Method for evaluating capability of demand side resources participating in auxiliary service market

Abstract

The invention discloses a demand side resource participation auxiliary service market capability evaluation method. The method comprises the following steps: constructing a three-level evaluation index system based on external market characteristics and internal resource characteristics; calculating the weight of each level of evaluation index through an entropy weight method and an order relation weighting method; calculating the comprehensive empowerment of each index through a multiplication synthesis method; constructing an improved cloud model market ability evaluation model based on an entropy weight-order relationship method; solving the cloud model market capability evaluation model to obtain a comprehensive cloud feature value of the demand side resources in the auxiliary service market; and according to the comprehensive cloud feature value, making an ability evaluation cloud picture of the demand side resources participating in the auxiliary service market, and according to the cloud picture, displaying a comprehensive evaluation result. The method has the beneficial effects that the comprehensive risk of demand side resource market interaction is reduced; scientific basis is provided for effective participation of demand side resources, and the power market can be promoted to develop in a more efficient, flexible and sustainable direction.

Description

Technical Field

[0001] The present invention relates to the technical field of power demand response, and in particular to a method for evaluating the ability of demand-side resources to participate in ancillary service markets. Background Art

[0002] Demand-side resources can support the integration of renewable energy by flexibly adjusting consumption patterns, optimizing the balance of power supply and demand and reducing costs, making significant contributions to grid stability and sustainable energy development. However, in the actual exploration process, demand-side resource interaction has problems such as low execution frequency, insufficient user coverage, weak response willingness, high single cost and difficulty in sustainable development.

[0003] In the process of demand-side resources participating in the electricity market, key technologies and market strategies are mainly considered. For example, some studies propose a method of cascade utilization of demand-side resources based on load quality characteristics, and design a fast demand response duration mechanism based on cascade utilization of load quality in combination with the market call method of demand response load; in other studies, in order to guide the transferable load to participate in demand response while improving the benefits of virtual power plants in the peak-shaving market, a step-type compensation demand response mechanism taking into account peak-valley transfer deviation is designed based on the energy-peak-shaving response model of flexible loads, and then a trading strategy for virtual power plants to participate in the energy and peak-shaving market is proposed. However, the above research results have ignored the impact of market environment volatility and other issues to a certain extent. For this reason, some studies have attempted to construct a spot market evaluation index system from the perspectives of source, network, load, and storage, but still failed to take into account the various complex factors that affect the participation of demand-side resources in the ancillary service market, especially the uncertainty of demand-side resources themselves and the limitation of response capacity, which greatly expands the risk coefficient of the transaction process of demand-side resources participating in the ancillary service market, thereby causing the impact of inefficient resource allocation and insensitive market response, resulting in potential risks and challenges to the entire energy supply system. Summary of the invention

[0004] The purpose of the present invention is to overcome the shortcomings of the above-mentioned prior art, and to propose a method for evaluating the ability of demand-side resources to participate in the ancillary service market, taking into account the external market characteristics and internal resource characteristics, based on the influence of internal and external factors on the demand-side participation in the market. This method provides a comprehensive and detailed evaluation method and scientific basis for the effective participation of demand-side resources, and helps to promote the development of the power market in a more efficient, flexible and sustainable direction. The method is specifically implemented by the following technical solutions:

[0005] The method for evaluating the ability of demand-side resources to participate in the auxiliary service market includes:

[0006] Constructing a three-level evaluation index system based on external market characteristics and internal resource characteristics, the three-level evaluation index system includes: a first-level index including market characteristic indicators and internal resource indicators; a second-level index belonging to the first-level index representing the specific evaluation of various risks; and a third-level index belonging to the second-level index representing the specific evaluation of various parameters;

[0007] The weights of evaluation indicators at all levels are calculated by entropy weight method and order relationship weighting method;

[0008] The comprehensive weighting of each indicator is calculated by multiplication synthesis method;

[0009] Construct a cloud model market capability evaluation model based on the improved entropy weight-order relationship method;

[0010] Solve the cloud model market capacity evaluation model to obtain the comprehensive cloud characteristic values ​​of the demand-side resources in the ancillary service market; make a capacity evaluation cloud map of the demand-side resources participating in the ancillary service market based on the comprehensive cloud characteristic values, and display the comprehensive evaluation results based on the cloud map.

[0011] The further design of the method for evaluating the ability of demand-side resources to participate in the auxiliary service market is that the secondary indicators include market monopoly risk indicators, market price volatility risk indicators, market disclosure risk indicators, startup deviation risk indicators, market matching risk indicators, resource adjustment margin risk indicators, and output curve matching risk indicators; the tertiary indicators include market concentration indicators, market information disclosure quality indicators, credit rating system, market price volatility indicators, resource ramping satisfaction indicators, startup time deviation satisfaction indicators, control capacity volatility indicators, control time effectiveness indicators, multi-energy equipment replacement rate indicators, energy storage proportion indicators, renewable energy proportion indicators, flexible load proportion indicators, and output curve and control curve matching indicators.

[0012] The further design of the method for evaluating the ability of demand-side resources to participate in the auxiliary service market is that the logical relationship and calculation of the indicators at each level of the three-level evaluation indicator system are specifically as follows:

[0013] External market characteristic indicators A1 include market monopoly risk B1, market price volatility risk B2, and market disclosure risk B3;

[0014] Market monopoly risk index B1: including market concentration index C1;

[0015] Market price volatility risk indicator B2: including market information disclosure quality indicator C2;

[0016] Market disclosure risk index B3 includes credit rating system index C3 and market price volatility index C4. Credit rating system index C3: supervises and manages the credit rating of market participants, reflecting the market credit level. Market price volatility C4: expressed by the standard deviation of the increase or decrease of fuel within a certain period of time, reflecting the risk level of the user's market assets. According to formula (1), it is expressed by the standard deviation of the increase or decrease of fuel within a set period of time, reflecting the risk level of the user's market assets:

[0017]

[0018] Where C4 represents the volatility of fuel prices in the user market; x i Indicates the fuel price at each time point in a certain period; It represents the average value of fuel price in a certain period of time;

[0019] Internal resource characteristic indicators A2 include: start-up deviation risk indicator B4, market matching risk indicator B5, resource adjustment margin risk indicator B6 and output curve matching risk indicator B7;

[0020] The startup deviation risk index B4 includes the resource ramping satisfaction index C5 and the startup time deviation satisfaction index C6;

[0021] Resource ramping satisfaction index C5: It characterizes the speed of change of the interactive capacity of the user market under unit time change, and reflects the flexibility of the user market in participating in the interaction of the power system. It is calculated according to formula (2):

[0022] C5=|P i,t -P i,t-1 |≥ΔP rate (2)

[0023] In formula (2), P i,t is the output power of device i at time t; C5 is the maximum power change value of device i per unit time, that is, the resource ramp satisfaction; ΔP rate It is the minimum resource ramp satisfaction of the power system;

[0024] The startup time deviation satisfaction index C6 represents the deviation between the actual response time and the benchmark response time after the user market receives the response instruction. It is calculated according to formula (3):

[0025]

[0026] In formula (3), C6 is the start time deviation satisfaction value, and its value range is (0,1]. When C6 is close to 0, it indicates that the user market time deviation satisfaction is poor. When C6 is close to 1, it indicates that the user market time deviation satisfaction is good. rel is the actual response time of the user market; tbase Benchmark response times for participating markets;

[0027] The market matching risk index B5 includes the regulation capacity volatility index C7 and the regulation time effectiveness index C8;

[0028] The regulation capacity volatility index C7 represents the deviation between the actual interaction capacity of the user market and the reported interaction capacity, and is calculated according to formula (4):

[0029]

[0030] In formula (4), C7 is the volatility of user market regulation capacity; P rel,t is the actual interactive capacity of the user market at time t; P pre,t Declare the interactive capacity for the user market at time t;

[0031] The control time effectiveness index C8 represents the overlap between the actual interaction period of the user market and the power system control period. When the interaction capacity of the user market meets the minimum interaction threshold during the control period, the period is determined to be an effective interaction period. According to formula (5),

[0032]

[0033] In formula (5), C8 is the time effectiveness of the user market regulation; x t P is the effective interaction flag of the time attribute at time t; hd,min is the minimum interaction threshold;

[0034] Resource regulation margin risk indicator B6 includes multi-energy equipment replacement rate indicator C9, energy storage ratio indicator C 10 、Renewable energy proportion index C 11 And the flexible load ratio index C 12 ;

[0035] The multi-energy equipment substitution rate index C9 represents the mutual substitution rate between multi-energy equipment under the same load demand within the user market; the higher the multi-energy equipment substitution rate within the user market, the more types of equipment there are within the user market, the higher the flexibility in participating in market interaction, and the more types of markets that can be participated in.

[0036] Energy storage ratio index C 10 Represents the installed capacity of energy storage equipment within the user market. The larger the proportion of energy storage equipment, the more sufficient the user's interactive capacity is. It is calculated according to formula (6):

[0037]

[0038] In formula (6), C 10 is the proportion of energy storage equipment; P ESP is the interactive capacity of energy storage equipment; hd The total capacity of the user market that can participate in the interaction;

[0039] Renewable energy share indicator C 11 Represents the unit capacity of renewable energy equipment within the user market. The larger the proportion of renewable energy, the greater the interactive potential of the user market. It is calculated according to formula (7):

[0040]

[0041] In formula (7), C 11 is the proportion of renewable energy; P ge Interactive capacity for renewable energy sources;

[0042] Flexible load ratio index C 12 Characterizes the proportion of non-rigid loads within the user market. The more flexible loads there are, the more flexible the user market interaction is. According to formula (8),

[0043]

[0044] In formula (8), C 12 is the proportion of flexible load; P rl Interactive capacity for flexible loads;

[0045] Output curve matching risk index B7 includes output curve and regulation curve matching index C 13 , represents the matching degree between the predicted output curve of the user market and the control curve of the power system. According to formula (9), the Euclidean distance is used to measure the similarity between the normalized power generation curve and the control curve of the demand side resources:

[0046]

[0047] In the formula, and are the normalized load values ​​of demand-side resources and power grid companies in the tth period respectively; d is the Euclidean distance between the market entity and the power grid demand; is the maximum power generation of demand-side resources; C 13 is the similarity index between the demand-side resource market entities and the grid demand; β d is the similarity coefficient.

[0048] The further design of the method for evaluating the ability of demand-side resources to participate in the auxiliary service market is to calculate the weights of evaluation indicators at all levels by using the entropy weight method and the order relationship weighting method, as follows:

[0049] The entropy weight method is used to calculate the weights of evaluation indicators at all levels, and the judgment matrix is ​​set to R = (r ij ) m×n , rij is the evaluation index value;

[0050] Order: r ij The value is the market capability evaluation value of the jth expert for the ith indicator when m samples are evaluated with n evaluation indicators; R = (r ij ) m×n The value is the matrix composed of the market capability evaluation value of the j-th expert for the i-th indicator;

[0051] The calculation formula of the objective weight vector of the indicator is as follows:

[0052]

[0053]

[0054]

[0055] V=(v1,v2,...,v n ) T (13)

[0056] In the formula, m is the number of samples; n is the number of evaluation indicators; P ij is the proportion of the i-th market capability evaluation value under the j-th indicator; Q j is the entropy value of the jth indicator; v j is the weight coefficient of the jth indicator; V is the objective weight vector of the indicator;

[0057] The weights of evaluation indicators at all levels are calculated by the ordinal relationship weighting method, and the indicator importance value is set to r k , the kth important indicator of the risk indicator system is x k ;

[0058] Setting r k The value is index x k-1 With x k The ratio of importance; then the calculation formula of the subjective weight vector of the indicator is:

[0059]

[0060] ω k-1 =r k ω k (k=n,n-1,…,2) (15)

[0061]

[0062] In the formula, ω n is the indicator weight coefficient; w k There are 13 third-level indicators for the two aspects of external market and internal resources. ksubjective weight.

[0063] The further design of the method for evaluating the ability of demand-side resources to participate in the auxiliary service market is to calculate the comprehensive weighting of each indicator through the multiplication synthesis method:

[0064] Determine the comprehensive weight according to formula (17):

[0065]

[0066] In formula (17), w i is the subjective weight of the ith indicator calculated by the ordinal relationship method; v j is the objective weight of the jth indicator calculated according to the entropy weight method;

[0067] The comprehensive weight W based on the minimum entropy weight principle i It is expressed as follows:

[0068]

[0069] The combined weights used for Lagrange multiplier optimization are:

[0070]

[0071]

[0072] Where W i is the comprehensive weight of the ith indicator.

[0073] The further design of the method for evaluating the market capability of demand-side resources participating in auxiliary services is to construct a cloud model market capability evaluation model based on the entropy weight-order relationship method, which is specifically:

[0074] Build forward cloud generator and reverse cloud generator:

[0075]

[0076]

[0077]

[0078] In the formula, x i represents the i-th sample, represents the arithmetic mean of the sample, S 2 is the sample variance, the reference formula Calculated; Ex is expectation, En is entropy; He is super entropy;

[0079] If the input values ​​are set to the characteristic values ​​Ex, En, and He, the output values ​​of the forward cloud generator are:

[0080]

[0081] In formula (24), x i is a normal random number that satisfies is a normal random number that satisfies

[0082] Construct a reverse cloud occurrence algorithm with the evaluation criteria as interval number, specifically:

[0083] Ex=(C min +C max ) / 2 (25)

[0084] En=(C max -C min ) / 6 (26)

[0085] He=K (27)

[0086] In the formula, C min , C max They represent the lower limit and upper limit of the comment set respectively. K refers to the linear relationship between entropy and super entropy, which is a constant. The specific value is adjusted according to the actual situation.

[0087] Build a virtual cloud, specifically:

[0088]

[0089]

[0090]

[0091] Where, Ex i is the expectation of each third-level indicator, En i is the entropy of each third-level index, He i is the super entropy of each third-level indicator, ω i is the weight of each third-level indicator.

[0092] The present invention also provides an electronic device, comprising a memory, a processor and a computer program, wherein the computer program is stored in the memory and is configured to be executed by the processor to implement the method for evaluating the ability of demand-side resources to participate in ancillary service market.

[0093] The present invention also provides a computer-readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the method for evaluating the ability of demand-side resources to participate in the auxiliary service market.

[0094] The advantages of the present invention are as follows:

[0095] The method for evaluating the ability of demand-side resources to participate in the ancillary service market of the present invention constructs an evaluation system for the ability of demand-side resources to participate in the ancillary service market. Based on the advantages of the weighting method of the weighting relationship and the cloud model in solving uncertainty evaluation information, an improved cloud model risk evaluation model and solution process based on the entropy weight-order relationship method are proposed, which ultimately reduces the comprehensive risk of demand-side resource market interaction. This provides a scientific basis for the effective participation of demand-side resources and helps promote the development of the electricity market in a more efficient, flexible and sustainable direction.

[0096] Description of the drawings

[0097] Figure 1 This is a cloud diagram of the demand-side resource risk assessment standard in an embodiment of the present invention.

[0098] Figure 2 Schematic diagram of the evaluation results of the ability to participate in the auxiliary service market in an embodiment of the present invention.

[0099] Figure 3 Flow chart of a method for evaluating the ability of demand-side resources to participate in ancillary service market in an embodiment of the present invention. DETAILED DESCRIPTION

[0100] The technical solution of the present invention is described in detail below.

[0101] The method for evaluating the ability of demand-side resources to participate in the ancillary service market in this embodiment includes:

[0102] Construct a three-level evaluation index system based on external market characteristics and internal resource characteristics. The three-level evaluation index system includes: first-level indicators including market characteristic indicators and internal resource indicators; second-level indicators belonging to the first-level indicators representing the specific evaluation of various risks; and third-level indicators belonging to the second-level indicators representing the specific evaluation of various parameters;

[0103] The weights of evaluation indicators at all levels are calculated by entropy weight method and order relationship weighting method;

[0104] The comprehensive weighting of each indicator is calculated by multiplication synthesis method;

[0105] Construct a cloud model market capability evaluation model based on the improved entropy weight-order relationship method;

[0106] Solve the cloud model market capacity evaluation model to obtain the comprehensive cloud characteristic values ​​of demand-side resources in the ancillary service market; make a capacity evaluation cloud map of demand-side resources participating in the ancillary service market based on the comprehensive cloud characteristic values, and display the comprehensive evaluation results based on the cloud map.

[0107] The further design of the method for evaluating the ability of demand-side resources to participate in the auxiliary service market is that the secondary indicators include market monopoly risk indicators, market price volatility risk indicators, market disclosure risk indicators, startup deviation risk indicators, market matching risk indicators, resource adjustment margin risk indicators, and output curve matching risk indicators; the tertiary indicators include market concentration indicators, market information disclosure quality indicators, credit rating system, market price volatility indicators, resource ramping satisfaction indicators, startup time deviation satisfaction indicators, control capacity volatility indicators, control time effectiveness indicators, multi-energy equipment replacement rate indicators, energy storage proportion indicators, renewable energy proportion indicators, flexible load proportion indicators, and output curve and control curve matching indicators.

[0108] Furthermore, the logical relationship and calculation of the indicators at each level of the three-level evaluation indicator system are as follows: the external market characteristic indicator A1 includes market monopoly risk B1, market price volatility risk B2, and market disclosure risk B3;

[0109] Market monopoly risk index B1: including market concentration index C1;

[0110] Market price volatility risk indicator B2: including market information disclosure quality indicator C2;

[0111] Market disclosure risk indicator B3 includes credit rating system indicator C3 and market price volatility indicator C4;

[0112] The market price volatility C4 is expressed by the standard deviation of the increase or decrease of fuel within a set period of time according to formula (1), reflecting the risk level of the user's market assets:

[0113]

[0114] Where C4 represents the volatility of fuel prices in the user market; x i represents the fuel price at each time point in a certain period of time; x represents the average fuel price in a certain period of time;

[0115] Internal resource characteristic indicators A2 include: start-up deviation risk indicator B4, market matching risk indicator B5, resource adjustment margin risk indicator B6 and output curve matching risk indicator B7;

[0116] The startup deviation risk index B4 includes the resource ramping satisfaction index C5 and the startup time deviation satisfaction index C6;

[0117] Resource climbing satisfaction index C5: It represents the speed of change of the interactive capacity of the user market under the change of unit time. It is calculated according to formula (2):

[0118] C5=|P i,t -P i,t-1|≥ΔP rate (2)

[0119] In formula (2), P i,t is the output power of device i at time t; C5 is the maximum power change value of device i per unit time, that is, the resource ramp satisfaction; ΔP rate It is the minimum resource ramp satisfaction of the power system;

[0120] The startup time deviation satisfaction index C6 represents the deviation between the actual response time and the benchmark response time after the user market receives the response instruction. It is calculated according to formula (3):

[0121]

[0122] In formula (3), C6 is the start time deviation satisfaction value, and its value range is (0,1]. When C6 is close to 0, it indicates that the user market time deviation satisfaction is poor. When C6 is close to 1, it indicates that the user market time deviation satisfaction is good. rel is the actual response time of the user market; t base Benchmark response times for participating markets;

[0123] The market matching risk index B5 includes the regulation capacity volatility index C7 and the regulation time effectiveness index C8;

[0124] The regulation capacity volatility index C7 represents the deviation between the actual interaction capacity of the user market and the reported interaction capacity, and is calculated according to formula (4):

[0125]

[0126] In formula (4), C7 is the volatility of user market regulation capacity; P rel,t is the actual interactive capacity of the user market at time t; P pre,t Declare the interactive capacity for the user market at time t;

[0127] The control time effectiveness index C8 represents the overlap between the actual interaction period of the user market and the power system control period. When the interactive capacity of the user market meets the minimum interaction threshold during the control period, the period is determined to be an effective interaction period. According to formula (5),

[0128]

[0129] In formula (5), C8 is the time effectiveness of the user market regulation; x t P is the effective interaction flag of the time attribute at time t; hd,min is the minimum interaction threshold;

[0130] Resource regulation margin risk indicator B6 includes multi-energy equipment replacement rate indicator C9, energy storage ratio indicator C 10 、Renewable energy proportion index C 11 And the flexible load ratio index C 12 ;

[0131] The multi-energy equipment substitution rate index C9 represents the mutual substitution rate between multi-energy equipment under the same load demand within the user market;

[0132] Energy storage ratio index C 10 The installed capacity of energy storage equipment within the user market is calculated according to formula (6):

[0133]

[0134] In formula (6), C 10 is the proportion of energy storage equipment; P ES P is the interactive capacity of energy storage equipment; hd The total capacity of the user market that can participate in the interaction;

[0135] Renewable energy share indicator C 11 The unit capacity representing the renewable energy equipment within the user market is calculated according to formula (7):

[0136]

[0137] In formula (7), C 11 is the proportion of renewable energy; P ge Interactive capacity for renewable energy sources;

[0138] Flexible load ratio index C 12 Characterizes the proportion of non-rigid load within the user market, calculated according to formula (8):

[0139]

[0140] In formula (8), C 12 is the proportion of flexible load; P rl Interactive capacity for flexible loads;

[0141] Output curve matching risk index B7 includes output curve and regulation curve matching index C 13 , represents the matching degree between the predicted output curve of the user market and the control curve of the power system. According to formula (9), the Euclidean distance is used to measure the similarity between the normalized power generation curve and the control curve of the demand side resources:

[0142]

[0143] In the formula, and are the normalized load values ​​of demand-side resources and power grid companies in the tth period respectively; d is the Euclidean distance between the market entity and the power grid demand; is the maximum power generation of demand-side resources; C 13 is the similarity index between the demand-side resource market entities and the grid demand; β d is the similarity coefficient.

[0144] The weights of evaluation indicators at all levels are calculated by entropy weight method and order relation weighting method respectively, as follows: The weights of evaluation indicators at all levels are calculated by entropy weight method, and the judgment matrix is ​​set to R = (r ij ) m×n , r ij is the evaluation index value;

[0145] Order: r ij The value is the market capability evaluation value of the jth expert for the ith indicator when m samples are evaluated with n evaluation indicators; R = (r ij ) m×n The value is the matrix composed of the market capability evaluation value of the j-th expert for the i-th indicator;

[0146] The calculation formula of the objective weight vector of the indicator is as follows:

[0147]

[0148]

[0149]

[0150] V=(v1,v2,...,v n ) T (13)

[0151] In the formula, m is the number of samples; n is the number of evaluation indicators; P ij is the proportion of the i-th market capability evaluation value under the j-th indicator; Q j is the entropy value of the jth indicator; v j is the weight coefficient of the jth indicator; V is the objective weight vector of the indicator;

[0152] The weights of evaluation indicators at all levels are calculated by the ordinal relationship weighting method, and the indicator importance value is set to r k , the kth important indicator of the risk indicator system is x k ;

[0153] Setting r k The value is index x k-1 With x k The ratio of importance; then the calculation formula of the subjective weight vector of the indicator is:

[0154]

[0155] ω k-1 =r k ω k (k=n,n-1,…,2) (15)

[0156]

[0157] In the formula, ω n is the indicator weight coefficient; w k There are 13 third-level indicators for the two aspects of external market and internal resources. k subjective weight.

[0158] The comprehensive weighting of each indicator is calculated by multiplication synthesis method as follows:

[0159] Determine the comprehensive weight according to formula (17):

[0160]

[0161] In formula (17), w i is the subjective weight of the ith indicator calculated by the ordinal relationship method; v j is the objective weight of the jth indicator calculated according to the entropy weight method;

[0162] The comprehensive weight W based on the minimum entropy weight principle i It is expressed as follows:

[0163]

[0164] The combined weights used for Lagrange multiplier optimization are:

[0165]

[0166]

[0167] Where W i is the comprehensive weight of the ith indicator.

[0168] The cloud model market capability evaluation model based on the entropy weight-order relationship method is constructed as follows:

[0169] Build forward cloud generator and reverse cloud generator:

[0170]

[0171]

[0172]

[0173] In the formula, xi represents the i-th sample, represents the arithmetic mean of the sample, S 2 is the sample variance, the reference formula Calculated; Ex is expectation, En is entropy; He is super entropy;

[0174] If the input values ​​are set to the characteristic values ​​Ex, En, and He, the output values ​​of the forward cloud generator are:

[0175]

[0176] In formula (24), x i is a normal random number that satisfies is a normal random number that satisfies

[0177] Construct a reverse cloud occurrence algorithm with the evaluation criteria as interval number, specifically:

[0178] Ex=(C min +C max ) / 2 (25)

[0179] En=(C max -C min ) / 6 (26)

[0180] He=K (27)

[0181] In the formula, C min , C max They represent the lower limit and upper limit of the comment set respectively. K refers to the linear relationship between entropy and super entropy, which is a constant. The specific value is adjusted according to the actual situation.

[0182] Build a virtual cloud, specifically:

[0183]

[0184]

[0185]

[0186] Where, Ex i is the expectation of each third-level indicator, En i is the entropy of each third-level index, He i is the super entropy of each third-level indicator, ω i is the weight of each third-level indicator.

[0187] The solution process using the present invention is described below through specific application examples.

[0188] The objective weights of the evaluation indicators are determined by the entropy weight method. The Likert six-level scale is used to indicate the degree of influence of external market risk and internal resource risk on the resource market capacity on the demand side. By inviting 25 experts to score, a judgment matrix is ​​constructed, as shown in Table 1.

[0189] Table 1. First-level indicator judgment matrix

[0190]

[0191]

[0192] After standardizing the matrix, calculate the entropy vector e of each indicator i as follows:

[0193] e i =(0.3846153846153850.307692307692308)

[0194] According to the weight calculation formula, the weight vector v is obtained i ,as follows:

[0195] v i =(0.4274723370.572527663)

[0196] The first-level indicator weights are obtained as shown in Table 2 below.

[0197] Table 2 Weights of first-level indicators

[0198] External market risks Internal resource risks 0.427 0.573

[0199] Similarly, the weight vector of the second-level index relative to the first-level index and the weight vector of the third-level index relative to the second-level index are calculated, as shown in Tables 3 and 4.

[0200] Table 3 Secondary index weights

[0201]

[0202] Table 4 Weights of the three-level indicators

[0203]

[0204] The subjective weight of the evaluation index is determined by the ordinal relationship method. After several rounds of discussion by 25 experts, the specific results of the first-level evaluation index of the demand-side resource risk evaluation index after ranking by importance are as follows:

[0205]

[0206] Assign order relations according to the importance of indicators:

[0207]

[0208] The demand-side resource market capacity is calculated, and the weight ranking of each first-level evaluation index is shown in Table 5:

[0209] Table 5: Weight ranking of primary indicators (sequential relationship method)

[0210]

[0211] The weight coefficients of the first-level indicators {x1, x2, x3} for demand-side resource risk assessment are shown in Table 6:

[0212] Table 6 Weights of first-level indicators (order relationship method)

[0213]

[0214] By analogy, the weights of the secondary and tertiary indicators are calculated to obtain the judgment matrix of various indicators for demand-side resource market capacity assessment, as shown in Tables 7 and 8.

[0215] Table 7 Secondary index weights (sequential relationship method)

[0216]

[0217] Table 8 Weights of the three-level indicators (sequential relationship method)

[0218]

[0219] Calculate the comprehensive weights under the comprehensive weighting method as shown in the following table.

[0220] Table 9 Weights of three-level indicators for demand-side resource market capacity evaluation

[0221]

[0222] The cloud model improved based on the entropy weight-order relationship is used to calculate the risk assessment cloud level in the ancillary service market scenario.

[0223] Well-known experts were invited to rate the 13 third-level indicators in terms of external market characteristics and internal resource characteristics, with the ratings being based on a 100-point system. Evaluation grade standards were set for each indicator, as shown in Table 10 below.

[0224] Table 10 Classification of demand-side resource market capabilities

[0225]

[0226] By calculating the cloud characteristic values ​​of the demand-side resource market capacity assessment standard from the level ranges divided in the above table, we can obtain: excellent (100, 10 / 3, 0.5), good (85, 5 / 3, 0.5), medium (75, 5 / 3, 0.5), poor (65, 5 / 3, 0.5), and inferior (0, 20, 0.5). Based on this, a cloud diagram of the demand-side resource risk assessment standard can be drawn, such as Figure 1 shown.

[0227] From the above, we can see that the demand-side resource market capacity assessment standard cloud is divided into five levels: excellent, good, medium, poor and poor. The excellent and poor levels are drawn using the half-cloud force method.

[0228] According to the comprehensive cloud computing method, the characteristic values ​​of the demand-side resource market capacity evaluation in the auxiliary service market scenario can be obtained, as shown in Table 11:

[0229] Table 11 Comprehensive cloud characteristic values ​​in the auxiliary service market

[0230] Scenario expect entropy Super Entropy Ancillary Services Market 87.2800 1.5600 2.2390

[0231] Making a cloud map of the ability of demand-side resources to participate in the ancillary service market can more clearly and intuitively display the comprehensive evaluation results. Figure 2 shown.

[0232] This embodiment also provides an electronic device, including a memory, a processor, and a computer program, wherein the computer program is stored in the memory and is configured to be executed by the processor to implement a method for evaluating the ability of demand-side resources to participate in ancillary service market.

[0233] This embodiment also provides a computer-readable storage medium, which stores a computer program. The computer program is executed by a processor to implement a method for evaluating the ability of demand-side resources to participate in ancillary service market.

[0234] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for evaluating the ability of demand-side resources to participate in ancillary service markets, characterized in that include: Constructing a three-level evaluation index system based on external market characteristics and internal resource characteristics, the three-level evaluation index system includes: a first-level index including market characteristic indicators and internal resource indicators; a second-level index belonging to the first-level index representing the specific evaluation of various risks; and a third-level index belonging to the second-level index representing the specific evaluation of various parameters; The weights of evaluation indicators at all levels are calculated by entropy weight method and order relationship weighting method; The comprehensive weighting of each indicator is calculated by multiplication synthesis method; Construct a cloud model market capability evaluation model based on the improved entropy weight-order relationship method; Solve the cloud model market capacity evaluation model to obtain the comprehensive cloud characteristic values ​​of the demand-side resources in the ancillary service market; make a capacity evaluation cloud map of the demand-side resources participating in the ancillary service market based on the comprehensive cloud characteristic values, and display the comprehensive evaluation results based on the cloud map.

2. The method for evaluating the ability of demand-side resources to participate in the auxiliary service market according to claim 1 is characterized in that The secondary indicators include market monopoly risk indicator, market price fluctuation risk indicator, market disclosure risk indicator, startup deviation risk indicator, market matching risk indicator, resource adjustment margin risk indicator, and output curve matching risk indicator; The three-level indicators include market concentration index, market information disclosure quality index, credit rating system, market price volatility index, resource ramping satisfaction index, start-up time deviation satisfaction index, control capacity volatility index, control time effectiveness index, multi-energy equipment replacement rate index, energy storage proportion index, renewable energy proportion index, flexible load proportion index and output curve and control curve matching index.

3. The method for evaluating the ability of demand-side resources to participate in the auxiliary service market according to claim 2 is characterized in that The logical relationship and calculation of the indicators at each level of the three-level evaluation indicator system are as follows: External market characteristic indicators A1 include market monopoly risk B1, market price volatility risk B2, and market disclosure risk B3; Market monopoly risk index B1: including market concentration index C1; Market price volatility risk indicator B2: including market information disclosure quality indicator C2; Market disclosure risk indicator B3 includes credit rating system indicator C3 and market price volatility indicator C4; The market price volatility C4 is expressed by the standard deviation of the increase or decrease of fuel within a set period of time according to formula (1), reflecting the risk level of the user's market assets: Where C4 represents the volatility of fuel prices in the user market; x i Indicates the fuel price at each time point in a certain period; It represents the average value of fuel price in a certain period of time; Internal resource characteristic indicators A2 include: start-up deviation risk indicator B4, market matching risk indicator B5, resource adjustment margin risk indicator B6 and output curve matching risk indicator B7; The startup deviation risk index B4 includes the resource ramping satisfaction index C5 and the startup time deviation satisfaction index C6; Resource climbing satisfaction index C5: It represents the speed of change of the interactive capacity of the user market under the change of unit time. It is calculated according to formula (2): C5=|P i,t -P i,t-1 |≥ΔP rate (2) In formula (2), P i,t is the output power of device i at time t; C5 is the maximum power change value of device i per unit time, that is, the resource ramp satisfaction; ΔP rate It is the minimum resource ramp satisfaction of the power system; The startup time deviation satisfaction index C6 represents the deviation between the actual response time and the benchmark response time after the user market receives the response instruction. It is calculated according to formula (3): In formula (3), C6 is the start time deviation satisfaction value, and its value range is (0,1]. When C6 is close to 0, it indicates that the user market time deviation satisfaction is poor. When C6 is close to 1, it indicates that the user market time deviation satisfaction is good. rel is the actual response time of the user market; t base Benchmark response times for participating markets; The market matching risk index B5 includes the regulation capacity volatility index C7 and the regulation time effectiveness index C8; The regulation capacity volatility index C7 represents the deviation between the actual interaction capacity of the user market and the reported interaction capacity, and is calculated according to formula (4): In formula (4), C7 is the volatility of user market regulation capacity; P rel,t is the actual interactive capacity of the user market at time t; P pre,t Declare the interactive capacity for the user market at time t; The control time effectiveness index C8 represents the overlap between the actual interaction period of the user market and the power system control period. When the interaction capacity of the user market meets the minimum interaction threshold during the control period, the period is determined to be an effective interaction period. According to formula (5), In formula (5), C8 is the time effectiveness of the user market regulation; x t P is the effective interaction flag of the time attribute at time t; hd,min is the minimum interaction threshold; Resource regulation margin risk indicator B6 includes multi-energy equipment replacement rate indicator C9, energy storage ratio indicator C 10 、Renewable energy proportion index C 11 And the flexible load ratio index C 12 ; The multi-energy equipment substitution rate index C9 represents the mutual substitution rate between multi-energy equipment under the same load demand within the user market; Energy storage ratio index C 10 The installed capacity of energy storage equipment within the user market is calculated according to formula (6): In formula (6), C 10 is the proportion of energy storage equipment; P ES P is the interactive capacity of energy storage equipment; hd The total capacity of the user market that can participate in the interaction; Renewable energy share indicator C 11 The unit capacity representing the renewable energy equipment within the user market is calculated according to formula (7): In formula (7), C 11 is the proportion of renewable energy; P ge Interactive capacity for renewable energy sources; Flexible load ratio index C 12 Characterizes the proportion of non-rigid load within the user market, calculated according to formula (8): In formula (8), C 12 is the proportion of flexible load; P rl Interactive capacity for flexible loads; Output curve matching risk index B7 includes output curve and regulation curve matching index C 13 , represents the matching degree between the predicted output curve of the user market and the control curve of the power system. According to formula (9), the Euclidean distance is used to measure the similarity between the normalized power generation curve and the control curve of the demand side resources: In the formula, and P t * are the normalized load values ​​of demand-side resources and power grid companies in the tth period respectively; d is the Euclidean distance between the market entity and the power grid demand; is the maximum power generation of demand-side resources; C 13 is the similarity index between the demand-side resource market entities and the grid demand; β d is the similarity coefficient.

4. The method for evaluating the ability of demand-side resources to participate in the auxiliary service market according to claim 1 is characterized in that The weights of evaluation indicators at all levels are calculated by entropy weight method and order relationship weighting method, as follows: The entropy weight method is used to calculate the weights of evaluation indicators at all levels, and the judgment matrix is ​​set to R = (r ij ) m×n , r ij is the evaluation index value; Order: r ij The value is the market capability evaluation value of the jth expert for the ith indicator when m samples are evaluated with n evaluation indicators; R = (r ij ) m×n The value is the matrix composed of the market capability evaluation value of the j-th expert for the i-th indicator; The calculation formula of the objective weight vector of the indicator is as follows: V=(v1,v2,...,v n ) T (13) In the formula, m is the number of samples; n is the number of evaluation indicators; P ij is the proportion of the i-th market capability evaluation value under the j-th indicator; Q j is the entropy value of the jth indicator; v j is the weight coefficient of the jth indicator; V is the objective weight vector of the indicator; The weights of evaluation indicators at all levels are calculated by the ordinal relationship weighting method, and the indicator importance value is set to r k , the kth important indicator of the risk indicator system is x k ; Setting r k The value is index x k-1 With x k The ratio of importance; then the calculation formula of the subjective weight vector of the indicator is: oh k-1 =r k oh k (k=n,n-1,…,2) (15) In the formula, ω n is the indicator weight coefficient; w k There are 13 third-level indicators for the two aspects of external market and internal resources. k subjective weight.

5. The method for evaluating the ability of demand-side resources to participate in the auxiliary service market according to claim 1 is characterized in that The comprehensive weighting of each indicator is calculated by multiplication synthesis method as follows: Determine the comprehensive weight according to formula (17): In formula (17), w i is the subjective weight of the ith indicator calculated by the ordinal relationship method; v j is the objective weight of the jth indicator calculated according to the entropy weight method; The comprehensive weight W based on the minimum entropy weight principle i It is expressed as follows: The combined weights used for Lagrange multiplier optimization are: Where W i is the comprehensive weight of the ith indicator.

6. The method for evaluating the ability of demand-side resources to participate in the auxiliary service market according to claim 1 is characterized in that The cloud model market capability evaluation model based on the entropy weight-order relationship method is constructed as follows: Build forward cloud generator and reverse cloud generator: In the formula, x i represents the i-th sample, represents the arithmetic mean of the sample, S 2 is the sample variance, the reference formula Calculated; Ex is expectation, En is entropy; He is super entropy; If the input values ​​are set to the characteristic values ​​Ex, En, and He, the output values ​​of the forward cloud generator are: In formula (24), x i is a normal random number that satisfies is a normal random number that satisfies Construct a reverse cloud occurrence algorithm with the evaluation criteria as interval number, specifically: Ex=(C min +C max ) / 2 (25) In=(C max -C min ) / 6 (26) He=K (27) In the formula, C min , C max They represent the lower and upper limits of the comment set respectively. K refers to the linear relationship between entropy and super entropy, which is a constant. The specific value is adjusted according to the actual situation. To build a virtual cloud, specifically: Where, Ex i is the expectation of each level 3 indicator, En i is the entropy of each third-level index, He i is the super entropy of each third-level indicator, ω i is the weight of each third-level indicator.

7. An electronic device, characterized in that: It includes a memory, a processor and a computer program, wherein the computer program is stored in the memory and is configured to be executed by the processor to implement the method for evaluating the ability of demand-side resources to participate in the auxiliary service market as described in any one of claims 1-6.

8. A computer-readable storage medium, characterized in that The storage medium stores a computer program, and the computer program is executed by a processor to implement the method for evaluating the ability of demand-side resources to participate in the auxiliary service market as described in any one of claims 1-6.

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