FAHP park enterprise electricity utilization potential evaluation and electricity price excitation method

Abstract

The invention belongs to the field of power utilization behavior evaluation of power enterprises, and particularly relates to an FAHP park enterprise power utilization potential evaluation and electricity price excitation method. The method comprises the following steps: collecting and acquiring power consumption behaviors and power consumption data of enterprises in a smart park, constructing a green intelligent power consumption evaluation model of the enterprises in the park, establishing a comprehensive evaluation matrix, and reasonably evaluating the green intelligent power consumption behaviors of the enterprises in the park by adopting a fuzzy comprehensive evaluation method to obtain green and intelligent power consumption behavior evaluation results of the enterprises in the park. And according to an evaluation result and an electricity price incentive model, providing a reference for formulating a detailed rule of electricity price incentive of enterprises in the next stage of the park.

Description

Technical Field

[0001] The invention belongs to the field of power enterprise electricity consumption behavior evaluation, and specifically is a FAHP park enterprise electricity consumption potential evaluation and electricity price incentive method. Background Art

[0002] At present, the planning and construction plan of the new power system requires the power grid to develop in the direction of greening and intelligence. As a key link in promoting social and economic development and energy transformation, the electricity consumption behavior of enterprises not only represents the current development status of enterprises, but also affects the safe and stable operation of regional power grids to a certain extent. In order to ensure the safe and stable operation of the power grid and further narrow the peak-valley load difference, the power department has introduced a series of measures such as time-of-use electricity prices to encourage enterprises to use electricity during peak hours. Reasonable evaluation of the electricity consumption behavior of enterprises and the formulation of an electricity price incentive system based on this will help encourage enterprises to scientifically and rationally plan their electricity consumption behavior and ensure the safe, stable and green operation of the power grid.

[0003] In view of the limited influencing factors involved in the existing enterprise electricity consumption behavior evaluation methods and the unscientific evaluation process, the present invention, on the basis of fully considering the electricity consumption habits of enterprises, collects and obtains the electricity consumption information of enterprises in the park, constructs a green and intelligent electricity consumption evaluation model for each enterprise, determines the weight of each evaluation index, and finally evaluates the electricity consumption potential of park enterprises based on the fuzzy comprehensive evaluation method FAHP, and gives an electricity price incentive method according to the evaluation results.

[0004] To this end, the present invention provides a method for evaluating the electricity consumption potential of park enterprises and providing electricity price incentives based on FAHP. Summary of the invention

[0005] In order to make up for the deficiencies of the prior art, at least one technical problem raised in the background technology is solved.

[0006] The technical solution adopted by the present invention to solve the technical problem is: a FAHP park enterprise electricity potential assessment and electricity price incentive method described in the present invention comprises the following steps:

[0007] S1. Collect and obtain the green and intelligent electricity consumption behavior and electricity consumption data of each enterprise in the park, and obtain the corresponding electricity consumption performance score, system peak load participation performance score, green energy participation performance score, electricity consumption prediction capability performance score, and social performance score through the electricity consumption evaluation model, system peak load participation evaluation model, green energy participation evaluation model, load forecasting capability evaluation model, and enterprise social image evaluation model;

[0008] S2. Obtain expert scoring results through the green and intelligent electricity consumption evaluation model of each enterprise in the park, and construct a fuzzy comprehensive evaluation matrix A1 to evaluate the electricity consumption behavior of each enterprise based on the expert scoring results. At the same time, construct a fuzzy comprehensive evaluation matrix A2 based on the S1 scoring results. Consider the membership of each factor to different evaluation levels with each factor having a weight of 50% to obtain the fuzzy comprehensive evaluation matrix A in the park;

[0009] S3. Determine the weight of each evaluation indicator on the evaluation results;

[0010] S4. Evaluate the electricity consumption behavior of enterprises based on the fuzzy comprehensive evaluation method and judge the electricity consumption behavior of each enterprise;

[0011] S5. Return to S2 to complete the electricity consumption behavior assessment of all enterprises in the park;

[0012] S6. Based on the results of S5, determine the details of electricity price incentives for each enterprise in the park.

[0013] Preferably, the method for constructing the electricity consumption evaluation model is:

[0014] According to the data of smart meters of each enterprise, the daily electricity consumption of each enterprise is counted, and the F total The power consumption performance of each enterprise in the park under different time scales is divided into several areas according to the power consumption under each time scale, and each area is scored to obtain F total The electricity consumption performance scores under different time scales are added together to obtain the total score of the enterprise, and the total score of the enterprise and F are obtained. total The ratio is used to obtain the enterprise’s electricity consumption performance score for that day.

[0015] Preferably, the method for constructing the system peak load participation evaluation model is:

[0016] Based on the energy consumption data of each enterprise in the park, the energy consumption of enterprises in F total The power consumption in peak, valley and flat time periods under different time scales is calculated, and then the proportion of the power consumption in the selected time period is obtained. The proportion value is divided into several areas, and each area is scored to obtain the enterprise's F total The total performance score in the peak load capacity evaluation at different time scales is obtained, and the total performance score in the peak load capacity evaluation and F total The ratio of the peak load and the peak load participation performance score of the system are obtained.

[0017] Preferably, the green energy participation evaluation model construction method is:

[0018] Statistics of enterprises in F total The proportion of green electricity usage under different time scales is used to score the green electricity usage performance; first, based on the energy consumption data of each enterprise in the park, the total F of the enterprises in the month, week and day is counted.total The green electricity generation in the peak, valley and flat time periods under different time scales is then obtained, and its proportion to the total green electricity generation in the selected time period is obtained. The proportion value is divided into several areas, and each area is scored to obtain the enterprise's F total The total score of the green energy participation of enterprises under different time scales is obtained, and the total score and F total The ratio of the green electricity participation performance of the enterprise is obtained.

[0019] Preferably, the load forecasting capability evaluation model construction method is:

[0020] Obtain the enterprise electricity consumption and forecast deviation values ​​reported in advance by each enterprise in the park, and calculate the enterprise F total The electricity consumption at each time scale is compared with the reported electricity consumption of enterprises and the actual statistics. total The difference of electricity consumption under each time scale is calculated respectively, and the ratio of the difference result to the predicted deviation value is obtained. The ratio is divided into several areas, and each area is scored to obtain the enterprise's F total The enterprise load forecast performance scores under different time scales are added together to obtain the total value of the enterprise load forecast performance score and F total The ratio of is used to obtain the enterprise's electricity consumption forecasting ability performance score.

[0021] Preferably, the method for constructing the corporate social image evaluation model is:

[0022] By retrieving the company's annual profit growth rate information, social image information, and electricity bill payment enthusiasm information, several scoring levels are set, and points are assigned to each scoring level. The corresponding scores are obtained according to the scoring levels of each type of company, and then the scores of each type of company are added together and the average is taken to obtain the social performance score of each company.

[0023] Preferably, the method for constructing the green intelligent electricity consumption evaluation model for each enterprise in the park is:

[0024] First, obtain the general set composed of multiple electricity consumption behavior scores of the evaluated enterprises and record it as U, U=(u1,u2,......u n ), where u i represents the i-th factor describing the green and smart electricity consumption behavior of the enterprise;

[0025] The set of possible evaluation results of the electricity consumption behavior of each enterprise in the park is represented by V, V = (V1, V2, .... V n ), where element v j represents the jth evaluation result; assigns points to V;

[0026] According to the expert scoring results, the contribution of each factor in the evaluation set is determined, the scores of each factor at different evaluation levels are counted, and the average number of scores at each evaluation level is calculated.

[0027] Preferably, the weight of each evaluation indicator on the evaluation result is determined as follows:

[0028] First, based on the expert evaluation, a judgment matrix focusing on the evaluation indicators was constructed;

[0029] Perform consistency check on the judgment matrix, corresponding to the maximum characteristic root λ of the judgment matrix max The eigenvector of is normalized and recorded as W;

[0030] After the consistency check is successful, the feature vector W(w1,w2,...w n ) represents the weight of each evaluation index’s impact on the enterprise’s electricity consumption behavior.

[0031] Preferably, the evaluation of the enterprise's electricity consumption behavior based on the fuzzy comprehensive evaluation method is as follows:

[0032] After determining the evaluation matrix A and the factor weight vector W, the fuzzy vector W on U is transformed into the fuzzy vector X on V through fuzzy transformation, that is, X = W 1*m ○A m*n =(x1,x 2, ...x n ), where ○ is called the comprehensive evaluation synthesis operator; finally, the electricity consumption behavior of each enterprise is judged according to the calculation result of X.

[0033] Preferably, the evaluation results of the enterprise's electricity consumption behavior are excellent, good, average, and poor, respectively.

[0034] The beneficial effects of the present invention are as follows:

[0035] The present invention discloses a FAHP method for evaluating the electricity consumption potential and providing electricity price incentives for enterprises in a park. The present invention fully analyzes the comprehensive energy consumption characteristics and behaviors of each enterprise, collects and obtains the electricity consumption behaviors and electricity consumption data of each enterprise in the smart park, constructs a green and intelligent electricity consumption evaluation model for each enterprise in the park, establishes a comprehensive evaluation matrix, and uses a fuzzy comprehensive evaluation method to reasonably evaluate the green and intelligent electricity consumption behaviors of each enterprise in the park, thereby obtaining evaluation results of the green and intelligent electricity consumption behaviors of enterprises in the park. According to the evaluation results and the electricity price incentive model, a reference is provided for the formulation of the electricity price incentive details for the next stage of enterprises in the park. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The present invention will be further described below in conjunction with the accompanying drawings.

[0037] Figure 1 is a flow chart of the method of the present invention;

[0038] Figure 2 This is a typical application scenario diagram of the method of the present invention. DETAILED DESCRIPTION

[0039] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the present invention is further explained below in conjunction with specific implementation methods.

[0040] Embodiment 1

[0041] like Figure 1 to Figure 2 As shown, a method for evaluating the electricity consumption potential and electricity price incentive of a park enterprise using FAHP according to an embodiment of the present invention includes the following steps:

[0042] S1. Collect and obtain the green and intelligent electricity consumption behavior and electricity consumption data of each enterprise in the park, and obtain the corresponding electricity consumption performance score, system peak load participation performance score, green energy participation performance score, and electricity consumption prediction performance score through the electricity consumption evaluation model, system peak load participation evaluation model, green energy participation evaluation model, load prediction capability evaluation model, and corporate social image evaluation model;

[0043] S2. Obtain expert scoring results through the green and intelligent electricity consumption evaluation model of each enterprise in the park, and construct a fuzzy comprehensive evaluation matrix A1 to evaluate the electricity consumption behavior of each enterprise based on the expert scoring results. At the same time, construct a fuzzy comprehensive evaluation matrix A2 based on the S1 scoring results. Consider the membership of each factor to different evaluation levels with each factor having a weight of 50% to obtain the fuzzy comprehensive evaluation matrix A in the park;

[0044] S3. Determine the weight of each evaluation indicator on the evaluation results;

[0045] S4. Evaluate the electricity consumption behavior of enterprises based on the fuzzy comprehensive evaluation method and judge the electricity consumption behavior of each enterprise;

[0046] S5. Return to S2 to complete the electricity consumption behavior assessment of all enterprises in the park;

[0047] S6. Based on the results of S5, determine the details of electricity price incentives for each enterprise in the park.

[0048] S1. Collect and obtain the green and intelligent electricity consumption behavior and electricity consumption data of each enterprise in the park:

[0049] Assume that the total number of electricity-consuming enterprises to be evaluated in the smart park is k. Based on the smart meters of each enterprise, the electricity consumption behavior of each enterprise is captured, and the four energy consumption indicators of each enterprise are counted: electricity consumption, system peak load participation, green energy participation, and electricity consumption prediction ability. The specific evaluation methods for each indicator are as follows:

[0050] The method for constructing the electricity consumption evaluation model is as follows:

[0051] According to the data of smart meters of each enterprise, the daily electricity consumption of each enterprise is counted, and the F total The power consumption performance of each enterprise in the park under different time scales is divided into several areas according to the power consumption under each time scale, and each area is scored to obtain F total The electricity consumption performance scores under different time scales are added together to obtain the total score of the enterprise, and the total score of the enterprise and F are obtained. total The ratio is used to obtain the enterprise’s electricity consumption performance score for that day.

[0052] In a specific embodiment, the method for constructing the power consumption evaluation model is:

[0053] Relying on the data of smart meters of each enterprise, the daily electricity consumption of each enterprise is counted, and the basic score f1 of the electricity consumption performance of the day is assigned according to the electricity consumption performance of the enterprise in the park last month. Then, according to the electricity consumption performance score f2 of the enterprise last week and the electricity consumption performance score f3 of the enterprise on the day, the average of the basic score and the performance score is calculated after the three-level scoring system to obtain the total score of the electricity consumption behavior performance of the enterprise on the day. The specific scoring rules are as follows:

[0054] Statistics for the month, week, and day total F total The electricity consumption performance of each enterprise in the park under different time scales. The monthly, weekly and daily electricity consumption of the kth enterprise in the park are respectively expressed as M k , W k , D k It means that the electricity consumption under the three time scales is ranked, the top 20% is assigned 5 points, the top 20% to 40% is assigned 4 points, the top 40% to 60% is assigned 3 points, the top 60% to 80% is assigned 2 points, and the remaining is assigned 1 point, as shown in the following table; finally, f k1 、f k2 、f k3 , Final score of enterprise electricity performance

[0055] Power consumption performance F Electricity usage behavior score Top 20% 5 Top 20% to 40% 4 Top 40%~60% 3 Top 60% to 80% 2 the remaining 1

[0056] In this example, there are 4 enterprises in the park, and the scoring results are as follows: F k_total (5,4,3,2).

[0057] The system peak load participation evaluation model construction method is:

[0058] Based on the energy consumption data of each enterprise in the park, the energy consumption of enterprises in F total The power consumption in peak, valley and flat time periods under different time scales is calculated, and then the proportion of the power consumption in the selected time period is obtained. The proportion value is divided into several areas, and each area is scored to obtain the enterprise's F totalThe total performance score in the peak load capacity evaluation at different time scales is obtained, and the total performance score in the peak load capacity evaluation and F total The ratio of the peak load and the peak load participation performance score of the system are obtained.

[0059] In a specific embodiment, the method for constructing a system peak load participation evaluation model is:

[0060] At present, the power grid implements a three-stage electricity price system of peak, valley and flat, so as to reduce the electricity load during peak hours and increase the electricity load during valley hours. According to the energy consumption data of each enterprise in the park, the total F of the kth enterprise in the month, week and day is counted. total The monthly peak, valley and average energy consumption data are PM kp 、PM kv 、PM kf ; Weekly peak, valley and average energy consumption data are PW kp , PW kv , PW kf ; Daily peak, valley and average energy consumption data are PD kp , PD kv , PD kf ; Calculate the electricity consumption performance of each enterprise in different time periods under different time scales and score them, and get the system peak load participation performance score V k , the specific calculation method is as follows:

[0061] First, calculate the proportion of monthly, weekly, and daily peak power consumption data to the total power consumption in the selected time period, MP k =PM kp / M k , MV k =PM kv / M k MF k =PM kf / M k , WP k =PW kp / W k , WV k =PW kv / W k 、WF k =PW kf / W k , DP k =PD kp / D k 、DV k =PD kv / D k 、DV k =PD kv / D k .

[0062] Based on the statistics of electricity consumption in different time periods, the company's contribution to system peak load regulation is examined. The specific scoring rules are shown in the following table:

[0063]

[0064] According to the scoring rules, the performance scores of the kth enterprise in the monthly, weekly, and daily peak load-shaving capability evaluation are p1, p2, and p 3， The final enterprise score is There are 4 enterprises in the park, and the scoring results are as follows: k_total (5,3,2,4).

[0065] The green energy participation evaluation model construction method is:

[0066] Statistics of enterprises in F total The proportion of green electricity usage under different time scales is used to score the green electricity usage performance; first, based on the energy consumption data of each enterprise in the park, the total F of the enterprises in the month, week and day is counted. total The green electricity generation in the peak, valley and flat time periods under different time scales is then obtained, and its proportion to the total green electricity generation in the selected time period is obtained. The proportion value is divided into several areas, and each area is scored to obtain the enterprise's F total The total score of the green energy participation of enterprises under different time scales is obtained, and the total score and F total The ratio of the green electricity participation performance of the enterprise is obtained.

[0067] In a specific embodiment, the method for constructing the green energy participation evaluation model is:

[0068] In order to meet the dual carbon goals, enterprises in each park have taken measures such as self-built photovoltaic panels to increase the utilization rate of green electricity and reduce the carbon emission rate of enterprises. total First, based on the energy consumption data of each enterprise in the park, the monthly, weekly, and daily electricity consumption of the kth enterprise is respectively expressed as M k , W k , D k Indicates that the total number of F of the k-th enterprise in the month, week, and day is total The green electricity generation in peak, valley and flat time periods under different time scales is represented by MG k WG k , DG k Indicates that the green power generation performance of each enterprise at different time scales is calculated and scored, and G is introduced, G = MG k / M k The calculation method of G in valley and time period is similar.

[0069] If the proportion of green electricity usage is more than 80%, 5 points will be assigned; if the proportion of green electricity usage is 60% to 80%, 4 points will be assigned; if the proportion of green electricity usage is 40% to 60%, 3 points will be assigned; if the proportion of green electricity usage is 20% to 40%, 2 points will be assigned; the remaining points will be assigned 1 point, as shown in the following table; i Indicates the corporate green energy participation scores at different time scales, and finally obtains the corporate green electricity participation performance score There are 4 enterprises in the park, and the scoring results are as follows: k_total (4,2,3,5).

[0070]

[0071]

[0072] The load forecasting capability evaluation model construction method is:

[0073] Obtain the enterprise electricity consumption and forecast deviation values ​​reported in advance by each enterprise in the park, and calculate the enterprise F total The electricity consumption at each time scale is compared with the reported electricity consumption of enterprises and the actual statistics. total The difference of electricity consumption under each time scale is calculated respectively, and the ratio of the difference result to the predicted deviation value is obtained. The ratio is divided into several areas, and each area is scored to obtain the enterprise's F total The enterprise load forecast performance scores under different time scales are added together to obtain the total value of the enterprise load forecast performance score and F total The ratio of is used to obtain the enterprise's electricity consumption forecasting ability performance score.

[0074] In a specific embodiment, the load forecasting capability evaluation model construction method is:

[0075] Accurate load forecasting is conducive to the scheduling of power grid dispatching. All enterprises in the park need to report their electricity consumption in advance. By statistically analyzing the electricity consumption forecast accuracy of enterprises on three time scales: monthly, weekly, and daily, the load forecasting ability of the enterprises is evaluated, and the load forecasting performance score L of the kth enterprise is obtained. k .

[0076] First, count the three Fs of the k-th enterprise on month, week and day total Power consumption forecast number MP under time scale k , WP k , DP k , respectively calculate the corresponding prediction deviation, σ i =|M k -MP k | / M k , points are assigned according to the prediction results. The scoring rules are shown in the following table:

[0077] Prediction bias Predictive ability score Less than 5% 5 More than 5% Less than 10% 4 More than 10% Less than 20% 3 More than 20% Less than 30% 2 the remaining 1

[0078] Finally, the enterprise electricity consumption forecasting ability performance score is obtained. There are 4 enterprises in the park, and the scoring results are as follows: J k_total (2,4,3,5).

[0079] The method for constructing the corporate social image evaluation model is as follows:

[0080] By retrieving the company's annual profit growth rate information, social image information, and electricity bill payment enthusiasm information, several scoring levels are set, and points are assigned to each scoring level. The corresponding scores are obtained according to the scoring levels of each type of company, and then the scores of each type of company are added together and the average is taken to obtain the social performance score of each company.

[0081] In one embodiment, the method for constructing a corporate social image evaluation model is:

[0082] By investigating the annual profit growth rate, social image, enthusiasm for electricity bill payment and other information of enterprises, the comprehensive social image of enterprises is scored from 1 to 5 points. Enterprises with excellent comprehensive performance are given 5 points, good performance are given 4 points, medium performance are given 3 points, general performance are given 2 points, and the rest are given 1 point. Finally, the social performance score of each enterprise is obtained. k_total There are 4 enterprises in the park, and the scoring results are as follows: k_total (2,3,4,5).

[0083] Embodiment 2

[0084] S2. Construct a green and intelligent electricity consumption evaluation model for each enterprise in the park:

[0085] The S1 score result is equivalent to the expert's score result. In order to scientifically and rationally evaluate the green and intelligent electricity performance of enterprises in the park, in addition to the newly formulated score, the park invited a team of 20 experts from the power department, scientific research institutes, government departments, etc. to evaluate and score the electricity performance of enterprises in the park. The scores are processed by fuzzy mathematics through fuzzy comprehensive evaluation method.

[0086] First, construct an evaluation factor set. The factor set is a general set composed of scores for evaluating multiple electricity consumption behaviors of enterprises, usually represented by U, U = (u1, u2, ... u n ), where u i represents the i-th factor describing the green and intelligent electricity consumption behavior of the enterprise. In addition, the set of possible evaluation results of the electricity consumption behavior of each enterprise in the park is represented by V, V = (V1, V2, .... V n ), where element v jRepresents the jth evaluation result, where V = (V1, V2, V3, V4). V1, V2, V3, V4 represent excellent, good, average, and poor corporate electricity consumption behavior, respectively. In order to unify the corporate electricity consumption scoring and final corporate electricity consumption behavior rating, it is stipulated that 5 points are excellent, 3-4 points are good, 2 points are average, and 1 point is poor.

[0087] According to the expert scoring results, determine the contribution of each factor to the evaluation set, and calculate the scores of each factor at different evaluation levels S nm , and calculate the average number of scores for each evaluation level. The specific method is shown in the following table: electricity consumption, system peak load participation, green energy participation, and electricity consumption forecasting capability.

[0088]

[0089] The average value of each factor is a nm =S nm / S total , S total The total number of experts who scored, S nm The number of people who scored each factor at each rating level is used to construct a fuzzy comprehensive evaluation matrix for evaluating the electricity consumption behavior of each enterprise. At the same time, according to the S1 scoring results, a fuzzy comprehensive evaluation matrix is ​​constructed. The S1 scoring results and the expert scoring results are combined with each factor's membership to different evaluation levels with a weight of 50%, and the fuzzy comprehensive evaluation matrix of the park is:

[0090] In this example, a company uses the S1 scoring and rating fuzzy evaluation matrix Statistics on the scores given by experts of the enterprise, and calculation Then the final fuzzy comprehensive evaluation matrix of the enterprise is

[0091] S3. Determine the weight of each evaluation indicator on the evaluation results:

[0092] To ensure the fairness of the evaluation work, the importance of various indicators describing the smart green electricity consumption behavior of enterprises varies. Therefore, it is necessary to take the weight of each factor. This description uses the hierarchical analysis method to determine the weight.

[0093] The analytic hierarchy process is a decision-making method that decomposes elements related to decision-making into levels such as goals, criteria, and plans, and conducts qualitative and quantitative analysis on this basis.

[0094] (1) First, based on expert evaluation, construct a judgment matrix H for the evaluation index based on the factors n×n ,h ij The scale determination method is shown in the following table:

[0095]

[0096]

[0097] In this example, the judgment matrix of the evaluation index factor set of electricity consumption behavior in the park is

[0098] (2) Perform consistency check on the judgment matrix.

[0099] Corresponding to the maximum characteristic root λ of the judgment matrix max The eigenvector of is normalized (so that the sum of the elements in the vector is 1) and recorded as W. The elements of W are the ranking weights of the relative importance of the elements at the same level to a factor at the previous level. This process is called hierarchical single sorting.

[0100] The consistency index CI (CI = λ-n / n-1) is defined. When CI = 0, it indicates complete consistency; when CI is close to 0, there is satisfactory consistency; the larger the CI, the more serious the inconsistency. The consistency index RI is introduced to measure the size of CI, as shown in the following table:

[0101] n 1 2 3 4 5 6 7 8 9 10 11 RI 0 0 0.58 0.90 1.12 1.24 1.32 1.41 1.45 1.49 1.51

[0102] In addition, the consistency ratio CR (CR = CI / RI) is introduced. It is generally believed that when the consistency ratio CR < 0.1, the inconsistency of H is considered to be within the allowable range, with satisfactory consistency, and passes the consistency test. Its normalized eigenvector can be used as the weight vector, otherwise the pairwise comparison matrix H must be reconstructed and h must be adjusted. ij .

[0103] After the consistency check is successful, the feature vector W(w1,w2,...w n ) represents the weight of each evaluation index’s impact on the enterprise’s electricity consumption behavior.

[0104] In this example, for a certain enterprise judgment matrix Perform consistency check to meet the requirements. After matrix normalization, it is Weight vector

[0105] S4. Evaluate the electricity consumption behavior of enterprises based on fuzzy comprehensive evaluation method:

[0106] After determining the evaluation matrix A and the factor weight vector W, the fuzzy vector W on U is transformed into the fuzzy vector X on V through fuzzy transformation, that is, X = W 1*m ○A m*n =(x1,x 2, ...x n). Among them, ○ is called the comprehensive evaluation synthesis operator, which can be taken as a general matrix multiplication.

[0107] Finally, based on the calculation results of X, the electricity consumption behavior of each enterprise is judged as excellent, good, average, or poor.

[0108] S5. Return to S2 to complete the electricity consumption behavior assessment of all enterprises in the park:

[0109] In the example, the evaluation results of the four companies are excellent, good, average, and poor.

[0110] S6. Based on the results of S5, determine the details of the electricity price incentives for each enterprise in the park:

[0111] In actual operation, the park's peak electricity price is Q1, the normal electricity price is Q2, and the valley electricity price is Q3. According to the S3 evaluation results, the excellent enterprises have a 90% discount on electricity prices during peak and valley periods, the good enterprises have a 95% discount on electricity prices during peak and valley periods, and the average enterprises have a 98% discount on electricity prices during peak and valley periods. The remaining enterprises do not participate in the park's electricity price discount activities, so as to enhance the electricity consumption potential of enterprises in the park.

[0112] According to the evaluation results of S5, the electricity price incentives for the four enterprises in the park in the example are 90%, 90%, 98% and no discount respectively.

[0113] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A FAHP method for evaluating the electricity consumption potential of enterprises in a park and for providing electricity price incentives, characterized by: The following steps are involved: S1. Collect and obtain the green and intelligent electricity consumption behavior and electricity consumption data of each enterprise in the park, and obtain the corresponding electricity consumption performance score, system peak load participation performance score, green energy participation performance score, electricity consumption prediction capability performance score, and social performance score through the electricity consumption evaluation model, system peak load participation evaluation model, green energy participation evaluation model, load forecasting capability evaluation model, and enterprise social image evaluation model; S2. Obtain expert scoring results through the green intelligent electricity consumption evaluation model of each enterprise in the park, and construct a fuzzy comprehensive evaluation matrix A1 to evaluate the electricity consumption behavior of each enterprise based on the expert scoring results. At the same time, construct a fuzzy comprehensive evaluation matrix A2 based on the S1 scoring results. Consider the membership of each factor to different evaluation levels with each factor having a weight of 50% to obtain the fuzzy comprehensive evaluation matrix A in the park; S3. Determine the weight of each evaluation indicator on the evaluation results; S4. Evaluate the electricity consumption behavior of enterprises based on the fuzzy comprehensive evaluation method and judge the electricity consumption behavior of each enterprise; S5. Return to S2 to complete the electricity consumption behavior assessment of all enterprises in the park; S6. Based on the results of S5, determine the details of electricity price incentives for each enterprise in the park.

2. The method for evaluating the electricity consumption potential and providing electricity price incentives for enterprises in a FAHP park according to claim 1 is characterized by: The method for constructing the electricity consumption evaluation model is as follows: According to the data of smart meters of each enterprise, the daily electricity consumption of each enterprise is counted, and the F total The power consumption performance of each enterprise in the park under different time scales is divided into several areas according to the power consumption under each time scale, and each area is scored to obtain F total The electricity consumption performance scores under different time scales are added together to obtain the total score of the enterprise, and the total score of the enterprise and F are obtained. total The ratio is used to obtain the enterprise’s electricity consumption performance score for that day.

3. The method for evaluating the electricity consumption potential and providing electricity price incentives for enterprises in a FAHP park according to claim 1 is characterized by: The method for constructing the system peak load participation evaluation model is as follows: Based on the energy consumption data of each enterprise in the park, the energy consumption of enterprises in F total The power consumption in peak, valley and flat time periods under different time scales is calculated, and then the proportion of the power consumption in the selected time period is obtained. The proportion value is divided into several areas, and each area is scored to obtain the enterprise's F total The total performance score in the peak load capacity evaluation at different time scales is obtained, and the total performance score in the peak load capacity evaluation and F total The ratio of the peak load and the peak load participation performance score of the system are obtained.

4. The method for evaluating the electricity consumption potential and providing electricity price incentives for enterprises in a FAHP park according to claim 1 is characterized by: The green energy participation evaluation model construction method is: Statistics of enterprises in F total The proportion of green electricity usage under different time scales is used to score the green electricity usage performance; first, based on the energy consumption data of each enterprise in the park, the total F of the enterprises in the month, week and day is counted. total The green electricity generation in the peak, valley and flat time periods under different time scales is then obtained, and its proportion to the total green electricity generation in the selected time period is obtained. The proportion value is divided into several areas, and each area is scored to obtain the enterprise's F total The total score of the green energy participation of enterprises under different time scales is obtained, and the total score and F total The ratio of the green electricity participation performance of the enterprise is obtained.

5. The method for evaluating the electricity consumption potential and providing electricity price incentives for enterprises in a FAHP park according to claim 1 is characterized by: The load forecasting capability evaluation model construction method is: Obtain the enterprise electricity consumption and forecast deviation values ​​reported in advance by each enterprise in the park, and calculate the enterprise F total The electricity consumption at each time scale is compared with the reported electricity consumption of enterprises and the actual statistics. total The difference of electricity consumption under each time scale is calculated respectively, and the ratio of the difference result to the predicted deviation value is obtained. The ratio is divided into several areas, and each area is scored to obtain the enterprise's F total The enterprise load forecast performance scores under different time scales are added together to obtain the total value of the enterprise load forecast performance score and F total The ratio of is used to obtain the enterprise's electricity consumption forecasting ability performance score.

6. The method for evaluating the electricity consumption potential and providing electricity price incentives for enterprises in a FAHP park according to claim 1, characterized in that: The method for constructing the corporate social image evaluation model is as follows: By retrieving the company's annual profit growth rate information, social image information, and electricity bill payment enthusiasm information, several scoring levels are set, and points are assigned to each scoring level. The corresponding scores are obtained according to the scoring levels of each type of company, and then the scores of each type of company are added together and the average is taken to obtain the social performance score of each company.

7. The method for evaluating the electricity consumption potential and providing electricity price incentives for enterprises in a park of FAHP according to claim 1 is characterized by: The construction method of the green intelligent electricity consumption evaluation model for each enterprise in the park is as follows: First, obtain the general set composed of multiple electricity consumption behavior scores of the evaluated enterprises and record it as U, U=(u1,u2,......u n ), where u i represents the i-th factor describing the green and smart electricity consumption behavior of the enterprise; The set of possible evaluation results of the electricity consumption behavior of each enterprise in the park is represented by V, V = (V1, V2, .... V n ), where element v j represents the jth evaluation result; assigns points to V; According to the expert scoring results, the contribution of each factor in the evaluation set is determined, the scores of each factor at different evaluation levels are counted, and the average number of scores at each evaluation level is calculated.

8. The method for evaluating the electricity consumption potential and providing electricity price incentives for enterprises in a FAHP park according to claim 7 is characterized by: Determine the weight of each evaluation indicator on the evaluation results, specifically: First, based on the expert evaluation, a judgment matrix focusing on the evaluation indicators was constructed; Perform consistency check on the judgment matrix, corresponding to the maximum characteristic root λ of the judgment matrix max The eigenvector of is normalized and recorded as W; After the consistency check is successful, the feature vector W(w1,w2,...w n ) represents the weight of each evaluation index’s impact on the enterprise’s electricity consumption behavior.

9. The method for evaluating the electricity consumption potential and providing electricity price incentives for enterprises in a FAHP park according to claim 8 is characterized by: The evaluation of the enterprise electricity consumption behavior based on the fuzzy comprehensive evaluation method is as follows: After determining the evaluation matrix A and the factor weight vector W, the fuzzy vector W on U is transformed into the fuzzy vector X on V through fuzzy transformation, that is, X = W 1*m ○A m*n =(x1,x 2, ...x n ), where ○ is called the comprehensive evaluation synthesis operator; finally, the electricity consumption behavior of each enterprise is judged according to the calculation result of X.

10. The method for evaluating the power consumption potential and providing power price incentives for enterprises in a FAHP park according to claim 9, characterized in that: The evaluation results of the enterprises' electricity usage behavior are excellent, good, average, and poor, respectively.

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