the matrix; m is the order of the matrix to be determined; The obtained geometric mean is normalized to obtain the relative weight average. Based on this, the improved DEMATEL-AHP weights are calculated as follows: (4) Consistency check In the formula, RI = α; λmax is the largest eigenvalue of the judgment matrix; RI is the average random consistency index value, which is obtained by looking up a table based on the order of the judgment matrix. If CR < 0.1, it means that the consistency test is satisfied; otherwise, the judgment will continue until the consistency test meets the requirements. Step 2: Objective Weighting Method Based on EWM An objective weighting method based on EWM[29,30] is used for correction to improve computational accuracy; The calculation steps are as follows: (1) Dimensionless data For attributes that are non-numerical, non-regular fuzzy numbers, or difficult to quantify, they need to be dimensionless and converted into numerical data. (2) Establish an evaluation index matrix (3) Evaluation matrix normalization, the calculation formula is: In the formula: i = 1, 2, ..., m; j = 1, 2, ..., m; (4) Calculate the index entropy value (5) Calculate the index weights Step 3: Combinatorial weighting method based on game theory The specific steps for combining and assigning weights are as follows: (1) The DEMATEL-AHP-EWM method is used to assign weights to each reliability index, and the set of weight vectors is as follows. (k=1, 2, ..., LL), where k is the set of weights determined by the k-th weighting method; m is the number of reliability indicators; LL is the number of methods for determining weights; assuming Let be the coefficients of the linear combination. Then any linear combination of these vectors is: (2) Based on the idea of ​​game theory aggregation model, the L linear combination coefficients in the above formula are... , … Optimize to and Minimizing the deviation of k is the objective, which yields... The optimal weights are determined, and the objective function is thus defined as follows: (3) According to the properties of matrix differentiation, the linear equation system with the same first-order derivative condition as the above equation is: (4) Normalize the optimal combination coefficients obtained from the above formula: (5) Comprehensive weighting based on game theory combinatorial weighting all means:

6. A renewable energy reliability confidence capacity assessment system, characterized in that, The method for performing the renewable energy reliability confidence capacity assessment method as described in claim 1 includes: The power system reliability index acquisition module is used to establish a power system reliability assessment model and obtain power system reliability indices. The assessment model building module is used to construct a renewable energy reliability confidence capacity assessment model based on power system reliability indicators. The reliability confidence capacity acquisition module is used to establish a renewable energy confidence capacity index system and calculate weights based on the renewable energy reliability confidence capacity assessment model using the game theory-based AHP-EWM subjective and objective weighting method, so as to obtain the final result of reliability confidence capacity.

7. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the renewable energy reliability confidence capacity assessment method as described in any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the renewable energy reliability confidence capacity assessment method as described in any one of claims 1 to 5.

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