Power utilization dispatching value evaluation method and related device

Abstract

The invention discloses a power utilization dispatching value evaluation method and a related device, and the method comprises the steps: carrying out the first classification processing of demand response related data with a user subjective response side, a user objective response side and a power grid dispatching value side as evaluation subjects, and obtaining M first-level evaluation indexes; performing second classification processing on the M first-level evaluation indexes to obtain P second-level evaluation indexes; determining an index matrix according to the Q primary evaluation indexes and the P secondary evaluation indexes; determining the weight of each evaluation index in the index matrix according to a plurality of weight evaluation methods to obtain a plurality of weight matrixes; fusing the plurality of weight matrixes to obtain a comprehensive weight matrix; and determining a power utilization dispatching value evaluation result according to the comprehensive weight matrix and the index matrix. According to the invention, the suitability degree and contribution degree of the user participating in the demand response measure can be accurately measured, the power utilization dispatching value evaluation of the user is realized, and the operation efficiency of the power grid is improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method for evaluating the value of electricity dispatching and related devices. Background Art

[0002] With the steady progress of the construction of new power systems, the reference value of user power dispatching value evaluation for power grid dispatching decision-making has become increasingly prominent. This is not only related to the efficient allocation of power resources, but also has a significant impact on the stability and reliability of power grid operation.

[0003] However, the current evaluation system is not comprehensive enough, which makes it difficult to obtain accurate and quantifiable basis for the evaluation results and to build a rigorous data support system. This can easily lead to the inability to fully meet the needs of intelligent power dispatching when providing power dispatching recommendations for the power grid, greatly restricting the scientificity and efficiency of power grid dispatching decisions. Summary of the invention

[0004] The embodiments of the present application provide a method and related devices for evaluating the value of power dispatching, so as to accurately measure the suitability and contribution of users in participating in demand response measures, realize the evaluation of the value of power dispatching of users, and thus improve the operating efficiency of the power grid.

[0005] In a first aspect, an embodiment of the present application provides a method for evaluating the value of power dispatching, including: Performing a first classification process on demand response related data with user subjective response side, user objective response side and power grid dispatch value side as evaluation subjects to obtain M first-level evaluation indicators, wherein there are overlapping first-level evaluation indicators among the M first-level evaluation indicators, and the overlapping first-level evaluation indicators correspond to different evaluation subjects; Performing a second classification process on the M first-level evaluation indicators to obtain P second-level evaluation indicators, where the P second-level evaluation indicators correspond to N first-level evaluation indicators, and N is less than M; Determine an indicator matrix according to the Q first-level evaluation indicators and the P second-level evaluation indicators, wherein the Q first-level evaluation indicators are other first-level evaluation indicators among the M first-level evaluation indicators except the N first-level evaluation indicators; Determine the weight of each evaluation indicator in the indicator matrix according to a plurality of weight evaluation methods to obtain a plurality of weight matrices; Fusing the multiple weight matrices to obtain a comprehensive weight matrix; The electricity dispatch value evaluation result is determined according to the comprehensive weight matrix and the indicator matrix.

[0006] In a possible embodiment, determining the indicator matrix according to the Q first-level evaluation indicators and the P second-level evaluation indicators includes: Obtain a first weight corresponding to the primary evaluation index and a second weight corresponding to the secondary evaluation index; Determining the indicator types of the Q first-level evaluation indicators and the P second-level evaluation indicators; The Q first-level evaluation indicators and the P second-level evaluation indicators are preprocessed according to the indicator type, the first weight, and the second weight to obtain the indicator matrix.

[0007] In a possible embodiment, the preprocessing of the Q first-level evaluation indicators and the P second-level evaluation indicators according to the indicator type, the first weight, and the second weight to obtain the indicator matrix includes: Preprocessing the Q first-level evaluation indicators and the P second-level evaluation indicators according to the indicator type to obtain a first indicator value corresponding to each evaluation indicator; determining an adjustment coefficient according to the first weight and the second weight; Adjust the first indicator value corresponding to each evaluation indicator according to the adjustment coefficient to obtain the second indicator value corresponding to each evaluation indicator; The indicator matrix is ​​constructed according to the second indicator value corresponding to each evaluation indicator.

[0008] In a possible embodiment, the indicator types include positive indicators and negative indicators, and the preprocessing method of the negative indicator is: , The preprocessing method of the positive indicator is: , in, is the first indicator value of a single evaluation indicator; is the original indicator value of the single evaluation indicator; is the maximum value of the single evaluation index; is the minimum value of the single evaluation indicator.

[0009] In a possible embodiment, the multiple weight matrices include a first weight matrix, a second weight matrix, and a third weight matrix, and the multiple weight matrices are fused to obtain a comprehensive weight matrix, including: Determine a first correlation represented by the first weight matrix on the whole of the second weight matrix and the third weight matrix; and, determine a second correlation represented by the second weight matrix on the whole of the first weight matrix and the third weight matrix; and, determine a third correlation represented by the third weight matrix on the whole of the first weight matrix and the second weight matrix; Determining a third weight of the first weight matrix, a fourth weight of the second weight matrix, and a fifth weight of the third weight matrix according to the first correlation, the second correlation, and the third correlation; The comprehensive weight matrix is ​​obtained according to the third weight, the fourth weight, the fifth weight, the first weight matrix, the second weight matrix and the third weight matrix.

[0010] In a possible embodiment, the M first-level evaluation indicators include a response potential indicator, a response reliability indicator, a response rapidity indicator, a response accuracy indicator, and a response contribution indicator with the user's objective response side as the evaluation subject; wherein the response potential indicator includes the user's predicted power consumption, peak load, and valley load, the response reliability indicator includes the number of terminal control devices of the user, the number of effective transmitted information, and the total amount of transmitted information, the response rapidity indicator includes the response time interval, the response start time, and the preset start response time, the response accuracy indicator includes the effective response power, the demand response instruction power, the effective response power, the demand response instruction power, and the response duration, and the response contribution indicator includes the actual load amount and the first predicted load amount during the peak power consumption period, and the actual load amount and the second predicted load amount during the demand response period; The M first-level evaluation indicators also include a first economic indicator, an environmental indicator, an economically sensitive indicator, a user participation willingness indicator, and a power supply demand indicator with the user's subjective response side as the evaluation subject; wherein the first economic indicator includes subsidy income, load adjustment amount during the demand response period, the second predicted load amount, the user's production plan, response time, the user's electricity utility, a first cost related to electricity consumption, and a second cost related to electricity consumption time; the environmental indicator includes the reduced emission of multiple pollutants; the economically sensitive indicator includes incentive price, user response electricity, time-of-use electricity price, user response load and electricity fee proportion expenditure; the user participation willingness indicator includes the number of responses, actual response capacity and invitation response capacity; the power supply demand indicator includes the user's power supply demand data; The M first-level evaluation indicators also include a second economic indicator with the grid dispatching value side as the evaluation subject and the environmental indicator; wherein the second economic indicator includes incentive costs, reduced start-up and shutdown costs of power generation equipment, reduced fuel costs and reduced electricity sales revenue.

[0011] In a possible embodiment, the P secondary evaluation indicators include a response speed indicator and a response timeliness indicator under the response rapidity indicator, a power indicator, an electricity indicator and a duration indicator under the response accuracy indicator, and a peak shaving indicator and a smoothing indicator under the response contribution indicator; wherein the response speed indicator is determined according to the response time interval, the response timeliness indicator is determined according to the response start time and the preset start response time, the power indicator is determined according to the effective response power and the demand response instruction power, the electricity indicator is determined according to the effective response electricity and the demand response instruction electricity, the duration indicator is determined according to the response duration, the peak shaving indicator is determined according to the actual load amount during the peak power consumption period and the first predicted load amount, and the smoothing indicator is determined according to the actual load amount during the demand response period and the second predicted load amount; The P secondary evaluation indicators also include a subsidy indicator, a first loss indicator, a second loss indicator, and an electricity utility indicator under the first economic indicator, multiple pollutant emission reduction indicators under the environmental indicator, and an incentive price sensitive indicator, a time-of-use electricity price sensitive indicator, and an electricity fee indicator under the economic sensitive indicator; wherein the subsidy indicator is determined according to the subsidy income, the first loss indicator is determined according to the load adjustment amount during the demand response period and the second predicted load amount, the second loss indicator is determined according to the user's production plan and the response time, the electricity utility indicator is determined according to the user's electricity utility, the first cost related to the electricity consumption, and the second cost related to the electricity consumption time, the incentive price sensitive indicator is determined according to the incentive price and the user's response electricity, the time-of-use electricity price sensitive indicator is determined according to the time-of-use electricity price and the user's response load, and the electricity fee indicator is determined according to the proportion of electricity fee expenditure; The P secondary evaluation indicators also include an incentive cost indicator, an equipment start-up and shutdown cost indicator, a fuel cost indicator, and a power sales revenue indicator under the second economic indicator.

[0012] In a second aspect, an embodiment of the present application provides a power dispatch value evaluation device, including: A first processing unit is used to perform a first classification process on the demand response related data with the user subjective response side, the user objective response side and the power grid dispatch value side as evaluation subjects to obtain M first-level evaluation indicators, wherein there are overlapping first-level evaluation indicators among the M first-level evaluation indicators, and the overlapping first-level evaluation indicators correspond to different evaluation subjects; A second processing unit is used to perform a second classification process on the M first-level evaluation indicators to obtain P second-level evaluation indicators, where the P second-level evaluation indicators correspond to the N first-level evaluation indicators, and N is less than M; A first determining unit is used to determine an indicator matrix according to the Q first-level evaluation indicators and the P second-level evaluation indicators, wherein the Q first-level evaluation indicators are other first-level evaluation indicators among the M first-level evaluation indicators except the N first-level evaluation indicators; A second determining unit is used to determine the weight of each evaluation indicator in the indicator matrix according to multiple weight evaluation methods to obtain multiple weight matrices; A fusion unit, used for fusing the multiple weight matrices to obtain a comprehensive weight matrix; The third determination unit is used to determine the electricity dispatch value evaluation result according to the comprehensive weight matrix and the indicator matrix.

[0013] In a third aspect, an embodiment of the present application provides an electronic device, comprising a memory, a processor, and an executable program code stored in the memory and executable on the processor, wherein the processor executes the steps of the method described in the first aspect when executing the executable program code.

[0014] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having executable program code stored thereon, wherein the executable program code includes execution instructions, and the execution instructions are used to execute the steps of the method described in the first aspect.

[0015] In a fifth aspect, an embodiment of the present application provides a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute some or all of the steps described in the first aspect of the embodiment of the present application. The computer program product may be a software installation package.

[0016] It can be seen that in the embodiment of the present application, first, the demand response related data with the user subjective response side, the user objective response side and the power grid dispatching value side as the evaluation subjects are subjected to a first classification process to obtain M first-level evaluation indicators, among which there are overlapping first-level evaluation indicators, and the overlapping first-level evaluation indicators correspond to different evaluation subjects; then, the M first-level evaluation indicators are subjected to a second classification process to obtain P second-level evaluation indicators, and the P second-level evaluation indicators correspond to N first-level evaluation indicators, where N is less than M; then, an indicator matrix is ​​determined based on the Q first-level evaluation indicators and the P second-level evaluation indicators, and the Q first-level evaluation indicators are other first-level evaluation indicators among the M first-level evaluation indicators except the N first-level evaluation indicators; then, the weight of each evaluation indicator in the indicator matrix is ​​determined according to a plurality of weight evaluation methods to obtain a plurality of weight matrices; then, the plurality of weight matrices are fused to obtain a comprehensive weight matrix; finally, the power dispatching value evaluation result is determined based on the comprehensive weight matrix and the indicator matrix.

[0017] It can be seen that this application establishes a user's electricity dispatch value evaluation index system from three dimensions: user-side objective response capability, subjective response willingness, and grid-side dispatch value, fully tapping the user's energy flexibility value, and using a variety of weight evaluation methods to comprehensively determine the weight of each indicator, making the weight value of each indicator more reasonable, and then accurately measuring the user's suitability and contribution to demand response measures, and realizing the user's electricity dispatch value evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 This is a system architecture diagram of an evaluation system provided by an embodiment of the present application; Figure 2 It is a flow chart of a method for evaluating the value of power dispatching provided in an embodiment of the present application; Figure 3 is a schematic diagram of an evaluation index system provided in an embodiment of the present application; Figure 4 is a schematic diagram of another evaluation index system provided in an embodiment of the present application; Figure 5 It is a flowchart of another method for evaluating the value of power dispatching provided in an embodiment of the present application; Figure 6 This is a functional unit composition block diagram of a power dispatch value evaluation device provided in an embodiment of the present application; Figure 7 This is a block diagram of the functional units of another power dispatch value evaluation device provided in an embodiment of the present application; Figure 8 It is a structural schematic diagram of an electronic device proposed in an embodiment of the present application. DETAILED DESCRIPTION

[0020] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0021] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0022] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0023] The current evaluation system is not comprehensive enough, which makes it difficult to obtain accurate and quantifiable basis for the evaluation results when evaluating the user's dispatch value, and it is impossible to build a rigorous data support system. This can easily lead to the inability to fully meet the needs of intelligent power dispatch when providing power dispatch recommendations for the power grid, which greatly restricts the scientificity and efficiency of power grid dispatch decisions.

[0024] In response to the above problems, an embodiment of the present application provides a method for evaluating the value of electricity scheduling and a related device. The embodiment of the present application is described in detail below in conjunction with the accompanying drawings.

[0025] See also Figure 1 , Figure 1 1 is a system architecture diagram of an evaluation system provided by an embodiment of the present application. Figure 1 As shown, the evaluation system 100 includes a data collection module 101, an indicator construction module 102, a weight determination module 103 and an evaluation module 104, wherein any two modules among the data collection module 101, the indicator construction module 102, the weight determination module 103 and the evaluation module 104 are interconnected for communication.

[0026] Among them, the data collection module 101 is used to collect relevant data of various evaluation subjects and response needs, such as participation willingness data and electricity consumption behavior data on the user's subjective response side, such as electricity consumption data and response execution status data on the user's objective response side, and electricity market transaction data on the power grid dispatch value side.

[0027] Among them, the indicator construction module 102 is used to receive various data collected by the data collection module 101, and perform multiple classification operations on the above data to construct a multi-level indicator. The weight determination module 103 is used to receive the multi-level indicators constructed by the indicator construction module 102, and determine the comprehensive weight of each indicator using multiple weight determination methods. The evaluation module 104 is used to receive the comprehensive weight of each indicator determined by the weight determination module 103 and the multi-level indicators constructed by the indicator construction module 102, and evaluate the user's power scheduling value according to the comprehensive weight of each indicator and the multi-level indicators.

[0028] Based on this, the present application provides a method for evaluating the value of electricity scheduling and related devices, and the present application is described in detail below in conjunction with the accompanying drawings.

[0029] See also Figure 2 , Figure 2 is a flow chart of a method for evaluating the value of power dispatching provided in an embodiment of the present application, such as Figure 2 As shown, the method comprises the following steps: S210, performing a first classification process on the demand response related data with the user subjective response side, the user objective response side and the power grid dispatch value side as evaluation subjects to obtain M first-level evaluation indicators.

[0030] Among the M first-level evaluation indicators, there are overlapping first-level evaluation indicators, and the overlapping first-level evaluation indicators correspond to different evaluation subjects.

[0031] Among them, when evaluating the value of power dispatch for users, it is necessary not only to carefully distinguish and measure the subjective and objective factors on the user side, but also to consider the impact of user participation in demand response on grid-side dispatch. The grid side needs to balance multiple goals when dispatching, including but not limited to ensuring the stability and reliability of power supply, optimizing the load distribution of the power grid, and achieving effective consumption of clean energy. Therefore, when evaluating the value of power dispatch, considering the impact of user response demand on grid-side dispatch is conducive to measuring the suitability and contribution of users' participation in demand response measures, so as to accurately evaluate the actual contribution of users in helping the stable operation of the power grid, improving power quality, and promoting energy structure optimization.

[0032] Among them, the data related to the evaluation of the value of power dispatching, that is, the demand response related data, are obtained, including the user's power consumption, power load curve, peak-to-valley difference and other data, as well as the power grid operation data, user satisfaction data on power supply and market transaction data. Then, the acquired data is sorted, the consistency and accuracy of the data are checked, and missing values ​​and outliers are processed. According to the characteristics of the data and the actual situation, appropriate methods can be selected to fill in the missing values, such as mean filling, median filling or regression filling based on other related variables; for outliers, the cause of their generation should be analyzed to determine whether it is a real abnormal situation or a data entry error, and decide whether to retain, correct or delete.

[0033] Among them, after the data is sorted out, the electricity dispatch value evaluation data will be divided to determine the demand response related data corresponding to each evaluation subject.

[0034] In one possible embodiment, the same data exists in the demand response related data corresponding to each evaluation subject. For example, both the user subjective response side and the user objective response side include the predicted load during the demand response period; exemplarily, both the user objective response side and the power grid dispatch value side include the reduced emissions of multiple pollutants.

[0035] After obtaining the data related to the demand response of each evaluation subject, a first classification process is performed. The first classification process can first assign at least one indicator to each data, and then summarize the data corresponding to the same indicator. If multiple indicators are assigned to a single data, the matching degree between the single data and each of the multiple indicators corresponding to the single data is calculated, and the correlation degree between the single data and other data under the same indicator is calculated, and secondary classification is performed according to the matching degree and / or correlation degree to obtain multiple primary evaluation indicators.

[0036] In a possible embodiment, secondary classification may be performed based on the matching degree. For example, if the matching degree between a single data and the assigned index i is greater than or equal to a preset matching degree, the single data belongs to the assigned index i.

[0037] In a possible embodiment, secondary classification may be performed based on the degree of association. For example, if the degree of association between a single data and any data under the same indicator is greater than or equal to a preset degree of association, the single data belongs to the assigned indicator i.

[0038] In a possible embodiment, secondary classification can be performed based on the degree of association and the degree of matching, with the priority of the degree of association being higher than the priority of the degree of matching. The degree of association between the data is first determined, and then the degree of matching between the data and the index is determined, and then the degrees of matching are sorted, and secondary classification is performed based on the degree of association and the degree of matching, and / or the degree of matching sorting. Exemplarily, if the degree of association between a single data and other data under the assigned index i is less than a preset degree of association, the first degree of matching between the single data and the assigned index i is determined, if the first degree of matching is greater than or equal to the preset degree of matching, the single data belongs to the assigned index i, if the first degree of matching is less than the preset degree of matching, the ranking of the first degree of matching among multiple degrees of matching is determined; if the first degree of matching is the largest, the single data belongs to the assigned index i, if the first degree of matching is less than any other degree of matching, the single data does not belong to the assigned index i.

[0039] In a possible embodiment, the degree of association may be a mathematical logic association, such as a numerical calculation association and a statistical correlation association. The degree of association may also be a business logic association, such as a causal association.

[0040] In a possible embodiment, the M first-level evaluation indicators include a response potential indicator, a response reliability indicator, a response rapidity indicator, a response accuracy indicator and a response contribution indicator with the user's objective response side as the evaluation subject; wherein the response potential indicator includes the user's predicted power consumption, peak load and valley load, the response reliability indicator includes the number of terminal control devices of the user, the number of effective transmitted information and the total amount of transmitted information, the response rapidity indicator includes the response time interval, the response start time and the preset start response time, the response accuracy indicator includes the effective response power, the demand response instruction power, the effective response power, the demand response instruction power and the response duration, the response contribution indicator includes the actual load amount and the first predicted load amount during the peak power consumption period, and the actual load amount and the second predicted load amount during the demand response period; the M first-level evaluation indicators also include the first economic indicator, Environmental indicators, economically sensitive indicators, user participation willingness indicators, and power supply demand indicators; wherein the first economic indicators include subsidy income, load adjustment during the demand response period, the second predicted load, the user's production plan, response time, the user's electricity utility, the first cost related to electricity consumption, and the second cost related to electricity consumption time; the environmental indicators include the reduced emissions of multiple pollutants; the economically sensitive indicators include incentive prices, user response electricity, time-of-use electricity prices, user response loads, and electricity fee proportion expenditures; the user participation willingness indicators include the number of responses, actual response capacity, and invited response capacity; the power supply demand indicators include the user's power supply demand data; the M first-level evaluation indicators also include the second economic indicators with the grid dispatch value side as the evaluation subject and the environmental indicators; wherein the second economic indicators include incentive costs, reduced start-up and shutdown costs of power generation equipment, reduced fuel costs, and reduced electricity sales revenue.

[0041] Among them, the first cost related to electricity consumption is the cost closely related to electricity consumption, such as the user's electricity purchase cost under the system time-of-use electricity price and the real-time electricity price; the second cost related to electricity consumption time is the electricity cost that is closely related to the electricity consumption period but not highly correlated with electricity consumption, such as the user's peak shifting cost.

[0042] The number of terminal control devices of the user refers to the number of terminal control devices available within a specified period of time. The preset start response time is the response start time specified by the response event.

[0043] Among them, the first-level evaluation indicators can be further divided into second-level evaluation indicators, or they can no longer be divided into second-level evaluation indicators. The specific details can be determined based on the data dimensions and data logical associations included in the first-level evaluation indicators.

[0044] For example, see Figure 3 , Figure 3is a schematic diagram of an evaluation index system provided in an embodiment of the present application, such as Figure 3 As shown, the evaluation index system includes multiple first-level rating indicators on the user's subjective response side, including the first economic indicator, environmental indicator, economic sensitivity indicator, user participation willingness indicator and power supply demand indicator.

[0045] Among them, the first economic indicator is used to describe the economic benefits and cost-effectiveness of users in the process of demand response. The environmental indicator is used to describe the positive impact of users' participation in demand response on the environment, that is, the degree to which the relevant pollutant emissions are indirectly reduced by adjusting electricity consumption behavior. The economic sensitivity indicator is used to describe the changes in relevant economic factors and their impact on users' economic behavior and decision-making during the process of users' participation in demand response. The user participation willingness indicator is used to describe the user's willingness to participate in demand response, which depends on the user's recognition of the incentive mechanism, the impact of the response strategy on the user's comfort, and the user type. The power supply demand indicator is used to describe the degree to which users can accept power supply problems. When the power supply reliability or quality requirements are high, it is difficult for users to interrupt, reduce or transfer the load, and the potential for participating in the response is low; on the contrary, when the power supply reliability or quality requirements are relatively low, after receiving the response signal, the user can stop the load as he wants, that is, the response potential is relatively high.

[0046] like Figure 3 As shown, the evaluation index system also includes multiple first-level rating indicators on the user's objective response side, including response potential index, response reliability index, response speed index, response accuracy index and response contribution index.

[0047] Among them, the response potential index is used to describe the potential load adjustment capability that users can provide during the demand response process. The response reliability index is used to describe the degree of control of users / equipment, which depends on the presence or absence of terminal control equipment, as well as the reliability of communication and whether the production process can be changed. The response accuracy index is used to describe the degree of proximity or compliance between the output results of users and the expected goals during the demand response process. The response contribution index is used to evaluate the role of users in the load balance and stability of the power grid in a specific time period.

[0048] like Figure 3 As shown, the evaluation index system also includes multiple first-level rating indicators on the grid dispatch value side, including the second economic indicator and the environmental indicator. Among them, the environmental indicator is the same as the environmental indicator on the user's subjective response side.

[0049] Among them, the second economic indicator is used to describe the economic value, cost-effectiveness and economic impact on the power market and related industries generated by grid dispatching in meeting the operation needs of the power system.

[0050] S220, performing a second classification process on the M first-level evaluation indicators to obtain P second-level evaluation indicators.

[0051] Among them, the P secondary evaluation indicators correspond to N primary evaluation indicators, and N is less than M.

[0052] Among them, each first-level evaluation indicator is classified again according to the dimensions of each data in each first-level evaluation indicator and the logical relationship between each data. Specifically, for the first-level evaluation indicators with multiple data dimensions, the data in each data dimension is expanded, that is, new data is calculated based on the logical relationship between each data in a single data dimension. After that, the data in the expanded multiple data dimensions are arbitrarily combined, and whether a new data dimension can be formed is determined based on the nature of the data and the impact of the data on the demand. If so, it is added to the first-level evaluation indicator, and finally multiple data dimensions under the first-level evaluation indicator are obtained, and a second-level evaluation indicator is assigned to each data dimension.

[0053] Exemplarily, the effective response power and demand response instruction power under the response accuracy index are the first data dimension, the effective response electricity and demand response instruction electricity are the second data dimension, and the response duration is the third data dimension; the response degree of power is determined according to the effective response power and the demand response instruction power, the response degree of electricity is determined according to the effective response electricity and the demand response instruction electricity, and the response degree of time is determined according to the response duration. Therefore, the first dimension data includes the effective response power, the demand response instruction power and the response degree of power, the second dimension data includes the effective response electricity, the demand response instruction electricity and the response degree of electricity, and the third dimension data includes the response duration and the response degree of time. Afterwards, any combination of the above data, such as the effective response electricity, the demand response instruction power and the response duration, determines that a new data dimension cannot be formed, such as the effective response electricity and the effective response power, determines that a new data dimension cannot be formed. Therefore, it is determined that the response accuracy index includes three data dimensions, and three secondary evaluation indicators are allocated, including power index, electricity index and duration index.

[0054] In a possible embodiment, the user objective response side is the response potential index and response reliability index of the evaluation subject, and the user subjective response side is the user participation willingness index and power supply demand index of the evaluation subject, and the secondary evaluation indicators are no longer split.

[0055] Specifically, the response potential index in different scenarios corresponds to different connotations, including objective peak shaving response potential and objective valley filling response potential. Among them, the peak load and valley load can be determined according to the typical load curve of the user. The peak load and valley load reflect the difference in the scale of electricity consumption of users in different periods. In order to ensure the basic electricity consumption of users, the average of the peak and valley values ​​is used as the basic electricity consumption value.

[0056] In a possible embodiment, the user's predicted electricity consumption can be determined based on historical data analysis. For example, historical electricity consumption data is obtained, and the collected data is cleaned to remove abnormal values, missing values ​​and other erroneous data. Then, the electricity consumption pattern is analyzed based on the above data to determine the user's peak and low electricity consumption periods in different seasons, the difference in electricity consumption on different date types, and the time distribution characteristics of daily electricity consumption. Finally, based on the analyzed electricity consumption pattern, a suitable prediction model is selected for modeling, and then the future electricity consumption is predicted.

[0057] In a possible embodiment, the predicted power consumption of the user can also be determined according to the load characteristic analysis method. For example, a power consumption survey of the equipment is first conducted, and a detailed survey is conducted on various types of power-consuming equipment used by the user, including information such as the power, usage time, and usage frequency of the equipment. And understand the impact of the user's production process, living habits, etc. on power consumption. Then classify and calculate the load, and classify the power-consuming equipment into different categories according to the power consumption characteristics of the equipment, such as lighting equipment, air-conditioning equipment, production equipment, etc. The load of each type of equipment is calculated separately, and the total load forecast value of the user is obtained by superposition according to the usage and time distribution of the equipment. At the same time, load change factors such as weather changes, economic development, and policy adjustments are considered. For example, changes in temperature will affect the power consumption of air-conditioning and heating equipment, economic growth may cause industrial users to expand production scale and thus increase power demand, and adjustments to power policies may affect users' power consumption behavior. By analyzing the relationship between these factors and power load, the prediction results are corrected and adjusted.

[0058] In a possible embodiment, the objective peak-shaving response potential or the objective valley-filling response potential of the user is calculated by the difference between the basic power consumption value and the predicted power consumption of the user.

[0059] Specifically, the basic electricity value , objective peak shaving response potential and objective valley-filling response potential Satisfies formula (1): , Among them, i refers to user i, is the peak load, is the valley load.

[0060] The response reliability evaluation can be represented by the terminal information transmission reliability and the number of terminal reliable devices. Specifically, different weights can be set for the terminal information transmission reliability and the number of terminal reliable devices, and the response reliability index can be obtained by weighted summation.

[0061] The terminal information transmission reliability is the ratio of the number of effective information transmitted by the user in a specified period to the total amount of information transmitted by the user in the specified period; the number of reliable terminal devices is determined by the terminal information transmission reliability and the number of terminal control devices of the user. Specifically, if the number of terminal control devices of the user is greater than 1, it means that the user has available terminal control devices in the specified period; if the number of terminal control devices of the user is not greater than 1, it means that the user does not have available terminal control devices in the specified period.

[0062] Specifically, the reliability of terminal information transmission and the number of reliable terminal devices Satisfies formula (2): , in, The number of effective information delivered by the user within a specified period of time. is the total amount of information transmitted by the user in a specified period of time. is a variable from 0 to 1, 1 indicates that user i has an available terminal control device during the specified period, and 0 indicates that user i does not have an available terminal control device during the specified period.

[0063] The user participation willingness index can be evaluated by the number of times the user participates in demand response and the fulfillment of the demand response contract. Specifically, different weights can be set for the number of times the user participates in demand response and the fulfillment of the demand response contract, and the user participation willingness index can be obtained by weighted summation. The fulfillment of the demand response contract is the ratio of the actual response capacity to the invited response capacity.

[0064] Among them, the power supply reliability and power supply quality requirements are determined through the user's power supply demand data, and then the power supply demand index is determined.

[0065] In a possible embodiment, the second-level evaluation indicators may be further classified to determine third-level evaluation indicators.

[0066] In a possible embodiment, the P secondary evaluation indicators include a response speed indicator and a response timeliness indicator under the response rapidity indicator, a power indicator, a quantity indicator and a duration indicator under the response accuracy indicator, and a peak shaving indicator and a smoothing indicator under the response contribution indicator; wherein the response speed indicator is determined according to the response time interval, the response timeliness indicator is determined according to the response start time and the preset start response time, the power indicator is determined according to the effective response power and the demand response instruction power, the quantity indicator is determined according to the effective response quantity and the demand response instruction quantity, the duration indicator is determined according to the response duration, the peak shaving indicator is determined according to the actual load during the peak power consumption period and the first predicted load, and the smoothing indicator is determined according to the actual load during the demand response period and the second predicted load; the P secondary evaluation indicators also include a subsidy indicator, a first economic indicator and a second economic indicator. loss index, a second loss index, an electricity utility index, multiple pollutant emission reduction indicators under the environmental index, an incentive price sensitive index, a time-of-use electricity price sensitive index and an electricity fee index under the economic sensitive index; wherein the subsidy index is determined according to the subsidy income, the first loss index is determined according to the load adjustment amount during the demand response period and the second predicted load amount, the second loss index is determined according to the user's production plan and the response time, the electricity utility index is determined according to the user's electricity utility, the first cost related to the electricity consumption, and the second cost related to the electricity consumption time, the incentive price sensitive index is determined according to the incentive price and the user's response electricity, the time-of-use electricity price sensitive index is determined according to the time-of-use electricity price and the user's response load, and the electricity fee index is determined according to the proportion of electricity fee expenditure; the P secondary evaluation indicators also include the incentive cost index, equipment start-stop cost index, fuel cost index and electricity sales revenue index under the second economic indicator.

[0067] In one possible embodiment, see Figure 4 , Figure 4 is a schematic diagram of another evaluation index system provided in the embodiment of the present application, such as Figure 4 As shown, the first economic indicator includes multiple secondary evaluation indicators, including a subsidy indicator, a first loss indicator, a second loss indicator and an electricity utility indicator.

[0068] Among them, subsidy indicators is the economic subsidy obtained by users after participating in demand-side response, satisfying formula (3): , Where N is the number of times user i participates in demand response during the demand response time period, is the jth response power of user i, is the duration of user i's jth participation in demand response, The subsidy unit price per power for user i’s jth participation in demand response.

[0069] Among them, the first loss index is used to describe the user's response comfort loss. It is an important indicator that affects whether the user participates in the response and the degree of response. The power consumption comfort is the highest when the user does not participate in the response, which is 100%; when the user begins to participate in the response and adjusts the power consumption method, the comfort will decrease with the change of the power consumption method. After participating in the power demand response, the power load is changed. Define the degree of change in power load is the first loss indicator, satisfying formula (4): , in, for The load change amount of user i participating in the power demand response during the time period, that is, the load adjustment amount during the demand response period; The range is 0-1, The larger the value, the higher the electricity comfort level, and vice versa.

[0070] Among them, the second loss indicator is used to describe the user's own utility loss caused by load participation in demand response, such as reduced production efficiency due to load reduction and the amount of orders that are not completed during the response period. It is determined according to the user's production plan and response time.

[0071] Among them, the electricity utility index is used to maximize the electricity utility. , satisfying formula (5): ,in, The user's electricity utility, the typical marginal electricity utility of the user is usually negatively correlated with the electricity consumption; It is a cost closely related to electricity consumption, that is, the first cost related to electricity consumption; It is the electricity cost that is closely related to the electricity consumption period but not highly related to the electricity consumption, that is, the second cost related to the electricity consumption time.

[0072] like Figure 4 As shown, the environmental indicators include multiple secondary evaluation indicators, including CO2 emission reduction indicators, SO2 emission reduction indicators, NO emission reduction indicators and NO2 emission reduction indicators.

[0073] Among them, demand response can greatly reduce the power generation of power plants on the power supply side, thereby reducing the emission of various pollutants caused by power generation. For example, thermal power generation requires a large amount of fossil energy, emitting carbon dioxide CO2, sulfur dioxide SO2, nitric oxide NO, nitrogen dioxide NO2 and other pollutants, posing a threat to the environment. Therefore, based on the reduced power generation, the emission reduction of various pollutants is calculated to construct multiple pollutant emission reduction indicators. Specifically, the multiple pollutant emission reduction indicators include CO2 emission reduction indicators, SO2 emission reduction indicators, NO emission reduction indicators, and NO2 emission reduction indicators. They can also be emission reductions of other pollutants, which are not limited here.

[0074] Among them, CO2 emission reduction Satisfies formula (6): ,in, To reduce the amount of electricity generated, The amount of CO2 emitted per unit of electricity generated.

[0075] Among them, SO2 emission reduction Satisfies formula (7): ,in, The amount of SO2 emitted per unit of electricity generated.

[0076] Among them, NOx emission reduction Satisfies formula (8): ,in, It is the amount of NOx emitted per unit of electricity generated. NOx emission reduction includes NO emission reduction and NO2 emission reduction.

[0077] like Figure 4 As shown, the economic sensitive indicators include multiple secondary evaluation indicators, including incentive price sensitive indicators, time-of-use electricity price sensitive indicators and electricity fee indicators.

[0078] Among them, a linear regression model is used to fit the relationship between the incentive price and the user's response electricity volume, as well as the relationship between the time-of-use electricity price and the user's response load. The least squares method is used to estimate the regression coefficient of the linear regression model. The final regression coefficient is the incentive price sensitivity index and the time-of-use electricity price sensitivity index.

[0079] Among them, the electricity fee index is determined by the proportion of the user's electricity fee expenditure to production. When the proportion of the user's electricity fee expenditure is low, the economic benefits of participating in demand response at the expense of production will be lower, and the user will not want to participate in the response. On the contrary, when the proportion of electricity fees is high, the reduction or increase of electricity prices will affect the production and operation profits, and the user's sensitivity to electricity prices will increase. Through the stimulation of electricity prices, users will be more likely to participate in the response.

[0080] like Figure 4As shown, the rapid response index includes multiple secondary evaluation indicators, including a response speed index and a response timeliness index.

[0081] Among them, the response speed index is determined by the length of the time interval from the user receiving the demand response event notification to the start of the response event, that is, the response time interval. By analyzing all the user's past response event record information, the user's average response speed is obtained, and the average response speed is used as the user's response speed to the newly received demand event. The faster the response speed, that is, the shorter the time interval from the user receiving the new event notification to the start of the event execution, the higher the user's response enthusiasm and the stronger the response willingness; conversely, the lower the user's response enthusiasm and the weaker the response willingness.

[0082] The timely response index is the difference between the user response start time and the event specified start response time, where the event specified start response time is the preset start response event. The more timely the user responds, the more attention the user pays to the demand event and the stronger the user's willingness to respond. Furthermore, the timely response index can be the difference of the most recent response event or the average difference of all past response events.

[0083] like Figure 4 As shown, the response accuracy index includes multiple secondary evaluation indicators, including power indicator, power indicator and duration indicator.

[0084] Among them, the power index Satisfies formula (9): , in, is the effective response power, Command power for demand response.

[0085] Among them, the power indicator Satisfies formula (10): , in, To effectively respond to power, The amount of electricity required for demand response.

[0086] Among them, the duration indicator is used to describe the actual duration of the user's historical response event. The closer the actual total duration of the user's past response to the demand event is to the time required by the demand event, the higher its accuracy.

[0087] like Figure 4 As shown, the response contribution index includes multiple secondary evaluation indicators, including a peak clipping index and a smoothing index.

[0088] Among them, the peak shaving index is used to describe the load participation response's ability to alleviate the power shortage problem during the peak power consumption of the local power system, reflecting the load participation response's peak shaving ability. Satisfies formula (11): , in, During the peak period of electricity consumption, represents the first predicted load at time t, Represents the actual load at time t after demand response.

[0089] Among them, the smoothing index is used to describe the ability of the load to improve the local load distribution and smooth the load curve by means of peak shaving and valley filling. Satisfies formula (12): , Where T is the demand response period.

[0090] like Figure 4 As shown, the second economic indicator includes multiple secondary evaluation indicators, including incentive cost indicator, equipment start-up and shutdown cost indicator, fuel cost indicator and electricity sales revenue indicator.

[0091] Among them, the incentive cost index is used to describe the compensation given by the power company to users for participating in the response. If the compensation mechanism has been established, each participation method provides a specific compensation unit price. Taking the three methods of time shifting, peak shifting and peak shaving as examples, the incentive cost index Satisfies formula (13): , in, is the total number of users; Incentives needed to dispatch load peaks; Incentives needed to dispatch loads to shift peaks and fill valleys; Incentives needed to dispatch load peak shaving; Incentive unit price for off-peak scheduling; The number of hours for staggered peak hours; To incentivize unit price for shifting peaks and filling valleys; To shift the peak electricity consumption to fill the valley electricity consumption; The unit price is for peak shaving incentives; To cut peak power.

[0092] Among them, when the power demand side resources are not involved in the grid regulation, in order to meet the requirements of load changes, the generator sets usually track the load. When the load changes sharply, the generator sets need to be started and stopped many times, which will lead to extra energy consumption and waste of time, and easily cause damage to the equipment. When the demand side resources participate in the grid regulation, the drastic changes in load can be suppressed to a certain extent, thereby reducing the start-up and stop costs of the units due to the sharp changes in load. Equipment start-up and stop cost indicators Satisfies formula (14): ,in, Indicates the cost of starting and stopping a generator set once; Indicates the number of times the generator set is started and stopped.

[0093] Among them, the fuel cost index is the fuel cost saved by reducing electricity consumption after participating in grid regulation. Satisfies formula (15): ,in, It indicates the reduction of electricity consumption after the power demand side participates in the grid regulation; Represents the marginal cost of fuel per unit of electricity.

[0094] Among them, the power demand side participates in grid regulation mainly by reducing power consumption to alleviate the impact of load peak on the grid. The power sales revenue indicator is used to describe the reduction in power sales revenue caused by the reduction in power sales. Satisfies formula (16): ,in, Indicates the electricity selling price.

[0095] Among them, the secondary rating indicators corresponding to the environmental indicators in the power grid dispatching value side, namely the CO2 emission reduction indicator, the SO2 emission reduction indicator, the NO emission reduction indicator and the NO2 emission reduction indicator, are the same as the secondary rating indicators corresponding to the environmental indicators in the user subjective response side, and will not be repeated here.

[0096] S230, determining an indicator matrix according to the Q first-level evaluation indicators and the P second-level evaluation indicators.

[0097] Among them, the Q first-level evaluation indicators are other first-level evaluation indicators among the M first-level evaluation indicators except the N first-level evaluation indicators.

[0098] Among them, the Q first-level evaluation indicators are evaluation indicators that have not been differentiated into second-level evaluation indicators. For example, the homogeneity of their own attributes and the contents they cover is too high, showing a single characteristic and lacking the dimension and space for further subdividing the second-level indicators.

[0099] In one possible embodiment, see Figure 5 , Figure 5is a flow chart of another method for evaluating the value of power dispatching provided in an embodiment of the present application. Figure 5 The step flow of determining the indicator matrix according to the Q first-level evaluation indicators and the P second-level evaluation indicators is shown as follows: Figure 5 As shown: S310, obtaining a first weight corresponding to the primary evaluation index and a second weight corresponding to the secondary evaluation index.

[0100] The weight of the first-level evaluation index is different from the weight of the second-level evaluation index. Specifically, the first weight is greater than the second weight. Exemplarily, the first weight is 0.73 and the second weight is 0.27.

[0101] S320, determining the indicator types of the Q first-level evaluation indicators and the P second-level evaluation indicators.

[0102] Among them, the indicator types include positive correlation indicators and negative correlation indicators, that is, positive indicators and negative indicators. For example, subsidy indicators and incentive price sensitive indicators are positive indicators, and time-of-use electricity price sensitive indicators and electricity fee indicators are negative indicators.

[0103] S330: Preprocess the Q first-level evaluation indicators and the P second-level evaluation indicators according to the indicator type, the first weight, and the second weight to obtain the indicator matrix.

[0104] Specifically, the Q first-level evaluation indicators and the P second-level evaluation indicators are preprocessed according to the indicator type to obtain a first indicator value corresponding to each evaluation indicator; an adjustment coefficient is determined according to the first weight and the second weight; the first indicator value corresponding to each evaluation indicator is adjusted according to the adjustment coefficient to obtain a second indicator value corresponding to each evaluation indicator; and the indicator matrix is ​​constructed according to the second indicator value corresponding to each evaluation indicator.

[0105] In a possible embodiment, different indicator types correspond to different preprocessing methods, and the preprocessing method for the negative indicator is: , The preprocessing method of the positive indicator is: , in, The preprocessed data for a single evaluation indicator; is the original indicator value of the single evaluation indicator, that is, the data before preprocessing; is the maximum value of the single evaluation indicator, that is, the maximum value of the indicator among users of the same type; is the minimum value of the single evaluation indicator, that is, the minimum value of the indicator among users of the same type.

[0106] The original indicator value corresponding to the evaluation indicator is preprocessed according to the preprocessing method corresponding to the indicator type to obtain the first indicator value of each evaluation indicator.

[0107] In a possible embodiment, the adjustment coefficient may be a ratio of the first weight to the second weight, and the adjustment coefficient may also be determined according to a linear relationship between the first weight and the second weight. Then, the adjustment coefficient is multiplied by the first indicator value to obtain the second indicator value. Then, according to a preset order, an indicator matrix is ​​constructed according to the second indicator value corresponding to each evaluation indicator.

[0108] In a possible embodiment, different evaluation subjects correspond to different weights, and the adjustment coefficient can be determined according to the weight corresponding to each evaluation subject, the first weight, and the second weight.

[0109] S240, determining the weight of each evaluation indicator in the indicator matrix according to a plurality of weight evaluation methods, and obtaining a plurality of weight matrices.

[0110] Among them, various weight evaluation methods can include hierarchical analysis method, rough set method, entropy weight method, grey correlation method and fuzzy comprehensive evaluation method.

[0111] In a possible embodiment, the weights of the above indicators are solved by using the hierarchical analysis method, rough set method and entropy weight method to obtain the first weight matrix , the second weight matrix and the third weight matrix Each weight matrix includes the weight of each evaluation indicator in the indicator matrix. For example, the evaluation indicator a in the indicator matrix is The weight in is A, The weight in is B, The weight in is C. Among them, A, B, and C can be the same or different.

[0112] In one possible embodiment, for the hierarchical analysis method, the factors involved in the problem are classified according to different levels to construct a multi-level structural model. Generally speaking, the hierarchical structure model can be divided into a target layer, a criterion layer, and a scheme layer. The target layer is the ultimate goal of the problem, that is, "evaluating the user scheduling value"; the criterion layer is the various factors that affect the realization of the goal, such as "environmental indicator quality", "incentive cost index", "response reliability index", etc.; the scheme layer is the specific evaluation object or scheme, such as the user's subjective response side, the user's objective response side, and the power grid scheduling value side. For an element of the previous level, the elements related to it in the next level are compared pairwise to determine their relative importance, and the corresponding numerical values ​​are assigned to form a judgment matrix. Then, the weight vector of each factor is determined by calculating the maximum eigenvalue of the judgment matrix and its corresponding eigenvector. Finally, a consistency test is performed to obtain the first weight matrix .

[0113] In one possible embodiment, for rough sets, the data in the indicator matrix are discretized. The data can be divided into different intervals by using equal width interval method, equal frequency interval method and other methods. According to the characteristics of the problem and the evaluation objectives, determine which evaluation indicators are used as conditional attributes and which indicators are used as decision attributes, and then fill the data into the decision table according to the values ​​of the conditional attributes and decision attributes, each row represents a sample object, and each column represents an attribute. Then, use the attribute importance definition of the rough set to calculate the importance of each conditional attribute to the decision attribute. Then, according to the importance of the attribute, delete those redundant attributes that have little impact on the decision attribute or have a strong correlation with other attributes. After obtaining the simplest attribute set, the objective weight of each attribute can be calculated according to the role of each attribute in the decision table.

[0114] Furthermore, if it is necessary to consider the subjective opinions of experts or other prior knowledge, the objective weights can be combined with the subjective weights to obtain the final comprehensive weights. The subjective weights can be obtained through expert scoring, hierarchical analysis, and other methods.

[0115] In one possible embodiment, for the entropy weight method, the information entropy of each indicator is calculated. The smaller the entropy value, the lower the information disorder of the indicator, and the greater the impact on the evaluation result. Then the difference coefficient of each evaluation indicator is calculated. The larger the difference coefficient, the greater the impact of the indicator on the evaluation result. And the difference coefficient is normalized to obtain the entropy weight of each evaluation indicator, and the sum of the entropy weights of all evaluation indicators is one. The weight obtained by the entropy weight method is determined based on the degree of variation of the data itself, and has a strong objectivity.

[0116] S250, fusing the multiple weight matrices to obtain a comprehensive weight matrix.

[0117] Among them, according to the weight of each weight matrix, multiple weight matrices are weighted and summed to obtain a comprehensive weight matrix.

[0118] In a possible embodiment, the multiple weight matrices include a first weight matrix, a second weight matrix and a third weight matrix, and the multiple weight matrices are merged to obtain a comprehensive weight matrix, including: determining a first correlation represented by the first weight matrix in the second weight matrix and the third weight matrix as a whole; and, determining a second correlation represented by the second weight matrix in the first weight matrix and the third weight matrix as a whole; and, determining a third correlation represented by the third weight matrix in the first weight matrix and the second weight matrix as a whole; determining a third weight of the first weight matrix, a fourth weight of the second weight matrix and a fifth weight of the third weight matrix according to the first correlation, the second correlation and the third correlation; and obtaining the comprehensive weight matrix according to the third weight, the fourth weight, the fifth weight, the first weight matrix, the second weight matrix and the third weight matrix.

[0119] In a possible embodiment, the first correlation Satisfies formula (17): , in, is the number of evaluation indicators; for The weight of the kth evaluation index in, for The mean of the overall weights in ; for and The combined weight of the kth indicator in , can be exemplarily and The weighted mean of the kth indicator in for and The mean of the overall weights in .

[0120] Among them, the second correlation The third correlation The determination method of the first correlation The method for determining is the same and will not be repeated here.

[0121] Among them, the third weight of the first weight matrix Satisfies formula (18): .

[0122] Among them, the fourth weight of the second weight matrix The fourth weight of the third weight matrix The determination method is based on the third weight of the first weight matrix The method for determining is the same and will not be repeated here.

[0123] According to the weight of each weight matrix, the first weight matrix, the second weight matrix and the third weight matrix are weighted and summed to obtain a comprehensive weight matrix . Comprehensive weight matrix Satisfies formula (19): .

[0124] S260, determining a power dispatch value evaluation result according to the comprehensive weight matrix and the indicator matrix.

[0125] Among them, the comprehensive weight matrix is ​​normalized to obtain the final weight matrix , the results of the power dispatch value evaluation Satisfies formula (20): ,in, is the indicator matrix. The higher the final score is, the higher the power dispatch value of the user is.

[0126] It can be seen that in the embodiment of the present application, a user's electricity dispatch value evaluation index system is established from three dimensions: objective response capability on the user side, subjective response willingness, and dispatch value on the grid side, so as to fully tap the user's energy flexibility value. At the same time, a variety of weight evaluation methods are used to comprehensively determine the weight of each indicator, so that the weight value of each indicator is more reasonable, thereby accurately measuring the user's suitability and contribution to demand response measures, and realizing the user's electricity dispatch value evaluation.

[0127] For the above embodiments, please refer to Figure 6 , Figure 6 is a functional unit block diagram of a power dispatch value evaluation device provided in an embodiment of the present application, such as Figure 6As shown, the power dispatch value evaluation device 60 includes: a first processing unit 61, which is used to perform a first classification process on the demand response related data with the user subjective response side, the user objective response side and the power grid dispatch value side as the evaluation subjects, to obtain M first-level evaluation indicators, wherein there are overlapping first-level evaluation indicators in the M first-level evaluation indicators, and the overlapping first-level evaluation indicators correspond to different evaluation subjects; a second processing unit 62, which is used to perform a second classification process on the M first-level evaluation indicators, to obtain P second-level evaluation indicators, wherein the P second-level evaluation indicators correspond to N first-level evaluation indicators, and N is less than M; the first A determination unit 63 is used to determine an indicator matrix based on Q first-level evaluation indicators and the P second-level evaluation indicators, wherein the Q first-level evaluation indicators are other first-level evaluation indicators among the M first-level evaluation indicators except the N first-level evaluation indicators; a second determination unit 64 is used to determine the weight of each evaluation indicator in the indicator matrix according to a plurality of weight evaluation methods to obtain a plurality of weight matrices; a fusion unit 65 is used to fuse the plurality of weight matrices to obtain a comprehensive weight matrix; a third determination unit 66 is used to determine a power dispatch value evaluation result based on the comprehensive weight matrix and the indicator matrix.

[0128] In a possible embodiment, in terms of determining an indicator matrix based on the Q first-level evaluation indicators and the P second-level evaluation indicators, the first determination unit 63 is specifically used to: obtain a first weight corresponding to the first-level evaluation indicator and a second weight corresponding to the second-level evaluation indicator; determine the indicator types of the Q first-level evaluation indicators and the P second-level evaluation indicators; and pre-process the Q first-level evaluation indicators and the P second-level evaluation indicators according to the indicator type, the first weight and the second weight to obtain the indicator matrix.

[0129] In a possible embodiment, in terms of preprocessing the Q first-level evaluation indicators and the P second-level evaluation indicators according to the indicator type, the first weight and the second weight to obtain the indicator matrix, the first determination unit 63 is specifically used to: preprocess the Q first-level evaluation indicators and the P second-level evaluation indicators according to the indicator type to obtain a first indicator value corresponding to each evaluation indicator; determine an adjustment coefficient according to the first weight and the second weight; adjust the first indicator value corresponding to each evaluation indicator according to the adjustment coefficient to obtain a second indicator value corresponding to each evaluation indicator; and construct the indicator matrix according to the second indicator value corresponding to each evaluation indicator.

[0130] In a possible embodiment, the indicator types include positive indicators and negative indicators, and the preprocessing method of the negative indicator is: , The preprocessing method of the positive indicator is: , in, is the first indicator value of a single evaluation indicator; is the original indicator value of the single evaluation indicator; is the maximum value of the single evaluation index; is the minimum value of the single evaluation indicator.

[0131] In a possible embodiment, the multiple weight matrices include a first weight matrix, a second weight matrix and a third weight matrix. In fusing the multiple weight matrices to obtain a comprehensive weight matrix, the fusion unit 65 is specifically used to: determine a first correlation represented by the first weight matrix in the second weight matrix and the third weight matrix as a whole; and, determine a second correlation represented by the second weight matrix in the first weight matrix and the third weight matrix as a whole; and, determine a third correlation represented by the third weight matrix in the first weight matrix and the second weight matrix as a whole; determine the third weight of the first weight matrix, the fourth weight of the second weight matrix and the fifth weight of the third weight matrix based on the first correlation, the second correlation and the third correlation; and obtain the comprehensive weight matrix based on the third weight, the fourth weight, the fifth weight, the first weight matrix, the second weight matrix and the third weight matrix.

[0132] In a possible embodiment, the M first-level evaluation indicators include a response potential indicator, a response reliability indicator, a response rapidity indicator, a response accuracy indicator and a response contribution indicator with the user's objective response side as the evaluation subject; wherein the response potential indicator includes the user's predicted power consumption, peak load and valley load, the response reliability indicator includes the number of terminal control devices of the user, the number of effective transmitted information and the total amount of transmitted information, the response rapidity indicator includes the response time interval, the response start time and the preset start response time, the response accuracy indicator includes the effective response power, the demand response instruction power, the effective response power, the demand response instruction power and the response duration, the response contribution indicator includes the actual load amount and the first predicted load amount during the peak power consumption period, and the actual load amount and the second predicted load amount during the demand response period; the M first-level evaluation indicators also include the first economic indicator, Environmental indicators, economically sensitive indicators, user participation willingness indicators, and power supply demand indicators; wherein the first economic indicators include subsidy income, load adjustment during the demand response period, the second predicted load, the user's production plan, response time, the user's electricity utility, the first cost related to electricity consumption, and the second cost related to electricity consumption time; the environmental indicators include the reduced emissions of multiple pollutants; the economically sensitive indicators include incentive prices, user response electricity, time-of-use electricity prices, user response loads, and electricity fee proportion expenditures; the user participation willingness indicators include the number of responses, actual response capacity, and invited response capacity; the power supply demand indicators include the user's power supply demand data; the M first-level evaluation indicators also include the second economic indicators with the grid dispatch value side as the evaluation subject and the environmental indicators; wherein the second economic indicators include incentive costs, reduced start-up and shutdown costs of power generation equipment, reduced fuel costs, and reduced electricity sales revenue.

[0133] In a possible embodiment, the P secondary evaluation indicators include a response speed indicator and a response timeliness indicator under the response rapidity indicator, a power indicator, a quantity indicator and a duration indicator under the response accuracy indicator, and a peak shaving indicator and a smoothing indicator under the response contribution indicator; wherein the response speed indicator is determined according to the response time interval, the response timeliness indicator is determined according to the response start time and the preset start response time, the power indicator is determined according to the effective response power and the demand response instruction power, the quantity indicator is determined according to the effective response quantity and the demand response instruction quantity, the duration indicator is determined according to the response duration, the peak shaving indicator is determined according to the actual load during the peak power consumption period and the first predicted load, and the smoothing indicator is determined according to the actual load during the demand response period and the second predicted load; the P secondary evaluation indicators also include a subsidy indicator, a first economic indicator and a second economic indicator. loss index, a second loss index, an electricity utility index, multiple pollutant emission reduction indicators under the environmental index, an incentive price sensitive index, a time-of-use electricity price sensitive index and an electricity fee index under the economic sensitive index; wherein the subsidy index is determined according to the subsidy income, the first loss index is determined according to the load adjustment amount during the demand response period and the second predicted load amount, the second loss index is determined according to the user's production plan and the response time, the electricity utility index is determined according to the user's electricity utility, the first cost related to the electricity consumption, and the second cost related to the electricity consumption time, the incentive price sensitive index is determined according to the incentive price and the user's response electricity, the time-of-use electricity price sensitive index is determined according to the time-of-use electricity price and the user's response load, and the electricity fee index is determined according to the proportion of electricity fee expenditure; the P secondary evaluation indicators also include the incentive cost index, equipment start-stop cost index, fuel cost index and electricity sales revenue index under the second economic indicator.

[0134] It can be understood that since the method embodiment and the device embodiment are different presentation forms of the same technical concept, the content of the method embodiment part in this application should be synchronously adapted to the device embodiment part and will not be repeated here.

[0135] In the case of integrated units, see Figure 7 , Figure 7 is a functional unit block diagram of another power dispatch value evaluation device provided in an embodiment of the present application, such as Figure 7As shown, the power dispatch value evaluation device 60 includes: a processing module 602 and a communication module 601. The processing module 602 is used to control and manage the actions of the power dispatch value evaluation device 60, for example, to execute the steps of the first processing unit, the second processing unit, the first determination unit, the second determination unit, the fusion unit and the third determination unit, and / or to execute other processes of the technology described herein. The communication module 601 is used for the interaction between the power dispatch value evaluation device 60 and other devices. Figure 7 As shown, the power scheduling value evaluation device 60 may further include a storage module 603 , and the storage module 603 is used to store program codes and data of the power scheduling value evaluation device 60 .

[0136] Among them, the processing module 602 can be a processor or a controller, for example, a central processing unit (CPU), a general processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It can implement or execute various exemplary logic boxes, modules and circuits described in conjunction with the disclosure of this application. The processor can also be a combination that implements a computing function, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like. The communication module 601 can be a transceiver, an RF circuit or a communication interface, etc. The storage module 603 can be a memory.

[0137] Among them, all relevant contents of each scenario involved in the above method embodiment can be referred to the functional description of the corresponding functional module, which will not be repeated here. The above power scheduling value evaluation device 60 can execute the above Figure 2 The electricity dispatch value evaluation method shown in FIG.

[0138] See also Figure 8 , Figure 8 is a schematic diagram of the structure of an electronic device proposed in an embodiment of the present application, such as Figure 8 As shown, the electronic device 800 includes a processor 810, a memory 820, a communication interface 830 and one or more programs 821. The one or more programs 821 are stored in the memory and are configured to be executed by the processor. When the program is executed, it includes part or all of the steps of any one of the electricity scheduling value evaluation methods recorded in the above method embodiments. The processor, memory and communication interface are interconnected and complete communication with each other.

[0139] The memory may be a volatile memory such as a dynamic random access memory (DRAM), or a non-volatile memory such as a mechanical hard disk. The memory is used to store a set of executable program codes, and the processor is used to call the executable program codes stored in the memory, and may execute part or all of the steps of any energy data management method recorded in the above-mentioned power dispatch value evaluation method embodiment.

[0140] It can be seen that the electronic device 800 described in the embodiment of the present application first performs a first classification process on the demand response related data with the user subjective response side, the user objective response side and the power grid dispatch value side as the evaluation subjects, and obtains M first-level evaluation indicators, among which there are overlapping first-level evaluation indicators, and the overlapping first-level evaluation indicators correspond to different evaluation subjects; then the M first-level evaluation indicators are subjected to a second classification process to obtain P second-level evaluation indicators, and the P second-level evaluation indicators correspond to N first-level evaluation indicators, and N is less than M; then an indicator matrix is ​​determined according to the Q first-level evaluation indicators and the P second-level evaluation indicators, and the Q first-level evaluation indicators are other first-level evaluation indicators in the M first-level evaluation indicators except the N first-level evaluation indicators; then the weight of each evaluation indicator in the indicator matrix is ​​determined according to a plurality of weight evaluation methods to obtain a plurality of weight matrices; then the plurality of weight matrices are merged to obtain a comprehensive weight matrix; finally, the power dispatch value evaluation result is determined according to the comprehensive weight matrix and the indicator matrix.

[0141] It can be seen that this application establishes a user's electricity dispatch value evaluation index system from three dimensions: user-side objective response capability, subjective response willingness, and grid-side dispatch value, fully tapping the user's energy flexibility value, and using a variety of weight evaluation methods to comprehensively determine the weight of each indicator, making the weight value of each indicator more reasonable, and then accurately measuring the user's suitability and contribution to demand response measures, and realizing the user's electricity dispatch value evaluation.

[0142] An embodiment of the present application also provides a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute part or all of the steps of any method described in the above method embodiments, and the above computer includes an electronic device.

[0143] The embodiment of the present application also provides a computer program product, the computer program product includes a non-transitory computer-readable storage medium storing a computer program, the computer program is operable to cause a computer to execute some or all of the steps of any method described in the method embodiment. The computer program product may be a software installation package, and the computer includes an electronic device.

[0144] It should be noted that, for the sake of simplicity, the aforementioned method implementations are all described as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the implementations described in the specification are all optional implementations, and the actions and modules involved are not necessarily required by the present application.

[0145] In the above-mentioned embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0146] In the several embodiments provided in this application, it should be understood that the disclosed device can be implemented in other ways. For example, the device implementation described above is only schematic, such as the division of the units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the device or unit can be electrical or other forms.

[0147] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0148] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above integrated unit may be implemented in the form of hardware or in the form of a software program module.

[0149] If the integrated unit is implemented in the form of a software program module and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a memory, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes: U disk, read-only memory (ROM), random access memory (RAM), mobile hard disk, disk or optical disk and other media that can store program codes.

[0150] A person of ordinary skill in the art can understand that all or part of the steps in the various methods of the above-mentioned embodiments can be completed by instructing the relevant hardware through a program, and the program can be stored in a computer-readable memory, which can include: a flash drive, a read-only memory, a random access memory, a magnetic disk or an optical disk, etc.

[0151] The above is a detailed introduction to the implementation methods of the present application. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above implementation methods is only used to help understand the method and core idea of ​​the present application. At the same time, for general technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A method for evaluating the value of power dispatching, characterized in that: include: Performing a first classification process on demand response related data with user subjective response side, user objective response side and power grid dispatch value side as evaluation subjects to obtain M first-level evaluation indicators, wherein there are overlapping first-level evaluation indicators among the M first-level evaluation indicators, and the overlapping first-level evaluation indicators correspond to different evaluation subjects; Performing a second classification process on the M first-level evaluation indicators to obtain P second-level evaluation indicators, where the P second-level evaluation indicators correspond to N first-level evaluation indicators, and N is less than M; Determine an indicator matrix according to the Q first-level evaluation indicators and the P second-level evaluation indicators, wherein the Q first-level evaluation indicators are other first-level evaluation indicators among the M first-level evaluation indicators except the N first-level evaluation indicators; Determine the weight of each evaluation indicator in the indicator matrix according to a plurality of weight evaluation methods to obtain a plurality of weight matrices; Fusing the multiple weight matrices to obtain a comprehensive weight matrix; The electricity dispatch value evaluation result is determined according to the comprehensive weight matrix and the indicator matrix.

2. The method according to claim 1, characterized in that: The determining of the indicator matrix according to the Q first-level evaluation indicators and the P second-level evaluation indicators includes: Obtain a first weight corresponding to the primary evaluation index and a second weight corresponding to the secondary evaluation index; Determining the indicator types of the Q first-level evaluation indicators and the P second-level evaluation indicators; The Q first-level evaluation indicators and the P second-level evaluation indicators are preprocessed according to the indicator type, the first weight, and the second weight to obtain the indicator matrix.

3. The method according to claim 2, characterized in that The preprocessing of the Q first-level evaluation indicators and the P second-level evaluation indicators according to the indicator type, the first weight, and the second weight to obtain the indicator matrix includes: Preprocessing the Q first-level evaluation indicators and the P second-level evaluation indicators according to the indicator type to obtain a first indicator value corresponding to each evaluation indicator; determining an adjustment coefficient according to the first weight and the second weight; Adjust the first indicator value corresponding to each evaluation indicator according to the adjustment coefficient to obtain the second indicator value corresponding to each evaluation indicator; The indicator matrix is ​​constructed according to the second indicator value corresponding to each evaluation indicator.

4. The method according to claim 3, characterized in that: The indicator types include positive indicators and negative indicators. The preprocessing method of the negative indicator is: ， The preprocessing method of the positive indicator is: ， in, is the first indicator value of a single evaluation indicator; is the original indicator value of the single evaluation indicator; is the maximum value of the single evaluation index; is the minimum value of the single evaluation indicator.

5. The method according to any one of claims 1 to 4, characterized in that: The multiple weight matrices include a first weight matrix, a second weight matrix, and a third weight matrix, and the multiple weight matrices are fused to obtain a comprehensive weight matrix, including: Determine a first correlation represented by the first weight matrix on the whole of the second weight matrix and the third weight matrix; and, determine a second correlation represented by the second weight matrix on the whole of the first weight matrix and the third weight matrix; and, determine a third correlation represented by the third weight matrix on the whole of the first weight matrix and the second weight matrix; Determining a third weight of the first weight matrix, a fourth weight of the second weight matrix, and a fifth weight of the third weight matrix according to the first correlation, the second correlation, and the third correlation; The comprehensive weight matrix is ​​obtained according to the third weight, the fourth weight, the fifth weight, the first weight matrix, the second weight matrix and the third weight matrix.

6. The method according to any one of claims 1 to 4, characterized in that: The M first-level evaluation indicators include a response potential indicator, a response reliability indicator, a response rapidity indicator, a response accuracy indicator and a response contribution indicator with the user's objective response side as the evaluation subject; wherein the response potential indicator includes the user's predicted power consumption, peak load and valley load, the response reliability indicator includes the number of terminal control devices of the user, the number of effective transmitted information and the total amount of transmitted information, the response rapidity indicator includes the response time interval, the response start time and the preset start response time, the response accuracy indicator includes the effective response power, the demand response instruction power, the effective response power, the demand response instruction power and the response duration, and the response contribution indicator includes the actual load amount and the first predicted load amount during the peak power consumption period, and the actual load amount and the second predicted load amount during the demand response period; The M first-level evaluation indicators also include a first economic indicator, an environmental indicator, an economically sensitive indicator, a user participation willingness indicator, and a power supply demand indicator with the user's subjective response side as the evaluation subject; wherein the first economic indicator includes subsidy income, load adjustment amount during the demand response period, the second predicted load amount, the user's production plan, response time, the user's electricity utility, a first cost related to electricity consumption, and a second cost related to electricity consumption time; the environmental indicator includes the reduced emission of multiple pollutants; the economically sensitive indicator includes incentive price, user response electricity, time-of-use electricity price, user response load and electricity fee proportion expenditure; the user participation willingness indicator includes the number of responses, actual response capacity and invitation response capacity; the power supply demand indicator includes the user's power supply demand data; The M first-level evaluation indicators also include a second economic indicator with the grid dispatching value side as the evaluation subject and the environmental indicator; wherein the second economic indicator includes incentive costs, reduced start-up and shutdown costs of power generation equipment, reduced fuel costs and reduced electricity sales revenue.

7. The method according to claim 6, characterized in that The P secondary evaluation indicators include a response speed indicator and a response timeliness indicator under the response rapidity indicator, a power indicator, an electric quantity indicator and a duration indicator under the response accuracy indicator, and a peak shaving indicator and a smoothing indicator under the response contribution indicator; wherein the response speed indicator is determined according to the response time interval, the response timeliness indicator is determined according to the response start time and the preset start response time, the power indicator is determined according to the effective response power and the demand response instruction power, the electric quantity indicator is determined according to the effective response electric quantity and the demand response instruction electric quantity, the duration indicator is determined according to the response duration, the peak shaving indicator is determined according to the actual load amount during the peak power consumption period and the first predicted load amount, and the smoothing indicator is determined according to the actual load amount during the demand response period and the second predicted load amount; The P secondary evaluation indicators also include a subsidy indicator, a first loss indicator, a second loss indicator, and an electricity utility indicator under the first economic indicator, multiple pollutant emission reduction indicators under the environmental indicator, and an incentive price sensitive indicator, a time-of-use electricity price sensitive indicator, and an electricity fee indicator under the economic sensitive indicator; wherein the subsidy indicator is determined according to the subsidy income, the first loss indicator is determined according to the load adjustment amount during the demand response period and the second predicted load amount, the second loss indicator is determined according to the user's production plan and the response time, the electricity utility indicator is determined according to the user's electricity utility, the first cost related to the electricity consumption, and the second cost related to the electricity consumption time, the incentive price sensitive indicator is determined according to the incentive price and the user's response electricity, the time-of-use electricity price sensitive indicator is determined according to the time-of-use electricity price and the user's response load, and the electricity fee indicator is determined according to the proportion of electricity fee expenditure; The P secondary evaluation indicators also include an incentive cost indicator, an equipment start-up and shutdown cost indicator, a fuel cost indicator, and a power sales revenue indicator under the second economic indicator.

8. A device for evaluating the value of power dispatching, characterized in that: include: A first processing unit is used to perform a first classification process on the demand response related data with the user subjective response side, the user objective response side and the power grid dispatch value side as evaluation subjects to obtain M first-level evaluation indicators, wherein there are overlapping first-level evaluation indicators among the M first-level evaluation indicators, and the overlapping first-level evaluation indicators correspond to different evaluation subjects; A second processing unit is used to perform a second classification process on the M first-level evaluation indicators to obtain P second-level evaluation indicators, where the P second-level evaluation indicators correspond to the N first-level evaluation indicators, and N is less than M; A first determining unit is used to determine an indicator matrix according to the Q first-level evaluation indicators and the P second-level evaluation indicators, wherein the Q first-level evaluation indicators are other first-level evaluation indicators among the M first-level evaluation indicators except the N first-level evaluation indicators; A second determining unit is used to determine the weight of each evaluation indicator in the indicator matrix according to multiple weight evaluation methods to obtain multiple weight matrices; A fusion unit, used for fusing the multiple weight matrices to obtain a comprehensive weight matrix; The third determination unit is used to determine the electricity dispatch value evaluation result according to the comprehensive weight matrix and the indicator matrix.

9. An electronic device, characterized in that: The device comprises: A memory, a processor, and an executable program code stored in the memory and executable on the processor, wherein the processor executes the steps of the method for evaluating the value of electricity scheduling as described in any one of claims 1 to 7 when executing the executable program code.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores executable program code, and the executable program code includes execution instructions, and the execution instructions are used to execute the steps of the electricity scheduling value evaluation method as described in any one of claims 1-7.

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