CN115545569B - Peak electricity price mechanism implementation effect evaluation method based on quantitative fee analysis - Google Patents
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Abstract
The invention relates to a peak electricity price mechanism implementation effect evaluation method based on quantitative fee analysis, and relates to the technical field of peak electricity price mechanism tracking evaluation. The method comprises the following steps: determining an evaluation time period and a reference time period; determining three types of user sets for executing a peak electricity price mechanism; acquiring evaluation time period electricity consumption data and reference time period electricity consumption data; determining response rate data, influence rate data and peak-to-valley ratio data; determining a user set execution effect score; and determining the total evaluation value of the peak electricity price mechanism implementation effect. According to the evaluation method, aiming at the time period of actually implementing the peak electricity price mechanism, 3 dimensions of peak electricity quantity reduction, peak electricity charge duty ratio and peak valley difference are subjected to refinement evaluation aiming at users with different volumes, so that the mechanism evaluation of the peak electricity price is systematic, and the evaluation result is accurate.
Description
Technical Field
The invention relates to the technical field of peak electricity price mechanism tracking and evaluation, in particular to a peak electricity price mechanism implementation effect evaluation method based on quantitative fee analysis.
Background
The power system needs to maintain the dynamic balance of supply and demand, so that the system always maintains corresponding spare capacity to ensure the reliable operation of the power system, but if the power system is in short supply under certain special conditions, the capacity shortage occurs, and the operation safety of the power system is endangered. The peak electricity price policy is a seasonal time-of-day electricity price policy, and is generally divided into a peak electricity price policy in summer and a peak electricity price policy in winter, and the policy utilizes a price lever to guide customers to actively avoid peaks, so that the safety and the efficiency of a power system are improved.
In the execution process of the peak electricity price mechanism, the related technology establishes an evaluation method for evaluating the execution effect of the peak electricity price policy, and the method collects, processes and evaluates data of peak electricity quantity, electricity charge and electricity quantity duty ratio from the whole dimension after seasonal peak electricity price execution is completed.
However, the related art has a coarse evaluation granularity, does not have flexibility to cope with various scenes, and lacks timeliness. Namely, the related technology cannot timely and finely reflect the implementation effect of the peak electricity price mechanism.
Disclosure of Invention
The invention aims to overcome the defects existing in the prior art, thereby providing a peak electricity price mechanism implementation effect evaluation method based on quantitative fee analysis, so that the mechanism evaluation of the peak electricity price is systemized, and the evaluation result is more accurate. The method comprises the following steps:
determining an evaluation time period and a reference time period, wherein the reference time period corresponds to the evaluation time period and is a time period in which a peak electricity price mechanism is not implemented;
determining three types of user sets for executing a peak electricity price mechanism, wherein the user sets comprise a full-scale user set, a typical industry user set and a large-scale user set;
acquiring evaluation time period power consumption data corresponding to the evaluation time period and reference time period power consumption data corresponding to the reference time period corresponding to each type of user set;
determining response rate data, influence rate data and peak-to-valley ratio data corresponding to the user set based on the evaluation period power consumption data and the reference period power consumption data;
determining user set execution effect scores corresponding to each type of user set based on the response rate data, the influence rate data, and the peak-to-valley ratio data;
and determining a total evaluation value of the peak electricity price mechanism implementation effect corresponding to the evaluation time period based on the execution effect score of each user set, wherein the total evaluation value of the peak electricity price mechanism implementation effect is used for quantitatively evaluating the evaluation effect of the peak electricity price mechanism.
In an alternative embodiment, when the evaluation period is a workday, the reference period is a workday;
when the evaluation time period is a non-working day, the reference time period is a non-working day;
the reference period precedes the evaluation period.
In an alternative embodiment, the duration range of the reference time period is greater than the duration range of the evaluation time period, and the duration range of the evaluation time period is 1 natural day;
corresponding to each type of user set, acquiring evaluation time period electricity consumption data corresponding to an evaluation time period and reference time period electricity consumption data corresponding to a reference time period, wherein the method comprises the following steps:
corresponding to each type of user set, acquiring daily evaluation time period electricity consumption data corresponding to the evaluation time period, and taking the daily evaluation time period electricity consumption data as evaluation time period electricity consumption data;
acquiring daily reference time period electricity consumption data corresponding to the reference time period corresponding to each type of user set;
and carrying out averaging treatment on the electricity consumption data of the reference time period every day to obtain the electricity consumption data of the reference time period.
In an alternative embodiment, the method further comprises:
determining a reference day-hour electric quantity and a reference day-hour electric quantity according to the reference time period electricity consumption data;
determining the electric quantity of the execution day and hour according to the electricity consumption data of the evaluation time period;
the execution day simulation hour power is determined based on the reference day hour power, the execution day hour power, and the reference day power.
In an alternative embodiment, determining the response rate data corresponding to the user set based on the evaluation period power usage data and the reference period power usage data includes:
determining a peak period corresponding to a peak electricity price mechanism;
and determining the response rate data corresponding to the user set according to the execution day simulation hour electric quantity and the execution day hour electric quantity corresponding to the peak period.
In an alternative embodiment, determining the impact rate data and the peak-to-valley ratio data corresponding to the user set based on the evaluation period power data and the reference period power data includes:
determining a peak period, a normal period and a valley period corresponding to the peak electricity price mechanism;
determining a peak electric quantity and a peak electric price corresponding to the peak time period, a flat electric quantity and a flat electric price corresponding to the normal time period, and a valley electric quantity and a valley electric price corresponding to the valley time period;
determining influence rate data corresponding to the user set based on the peak power, the peak power price, the flat power price, the valley power, and the valley power price;
and determining peak-to-valley ratio data corresponding to the user set based on the peak power and the valley power.
In an alternative embodiment, determining user set performance effect scores corresponding to each type of user set based on the response rate data, the impact rate data, and the peak-to-valley ratio data includes:
determining a response rate data scoring rule, a peak-to-valley ratio data scoring rule and an influence rate data scoring rule;
determining a score gear where the response rate data are located and a corresponding response rate score according to the response rate data allocation rule;
determining the score gear where the peak-to-valley ratio data are located and the corresponding peak-to-valley ratio score based on the peak-to-valley ratio data scoring rule;
determining a score gear where the influence rate data is located and a corresponding influence rate score based on the influence rate data allocation rule;
determining response rate weight, influence rate weight and peak-to-valley ratio weight according to the type of the user set;
according to the response rate weight, the influence rate weight and the peak-to-valley ratio weight, weighting and summing the response rate score, the influence rate score and the peak-to-valley ratio score to obtain a user set execution effect score corresponding to the user set;
and summarizing the user set execution effect scores corresponding to each type of user set.
In an alternative embodiment, determining a peak electricity price mechanism implementation effect total evaluation value corresponding to an evaluation period based on each user set execution effect score includes:
determining the user set weight corresponding to the execution effect score of each type of user set through a weight vector matrix;
and determining the total evaluation value of the spike electricity price mechanism implementation effect corresponding to the evaluation time period based on the user set score weight and the execution effect score of each type of user set.
In an alternative embodiment, after determining the peak electricity price mechanism implementation effect total evaluation score corresponding to the evaluation period based on each user set execution effect score, the method includes:
determining total evaluation values of the implementation effects of the peak electricity price mechanisms corresponding to at least two evaluation time periods;
and comparing the total evaluation values of the implementation effects of the at least two peak electricity price mechanisms to obtain a real-time effect comparison result.
The technical scheme provided by the invention has the beneficial effects that at least:
in the process of evaluating the implementation effect of the peak electricity price mechanism, a reference time period is selected, after the reference time period is determined, the response rate data, the influence rate data and the peak-to-valley ratio data are determined corresponding to each type of user set according to the set type of the user, so that the implementation effect score corresponding to each type of user set is obtained, and finally the evaluation effect score for evaluating the peak electricity price mechanism is obtained. According to the evaluation method, aiming at the time period of actually implementing the peak electricity price mechanism, 3 dimensions of peak electricity quantity reduction, peak electricity charge duty ratio and peak valley difference are subjected to refinement evaluation aiming at users with different volumes, so that the mechanism evaluation of the peak electricity price is systematic, and the evaluation result is accurate.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a peak electricity price mechanism implementation effect evaluation method based on quantitative fee analysis according to an exemplary embodiment of the present application.
FIG. 2 is a flow chart illustrating another method for evaluating the performance of a peak electricity price mechanism based on a royalty analysis according to an exemplary embodiment of the present application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the embodiments of the present invention will be described in further detail with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart of a peak electricity price mechanism implementation effect evaluation method based on quantitative fee analysis according to an exemplary embodiment of the present application, and the method is used in a computer device for illustration, and includes:
step 101, determining an evaluation period and a reference period.
In the embodiment of the present application, the real-time effect evaluation process of the peak electricity price mechanism is performed after the evaluation period, in which case, the setting of the evaluation period and the reference period is required, where the reference period corresponds to the evaluation period and is a period in which the peak electricity price mechanism is not implemented.
Optionally, the peak electricity price mechanism is executed in response to a peak electricity price policy, and the peak electricity price mechanism corresponds to an evaluation period, an execution range and an execution condition. In various embodiments of the present application, the evaluation period, execution range, and execution condition of the peak electricity price mechanism will be exemplified and schematically explained.
Step 102, determining three types of user sets for executing the peak electricity price mechanism.
In the embodiment of the application, the user set comprises a full-scale user set, a typical industry user set and a large-scale user set.
The full-quantity user set is a set of all users meeting the peak electricity price execution condition in the peak electricity price policy. In one example, industrial users with power usage above 315kVA all belong to a full set of users within a certain province execution range.
In the national economy industry classification, 31 sub-industries of the manufacturing industry are subordinate, and all industrial users corresponding to the sub-industries with the first five electric power consumption are selected as a typical industry user set.
And within the evaluation time period, selecting the industrial users with the power consumption ranking of top 100 to form a large user set.
Optionally, there is a situation that the user repeatedly appears in the three types of sets, and the intersection relationship of the three types of sets is not limited in the application.
Step 103, corresponding to each type of user set, acquiring evaluation time period electricity consumption data corresponding to the evaluation time period and reference time period electricity consumption data corresponding to the reference time period.
In the embodiment of the application, the computer device obtains and counts the reference time period electricity consumption data and the evaluation time period electricity consumption data corresponding to the reference time period and the evaluation time period.
Step 104, determining response rate data, influence rate data and peak-to-valley ratio data corresponding to the user set based on the evaluation period power consumption data and the reference period power consumption data.
In the embodiment of the application, the response rate data, the influence rate data and the peak-to-valley ratio data are all obtained by processing the reference time period power consumption data and the evaluation time period power consumption data by the computer equipment according to the processing rules.
Step 105, determining a user set execution effect score corresponding to each type of user set based on the response rate data, the influence rate data, and the peak-to-valley ratio data.
In the embodiment of the application, each type of user set corresponds to a user set execution effect score, and the score is used for representing the execution effect of the peak electricity price mechanism on a single type of user.
And step 106, determining the total evaluation value of the spike electricity price mechanism implementation effect corresponding to the evaluation time period based on the execution effect score of each user set.
In the embodiment of the application, summarizing is performed by the computer equipment through summarizing calculation on the effect scores of the plurality of user sets, namely, the total effect evaluation score of the spike electricity price mechanism implementation is determined. In the embodiment of the application, the total evaluation value of the implementation effect of the peak electricity price mechanism is used for quantitatively evaluating the evaluation effect of the peak electricity price mechanism. That is, the score may directly characterize the implementation of the peak price policy, or the score may be compared with the historical score to compare the implementation of the current peak price policy with the historical peak price policy.
In summary, in the method provided in the embodiment of the present application, during the process of evaluating the implementation effect of the spike motor mechanism, the implementation period of the spike motor mechanism is selected for the reference period, and after the reference period is determined, the response rate data, the impact rate data, and the peak-to-valley ratio data are determined for each type of user set according to the set type of the user, so as to obtain the implementation effect score corresponding to each type of user set, and finally obtain the evaluation effect score for evaluating the spike motor mechanism. According to the evaluation method, aiming at the time period of actually implementing the peak electricity price mechanism, 3 dimensions of peak electricity quantity reduction, peak electricity charge duty ratio and peak valley difference are subjected to refinement evaluation aiming at users with different volumes, so that the mechanism evaluation of the peak electricity price is systematic, and the evaluation result is accurate.
FIG. 2 shows another method for evaluating the effect of peak electricity price mechanism based on quantitative fee analysis according to an exemplary embodiment of the present application, and the method is applied to a computer device for illustration, and includes:
in step 201, an evaluation period and a reference period are determined.
This step is the same as the step described in step 101 and will not be described here.
Alternatively, in the embodiment of the present application, a specific correspondence relationship between the reference period and the evaluation period is described. The reference time period should be selected for the evaluation time period, respectively, due to the different production behaviors of the workday and the holiday. When the evaluation time period is a working day, the reference time period is a working day; when the evaluation period is a non-working day, the reference period is a non-working day. And the reference time period precedes the evaluation time period due to the impact of the peak price policy on the enterprise production plan. In one example, peak electricity prices are performed starting from 7 months and 7 days, the evaluation time is selected from 0 to 24 points on four days of 2022 7 months and 7 days, and the reference time period may be selected from the working days 7 months and 7 days in the week, i.e., 2022 7 months and 4 days (monday) -7 months and 6 days (wednesday).
In some embodiments of the present application, the range of the duration of the reference time period is greater than the range of the duration of the evaluation time period, and the range of the duration of the evaluation time period is 1 natural day.
Step 202, a set of three classes of users performing a peak electricity price mechanism is determined.
In an embodiment of the present application, a full set of users, a typical industry set of users, and a large set of users are determined based on the rules specified in step 102.
Step 203, corresponding to each type of user set, acquiring daily evaluation period electricity consumption data corresponding to the evaluation period, as evaluation period electricity consumption data.
In the embodiment of the present application, when the range of the evaluation period is 1 natural day, that is, one natural day is taken as the electricity consumption data acquisition unit, the data acquisition is performed, alternatively, the electricity consumption data of the evaluation period may be subdivided according to each hour and each 15 minutes, and the electricity consumption data of the evaluation period includes the electricity consumption related data and the electricity price related data.
Step 204, corresponding to each user set, acquiring electricity consumption data of a daily reference time period corresponding to the reference time period.
And 205, carrying out averaging treatment on the daily electricity data of the reference time period to obtain the electricity data of the reference time period.
Step 204 and step 205 correspond to the case of the example in step 203, if the evaluation period indicates a single day time, the reference period may be a period exceeding the single day time, for example, the evaluation period is one working day, the reference period is all working days of the last week of the working day, that is, in each reference period, the electricity consumption data of five daily reference periods may be obtained, and after the average processing, the value after the average processing is taken as the electricity consumption data of the reference period.
And 206, determining the reference day-hour electric quantity and the reference day-hour electric quantity according to the reference time period power consumption data.
Step 207, determining the electric quantity of the execution day and hour according to the electric data of the evaluation time period.
In step 208, the execution day simulation hour power is determined based on the reference day hour power, the execution day hour power, and the reference day power.
The processes shown in steps 206 to 208 are used for indicating the determination method of the electric quantity of the day simulation hour. The simulated hour electric quantity of the execution day is compared with the actual hour electric quantity of the execution day, so that the deviation between the predicted electric quantity and the actual electric quantity in a certain hour in the execution day can be obtained, and the deviation has various generating factors, but can characterize the feedback of enterprise users on the peak electricity price policy in the peak period.
Optionally, in the embodiment of the present application, the calculation manner of the daily simulation hour electric quantity is as follows in equation 1:
where i indicates the time period of the hour corresponding to the day, i is an integer from 1 to 24, i.e., indicates from 0 to 1 when i=1, and the related data is generated by the user. P (i) represents the execution day analog hour power, G (i) represents the reference day hour power, and R (i) represents the execution day hour power.
Step 209, determining a peak period corresponding to the peak electricity price mechanism.
In the embodiment of the present application, the spike period belongs to the evaluation period.
Step 210, determining response rate data corresponding to the user set according to the execution day simulation hour electric quantity and the execution day hour electric quantity corresponding to the peak period.
In one example, the spike period is 10:00-11:00 and 14:00-15:00, at this time, i is 10 and 14. In this case, the manner of calculation of the response rate data is as follows in equation 2:
in the method, in the process of the invention,for the response rate, P (10), P (14) represent the simulated hour power of the two spike periods, respectively, and G (10), G (14) represent the actual hour power of the two spike periods, respectively.
Step 211, determining a peak period, a flat period, and a valley period corresponding to the peak electricity price mechanism.
Step 212, determining the peak power and peak power price corresponding to the peak period, the flat power and flat power price corresponding to the normal period, and the valley power and valley power price corresponding to the valley period.
It should be noted that the "spike period" in steps 211 and 212 corresponds to the foregoing "spike period". The peak time period is a time period with higher power consumption load, the normal time period is a time period with stable power consumption load, and the valley time period is a time period with lower power consumption load. The spike period is a subset of the peak periods, being the highest loaded period of the peak periods. The electricity prices of each period are respectively the peak electricity prices, the flat electricity prices and the valley electricity prices. Exemplary, peak periods are 8:00-12:00,17:00-21:00; flat period 12:00-17:00,21:00-24:00; valley period 0:00-8:00, peak period 10:00-11:00 and 14:00-15:00.
And step 213, determining the influence rate data corresponding to the user set based on the peak power, the flat power, the valley power and the valley power.
In the embodiment of the present application, the determination formula of the influence rate data is shown in the following formula 3:
in the method, in the process of the invention,to influence the rate data, P Tip of the tip 、P Peak to peak 、P Flat plate 、P Cereal grain Respectively represent tip, peak, flat, gu Dianliang, C Tip of the tip 、C Peak to peak 、C Flat plate 、C Cereal grain Respectively represent the electricity prices of the tip, the peak, the flat and the valley.
Step 214, determining peak-to-valley ratio data corresponding to the user set based on the peak power and the valley power.
In the embodiment of the present application, the calculation mode of the peak-to-valley ratio data is as follows in formula 4:
step 215, determining a response rate data scoring rule, a peak-to-valley ratio data scoring rule and an influence rate data scoring rule.
In one example, the scoring rules are shown in table 1 below:
that is, in the embodiment of the present application, the response rate score, the influence rate score, and the peak-to-valley ratio score are all expressed in integer form. Alternatively, in the present example, the correlation value is obtained by comparing with the contemporaneous period.
The specific scoring rules are not limited in this application, and table 2 provides only one exemplary scoring scheme.
And step 216, determining the score gear where the response rate data is located and the corresponding response rate score according to the response rate data allocation rule.
And step 217, determining the score gear where the peak-to-valley ratio data is located and the corresponding peak-to-valley ratio score based on the peak-to-valley ratio data assignment rule.
Step 218, determining the score gear where the influence rate data is located and the corresponding influence rate score based on the influence rate data assignment rule.
Steps 216 through 218 are data usage and score determination processes corresponding to the computer device.
Step 219, determining the response rate weight, the influence rate weight and the peak-to-valley ratio weight according to the type of the user set.
In the embodiment of the application, the response rate, the influence rate and the weight corresponding to the three types of dimensions of the peak-to-valley ratio are determined by adopting an expert discussion method, and in one example, the determination of the three types of weights is performed by establishing a matrix form.
And 220, carrying out weighted summation on the response rate score, the influence rate score and the peak-to-valley ratio score according to the response rate weight, the influence rate weight and the peak-to-valley ratio weight to obtain a user set execution effect score corresponding to the user set.
In the embodiment of the application, the full-scale user set corresponds to a full-scale user set execution effect score, and the typical industry user set corresponds to a typical industry user execution effect score:
wherein, the response rate score, the influence rate score, the peak-to-valley ratio score, and the response rate weight, the influence rate weight and the peak-to-valley ratio weight are respectively.
Step 221, summarizing the user set execution effect scores corresponding to each type of user set.
In the embodiment of the application, after determining the execution effect scores of each type of user set, the computer equipment performs statistics of various scores.
Step 222, determining the user set weight corresponding to the execution effect score of each type of user set through the weight vector matrix.
In the example of the present application,the determination mode of the weights of various user sets is also determined by an expert discussion method and a matrix establishment mechanism. Optionally, finally determining that the user set weight corresponding to the full user set is H 1 The user set weight corresponding to the typical industry user set is H 2 The user set weight corresponding to the large user set is H 3 。
Step 223, determining the peak electricity price mechanism implementation effect total evaluation value corresponding to the evaluation time period based on the user set score weight and the per-class user set execution effect score.
In this embodiment of the present application, the calculation mode of the total evaluation value of the implementation effect of the peak electricity price mechanism is as follows in equation 6:
it should be noted that, in the embodiment of the present application, after the evaluation values are determined, at least two evaluation peaks may be compared longitudinally according to different evaluation values stored in the computer device, so as to obtain a real-time effect comparison result.
In summary, in the method provided in the embodiment of the present application, during the process of measuring the implementation effect of the spike motor mechanism, the implementation period of the spike motor mechanism is selected for the reference period, and after the reference period is determined, the response rate data, the impact rate data, and the peak-to-valley ratio data are determined for each type of user set according to the set type of the user, so as to obtain the implementation effect score corresponding to each type of user set, and finally obtain the evaluation effect score for evaluating the spike motor mechanism. The evaluation method corresponds to the time period of actually implementing the peak electricity price mechanism, and performs refined evaluation on users with different volumes from 3 dimensions of peak electricity quantity reduction, peak electricity charge duty ratio and peak valley difference, so that the mechanism evaluation of the peak electricity price is systemized, and the evaluation result is more accurate.
According to the method provided by the embodiment of the application, the peak price policy is divided into 3 dimensions of a whole, a typical industry and a large user, the weight is calculated by adopting an analytic hierarchy process, comprehensive scoring is carried out, and the evaluation result is quantized; according to the evaluation method, 3 indexes of response rate, influence rate and peak-valley ratio are selected, evaluation is carried out from 3 dimensions of peak electric quantity reduction, peak electric charge occupation ratio and peak-valley difference, and evaluation indexes are refined; the evaluation method has adjustable time dimension, can evaluate the season and the day, and is favorable for timely grasping the execution condition of peak electricity price.
The foregoing description of the preferred embodiments of the present invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements within the spirit and principles of the present invention.
Claims (6)
1. A peak electricity price mechanism implementation effect evaluation method based on quantitative fee analysis, which is characterized in that the method is applied to computer equipment and comprises the following steps:
determining an evaluation period and a reference period, the reference period corresponding to the evaluation period and being a period in which the spike electricity price mechanism is not implemented;
determining three types of user sets for executing the peak electricity price mechanism, wherein the user sets comprise a full-quantity user set, a typical industry user set and a large-scale user set;
acquiring evaluation time period electricity consumption data corresponding to the evaluation time period and reference time period electricity consumption data corresponding to the reference time period corresponding to each type of user set;
determining the reference day-hour electric quantity and the reference day-hour electric quantity according to the reference time period electricity consumption data;
determining the electric quantity of the execution day and hour according to the electric data of the evaluation time period;
determining an execution day simulation hour electric quantity based on the reference day hour electric quantity, the execution day hour electric quantity and the reference day electric quantity;
determining a peak period corresponding to the peak electricity price mechanism;
determining response rate data corresponding to the user set according to the execution day simulation hour electric quantity and the execution day hour electric quantity corresponding to the peak period;
determining a peak period, a normal period and a valley period corresponding to the peak electricity price mechanism;
determining a peak electric quantity and a peak electric price corresponding to the peak period, a flat electric quantity and a flat electric price corresponding to the usual period, and a valley electric quantity and a valley electric price corresponding to the valley period;
determining influence rate data corresponding to the user set based on the peak power, peak power price, flat power price, valley power price;
determining peak-to-valley ratio data corresponding to the set of users based on the peak power and the Gu Dianliang;
determining a user set execution effect score corresponding to each type of user set based on the response rate data, the impact rate data, and the peak-to-valley ratio data;
and determining a peak electricity price mechanism implementation effect total evaluation value corresponding to the evaluation time period based on the user set execution effect score of each type, wherein the peak electricity price mechanism implementation effect total evaluation value is used for quantitatively evaluating the evaluation effect of the peak electricity price mechanism.
2. The method of claim 1, wherein the step of determining the position of the substrate comprises,
when the evaluation period is a workday, the reference period is a workday;
when the evaluation period is a non-working day, the reference period is a non-working day;
the reference time period is selected before the evaluation time period.
3. The method of claim 1, wherein the reference time period has a duration range that is greater than a duration range of the evaluation time period, the duration range of the evaluation time period being 1 natural day;
the corresponding user set of each type obtains evaluation time period electricity consumption data corresponding to the evaluation time period and reference time period electricity consumption data corresponding to the reference time period, and the method comprises the following steps:
acquiring daily evaluation time period electricity consumption data corresponding to the evaluation time period as evaluation time period electricity consumption data corresponding to each type of user set;
acquiring daily reference time period electricity consumption data corresponding to the reference time period corresponding to each type of user set;
and carrying out averaging treatment on the daily electricity consumption data of the reference time period to obtain the electricity consumption data of the reference time period.
4. The method of claim 1, wherein the determining a user set execution effectiveness score corresponding to each type of user set based on the response rate data, the impact rate data, and the peak-to-valley ratio data comprises:
determining a response rate data scoring rule, a peak-to-valley ratio data scoring rule and an influence rate data scoring rule;
determining a score gear where the response rate data are located and a corresponding response rate score according to the response rate data allocation rule;
determining a score gear where the peak-to-valley ratio data is located and a corresponding peak-to-valley ratio score based on the peak-to-valley ratio data scoring rule;
determining a score gear where the influence rate data is located and a corresponding influence rate score based on the influence rate data allocation rule;
determining response rate weight, influence rate weight and peak-to-valley ratio weight according to the type of the user set;
according to the response rate weight, the influence rate weight and the peak-to-valley ratio weight, carrying out weighted summation on the response rate score, the influence rate score and the peak-to-valley ratio score to obtain a user set execution effect score corresponding to the user set;
and summarizing the user set execution effect scores corresponding to each type of user set.
5. The method of claim 1, wherein the determining a spiking electricity price mechanism implementation effect total evaluation value corresponding to the evaluation period based on each of the user set implementation effect scores comprises:
determining the user set weight corresponding to the execution effect score of each type of user set through a weight vector matrix;
and determining a peak electricity price mechanism implementation effect total evaluation value corresponding to the evaluation time period based on the user set score weight and each type of user set implementation effect score.
6. The method of claim 1, wherein after determining a total evaluation score of the electricity spike rating mechanism implementation effect corresponding to the evaluation period based on each of the user set execution effect scores, comprising:
determining total evaluation values of the implementation effects of the peak electricity price mechanisms corresponding to at least two evaluation time periods;
and comparing the total evaluation values of the implementation effects of at least two peak electricity price mechanisms to obtain a real-time effect comparison result.
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