High-energy-consumption enterprise load interaction strategy implementation effect evaluation system and method

Abstract

The invention discloses a high-energy-consumption enterprise load interaction strategy implementation effect evaluation system, which is characterized in that scores of various strategy indexes in a load interaction strategy evaluation index system are calculated by the system, and a strategy index score set is formed; calculating scores of each effect index in the implementation effect evaluation index system to form an implementation effect index score set; calculating an average value based on the strategy index score set, determining a strategy total weight coefficient and an effect total weight coefficient according to the average value, distributing the weight coefficient of each strategy index by utilizing the strategy total weight coefficient, and calculating the weight coefficient of each effect index through an adaptive algorithm by utilizing the implementation effect index score set and the effect total weight coefficient. And performing weighted accumulation on the score of each strategy index and the effect index and the corresponding weight coefficient to obtain an interaction effect comprehensive evaluation value. According to the invention, the effectiveness of high-energy-consumption enterprise load interaction and the power grid stability are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power demand response, in particular to a high energy consumption enterprise load interaction strategy implementation effect evaluation system and method. BACKGROUND

[0002] With the large-scale access of renewable energy such as wind power and photovoltaic power, the power load characteristics in China have changed significantly and are challenging. These changes have brought many problems. On the one hand, the load characteristics in winter and summer are increasingly prominent, which leads to the continuous expansion of the peak-valley difference of the power grid and the downward trend of the load rate, which increases the difficulty and stability risk of power grid dispatching. On the other hand, renewable energy generation has intermittency, volatility and uncontrollability, which further aggravates the fluctuation of power grid operation, making it difficult for traditional load management methods to effectively respond.

[0003] In order to solve this problem, power grid companies and related research institutions have carried out research on load regulation of high energy consumption users, and have proposed strategies and methods to tap the response potential of the load side and improve the interaction ability of "source-grid-load". However, the research on "source-grid-load" interaction mostly stays in the stage of theoretical research and simulation verification, and it is not clear whether it can be applied in practice and the actual application effect. SUMMARY

[0004] The purpose of the present application is to provide a high energy consumption enterprise load interaction strategy implementation effect evaluation system and method. The present application realizes comprehensive and accurate evaluation of high energy consumption enterprise load interaction strategy and implementation effect, and improves the effectiveness of high energy consumption enterprise load interaction and power grid stability.

[0005] To achieve this purpose, the present application designs a high energy consumption enterprise load interaction strategy implementation effect evaluation system, which comprises: The load interaction strategy evaluation module is used to calculate the score of each strategy index in the load interaction strategy evaluation index system based on the information data required for strategy evaluation, and form a load interaction strategy index score set. The implementation effect evaluation module is used to calculate the score of each effect index in the implementation effect evaluation index system based on the actual interaction data of the user and the operation data of the power grid, and form an implementation effect index score set. The interactive effect comprehensive evaluation module is configured to calculate an average value of each strategy index score based on the set of load interaction strategy index scores, determine a total weight coefficient of the load interaction strategy evaluation index system and a total weight coefficient of the implementation effect evaluation index system according to the average value, obtain a weight coefficient of each strategy index by using the total weight coefficient, calculate a weight coefficient of each effect index by using the set of implementation effect index scores and the total weight coefficient through an adaptive algorithm, and obtain an interactive effect comprehensive evaluation value by weighting and accumulating the scores of each strategy index and effect index respectively with the corresponding weight coefficients.

[0006] Preferably, the strategy indexes in the load interaction strategy evaluation index system include an input parameter actual acquisition rate, a strategy adaptability, a load prediction accuracy and a controllable object executability; and the effect indexes in the implementation effect evaluation index system include a user actual response degree, a user response time length coverage rate, a peak shaving time period matching degree, a valley filling time period matching degree and a user load adjustment matching degree.

[0007] Preferably, in the load interaction strategy evaluation module, the score of each strategy index in the load interaction strategy evaluation index system is calculated based on information data required for strategy evaluation, and the load interaction strategy evaluation module is specifically configured to: The input parameter actual acquisition rate is calculated by a ratio of the number of actually acquired parameters to the number of parameters required by the load interaction strategy, and the calculation formula is: wherein, I m represents the score of the input parameter actual acquisition rate, N r represents the number of actually acquired parameters, N m represents the number of parameters required by the load interaction strategy; The strategy adaptability is determined according to the type of the optimization target of the load interaction strategy: when the load interaction strategy only takes peak shaving and valley filling as the optimization target, A m =A; when the load interaction strategy takes peak shaving, valley filling and load interaction as the optimization target, A m =B; when the optimization target of the load interaction strategy does not include peak shaving, valley filling and load interaction, A m =C, wherein A m represents the score of the strategy adaptability; The load prediction accuracy is calculated by an error between the predicted load value and the actual load value, and the calculation formula is: wherein, Pm The score represents the accuracy of load forecasting. N 1 This represents the amount of data predicted by the load interaction strategy. M p This represents the maximum value of the predicted data. p ri Indicates the first i The actual load value at any given time. p mi Indicates the first i Load values ​​predicted by the real-time load interaction strategy; The executability of the control object is determined by whether the control object can be adjusted during actual operation. If the control object can be adjusted during actual operation, then... Q m =D; If the object to be regulated cannot be adjusted during actual operation, then Q m =E, where Q m The score indicates the feasibility of the regulated object.

[0008] Preferably, in the implementation effect evaluation module, based on actual user interaction data and power grid operation data, the score of each effect indicator in the implementation effect evaluation indicator system is calculated, specifically for: The actual user responsiveness is calculated by the error between the actual user load adjustment and the load adjustment output by the load interaction strategy. The calculation formula is as follows: in, F u A score representing the user's actual responsiveness. N 2 This indicates the actual amount of data sampled. M R This indicates the maximum value of the load regulation output by the load interaction strategy. p ni Indicates the first i The real-time load interaction strategy outputs the amount of load reduction or increase. p ui Indicates the first i The amount of reduction or increase in the actual user load at any given time; The user response time coverage rate is calculated as the ratio of the actual user response time to the duration set by the load interaction strategy. The calculation formula is as follows: in, T u The score represents the coverage of user response time. T ra duration of actual peak shaving and filling response of the user in the target regulation time period of the load interaction strategy, T m a duration of peak shaving and filling set by the load interaction strategy; The peak shaving period matching degree is calculated by a ratio of an overlapping duration of the estimated peak shaving period of the load interaction strategy and the actual peak shaving period of the power grid to a duration of the actual peak shaving period of the power grid, and a calculation formula is: wherein, T pc a score of the peak shaving period matching degree, T mpc an overlapping duration of the estimated peak shaving period of the load interaction strategy and the actual peak shaving period of the power grid, T epc a duration of the actual peak shaving period of the power grid; The valley filling period matching degree is calculated by a ratio of an overlapping duration of the estimated valley filling period of the load interaction and the actual valley filling period of the power grid to a duration of the actual valley filling period of the power grid, and a calculation formula is: wherein, T vf a score of the valley filling period matching degree, T mvf an overlapping duration of the estimated valley filling period of the load interaction strategy and the actual valley filling period of the power grid, T evf a duration of the actual valley filling period of the power grid; The user load adjustment matching degree is calculated by a ratio of a number of the same devices in the actual adjustment devices of the user and a number of the adjustment devices set by the load interaction strategy, and a calculation formula is: wherein, P u a score of the user load adjustment matching degree, D um a number of the same devices in the actual adjustment devices of the user, D m a number of the adjustment devices set by the load interaction strategy.

[0009] Preferably, the interaction effect comprehensive evaluation module calculates an average value of the scores of the strategy indicators based on the set of scores of the load interaction strategy indicators, and determines a total weight coefficient of the strategy of the load interaction strategy evaluation indicator system and a total weight coefficient of the effect of the implementation effect evaluation indicator system according to the average value, and is specifically used for: The average score (Ave) of each strategy indicator in the load interaction strategy indicator score set is calculated using the following formula: The total weight coefficient Q1 of the load interaction strategy evaluation index system and the total weight coefficient Q2 of the implementation effect evaluation index system are obtained using the average value Ave. The calculation formula is as follows: in, ω This is a preset constant.

[0010] Preferably, the comprehensive evaluation module for interactive effects uses the total weight coefficient of the strategy to obtain the weight coefficient of each strategy indicator. Using the set of implementation effect indicator scores and the total weight coefficient of the effect, an adaptive algorithm calculates the weight coefficient of each effect indicator. The scores of each strategy indicator and effect indicator are then weighted and accumulated with their corresponding weight coefficients to obtain the comprehensive evaluation value of the interactive effect. Specifically, this is used for: Dividing the total strategy weight coefficient Q1 by the number of strategy indicators yields the weight coefficient for each strategy indicator. β ; Using the set of implementation effect indicator scores and the total effect weight coefficient, an adaptive algorithm is used to calculate the weight coefficient of each effect indicator. The calculation formula is as follows: in, K i Indicates the first i The weighting coefficient of each performance indicator. S M This represents the set adaptive weight coefficient. S i Refers to the first i The score of each performance indicator S j Refers to the first j The scores for each performance metric, with the first performance metric being the actual user response rate. F u The second performance metric is user response time coverage. T u The third performance indicator is the matching degree of peak shaving periods. T pc The fourth performance indicator is the matching degree of the valley filling period. T vf The fifth performance indicator is the user load adjustment matching degree. P u ; The scores of each strategy indicator and effect indicator are multiplied by their corresponding weighting coefficients and then summed to obtain the weighted comprehensive evaluation value of the interaction effect.S A , the calculation formula is: Thus, the interactive effect comprehensive evaluation value is obtained S A .

[0011] The application also provides a high energy consumption enterprise load interaction strategy implementation effect evaluation method, comprising: Based on the information data required for strategy evaluation, the score of each strategy index in the load interaction strategy evaluation index system is calculated, and a load interaction strategy index score set is formed; Based on the actual interaction data of the user and the grid operation data, the score of each effect index in the implementation effect evaluation index system is calculated, and an implementation effect index score set is formed; Based on the average value of the scores of each strategy index calculated based on the load interaction strategy index score set, the total weight coefficient of the strategy evaluation index system of the load interaction strategy and the total weight coefficient of the effect evaluation index system of the implementation effect evaluation index system are determined, the weight coefficient of each strategy index is obtained by using the total weight coefficient of the strategy, and the weight coefficient of each effect index is calculated by using the implementation effect index score set and the total weight coefficient of the effect through an adaptive algorithm, and the scores of each strategy index and effect index are respectively weighted and accumulated with the corresponding weight coefficient, to obtain the interactive effect comprehensive evaluation value.

[0012] The application also provides a computer storage medium, the computer readable storage medium stores a computer program, and the computer program is executed by a processor to realize the steps of the high energy consumption enterprise load interaction strategy implementation effect evaluation method as described above.

[0013] The application has the following beneficial effects: The application can simultaneously evaluate the rationality of the load interaction strategy of the high energy consumption enterprise and the actual implementation effect of the load interaction strategy by constructing a comprehensive evaluation system, effectively filling the gap in the prior art that only focuses on theoretical simulation and lacks practical application verification. The application considers the actual acquisition rate of the input parameters of the load interaction strategy itself, the strategy adaptability, the load prediction accuracy and the executable of the control object, and also integrates the effect indexes such as the actual response degree of the user, the response time coverage rate of the user, the peak shaving period matching degree, the valley filling period matching degree and the user load adjustment matching degree, dynamically adjusts the contribution degree of the effect indexes through an adaptive weight algorithm, and improves the objectivity and reliability of the evaluation result; the application is helpful for the power grid dispatching department to quickly identify the optimal interaction control strategy, improves the stability of the power grid operation, optimizes the load management efficiency, and provides a solid support for the energy saving and consumption reduction of the high energy consumption enterprise and the safe and economic dispatching of the power grid.

[0014] The above description is only a summary of the technical solutions of the present application. In order to more clearly understand the technical means of the present application and to be able to implement the content of the description, the following will be described in detail with the preferred embodiments of the present application and in conjunction with the accompanying drawings. The specific embodiments of the present application are given in detail by the following embodiments and their accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS

[0015] The accompanying drawings described herein are used to provide further understanding of the present application, and form a part of the present application. The schematic embodiments of the present application and their descriptions are used to explain the present application, and do not constitute an improper limitation on the present application. In the drawings: Figure 1 is a structural schematic diagram of the present application; Figure 2 is a flowchart of the present application; Figure 3 is the grid load prediction value and the load true value in embodiment 3 of the present application. DETAILED DESCRIPTION

[0016] The principles and characteristics of the present application are described below in conjunction with the accompanying drawings. The examples are only used to explain the present application, and are not used to limit the scope of the present application.

[0017] Embodiment 1 A high energy consumption enterprise load interaction strategy implementation effect evaluation system, as shown in Figure 1 , it comprises: The load interaction strategy evaluation module is used to calculate the score of each strategy index in the load interaction strategy evaluation index system based on the information data required for strategy evaluation (the information data required for strategy evaluation includes the basic information required for load interaction strategy design and evaluation, specifically including the parameter list required for load interaction strategy, the actual number of parameters that can be collected, the optimization target type of load interaction strategy, the prediction value of load interaction strategy, the actual load true value, the control object set by the load interaction strategy and the actual participating control object information), to form a load interaction strategy index score set. This design directly reflects the integrity of the load interaction strategy input data, the target matching degree, the prediction accuracy and the execution possibility by calculating the score of the specific strategy index, can quantitatively evaluate the rationality and feasibility of the load interaction strategy itself, ensures that the load interaction strategy design is based on actual data and application scenarios, and improves the practicability and feasibility. The implementation effect evaluation module is configured to calculate the score of each effect index in the implementation effect evaluation index system based on the user actual interaction data (the user actual interaction data including the peak shaving or valley filling amount (i.e. load adjustment amount) of the user in actual operation, the theoretical peak shaving or valley filling amount of the load interaction strategy output, the specific start and end time of the user participating in the load interaction, the interaction period set by the load interaction strategy, the list of devices actually adjusted by the user, and the expected adjustment device list output by the load interaction strategy) and the power grid operation data (the power grid operation data including the actual start and end time of the power grid needing to shave the peak, the actual start and end time of the power grid needing to fill the valley, and the overlapping length and total length of the related period), and form an implementation effect index score set. The design uses the user actual interaction data and the power grid operation data to calculate the score of the effect index, can truly reflect the performance of the load interaction strategy in actual application, assesses the user participation and the power grid interaction effect through empirical data, and helps to identify the pros and cons of the load interaction strategy. The interaction effect comprehensive evaluation module is configured to calculate the average value of the scores of each strategy index based on the load interaction strategy index score set, determine the total weight coefficient of the strategy of the load interaction strategy evaluation index system and the total weight coefficient of the effect of the implementation effect evaluation index system according to the average value, obtain the weight coefficient of each strategy index by using the total weight coefficient of the strategy, calculate the weight coefficient of each effect index by using the implementation effect index score set and the total weight coefficient of the effect through an adaptive algorithm, and obtain the comprehensive evaluation value of the interaction effect by weighting and accumulating the scores of each strategy index and effect index with the corresponding weight coefficients. The design dynamically adjusts the weight according to the score of the effect index through the adaptive algorithm, avoids subjective bias, makes the evaluation result more fair and comprehensive, effectively supports load management optimization to improve the stability of the power grid, organically combines the load interaction strategy and the implementation effect, gives a well-divided evaluation level, and can provide intuitive reference for power grid dispatching decision.

[0018] In the technical solution, the strategy indexes in the load interaction strategy evaluation index system include the actual acquisition rate of input parameters, strategy adaptability, load prediction accuracy, and controllability of the adjustment object; the effect indexes in the implementation effect evaluation index system include the actual response degree of the user, the response time length coverage of the user, the peak shaving period matching degree, the valley filling period matching degree, and the load adjustment matching degree of the user; the strategy indexes in the design can systematically evaluate the rationality and executability of the load interaction strategy itself; the effect indexes can objectively reflect the actual performance of the load interaction strategy after implementation, help to accurately identify the strong points and weaknesses of the load interaction strategy, provide a reliable basis for power grid dispatching, and optimize load interaction management.

[0019] In the technical solution, the score of each strategy index in the load interaction strategy evaluation index system is calculated based on the information data required for strategy evaluation, and specifically used for: The actual acquisition rate of input parameters is calculated as the ratio of the number of parameters actually acquired to the number of parameters required by the load interaction strategy. The calculation formula is as follows: in, I m The score represents the actual acquisition rate of the input parameters. N r This indicates the actual number of parameters acquired. N m Indicates the number of parameters required for the load interaction strategy; The fitness of the strategy is determined based on the type of optimization objective of the load interaction strategy: when the load interaction strategy only uses peak shaving and valley filling as its optimization objective... A m =A (A=1); When the load interaction strategy uses peak shaving and load interaction as optimization objectives... A m =B (B=0.6); When the optimization objective of the load interaction strategy does not include peak shaving and valley filling and load interaction, A m =C (C=0.2), where A m The score represents the policy fitness score; The load forecast accuracy is calculated by the error between the predicted load value and the actual load value. The calculation formula is as follows: in, P m The score represents the accuracy of load forecasting. N 1 This represents the amount of data predicted by the load interaction strategy. M p This represents the maximum value of the predicted data. p ri Indicates the first i The actual load value at any given time. p mi Indicates the first i Load values ​​predicted by the real-time load interaction strategy; The executability of the control object is determined by whether the control object can be adjusted during actual operation. If the control object can be adjusted during actual operation, then... Q m =D (D=1); If the object to be regulated cannot be adjusted during actual operation, then Q m =E (E=0), where Q mThe score represents the executability of the control object; in the above design, the actual acquisition rate of input parameters directly reflects the data integrity through the ratio of the number of parameters, the strategy fitness is assigned according to the optimization target type to ensure target alignment, the load forecast accuracy is quantified by error calculation to quantify the forecast reliability, and the executability of the control object is simplified by binary judgment to assess actual feasibility. By quantifying and scoring each strategy indicator, the bias of subjective judgment can be avoided, and an objective and standardized assessment of the rationality of the load interaction strategy can be achieved.

[0020] In the above technical solution, based on actual user interaction data and power grid operation data, the score of each performance indicator in the implementation effect evaluation index system is calculated, specifically for: The actual user responsiveness is calculated by the error between the actual user load adjustment and the load adjustment output by the load interaction strategy. The calculation formula is as follows: in, F u A score representing the user's actual responsiveness. N 2 This indicates the actual amount of data sampled. M R This indicates the maximum value of the load regulation output by the load interaction strategy. p ni Indicates the first i The real-time load interaction strategy outputs the amount of load reduction or increase. p ui Indicates the first i The amount of reduction or increase in the actual user load at any given time; The user response time coverage rate is calculated as the ratio of the actual user response time to the duration set by the load interaction strategy. The calculation formula is as follows: in, T u The score represents the coverage of user response time. T r This indicates the actual duration of peak shaving and valley filling responses performed by users within the target control period of the load interaction strategy. T m This indicates the peak shaving and valley filling duration set by the load interaction strategy; The peak shaving period matching degree is calculated by the ratio of the overlap duration between the peak shaving period and the actual peak shaving period of the power grid, as estimated by the load interaction strategy, to the actual peak shaving period of the power grid. The calculation formula is as follows: in, T pc The score represents the matching degree during peak shaving periods. Tmpc This indicates the overlap between the peak shaving period estimated by the load interaction strategy and the actual peak shaving period of the power grid. T epc This indicates the actual duration of peak shaving periods in the power grid; The valley filling time matching degree is calculated by the ratio of the overlap duration between the load interaction-estimated valley filling time and the actual valley filling time of the power grid to the actual valley filling time of the power grid. The calculation formula is as follows: in, T vf The score represents the matching degree during the valley filling period. T mvf This indicates the overlap between the valley filling period estimated by the load interaction strategy and the actual valley filling period of the power grid. T evf This indicates the actual duration of the valley filling period in the power grid; The user load regulation matching degree is calculated as the ratio of the number of devices in the user's actual regulation equipment that are the same as the regulation equipment set by the load interaction strategy to the total number of regulation equipment set by the load interaction strategy. The calculation formula is as follows: in, P u The score represents the user's load adjustment matching degree. D um This indicates the number of devices in the user's actual control equipment that are the same as the control equipment set in the load interaction strategy. D m This indicates the number of regulating devices set for the load interaction strategy. In the above design, the actual user response rate reflects the execution consistency through load regulation error, the user response time coverage rate measures time compliance through the time ratio, the peak shaving period matching degree and valley filling period matching degree assess the synchronization between the load interaction strategy and grid demand through the overlap time, and the user load regulation matching degree checks the operational consistency through the device number ratio. By quantifying and scoring each effect indicator, subjective bias can be effectively eliminated, ensuring that the scoring results can truly reflect the implementation effect of the load interaction strategy, and achieving an objective and refined evaluation of the actual execution of the load interaction strategy.

[0021] In the above technical solution, the average score of each strategy indicator is calculated based on the set of load interaction strategy indicator scores. The total weight coefficient of the load interaction strategy evaluation indicator system and the total weight coefficient of the implementation effect evaluation indicator system are then determined based on the average value. Specifically, this is used for: The average score (Ave) of each strategy indicator in the load interaction strategy indicator score set is calculated using the following formula: The total weight coefficient Q1 of the load interaction strategy evaluation index system and the total weight coefficient Q2 of the implementation effect evaluation index system are obtained using the average value Ave. The calculation formula is as follows: in, ω For preset constants ( ω (Values ​​can be 1.1); In the above design, when the load interaction strategy itself is highly reasonable (i.e., the average value Ave is large), Q1 decreases and Q2 increases. At this time, more emphasis is placed on evaluating the implementation effect, thereby avoiding evaluation bias caused by defects in the load interaction strategy or implementation problems.

[0022] In the above technical solution, the weight coefficient of each strategy indicator is obtained using the total strategy weight coefficient. Using the set of implementation effect indicator scores and the total effect weight coefficient, an adaptive algorithm calculates the weight coefficient of each effect indicator. The scores of each strategy indicator and effect indicator are then weighted and accumulated with their corresponding weight coefficients to obtain a comprehensive evaluation value of the interaction effect, specifically used for: Dividing the total strategy weight coefficient Q1 by the number of strategy indicators yields the weight coefficient for each strategy indicator. β ; Using the set of implementation effect indicator scores and the total effect weight coefficient, an adaptive algorithm is used to calculate the weight coefficient of each effect indicator. The calculation formula is as follows: in, K i Indicates the first i The weighting coefficient of each performance indicator. S M This indicates the set adaptive weight coefficient ( S M The value ranges from 1 to 2, and can be 1.5). S i Refers to the first i The score of each performance indicator S j Refers to the first j The scores for each performance metric, with the first performance metric being the actual user response rate. F u The second performance metric is user response time coverage. T u The third performance indicator is the matching degree of peak shaving periods. T pc The fourth performance indicator is the matching degree of the valley filling period. T vf The fifth performance indicator is the user load adjustment matching degree. P u ; The scores of each strategy indicator and effect indicator are multiplied by their corresponding weighting coefficients and then summed to obtain the weighted comprehensive evaluation value of the interaction effect. S A The calculation formula is: This yields a comprehensive evaluation value for the interaction effect. S A If 0≤ S A If F < F (F=0.6), the interaction effect is poor; if F≤ S A If G < G (G=0.7), the interaction effect is satisfactory; if G ≤ S A If H < H (H=0.8), the interaction effect is moderate; if H ≤ S A If I < I (I=0.9), the interaction effect is good; if I ≤ S A If the value is less than 1, the interaction effect is considered excellent. The above design obtains a comprehensive evaluation value of the interaction effect by uniformly distributing the weights of the strategy indicators and adaptively calculating the weights of the effect indicators, and then weighted and summing them. This provides an intuitive quantitative result, visually demonstrates the interaction effect, and provides effective support for the optimization of power grid load management.

[0023] Example 2 like Figure 2 As shown in the figure, this embodiment provides a method for evaluating the implementation effect of load interaction strategy for high-energy-consuming enterprises, including the following steps: S1. Based on the information and data required for strategy evaluation, calculate the score of each strategy indicator in the load interaction strategy evaluation index system, forming a set of load interaction strategy indicator scores, specifically including: The strategy indicators in the load interaction strategy evaluation index system include the actual acquisition rate of input parameters, strategy adaptability, load forecasting accuracy, and the executability of the control object. The actual acquisition rate of input parameters is calculated as the ratio of the number of parameters actually acquired to the number of parameters required by the load interaction strategy. The calculation formula is as follows: in, I m The score represents the actual acquisition rate of the input parameters. N r This indicates the actual number of parameters acquired. N m Indicates the number of parameters required for the load interaction strategy; The fitness of the strategy is determined based on the type of optimization objective of the load interaction strategy: when the load interaction strategy only uses peak shaving and valley filling as its optimization objective... A m =A; When the load interaction strategy uses peak shaving and load interaction as optimization objectives... A m =B; When the optimization objective of the load interaction strategy does not include peak shaving and valley filling and load interaction, A m =C, where A m The score represents the policy fitness score; The load forecast accuracy is calculated by the error between the predicted load value and the actual load value. The calculation formula is as follows: in, P m The score represents the accuracy of load forecasting. N 1 This represents the amount of data predicted by the load interaction strategy. M p This represents the maximum value of the predicted data. p ri Indicates the first i The actual load value at any given time. p mi Indicates the first i Load values ​​predicted by the real-time load interaction strategy; The executability of the control object is determined by whether the control object can be adjusted during actual operation. If the control object can be adjusted during actual operation, then... Q m =D; If the object to be regulated cannot be adjusted during actual operation, then Q m =E, where Q m The score indicates the feasibility of the regulated object.

[0024] S2. Based on actual user interaction data and power grid operation data, calculate the score for each effectiveness indicator in the implementation effectiveness evaluation indicator system, forming a set of implementation effectiveness indicator scores, specifically including: The performance indicators in the implementation effect evaluation index system include actual user response rate, user response time coverage rate, peak shaving period matching degree, valley filling period matching degree, and user load adjustment matching degree. The actual user responsiveness is calculated by the error between the actual user load adjustment and the load adjustment output by the load interaction strategy. The calculation formula is as follows: in,F u A score representing the user's actual responsiveness. N 2 This indicates the actual amount of data sampled. M R This indicates the maximum value of the load regulation output by the load interaction strategy. p ni Indicates the first i The real-time load interaction strategy outputs the amount of load reduction or increase. p ui Indicates the first i The amount of reduction or increase in the actual user load at any given time; The user response time coverage rate is calculated as the ratio of the actual user response time to the duration set by the load interaction strategy. The calculation formula is as follows: in, T u The score represents the coverage of user response time. T r This indicates the actual duration of peak shaving and valley filling responses performed by users within the target control period of the load interaction strategy. T m This indicates the peak shaving and valley filling duration set by the load interaction strategy; The peak shaving period matching degree is calculated by the ratio of the overlap duration between the peak shaving period and the actual peak shaving period of the power grid, as estimated by the load interaction strategy, to the actual peak shaving period of the power grid. The calculation formula is as follows: in, T pc The score represents the matching degree during peak shaving periods. T mpc This indicates the overlap between the peak shaving period estimated by the load interaction strategy and the actual peak shaving period of the power grid. T epc This indicates the actual duration of peak shaving periods in the power grid; The valley filling time matching degree is calculated by the ratio of the overlap duration between the load interaction-estimated valley filling time and the actual valley filling time of the power grid to the actual valley filling time of the power grid. The calculation formula is as follows: in, T vf The score represents the matching degree during the valley filling period. T mvf This indicates the overlap between the valley filling period estimated by the load interaction strategy and the actual valley filling period of the power grid. T evf This indicates the actual duration of the valley filling period in the power grid; The user load regulation matching degree is calculated as the ratio of the number of devices in the user's actual regulation equipment that are the same as the regulation equipment set by the load interaction strategy to the total number of regulation equipment set by the load interaction strategy. The calculation formula is as follows: in, P u The score represents the user's load adjustment matching degree. D um This indicates the number of devices in the user's actual control equipment that are the same as the control equipment set in the load interaction strategy. D m This indicates the number of regulating devices configured in the load interaction strategy.

[0025] S3. Calculate the average score of each strategy indicator based on the set of load interaction strategy indicator scores. Determine the total weight coefficient of the strategy evaluation index system and the total weight coefficient of the implementation effect evaluation index system based on the average value. Use the total weight coefficient to obtain the weight coefficient of each strategy indicator. Use the set of implementation effect indicator scores and the total weight coefficient of the effect to calculate the weight coefficient of each effect indicator using an adaptive algorithm. Weight and accumulate the scores of each strategy indicator and effect indicator with their corresponding weight coefficients to obtain the comprehensive evaluation value of the interaction effect, specifically including: 3.1 Based on the set of load interaction strategy indicator scores, calculate the average score of each strategy indicator. Then, determine the total weight coefficient of the load interaction strategy evaluation indicator system and the total weight coefficient of the implementation effect evaluation indicator system based on the average value. The specific process is as follows: The average score (Ave) of each strategy indicator in the load interaction strategy indicator score set is calculated using the following formula: The total weight coefficient Q1 of the load interaction strategy evaluation index system and the total weight coefficient Q2 of the implementation effect evaluation index system are obtained using the average value Ave. The calculation formula is as follows: in, ω This is a preset constant.

[0026] 3.2 The weight coefficient of each strategy indicator is obtained using the total weight coefficient of the strategy. Using the set of implementation effect indicator scores and the total weight coefficient of the effect, the weight coefficient of each effect indicator is calculated through an adaptive algorithm. The scores of each strategy indicator and effect indicator are weighted and accumulated with their corresponding weight coefficients to obtain the comprehensive evaluation value of the interaction effect. The specific process is as follows: Dividing the total strategy weight coefficient Q1 by the number of strategy indicators yields the weight coefficient for each strategy indicator. β ; Using the set of implementation effect indicator scores and the total effect weight coefficient, an adaptive algorithm is used to calculate the weight coefficient of each effect indicator. The calculation formula is as follows: in, K i Indicates the first i The weighting coefficient of each performance indicator. S M This represents the set adaptive weight coefficient. S i Refers to the first i The score of each performance indicator S j Refers to the first j The scores for each performance metric, with the first performance metric being the actual user response rate. F u The second performance metric is user response time coverage. T u The third performance indicator is the matching degree of peak shaving periods. T pc The fourth performance indicator is the matching degree of the valley filling period. T vf The fifth performance indicator is the user load adjustment matching degree. P u ; The scores of each strategy indicator and effect indicator are multiplied by their corresponding weighting coefficients and then summed to obtain the weighted comprehensive evaluation value of the interaction effect. S A The calculation formula is: This yields a comprehensive evaluation value for the interaction effect. S A .

[0027] Example 3 Taking the implementation of load-interaction in a cement company as an example: Calculate the scores of each strategy indicator in the load interaction strategy evaluation index system: The actual number of parameters acquired includes information such as the operating power and adjustment ratio of the main adjustable equipment, as well as the company's historical electricity load. Since the actual number of parameters acquired fully meets the parameter requirements of the load interaction strategy, then... I m =3 / 3=1; In the calculation of the load interaction strategy, peak shaving and load interaction are used as optimization objectives. A m =0.6; The load interaction strategy predicts future load data of the power grid, and the predicted load value and the actual load value are as follows:Figure 3 As shown, according to the formula P m =0.965; Finally, the control equipment output by the load interaction strategy all correspond to the adjustable equipment of the enterprise, then Q m =1.

[0028] Calculate the scores of each performance indicator in the implementation effectiveness evaluation index system: The user's actual peak shaving capacity is 488kW, and the user's actual valley filling capacity is 280kW; the load interaction strategy estimates the peak shaving capacity as 500kW and the valley filling capacity as 250kW; the user's participation in peak shaving starts and ends at 18:30 and 20:40, respectively, and the user's participation in valley filling starts and ends at 4:00 and 6:15, respectively; the load interaction strategy outputs the peak shaving start and end times as 18:25 and 20:30, respectively, and the valley filling start and end times as 4:00 and 6:10, respectively; the grid's actual peak shaving start and end times are 18:40 and 20:35, respectively, and the grid's actual valley filling start and end times are 3:55 and 6:15, respectively; the user's actual interaction equipment is a crusher and a screening machine, consistent with the interaction equipment output by the load interaction strategy; Based on the above information, the user's actual response rate can be calculated. F u =0.928, User Response Time Coverage T u =0.98, matching degree during peak shaving periods T pc =0.956, matching degree during valley filling period T vf =0.923, User load regulation matching degree P u =1.

[0029] A comprehensive evaluation of the interactive effect: Based on the calculation results of various strategy indicators and effect indicators, the average value of the load interaction strategy indicator score set Ave = 0.891, then the total strategy weight coefficient of the load interaction strategy evaluation indicator system Q1 = 0.209, and the total effect weight coefficient of the implementation effect evaluation indicator system Q2 = 0.791. Based on Q1 and Q2, the weight coefficient of each strategy indicator in the load interaction strategy evaluation index system is as follows: β= 0.209 / 4 = 0.05225, actual user response rate F u The weighting coefficient is K 1 =0.167, User Response Time Coverage Tu The weighting coefficient is K 2 =0.152, matching degree during peak shaving periods T pc The weighting coefficient is K 3 =0.158, matching degree during valley filling period T vf The weighting coefficient is K 4 =0.168, User load regulation matching degree P u The weighting coefficient is K 5 =0.146; Finally, the overall evaluation value of the interaction effect was obtained. S A =0.942, then the interaction effect is excellent.

[0030] Example 4 This embodiment provides a computer storage medium storing a computer program, which, when executed by a processor, implements the steps of the method described in Embodiment 2.

[0031] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0032] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A system that specifies functions in one or more boxes.

[0033] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including an instruction set implemented in a process. Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0034] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0035] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit its scope of protection. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that after reading the present invention, they can still make various changes, modifications or equivalent substitutions to the specific implementation of the invention, but these changes, modifications or equivalent substitutions are all within the scope of protection of the pending claims of the invention.

[0036] The contents not described in detail in this specification are existing technologies known to those skilled in the art.

Claims

1. A system for evaluating the implementation effect of load interaction strategies for high-energy-consuming enterprises, characterized in that, include: The load interaction strategy evaluation module is used to calculate the score of each strategy indicator in the load interaction strategy evaluation index system based on the information data required for strategy evaluation, and form a set of load interaction strategy indicator scores. The implementation effect evaluation module is used to calculate the score of each effect indicator in the implementation effect evaluation indicator system based on actual user interaction data and power grid operation data, forming a set of implementation effect indicator scores; The comprehensive evaluation module for interactive effects is used to calculate the average score of each strategy indicator based on the set of scores for the load interaction strategy indicators. Based on the average value, the module determines the total weight coefficient of the strategy evaluation index system and the total weight coefficient of the implementation effect evaluation index system. The module uses the total weight coefficient of the strategy to obtain the weight coefficient of each strategy indicator. Using the set of scores for the implementation effect indicators and the total weight coefficient of the effect, the module calculates the weight coefficient of each effect indicator through an adaptive algorithm. The module then weights and sums the scores of each strategy indicator and effect indicator with their corresponding weight coefficients to obtain the comprehensive evaluation value of the interactive effects.

2. The system for evaluating the implementation effect of load interaction strategy for high-energy-consuming enterprises according to claim 1, characterized in that: The strategy indicators in the load interaction strategy evaluation index system include the actual acquisition rate of input parameters, strategy adaptability, load forecast accuracy, and executability of the control object; the effect indicators in the implementation effect evaluation index system include the actual user response rate, user response time coverage rate, peak shaving period matching degree, valley filling period matching degree, and user load adjustment matching degree.

3. The system for evaluating the implementation effect of load interaction strategy for high-energy-consuming enterprises according to claim 2, characterized in that, In the load interaction strategy evaluation module, based on the information and data required for strategy evaluation, the score of each strategy indicator in the load interaction strategy evaluation index system is calculated, specifically for: The actual acquisition rate of input parameters is calculated as the ratio of the number of parameters actually acquired to the number of parameters required by the load interaction strategy. The calculation formula is as follows: in, I m The score represents the actual acquisition rate of the input parameters. N r This indicates the actual number of parameters acquired. N m Indicates the number of parameters required for the load interaction strategy; The fitness of the strategy is determined based on the type of optimization objective of the load interaction strategy: when the load interaction strategy only uses peak shaving and valley filling as its optimization objective... A m =A; When the load interaction strategy uses peak shaving and load interaction as optimization objectives... A m =B; When the optimization objective of the load interaction strategy does not include peak shaving and valley filling and load interaction, A m =C, where A m The score represents the policy fitness score; The load forecast accuracy is calculated by the error between the predicted load value and the actual load value. The calculation formula is as follows: in, P m The score represents the accuracy of load forecasting. N 1 This represents the amount of data predicted by the load interaction strategy. M p This represents the maximum value of the predicted data. p ri Indicates the first i The actual load value at any given time. p mi Indicates the first i Load values ​​predicted by the real-time load interaction strategy; The executability of the control object is determined by whether the control object can be adjusted during actual operation. If the control object can be adjusted during actual operation, then... Q m =D; If the object to be regulated cannot be adjusted during actual operation, then Q m =E, where Q m The score indicates the feasibility of the regulated object.

4. The system for evaluating the implementation effect of load interaction strategy for high-energy-consuming enterprises according to claim 2, characterized in that, In the implementation effectiveness evaluation module, based on actual user interaction data and power grid operation data, the score of each effectiveness indicator in the implementation effectiveness evaluation index system is calculated, specifically for: The actual user responsiveness is calculated by the error between the actual user load adjustment and the load adjustment output by the load interaction strategy. The calculation formula is as follows: in, F u A score representing the user's actual responsiveness. N 2 This indicates the actual amount of data sampled. M R This indicates the maximum value of the load regulation output by the load interaction strategy. p ni Indicates the first i The real-time load interaction strategy outputs the amount of load reduction or increase. p ui Indicates the first i The amount of reduction or increase in the actual user load at any given time; The user response time coverage rate is calculated as the ratio of the actual user response time to the duration set by the load interaction strategy. The calculation formula is as follows: in, T u The score represents the coverage of user response time. T r This indicates the actual duration of peak shaving and valley filling responses performed by users within the target control period of the load interaction strategy. T m This indicates the peak shaving and valley filling duration set by the load interaction strategy; The peak shaving period matching degree is calculated by the ratio of the overlap duration between the peak shaving period and the actual peak shaving period of the power grid, as estimated by the load interaction strategy, to the actual peak shaving period of the power grid. The calculation formula is as follows: in, T pc The score represents the matching degree during peak shaving periods. T mpc This indicates the overlap between the peak shaving period estimated by the load interaction strategy and the actual peak shaving period of the power grid. T epc This indicates the actual duration of peak shaving periods in the power grid; The valley filling time matching degree is calculated by the ratio of the overlap duration between the load interaction-estimated valley filling time and the actual valley filling time of the power grid to the actual valley filling time of the power grid. The calculation formula is as follows: in, T vf The score represents the matching degree during the valley filling period. T mvf This indicates the overlap between the valley filling period estimated by the load interaction strategy and the actual valley filling period of the power grid. T evf This indicates the actual duration of the valley filling period in the power grid; The user load regulation matching degree is calculated as the ratio of the number of devices in the user's actual regulation equipment that are the same as the regulation equipment set by the load interaction strategy to the total number of regulation equipment set by the load interaction strategy. The calculation formula is as follows: in, P u The score represents the user's load adjustment matching degree. D um This indicates the number of devices in the user's actual control equipment that are the same as the control equipment set in the load interaction strategy. D m This indicates the number of regulating devices configured in the load interaction strategy.

5. The system for evaluating the implementation effect of load interaction strategy for high-energy-consuming enterprises according to claim 3, characterized in that, The comprehensive evaluation module for interactive effects calculates the average score of each strategy indicator based on the set of load interaction strategy indicator scores, and determines the total weight coefficient of the strategy evaluation index system and the total weight coefficient of the implementation effect evaluation index system based on the average value. Specifically, it is used for: The average score (Ave) of each strategy indicator in the load interaction strategy indicator score set is calculated using the following formula: The total weight coefficient Q1 of the load interaction strategy evaluation index system and the total weight coefficient Q2 of the implementation effect evaluation index system are obtained using the average value Ave. The calculation formula is as follows: in, ω This is a preset constant.

6. The system for evaluating the implementation effect of load interaction strategy for high-energy-consuming enterprises according to claim 4 or 5, characterized in that, The comprehensive evaluation module for interactive effects uses the total weight coefficient of the strategy to obtain the weight coefficient of each strategy indicator. It then uses the set of implementation effect indicator scores and the total weight coefficient to calculate the weight coefficient of each effect indicator through an adaptive algorithm. Finally, it weights and sums the scores of each strategy indicator and effect indicator with their corresponding weight coefficients to obtain the comprehensive evaluation value of the interactive effect. This value is specifically used for: Dividing the total strategy weight coefficient Q1 by the number of strategy indicators yields the weight coefficient for each strategy indicator. β ; Using the set of implementation effect indicator scores and the total effect weight coefficient, an adaptive algorithm is used to calculate the weight coefficient of each effect indicator. The calculation formula is as follows: in, K i Indicates the first i The weighting coefficient of each performance indicator. S M This represents the set adaptive weight coefficient. S i Refers to the first i The score of each performance indicator S j Refers to the first j The scores for each performance metric, with the first performance metric being the actual user response rate. F u The second performance metric is user response time coverage. T u The third performance indicator is the matching degree of peak shaving periods. T pc The fourth performance indicator is the matching degree of the valley filling period. T vf The fifth performance indicator is the user load adjustment matching degree. P u ; The scores of each strategy indicator and effect indicator are multiplied by their corresponding weighting coefficients and then summed to obtain the weighted comprehensive evaluation value of the interaction effect. S A The calculation formula is: This yields a comprehensive evaluation value for the interaction effect. S A .

7. A method for evaluating the implementation effect of load interaction strategy for high-energy-consuming enterprises, characterized in that, Includes the following steps: Based on the information and data required for strategy evaluation, the score of each strategy indicator in the load interaction strategy evaluation index system is calculated to form a set of load interaction strategy indicator scores. Based on actual user interaction data and power grid operation data, the score of each effect indicator in the implementation effect evaluation index system is calculated to form a set of implementation effect indicator scores. Based on the set of scores for the load interaction strategy indicators, the average score of each strategy indicator is calculated. The total weight coefficient of the strategy evaluation index system and the total weight coefficient of the implementation effect evaluation index system are determined according to the average value. The weight coefficient of each strategy indicator is obtained using the total weight coefficient of the strategy. Using the set of scores for the implementation effect indicators and the total weight coefficient of the effect, the weight coefficient of each effect indicator is calculated through an adaptive algorithm. The scores of each strategy indicator and effect indicator are weighted and accumulated with their corresponding weight coefficients to obtain the comprehensive evaluation value of the interaction effect.

8. The method for evaluating the implementation effect of a load interaction strategy for high-energy-consuming enterprises according to claim 7, characterized in that, Based on the information and data required for strategy evaluation, the score of each strategy indicator in the load interaction strategy evaluation index system is calculated, including: The actual acquisition rate of input parameters is calculated as the ratio of the number of parameters actually acquired to the number of parameters required by the load interaction strategy. The calculation formula is as follows: in, I m The score represents the actual acquisition rate of the input parameters. N r This indicates the actual number of parameters acquired. N m Indicates the number of parameters required for the load interaction strategy; The fitness of the strategy is determined based on the type of optimization objective of the load interaction strategy: when the load interaction strategy only uses peak shaving and valley filling as its optimization objective... A m =A; When the load interaction strategy uses peak shaving and load interaction as optimization objectives... A m =B; When the optimization objective of the load interaction strategy does not include peak shaving and valley filling and load interaction, A m =C, where A m The score represents the policy fitness score; The load forecast accuracy is calculated by the error between the predicted load value and the actual load value. The calculation formula is as follows: in, P m The score represents the accuracy of load forecasting. N 1 This represents the amount of data predicted by the load interaction strategy. M p This represents the maximum value of the predicted data. p ri Indicates the first i The actual load value at any given time. p mi Indicates the first i Load values ​​predicted by the real-time load interaction strategy; The executability of the control object is determined by whether the control object can be adjusted during actual operation. If the control object can be adjusted during actual operation, then... Q m =D; If the object to be regulated cannot be adjusted during actual operation, then Q m =E, where Q m The score indicates the feasibility of the regulated object.

9. The method for evaluating the implementation effect of a load interaction strategy for high-energy-consuming enterprises according to claim 7, characterized in that, Based on actual user interaction data and power grid operation data, the score for each effectiveness indicator in the implementation effectiveness evaluation index system is calculated, including: The actual user responsiveness is calculated by the error between the actual user load adjustment and the load adjustment output by the load interaction strategy. The calculation formula is as follows: in, F u A score representing the user's actual responsiveness. N 2 This indicates the actual amount of data sampled. M R This indicates the maximum value of the load regulation output by the load interaction strategy. p ni Indicates the first i The real-time load interaction strategy outputs the amount of load reduction or increase. p ui Indicates the first i The amount of reduction or increase in the actual user load at any given time; The user response time coverage rate is calculated as the ratio of the actual user response time to the duration set by the load interaction strategy. The calculation formula is as follows: in, T u The score represents the coverage of user response time. T r This indicates the actual duration of peak shaving and valley filling responses performed by users within the target control period of the load interaction strategy. T m This indicates the peak shaving and valley filling duration set by the load interaction strategy; The peak shaving period matching degree is calculated by the ratio of the overlap duration between the peak shaving period and the actual peak shaving period of the power grid, as estimated by the load interaction strategy, to the actual peak shaving period of the power grid. The calculation formula is as follows: in, T pc The score represents the matching degree during peak shaving periods. T mpc This indicates the overlap between the peak shaving period estimated by the load interaction strategy and the actual peak shaving period of the power grid. T epc This indicates the actual duration of peak shaving periods in the power grid; The valley filling time matching degree is calculated by the ratio of the overlap duration between the load interaction-estimated valley filling time and the actual valley filling time of the power grid to the actual valley filling time of the power grid. The calculation formula is as follows: in, T vf The score represents the matching degree during the valley filling period. T mvf This indicates the overlap between the valley filling period estimated by the load interaction strategy and the actual valley filling period of the power grid. T evf This indicates the actual duration of the valley filling period in the power grid; The user load regulation matching degree is calculated as the ratio of the number of devices in the user's actual regulation equipment that are the same as the regulation equipment set by the load interaction strategy to the total number of regulation equipment set by the load interaction strategy. The calculation formula is as follows: in, P u The score represents the user's load adjustment matching degree. D um This indicates the number of devices in the user's actual control equipment that are the same as the control equipment set in the load interaction strategy. D m This indicates the number of regulating devices configured in the load interaction strategy.

10. The method for evaluating the implementation effect of a load interaction strategy for high-energy-consuming enterprises according to claim 8, characterized in that, The weight coefficient of each strategy indicator is obtained using the overall strategy weight coefficient. Using the set of implementation effect indicator scores and the overall effect weight coefficient, an adaptive algorithm calculates the weight coefficient of each effect indicator. The scores of each strategy indicator and effect indicator are then weighted and summed with their corresponding weight coefficients to obtain a comprehensive evaluation value of the interaction effect, including: The average score (Ave) of each strategy indicator in the load interaction strategy indicator score set is calculated using the following formula: The total weight coefficient Q1 of the load interaction strategy evaluation index system and the total weight coefficient Q2 of the implementation effect evaluation index system are obtained using the average value Ave. The calculation formula is as follows: in, ω This is a preset constant.

11. The method for evaluating the implementation effect of a load interaction strategy for high-energy-consuming enterprises according to claim 9 or 10, characterized in that... The weight coefficient of each strategy indicator is obtained using the overall strategy weight coefficient. Using the set of implementation effect indicator scores and the overall effect weight coefficient, an adaptive algorithm calculates the weight coefficient of each effect indicator. The scores of each strategy indicator and effect indicator are then weighted and summed with their corresponding weight coefficients to obtain a comprehensive evaluation value of the interaction effect, including: Dividing the total strategy weight coefficient Q1 by the number of strategy indicators yields the weight coefficient for each strategy indicator. β ; Using the set of implementation effect indicator scores and the total effect weight coefficient, an adaptive algorithm is used to calculate the weight coefficient of each effect indicator. The calculation formula is as follows: in, K i Indicates the first i The weighting coefficient of each performance indicator. S M This represents the set adaptive weight coefficient. S i Refers to the first i The score of each performance indicator S j Refers to the first j The scores for each performance metric, with the first performance metric being the actual user response rate. F u The second performance metric is user response time coverage. T u The third performance indicator is the matching degree of peak shaving periods. T pc The fourth performance indicator is the matching degree of the valley filling period. T vf The fifth performance indicator is the user load adjustment matching degree. P u ; The scores of each strategy indicator and effect indicator are multiplied by their corresponding weighting coefficients and then summed to obtain the weighted comprehensive evaluation value of the interaction effect. S A The calculation formula is: This yields a comprehensive evaluation value for the interaction effect. S A .

12. A computer storage medium, wherein the computer-readable storage medium stores a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for evaluating the implementation effect of a load interaction strategy for high-energy-consuming enterprises as described in any one of claims 7-11.

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