5G intelligent power grid demand side user response potential evaluation method

Abstract

The invention relates to a 5G intelligent power grid demand side user response potential evaluation method, which comprises the following steps of 1, constructing a demand side user response potential index system considering information value for evaluating the response potential of a demand side user; 2, index weight distribution based on a three-scale analytic hierarchy process: calculating the weight of a response potential evaluation index by using the three-scale analytic hierarchy process; 3, evaluating the response potential of the demand side user by using the index system and adopting a method based on combination of set pair analysis and a variable fuzzy set; 4, the demand side response peak clipping potential of the user is judged by analyzing the demand side load characteristics of the user; 5, constructing an enterprise demand side response capability potential evaluation model; the method has the advantages that the distribution accuracy is effectively improved, the evaluation index system is established, and the response potential model is established.

Description

Technical Field

[0001] The present invention belongs to the technical field of smart grids, and specifically relates to a method for evaluating the user response potential on the demand side of a 5G smart grid. Background Art

[0002] The imbalance of electricity supply and demand during the peak and valley periods is still a very serious problem. Under the reform of the power market, the demand response operation mode with load aggregators as intermediaries, integrating emerging technologies such as the Internet, distributed generation, and smart grids, can effectively reduce the peak and valley gap of electricity consumption and ensure the safe, stable and reliable operation of the power grid. In the process of implementing demand response, the response potential of users is evaluated in advance, and the load response amount of users participating in the response can be arranged more reasonably. The evaluation index system of existing technologies mostly considers the basic situation of users, user behavior, economic incentives, etc., and in the process of transmitting this information to aggregators, there will be transmission problems such as delay and damage, and the credibility of the user's own information needs to be verified. Therefore, incorporating information value factors in the evaluation process can more comprehensively evaluate the user's response potential. Therefore, it is very necessary to provide a 5G smart grid demand-side user response potential evaluation method that effectively improves the allocation accuracy, establishes an evaluation index system, and establishes a response potential model. Summary of the invention

[0003] The purpose of the present invention is to overcome the shortcomings of the prior art and to provide a 5G smart grid demand-side user response potential evaluation method that effectively improves allocation accuracy, establishes an evaluation index system, and establishes a response potential model.

[0004] The object of the present invention is achieved by: a method for evaluating the user response potential on the demand side of a 5G smart grid, the method comprising the following steps:

[0005] Step 1: To evaluate the response potential of demand-side users, a demand-side user response potential indicator system taking into account the value of information is constructed;

[0006] Step 2: Indicator weight allocation based on three-scale analytic hierarchy process: The weights of response potential assessment indicators are calculated using the three-scale analytic hierarchy process;

[0007] Step 3: Using the above indicator system, the demand-side user response potential is evaluated by combining the method based on set pair analysis and variable fuzzy sets;

[0008] Step 4: Determine the peak-shaving potential of the user's demand-side response by analyzing the user's demand-side load characteristics;

[0009] Step 5: Construct a model to evaluate the potential of enterprise demand-side response capabilities.

[0010] The demand-side user response potential indicator system in step 1 includes: user willingness level, user response coordination, load importance, equipment capacity that can be used for response, user economic level, compensation for user response, timeliness, accuracy and completeness.

[0011] The compensation received by the user in response is specifically: In the formula, C i is the total compensation received by user i; Compensation for participating in peak shifting; Δt i is the number of hours for peak shifting; α i Compensation unit price for peak time shifting; Compensation for participating in peak-to-valley shifting; β is the peak shifting and valley filling power; i Compensation unit price for peak shifting and valley filling; Compensation for participation in peak load shaving; is the peak power reduction; i The unit price for peak shaving compensation.

[0012] The timeliness is specifically: the smart grid has a time delay requirement for information communication, and it is necessary to ensure that the acquired information can be updated in real time over time in order to make the latest judgment. j (t,k)∈(0,1] is the specified service w j At the evaluation time t, according to the service identifier j in the user evaluation request, obtain the service w at the current time t j The forgetting rate k: s j (t,k)=e -kt .

[0013] The indicator weight allocation based on the three-scale hierarchical analysis method in step 2 includes the following steps:

[0014] Step 2.1: Use the three scales (1, 0.5, 0) to compare each indicator in pairs and establish a priority judgment matrix for the first-level indicators;

[0015] Step 2.2: Transform the judgment matrix F into the fuzzy consistency judgment matrix R. The calculation method is as follows:

[0016]

[0017] Step 2.3: Check the consistency of R obtained in the previous step. And p≠q≠λ, if: r pλ r λq r qp =r λp r qλ r pq , then the matrix R satisfies consistency; if R does not satisfy consistency, then: Convert it into a consistency matrix;

[0018] Step 2.4: Use the row normalization method to obtain the weight vector w for the consistency matrix R or R′, that is:

[0019]

[0020] The set pair analysis in step 3 is specifically as follows: the evaluation samples and evaluation levels are regarded as a set pair, and quantitative analysis of similarities, differences, and opposites is performed to calculate the degree of connection: u=a+bI+cJ, where a, b, and c are respectively the degree of identity, the degree of difference, and the degree of opposition, all of which are non-negative, and a+b+c=1; I is the coefficient of difference; and J is the coefficient of opposition.

[0021] The step 3 specifically includes the following steps:

[0022] Step 3.1: Aggregators establish an evaluation index system for user response potential: From the perspective of users and incentives, the user response potential evaluation index system is established from four aspects: user historical behavior, user basic situation, aggregator incentives to users, and information value. The three-scale hierarchical analysis method is used to determine the weight of the index w = (w1, w2, ..., w m ), m is the number of indicators;

[0023] Step 3.2: To make the user's response potential more intuitive, the aggregator establishes a potential assessment level standard. kl k=1,2,...,n,l=1,2,...,m}, i.e., the upper and lower limits of the evaluation criteria for the indicator level; the indicator value corresponding to the indicator l of the user is recorded as {x l l=1,2,...,m}; the level of the indicator is the level k corresponding to the standard limit interval within which its indicator value falls; where n is the number of divided levels; m is the number of indicators;

[0024] Step 3.3: The aggregator uses the set pair analysis theory to calculate the connection between the evaluation index value and each evaluation level: the user's index value and its corresponding level are regarded as a set pair with m characteristics, and the weight of each index is considered to make a comprehensive judgment from the three aspects of similarity, difference and opposite;

[0025] Step 3.4: The aggregator calculates the relative membership of the evaluation index value and each evaluation level based on the difference: using the connection between the evaluation index value and each evaluation level k, that is, the difference u k , use fuzzy set pair theory to calculate the membership degree: v k =(1+u k ) / 2;

[0026] Step 3.5: Aggregator calculates user response potential level: Based on the relative membership in step 3.4, the aggregator uses the maximum membership criterion to determine the level of user response potential on the demand side; to avoid distortion caused by using the maximum membership principle, the eigenvalue h is used here to quantify the potential assessment result: In the formula, k is the level; n is the number of levels; It is the normalized relative membership. Different levels are multiplied by their relative membership and then added together to get the eigenvalue.

[0027] Step 3.6: When the aggregator assigns reduction tasks to users, the aggregator normalizes the potential evaluation levels of all users h j , which is the ratio of the reduction tasks assigned to each user to the total tasks. Then the reduction tasks of each user are: Among them, TA j is the reduction task of user j; CA is the contracted amount of load reduction of the aggregator; u is the total number of users.

[0028] The step 3.3 is specifically as follows: assuming that the user's potential evaluation indicators 1, 2, ..., t1 are in level k, then the identity a is: In the formula, w l is the normalized weight of the lth indicator; assuming that the user's response potential evaluation indicators t1+1, t1+2, ..., t2 are in the interval level of level k, then the opposition degree c is: Assume that the user's response potential evaluation index t2+1, t2+2, ..., m is in the adjacent level of level k, then the difference degree b of the single index is l =w l , l=t2+1,t2+2,...,m, the single index difference coefficient is: In the formula, x l ∈grad(k-1) means the indicator value falls into level k-1; x l ∈grad(k+1) means that the indicator value falls into level k+1.

[0029] The step 4 is specifically as follows: statistically analyzing the historical power consumption data of a single user on a typical day during the demand side response period, and using the standard deviation formula to measure the load dispersion, that is, to calculate the power consumption volatility α of the user during the period, the minimum power consumption x during the demand side response period min And the average power consumption during the demand-side response period x av , where the power consumption volatility is: In the formula, x i is the electricity consumption of the user in the demand-side response period i; n is the number of time periods for demand-side response.

[0030] The enterprise demand-side response capability potential evaluation model in step 5 is specifically as follows: Based on the above analysis, combined with the maximum response capacity measured by power consumption, the user's demand-side response capability determination model for a single hour can be set as follows: η i =(x av -x min )×α×λ i ×10 5 Substituting the expression of power consumption fluctuation α into the above formula, we get: Where η i is the demand-side response potential of the user in response period i; i is the ratio of the number of demand-side responses carried out in response period i to the number of high-frequency demand-side responses; summing up all response potentials, the resulting scale interval is the comprehensive potential of user demand-side response η:

[0031] Beneficial effects of the present invention: The present invention is a method for evaluating the user response potential on the demand side of a 5G smart grid. During the implementation of demand response, the prior evaluation of the user response potential can effectively improve the accuracy of the aggregator's task allocation. During use, the present invention takes into account the accuracy of each user's information itself and the error caused during the transmission process, and incorporates the information value into the evaluation index system. First, the factors related to the user's response potential are comprehensively analyzed, an evaluation index system is established and the weights are calculated to achieve quantitative analysis of each index; then, the potential level is calculated by combining set pair analysis with variable fuzzy sets, and the user load response task volume is allocated using the aggregator evaluation results; the present invention is based on the characteristic analysis of user electricity consumption data in the response period, sets a plurality of response capability evaluation indicators, and proposes a highly operational method for evaluating the demand side response potential of power users, which can be used to judge the demand side response potential of users to be aggregated by load aggregators, and screen out power users who are not suitable for demand side response; the present invention has the advantages of effectively improving the allocation accuracy, establishing an evaluation index system, and establishing a response potential model. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 Schematic diagram of the demand-side aggregator-user interaction model of the present invention.

[0033] Figure 2 It is a schematic diagram of the user response potential evaluation index system of the present invention.

[0034] Figure 3 This is a flow chart for evaluating the demand-side user response potential of the present invention. DETAILED DESCRIPTION

[0035] The present invention will be further described below in conjunction with the accompanying drawings.

[0036] Example 1

[0037] like Figure 1-3 As shown, a method for evaluating the user response potential on the demand side of a 5G smart grid comprises the following steps:

[0038] Step 1: To evaluate the response potential of demand-side users, a demand-side user response potential indicator system taking into account the value of information is constructed;

[0039] In this embodiment, the demand-side user response potential index system includes: user willingness level, user response coordination, load importance, equipment capacity that can be used for response, user economic level, compensation for user response, timeliness, accuracy and completeness.

[0040] ① The compensation for user response is specifically: the compensation given by the power company and the government for the user's participation in the response. If the compensation mechanism has been established, each participation method will provide a specific compensation unit price. Taking the three methods of time shifting, peak shifting and peak shaving as examples, the compensation received by the user is: In the formula, C i is the total compensation received by user i; Compensation for participating in peak shifting; Δt i is the number of hours for peak shifting; α i Compensation unit price for peak time shifting; Compensation for participating in peak-to-valley shifting; β is the peak shifting and valley filling power; i Compensation unit price for peak shifting and valley filling; Compensation for participation in peak load shaving; is the peak power reduction; i The unit price for peak shaving compensation. If such compensation mechanism has not yet been formed and improved, it will be replaced by an abstract unit price for compensation.

[0041] ② Timeliness: Smart grids require information communication to have a certain delay, and the information obtained must be updated in real time to make the latest judgment. j (t,k)∈(0,1] is the specified service w j At the evaluation time t, according to the service identifier j in the user evaluation request, obtain the service w at the current time t j The forgetting rate k: s j (t,k)=e -kt .

[0042] Step 2: Indicator weight allocation based on three-scale analytic hierarchy process: Use three-scale analytic hierarchy process to calculate the weight of response potential evaluation indicators; first, use three scales to rank the importance of each indicator; then, prioritize the importance of each indicator and obtain the weight of each indicator to the upper level indicator; finally, calculate the weight of all indicators at the same level to the response potential;

[0043] In this embodiment, the indicator weight allocation based on the three-scale analytic hierarchy process includes the following steps:

[0044] Step 2.1: Use the three scales (1, 0.5, 0) to compare each indicator in pairs and establish a priority judgment matrix for the first-level indicators; for example, for a priority judgment matrix F containing n elements, it is itself an n-order square matrix, and the element f in the matrix pq Indicator u that affects the user response potential on the demand side p with u q The mutual relationship: where f pq The judgment rules are as follows:

[0045] Step 2.2: Transform the judgment matrix F into the fuzzy consistency judgment matrix R. The calculation method is as follows:

[0046]

[0047] Step 2.3: Check the consistency of R obtained in the previous step. And p≠q≠λ, if: r pλ r λq r qp =r λp r qλ r pq , then the matrix R satisfies consistency; if R does not satisfy consistency, then: Convert it into a consistency matrix;

[0048] Step 2.4: Use the row normalization method to obtain the weight vector w for the consistency matrix R or R′, that is:

[0049]

[0050] Therefore, the weight of the first-level indicator of the evaluation index system without considering the information value is (0.444, 0.333, 0.222), and the weight of the second-level indicator to the corresponding first-level indicator is [(0.625, 0.375), (0.333, 0.444, 0.222), 1]; therefore, the weight of the second-level indicator relative to the demand-side user response potential is (0.278, 0.167, 0.111, 0.148, 0.074, 0.222);

[0051] Similarly, the weights of the first-level indicators of the evaluation index system considering information value are (0.3438, 0.2188, 0.1563, 0.2813), and the weights of the second-level indicators relative to the corresponding indicators of the first level are [(0.625, 0.375), (0.333, 0.444, 0.222), 1, (0.444, 0.222, 0.333)]. Therefore, the weights of the second-level indicators relative to the demand-side user response potential are (0.2149, 0.1289, 0.0729, 0.0971, 0.0486, 0.2813, 0.0694, 0.0347, 0.0520).

[0052] Step 3: Using the above indicator system, the demand-side user response potential is evaluated by combining the method based on set pair analysis and variable fuzzy sets;

[0053] In this embodiment, the set pair analysis is specifically as follows: the evaluation sample and the evaluation level are regarded as a set pair, and a quantitative analysis of similarities, differences and opposites is performed to calculate the degree of connection: u=a+bI+cJ, wherein a, b, c are respectively the degree of identity, the degree of difference and the degree of opposition, all of which are non-negative, and a+b+c=1; I is the coefficient of difference, I∈[-1,1] when I=-1 or 1, it means that b is deterministic, and as I approaches 0, the uncertainty of b increases; J is the coefficient of opposition, J=-1; the evaluation sample and the evaluation level are regarded as a set pair with m characteristics (m evaluation indicators), and under the premise of considering the importance (weight) of each characteristic, the degree of similarities, differences and opposites between the evaluation sample and the evaluation level and the comprehensive degree of connection are analyzed.

[0054] In this embodiment, step 3 specifically includes the following steps:

[0055] Step 3.1: Aggregators establish an evaluation index system for user response potential: From the perspective of users and incentives, an evaluation index system for user response potential will be established from four aspects: user historical behavior, user basic information, aggregator incentives to users, and information value. Figure 3 As shown in the figure, the weight of the index w=(w1,w2,...,w m ), m is the number of indicators;

[0056] Step 3.2: To make the user's response potential more intuitive, the aggregator establishes a potential assessment level standard. kl k=1,2,...,n,l=1,2,...,m}, i.e., the upper and lower limits of the evaluation criteria for the indicator level; the indicator value corresponding to the indicator l of the user is recorded as {x ll=1,2,...,m}, the index values ​​of the above 9 evaluation indicators are all better if the value is large, that is, the larger the value, the greater the potential level of user response; the level of the indicator is the level k corresponding to the standard limit range within which its index value falls; where n is the number of divided levels; m is the number of indicators;

[0057] Step 3.3: The aggregator uses the set pair analysis theory to calculate the connection between the evaluation index value and each evaluation level: the user's index value and its corresponding level are regarded as a set pair with m characteristics, and the weight of each index is considered to make a comprehensive judgment from the three aspects of similarity, difference and opposite;

[0058] In this embodiment, step 3.3 is specifically as follows: assuming that the user's potential evaluation indicators 1, 2, ..., t1 are in level k, then the identity a is: In the formula, w l is the normalized weight of the lth indicator;

[0059] Assuming that the user's response potential evaluation indicators t1+1, t1+2, ..., t2 are in the interval level of level k, the opposition degree c is:

[0060] Assume that the user's response potential evaluation index t2+1, t2+2, ..., m is in the adjacent level of level k, then the difference degree b of the single index is l =w l , l=t2+1,t2+2,...,m, the single index difference coefficient is: In the formula, x l ∈grad(k-1) means the indicator value falls into level k-1; x l ∈grad(k+1) means that the indicator value falls into level k+1;

[0061] For example, for level k=9, the weights of indicators at level 9 are added together to get the same degree a; the difference b of a single indicator at level 10 or 8 is its own weight, and each indicator also corresponds to a difference coefficient; the opposition c is the sum of the weights of the other indicators at the level separated from level 9; then the connection u is:

[0062]

[0063] Step 3.4: The aggregator calculates the relative membership of the evaluation index value and each evaluation level based on the difference: Obviously, the connection degree u∈[-1,1], if the identity between each user and the evaluation level is smaller, u is closer to -1, and the user is more inclined not to belong to the evaluation level k; if the identity between the user and the evaluation level k is greater, u is closer to 1, and the sample is more inclined to belong to the evaluation level k; it can be seen that the connection degree u is also a relative difference degree; in order to further improve the comprehensive evaluation effect of the aggregator on the user response potential level, the connection degree between the evaluation index value and each evaluation level k, that is, the difference degree u, is used here k , use fuzzy set pair theory to calculate the membership degree: v k =(1+u k ) / 2;

[0064] Step 3.5: Aggregator calculates user response potential level: Based on the relative membership in step 3.4, the aggregator uses the maximum membership criterion to determine the level of user response potential on the demand side; to avoid distortion caused by using the maximum membership principle, the eigenvalue h is used here to quantify the potential assessment result: In the formula, k is the level; n is the number of levels; It is the normalized relative membership. Different levels are multiplied by their relative membership and then added together to get the eigenvalue.

[0065] Step 3.6: When the aggregator assigns reduction tasks to users, the aggregator normalizes the potential evaluation levels of all users h j , which is the ratio of the reduction tasks assigned to each user to the total tasks. Then the reduction tasks of each user are: Among them, TA j is the load reduction task of user j; CA is the load reduction contract of the aggregator; u is the total number of users

[0066] Step 4: Determine the peak-shaving potential of the user's demand-side response by analyzing the user's demand-side load characteristics;

[0067] Step 5: Construct a model to evaluate the potential of enterprise demand-side response capabilities.

[0068] The present invention is a method for evaluating the user response potential on the demand side of a 5G smart grid. During the implementation of demand response, the prior evaluation of the user response potential can effectively improve the accuracy of the aggregator's task allocation. During use, the present invention takes into account the accuracy of each user's information itself and the error caused during the transmission process, and incorporates the information value into the evaluation index system. First, the factors related to the user's response potential are comprehensively analyzed, an evaluation index system is established and the weights are calculated to achieve quantitative analysis of each index; then, the potential level is calculated by combining set pair analysis with variable fuzzy sets: the user load response task volume is allocated using the aggregator evaluation results; the present invention has the advantages of effectively improving the allocation accuracy, establishing an evaluation index system, and establishing a response potential model.

[0069] Example 2

[0070] like Figure 1-3 As shown, a method for evaluating the user response potential on the demand side of a 5G smart grid comprises the following steps:

[0071] Step 1: To evaluate the response potential of demand-side users, a demand-side user response potential indicator system taking into account the value of information is constructed;

[0072] Step 2: Indicator weight allocation based on three-scale analytic hierarchy process: The weights of response potential assessment indicators are calculated using the three-scale analytic hierarchy process;

[0073] Step 3: Using the above indicator system, the demand-side user response potential is evaluated by combining the method based on set pair analysis and variable fuzzy sets;

[0074] Step 4: Determine the peak-shaving potential of the user's demand-side response by analyzing the user's demand-side load characteristics;

[0075] In this embodiment, step 4 is specifically as follows: statistically analyze the historical power consumption data of a single user on a typical day during the demand-side response period, and use the standard deviation formula to measure the load dispersion, that is, to calculate the power consumption volatility α of the user during the period, the minimum power consumption x during the demand-side response period, and the power consumption fluctuation α of the user during the period. min And the average power consumption during the demand-side response period x av , where the power consumption volatility is: In the formula, x i is the electricity consumption of the user in the demand-side response period i; n is the number of time periods for demand-side response.

[0076] Step 5: Construct a model to evaluate the potential of enterprise demand-side response capabilities.

[0077] In this embodiment, the enterprise demand side response capability potential evaluation model in step 5 is specifically as follows: Based on the above analysis, combined with the maximum response capacity measured by power consumption, the user's single hour demand side response capability determination model can be set as follows: η i =(x av -x min )×α×λ i ×10 5 Substituting the expression of power consumption fluctuation α into the above formula, we get: Where η i is the demand-side response potential of the user in response period i; i is the ratio of the number of demand-side responses carried out in response period i to the number of high-frequency demand-side responses; summing up all response potentials, the resulting scale interval is the comprehensive potential of user demand-side response η:

[0078] The present invention is a method for evaluating the user response potential on the demand side of a 5G smart grid. During use, the present invention starts from the incentive-type demand side response, collects and analyzes the historical electricity consumption data of electricity users, establishes a demand side response potential calculation model for electricity users, uses the historical electricity consumption data to analyze the user response degree and the load adjustable capacity, and proposes a quantitative evaluation method for the demand side response potential based on the user's actual electricity consumption data. The load aggregator uses this method to quantitatively evaluate the user, and uses the potential evaluation quantitative data to effectively evaluate the importance of the user to the load aggregator. According to the evaluation results, high-quality users can be screened to guide the development direction of the load aggregator. The present invention has the advantages of effectively improving the allocation accuracy, establishing an evaluation index system, and establishing a response potential model.

Claims

1. 5G smart grid demand side user response potential assessment method, characterized by: The method comprises the following steps: Step 1: To evaluate the response potential of demand-side users, a demand-side user response potential indicator system taking into account the value of information is constructed; Step 2: Indicator weight allocation based on three-scale analytic hierarchy process: The weights of response potential assessment indicators are calculated using the three-scale analytic hierarchy process; Step 3: Using the above indicator system, the demand-side user response potential is evaluated by combining the method based on set pair analysis and variable fuzzy sets; Step 4: Determine the peak-shaving potential of the user's demand-side response by analyzing the user's demand-side load characteristics; Step 5: Construct a model to evaluate the potential of enterprise demand-side response capabilities.

2. The 5G smart grid demand side user response potential assessment method according to claim 1, characterized in that: The demand-side user response potential indicator system in step 1 includes: user willingness level, user response coordination, load importance, equipment capacity that can be used for response, user economic level, compensation for user response, timeliness, accuracy and completeness.

3. The 5G smart grid demand side user response potential assessment method according to claim 2, characterized in that: The compensation received by the user in response is specifically: In the formula, C i is the total compensation received by user i; Compensation for participating in peak shifting; Δt i is the number of hours for peak shifting; α i Compensation unit price for peak time shifting; Compensation for participating in peak-to-valley shifting; β is the peak shifting and valley filling power; i Compensation unit price for peak shifting and valley filling; Compensation for participation in peak load shaving; is the peak power reduction; i The unit price for peak shaving compensation.

4. The 5G smart grid demand side user response potential assessment method according to claim 2, characterized in that: The timeliness is specifically: the smart grid has a time delay requirement for information communication, and it is necessary to ensure that the acquired information can be updated in real time over time in order to make the latest judgment. j (t,k)∈(0,1] is the specified service w j At the evaluation time t, according to the service identifier j in the user evaluation request, obtain the service w at the current time t j The forgetting rate k: s j (t,k)=e -kt .

5. The 5G smart grid demand side user response potential assessment method according to claim 1, characterized in that: The indicator weight allocation based on the three-scale hierarchical analysis method in step 2 includes the following steps: Step 2.1: Use the three scales (1, 0.5, 0) to compare each indicator in pairs and establish a priority judgment matrix for the first-level indicators; Step 2.2: Transform the judgment matrix F into the fuzzy consistency judgment matrix R. The calculation method is as follows: Step 2.3: Check the consistency of R obtained in the previous step. And p≠q≠λ, if: r pλ r λq r qp =r λp r qλ r pq , then the matrix R satisfies consistency; if R does not satisfy consistency, then: Convert it into a consistency matrix; Step 2.4: Use the row normalization method to obtain the weight vector w for the consistency matrix R or R′, that is:

6. The 5G smart grid demand side user response potential assessment method according to claim 1, characterized in that: The set pair analysis in step 3 is specifically as follows: the evaluation samples and evaluation levels are regarded as a set pair, and quantitative analysis of similarities, differences, and opposites is performed to calculate the degree of connection: u=a+bI+cJ, where a, b, and c are respectively the degree of identity, the degree of difference, and the degree of opposition, all of which are non-negative, and a+b+c=1; I is the coefficient of difference; and J is the coefficient of opposition.

7. The 5G smart grid demand side user response potential assessment method according to claim 6, characterized in that: The step 3 specifically comprises the following steps: Step 3.1: Aggregators establish an evaluation index system for user response potential: From the perspective of users and incentives, the user response potential evaluation index system is established from four aspects: user historical behavior, user basic situation, aggregator incentives to users, and information value. The three-scale hierarchical analysis method is used to determine the weight of the index w = (w1, w2, ..., w m ), m is the number of indicators; Step 3.2: To make the user's response potential more intuitive, the aggregator establishes a potential assessment level standard. kl k=1,2,...,n,l=1,2,...,m}, i.e., the upper and lower limits of the evaluation criteria for the indicator level; the indicator value corresponding to the indicator l of the user is recorded as {x l l=1,2,...,m}; the level of the indicator is the level k corresponding to the standard limit interval within which its indicator value falls; where n is the number of divided levels; m is the number of indicators; Step 3.3: The aggregator uses the set pair analysis theory to calculate the connection between the evaluation index value and each evaluation level: the user's index value and its corresponding level are regarded as a set pair with m characteristics, and the weight of each index is considered to make a comprehensive judgment from the three aspects of similarity, difference and opposite; Step 3.4: The aggregator calculates the relative membership of the evaluation index value and each evaluation level based on the difference: using the connection degree between the evaluation index value and each evaluation level k, that is, the difference u k , use fuzzy set pair theory to calculate the membership degree: v k =(1+u k ) / 2; Step 3.5: Aggregator calculates user response potential level: Based on the relative membership in step 3.4, the aggregator uses the maximum membership criterion to determine the level of user response potential on the demand side; to avoid distortion caused by using the maximum membership principle, the eigenvalue h is used here to quantify the potential assessment result: In the formula, k is the level; n is the number of levels; It is the normalized relative membership. Different levels are multiplied by their relative membership and then added together to get the eigenvalue. Step 3.6: When the aggregator assigns reduction tasks to users, the aggregator normalizes the potential evaluation levels of all users h j , which is the ratio of the reduction tasks assigned to each user to the total tasks. Then the reduction tasks of each user are: Among them, TA j is the reduction task of user j; CA is the contracted amount of load reduction of the aggregator; u is the total number of users.

8. The 5G smart grid demand side user response potential assessment method according to claim 7, characterized in that: The step 3.3 is specifically as follows: assuming that the user's potential evaluation indicators 1, 2, ..., t1 are in level k, then the identity a is: In the formula, w l is the normalized weight of the lth indicator; assuming that the user's response potential evaluation indicators t1+1, t1+2, ..., t2 are in the interval level of level k, then the opposition degree c is: Assume that the user's response potential evaluation index t2+1, t2+2, ..., m is in the adjacent level of level k, then the difference degree b of the single index is l =w l , l=t2+1,t2+2,...,m, the single index difference coefficient is: In the formula, x l ∈grad(k-1) means the indicator value falls into level k-1; x l ∈grad(k+1) means that the indicator value falls into level k+1.

9. The 5G smart grid demand side user response potential assessment method according to claim 1, characterized in that: The step 4 is specifically as follows: statistically analyzing the historical power consumption data of a single user on a typical day during the demand side response period, and using the standard deviation formula to measure the load dispersion, that is, to calculate the power consumption volatility α of the user during the period, the minimum power consumption x during the demand side response period min And the average power consumption during the demand-side response period x av , where the power consumption volatility is: In the formula, x i is the electricity consumption of the user in the demand-side response period i; n is the number of time periods for demand-side response.

10. The 5G smart grid demand side user response potential assessment method according to claim 1, characterized in that: The enterprise demand-side response capability potential evaluation model in step 5 is specifically as follows: Based on the above analysis, combined with the maximum response capacity measured by power consumption, the user's demand-side response capability determination model for a single hour can be set as follows: η i =(x av -x min )×α×λ i ×10 5 Substituting the expression of power consumption fluctuation α into the above formula, we get: Where η i is the demand-side response potential of the user in response period i; λ i is the ratio of the number of demand-side responses carried out in response period i to the number of high-frequency demand-side responses; summing up all response potentials, the resulting scale interval is the comprehensive potential of user demand-side response η:

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