Building demand response potential determination method and device, computer equipment and medium

Abstract

The application relates to a building demand response potential determination method and device, computer equipment and medium. The method comprises the following steps: determining the respective index values of each building relative to each demand response evaluation index according to the building power load information of each type of building and the regional power load information; performing subjective and objective weighting processing on each demand response evaluation index based on the respective index values of each building relative to each demand response evaluation index, to obtain the subjective weight and the objective weight of each demand response evaluation index; performing weight comprehensive analysis on the demand response evaluation index according to the subjective weight and the objective weight, to obtain the comprehensive weight of the demand response evaluation index; and performing demand response potential analysis on the building based on the comprehensive weight, to obtain the demand response potential result of the building. The method can improve the accuracy and reliability of the determination of the demand response potential of the building.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of smart grid, in particular to a building demand response potential determination method and device, computer equipment, storage medium and computer program product. BACKGROUND

[0002] High proportion of renewable energy fluctuates greatly and has strong randomness, and the balance pressure of the power system is increasingly severe, and the extreme lack of regulation capacity has become the main challenge faced by the new power system. Demand response can help the power grid smooth the load curve and relieve the peak electricity pressure by means of price signals and incentive mechanisms to guide users to adjust the power consumption mode. In the power demand response, buildings are high-quality resources for demand response. On the one hand, the power consumption of buildings accounts for about 40% of the total power consumption of society, and has great flexible adjustment potential. On the other hand, devices such as air conditioners and charging piles in buildings have energy storage characteristics, which can effectively cooperate with the power grid for flexible adjustment. In order to select suitable buildings to participate in the grid-load interaction in a large number of buildings, the demand response potential of the buildings needs to be evaluated.

[0003] However, the traditional research on building demand response is often based on the response capacity of each device of the building itself to determine the demand response potential of the building. Although this method can provide guidance for the demand response control of the building to some extent, it also has the problems of low accuracy and low reliability. Therefore, how to accurately determine the demand response potential of the building to make up for the lack of regulation resources of the target new power system is the key problem concerned by power workers at present. SUMMARY

[0004] Therefore, it is necessary to provide a building demand response potential determination method, device, computer equipment, computer readable storage medium and computer program product capable of improving the accuracy and reliability of building demand response potential determination in view of the above technical problems.

[0005] In a first aspect, the present application provides a building demand response potential determination method. The method comprises:

[0006] determining the index value of each demand response evaluation index of each building relative to each building power load information of each type of building and the regional power load information of the region where each building is located;

[0007] For each building, based on the index value of each demand response evaluation index of the building relative to each demand response evaluation index, the subjective weight and the objective weight of each demand response evaluation index are obtained by respectively performing subjective and objective weighting processing on each demand response evaluation index;

[0008] The weight comprehensive analysis module is configured to, for each of the demand response evaluation indexes, perform weight comprehensive analysis on the demand response evaluation index according to the subjective weight and the objective weight, and obtain a comprehensive weight of the demand response evaluation index.

[0009] The demand response potential determination module is configured to perform demand response potential analysis on the building based on each of the comprehensive weights, and obtain a demand response potential result of the building.

[0010] In a second aspect, the present application further provides a building demand response potential determination device. The device comprises:

[0011] The index value determination module is configured to determine, according to building power load information of each type of building and regional power load information of a region where each of the buildings is located, an index value of each of the buildings with respect to each of the demand response evaluation indexes.

[0012] The subjective and objective weighting module is configured to, for each of the buildings, perform subjective and objective weighting processing on each of the demand response evaluation indexes based on the index value of the building with respect to each of the demand response evaluation indexes, and obtain a subjective weight and an objective weight of each of the demand response evaluation indexes.

[0013] The weight comprehensive analysis module is configured to, for each of the demand response evaluation indexes, perform weight comprehensive analysis on the demand response evaluation index according to the subjective weight and the objective weight, and obtain a comprehensive weight of the demand response evaluation index.

[0014] The demand response potential determination module is configured to perform demand response potential analysis on the building based on each of the comprehensive weights, and obtain a demand response potential result of the building.

[0015] In a third aspect, the present application further provides a computer device. The computer device comprises a memory and a processor, the memory stores a computer program, and the processor implements the steps of the building demand response potential determination method when executing the computer program.

[0016] In a fourth aspect, the present application further provides a computer readable storage medium. The computer readable storage medium stores a computer program, and the computer program implements the steps of the building demand response potential determination method when executed by a processor.

[0017] In a fifth aspect, the present application further provides a computer program product. The computer program product comprises a computer program, and the computer program implements the steps of the building demand response potential determination method when executed by a processor.

[0018] The building demand response potential determination method, device, computer equipment, storage medium and computer program product determine the index values of each building with respect to each demand response evaluation index according to the building power load information of each type of building and the regional power load information of the region where each building is located. The determination of each index value takes into account the power load condition of the building itself and the regional power load condition of the region, and realizes the interaction of building load and power grid load from the data perspective. For each building, the subjective weight and objective weight of each demand response evaluation index are obtained by respectively performing subjective and objective weighting processing on each demand response evaluation index based on the index values of each building with respect to each demand response evaluation index. For each demand response evaluation index, the comprehensive weight of the demand response evaluation index is obtained by performing weight comprehensive analysis on the demand response evaluation index according to the subjective weight and the objective weight. The comprehensive weight takes into account the subjective expert opinion and the objective data information. The demand response potential of the building is analyzed based on the comprehensive weight, which improves the accuracy and reliability of determining the demand response potential of the building. The demand response potential result obtained can truly and comprehensively reflect the potential of the building to cooperate with the power grid for demand response, and can provide accurate guidance for subsequent demand response control of the building. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 An application environment diagram of a building demand response potential determination method in an embodiment;

[0020] Figure 2 A flowchart of a building demand response potential determination method in an embodiment;

[0021] Figure 3 A flowchart of determining the index values of each building with respect to each demand response evaluation index according to the building power load information of each type of building and the regional power load information of the region where each building is located in an embodiment;

[0022] Figure 4 A flowchart of performing subjective and objective weighting processing on each demand response evaluation index based on the index values of each building with respect to each demand response evaluation index in an embodiment, to obtain the subjective weight and the objective weight of each demand response evaluation index;

[0023] Figure 5 A flowchart of performing load closeness evaluation on a building based on the comprehensive weight to obtain a load closeness evaluation result of the building in an embodiment;

[0024] Figure 6 A flowchart of a building demand response potential determination method in another embodiment;

[0025] Figure 7A structural block diagram of a building demand response potential determination device in an embodiment;

[0026] Figure 8 An internal structural diagram of a computer device in an embodiment. DETAILED DESCRIPTION

[0027] In order to make the purposes, technical solutions and advantages of the present application clearer, the present application is further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0028] The building demand response potential determination method provided by the embodiments of the present application can be applied to an application environment as shown in Figure 1 . The demand response potential analysis platform 102 communicates with the server 104 through a network. The data storage system can store data required to be processed by the demand response potential analysis platform 102. The data storage system can be integrated on the demand response potential analysis platform 102, or placed on a cloud or other network server. The demand response potential analysis platform 102 can obtain building power load information of various types of buildings and regional power load information of regions where the buildings are located from the server 104, determine respective index values of the buildings with respect to various demand response evaluation indexes according to the building power load information of various types of buildings and the regional power load information of the regions where the buildings are located, perform subjective and objective weighting processing on various demand response evaluation indexes based on the respective index values of the buildings with respect to various demand response evaluation indexes for each building, and obtain subjective weights and objective weights of the various demand response evaluation indexes. For each demand response evaluation index, the demand response evaluation index is analyzed by weight synthesis according to the subjective weight and the objective weight, and a comprehensive weight of the demand response evaluation index is obtained. The buildings are analyzed for demand response potential based on the comprehensive weights, and a demand response potential result of the buildings is obtained.

[0029] The demand response potential analysis platform 102 can be integrated on a terminal or a service. The terminal can be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, etc. The server can be implemented by an independent server or a server cluster composed of multiple servers, like the server 104.

[0030] In an embodiment, as shown in Figure 2 , a building demand response potential determination method is provided. The method is taken as an example to illustrate the demand response potential analysis platform 102 in Figure 1 , and includes the following steps:

[0031] S202, determine the index value of each building with respect to each demand response evaluation index according to the building power load information of each type of building and the regional power load information of the region where each building is located.

[0032] The type of each building is a type parameter for classifying buildings from the perspective of use function, for example, the type of a building can include but is not limited to office building, commercial building, hospital, etc. It can be understood that the building power load information corresponding to different types of buildings is different.

[0033] The building power load information is information data for characterizing the power load condition of the building, and the building power load information can include the overall power load curve of the building and the energy consumption data of equipment such as air conditioners, energy storage devices, etc.

[0034] The regional power load information is information data for characterizing the regional power load condition of the region where the building is located, and the regional power load information can include the power load curve of the regional power grid.

[0035] The demand response evaluation index is an index parameter that affects the demand response potential of the building, and the index value of the demand response evaluation index can represent the correlation between the power load condition of the building and the regional power load condition of the region in the corresponding index dimension. For example, the demand response evaluation index can include but is not limited to the daily peak valley difference rate of the building, the daily load rate, the daily load peak period length, the grid peak period overlap rate, etc.

[0036] In some embodiments, each demand response evaluation index can be pre-set in the demand response potential analysis platform by a designer according to empirical data or experimental data.

[0037] Specifically, the demand response potential analysis platform can determine the index value of each building with respect to each demand response evaluation index according to the building power load information of each type of building and the regional power load information of the region where each building is located.

[0038] In some embodiments, the demand response potential analysis platform has pre-set an index calculation model of the demand response evaluation index, and after obtaining the building power load information of each type of building and the regional power load information of the region where each building is located, the index calculation model can be called, and the building power load information and the regional power load information of each building are respectively input into the index calculation model to obtain the index value of each building with respect to each demand response evaluation index.

[0039] S204, for each building, respectively process the subjective and objective weights of each demand response evaluation index based on the index value of the building with respect to each demand response evaluation index.

[0040] The subjective and objective weighting processing is a weight processing operation of respectively performing subjective weighting processing and objective weighting processing on each demand response evaluation index. The subjective weighting processing is a weight determination operation of subjectively weighting each demand response evaluation index by a professional, such as an expert in the power grid field, according to the index value of each demand response evaluation index and the information reflected by the index, and the knowledge and experience or preference of the professional.

[0041] The objective weighting processing is a weight determination operation of objectively weighting each demand response evaluation index according to the index value of each demand response evaluation index from the data objectivity.

[0042] Specifically, for each building, the demand response potential analysis platform can perform subjective and objective weighting processing on each demand response evaluation index based on the index value of each demand response evaluation index of the building, to obtain the subjective weight and the objective weight of each demand response index.

[0043] In some embodiments, the demand response potential analysis platform is configured with an objective weighting model generated in advance according to an objective weighting rule, such as a principal component analysis rule or an entropy weight method. The demand response potential analysis platform can input the index value of each demand response evaluation index of the building into the objective weighting model, to obtain the objective weight of each demand response index.

[0044] S206, for each demand response evaluation index, performing weight comprehensive analysis on the demand response evaluation index according to the subjective weight and the objective weight, to obtain the comprehensive weight of the demand response evaluation index.

[0045] The weight comprehensive analysis is a weight analysis method that comprehensively considers the advantages of the subjective weight and the objective weight, and uses a corresponding combination strategy to calculate a more accurate and comprehensive comprehensive weight. The weight comprehensive analysis can reasonably combine the subjective weight and the objective weight to obtain the optimal weight assignment. It can be understood that the analysis algorithm used by the weight comprehensive analysis can be any analysis algorithm that can achieve reasonable combination and obtain the optimal weight assignment, such as additive synthesis method, multiplicative synthesis method, and range maximization.

[0046] Specifically, for each demand response evaluation index, the demand response potential analysis platform can perform weight comprehensive analysis on the demand response evaluation index according to the subjective weight and the objective weight after determining the subjective weight and the objective weight of the demand response evaluation index, to obtain the comprehensive weight of the demand response evaluation index.

[0047] S208, performing demand response potential analysis on the building based on the comprehensive weights, to obtain the demand response potential result of the building.

[0048] The demand response potential analysis is used to determine the correlation degree of the building power load condition and the regional power grid power load condition according to the respective comprehensive weights of the respective demand response evaluation indexes.

[0049] Specifically, after obtaining the respective comprehensive weights of the respective demand response evaluation indexes, the demand response potential analysis platform can perform demand response potential analysis on the building based on the respective comprehensive weights, determine the correlation degree of the building power load condition and the regional power grid power load condition, and obtain the demand response potential result of the building. It can be understood that the correlation degree of the building power load condition and the regional power grid power load condition is positively correlated with the demand response potential result of the building, that is, the higher the correlation degree of the building power load condition and the regional power grid power load condition, the greater the demand response potential of the building, and vice versa, the lower the correlation degree of the building power load condition and the regional power grid power load condition, the smaller the demand response potential of the building.

[0050] In the above building demand response potential determination method, the respective index values of the respective demand response evaluation indexes of each building are determined according to the building power load information of each type of building and the regional power load information of the region where each building is located. The determination of the respective index values takes into account the building power load condition and the regional power load condition of the region where the building is located, thereby realizing the interaction of building load and power grid load from the data perspective. For each building, the subjective weight and the objective weight of each demand response evaluation index are obtained by performing subjective and objective weighting processing on each demand response evaluation index based on the respective index values of the respective demand response evaluation indexes of the building. For each demand response evaluation index, the comprehensive weight of the demand response evaluation index is obtained by performing weight comprehensive analysis on the demand response evaluation index according to the subjective weight and the objective weight, so that the comprehensive weight takes into account the subjective expert opinion and the objective data information. Subsequently, the demand response potential analysis is performed on the building based on the respective comprehensive weights, thereby improving the accuracy and reliability of determining the demand response potential of the building. The obtained demand response potential result can truly and comprehensively reflect the potential of the building to cooperate with the power grid for demand response, and can provide accurate guidance for subsequent demand response control of the building.

[0051] In one embodiment, as shown in FIG. 2, Figure 3 S202 determines the respective index values of the respective demand response evaluation indexes of each building according to the building power load information of each type of building and the regional power load information of the region where each building is located, including:

[0052] S302, obtaining building power load information of each type of building and regional power load information of the region where each building is located.

[0053] Specifically, the demand response potential analysis platform can obtain building power load information of each type of building and regional power load information of each region where each building is located from the server.

[0054] In S304, according to a preset demand response potential evaluation system, each demand response evaluation index that has an influence on the demand response potential of the building is determined, and an index calculation method of each demand response evaluation index is determined.

[0055] The preset demand response potential evaluation system is a comprehensive evaluation framework for quantifying the ability of a building to adjust load when participating in demand response. The preset demand response potential evaluation system can be established by a designer based on actual load curve data of each type of building and power grid, and by comprehensively considering the positive and negative effects of building energy consumption characteristics, matching power grid, and economic and social aspects on the expected implementation of demand response of the building.

[0056] In some embodiments, the preset demand response potential evaluation system is as shown in the following table:

[0057]

[0058] The preset demand response potential evaluation system is provided with a plurality of demand response evaluation indexes, each of which corresponds to a respective index calculation method, so as to calculate the index value of the demand response evaluation index according to the building power load information of the building and / or the regional power load information of the region where the building is located.

[0059] Specifically, the demand response potential analysis platform can determine each demand response evaluation index that has an influence on the demand response potential of the building according to the preset demand response potential evaluation system, and then determine the index calculation method of each demand response evaluation index based on the preset correspondence between the demand response evaluation index and the index calculation method.

[0060] In S306, for each demand response evaluation index, the index value of each building with respect to the demand response evaluation index is calculated based on the building power load information and / or the regional power load information of each building and the index calculation method of the demand response evaluation index.

[0061] Specifically, for each demand response evaluation index, the demand response potential analysis platform can calculate the index value of each building with respect to the demand response evaluation index based on the building power load information and / or the regional power load information of each building and the index calculation method of the demand response evaluation index.

[0062] The calculation methods of some demand response evaluation indexes will be described below through several embodiments:

[0063] In some embodiments, the demand response evaluation index can include a daily peak-valley difference rate, and the demand response potential analysis platform can calculate the index value of the daily peak-valley difference rate by the above method.

[0064] Wherein, the daily peak-valley difference rate is the ratio of the difference between the maximum value and the minimum value of the daily load curve data and the maximum value, reflecting the degree of daily load peak-valley change, and is a positive index.

[0065] Specifically, the daily peak-valley difference rate B1 calculation formula is as follows:

[0066] ;

[0067] Wherein, N is the number of typical days of the building, and respectively represent the maximum value and the minimum value of the power of the nth typical day load curve of the building.

[0068] In some embodiments, the demand response evaluation index can include a daily load rate, and the demand response potential analysis platform can calculate the index value of the daily load rate by the above method.

[0069] Wherein, the daily load rate is the ratio of the average value to the maximum value in the daily load curve, indicating the degree of load fluctuation, and is a negative index.

[0070] Specifically, the daily load rate B2 calculation formula is as follows:

[0071] ;

[0072] Wherein, is the average value of the power of the nth typical day load curve of the building.

[0073] In some embodiments, the demand response evaluation index can include a daily load peak period duration, and the demand response potential analysis platform can calculate the index value of the daily load peak period duration by the above method.

[0074] Wherein, the daily load peak period duration is used to define the period in which the load value is more than 1.1 times the average load, reflecting the duration of the building peak electricity consumption.

[0075] Specifically, the daily load peak period duration B3 calculation formula is as follows:

[0076] ;

[0077] Wherein, is the peak period state of the nth typical day t period of the building, and is 1 if the period is a peak period, and is 0 if the period is not a peak period.

[0078] In some embodiments, the demand response evaluation index can include a daily load valley period length, and the demand response potential analysis platform can calculate the index value of the daily load valley period length by the above method.

[0079] The daily load valley period length is used to define a period in which the load value is greater than or equal to 0.9 times the average load, and reflects the length of the building's low valley electricity.

[0080] Specifically, the daily load valley period length B4 is calculated according to the following formula:

[0081] ;

[0082] Wherein, is the valley period state of the nth typical day t period of the building, and is 1 if the period is a peak period, and is 0 if the period is not a peak period.

[0083] In some embodiments, the demand response evaluation index can include a grid peak period overlap rate, and the demand response potential analysis platform can calculate the index value of the grid peak period overlap rate by the above method.

[0084] The grid peak period overlap rate is the overlap time of building load and grid load peak period / building load peak time length, and is a positive index.

[0085] Specifically, the grid peak period overlap rate B5 is calculated according to the following formula:

[0086] ;

[0087] Wherein, is the valley period state of the nth typical day t period of the building, and is 1 if the period is a peak period, and is 0 if the period is not a peak period.

[0088] In some embodiments, the demand response evaluation index can include a grid valley period overlap rate, and the demand response potential analysis platform can calculate the index value of the grid valley period overlap rate by the above method.

[0089] The grid valley period overlap rate is the overlap time of building load and grid load valley period / building load valley time length, and is a positive index.

[0090] Specifically, the grid valley period overlap rate B6 is calculated according to the following formula:

[0091]

[0092] ;

[0093] In some embodiments, the demand response evaluation index can include a building grid load curve similarity coefficient, and the demand response potential analysis platform can calculate the index value of the building grid load curve similarity coefficient by the above method.

[0094] wherein, the building grid load curve similarity coefficient is the root mean square error (RMSE) calculated after normalizing the load curve, reflecting the time sequence similarity of each type of building and grid load. It is a negative index.

[0095] Specifically, the building grid load curve similarity coefficient B7 is calculated according to the following formula:

[0096]

[0097] wherein, , is the normalized value of the building and grid load data.

[0098] In some embodiments, the demand response evaluation index can include total response power, and the demand response potential analysis platform can calculate the index value of the total response power by the above method.

[0099] wherein, the building internal flexibility resources mainly include air conditioning systems, lighting systems, electric vehicle charging piles and energy storage devices, etc., and the total response power index calculation method is the sum of the total building load up or down, with units of kilowatts (kW) or megawatts (MW), reflecting the size of the building's instantaneous response capability. It is a positive index.

[0100] In some embodiments, the demand response evaluation index can include total response capacity, and the demand response potential analysis platform can calculate the index value of the total response capacity by the above method.

[0101] wherein, the total response capacity is a combination of response capability and durability, i.e., how long the building demand response can support operation. The unit is kilowatt-hour (kWh) or megawatt-hour (MWh). It is a positive index.

[0102] In some embodiments, the demand response evaluation index can include building comprehensive response performance coefficient, and the demand response potential analysis platform can calculate the index value of the building comprehensive response performance coefficient by the above method.

[0103] wherein, demand response often has requirements for the response time, speed and accuracy of participants, and the calculation method of the building comprehensive response performance coefficient is as follows:

[0104] ;

[0105] wherein, is the comprehensive response performance coefficient of unit i, which can also be referred to as the comprehensive frequency modulation performance index, , , is the adjustment rate, response time and adjustment accuracy of unit i, , , The weight factor is 3 components. According to the actual operation experience, the adjustment rate has the greatest influence on the frequency regulation performance, so the weight factor is taken as 1, the weight factor of the response time is taken as 0.5, and the weight factor of the adjustment error is taken as 0.5. . is the actual frequency regulation rate of the unit i, is the average frequency regulation rate of all units. i is the time from when the unit i receives the frequency regulation instruction to when the unit i responds to the frequency regulation instruction, N is the standard response time (determined according to the type of demand response). i is the adjustment error of the actual output of the unit i and the required output, available is the allowable error.

[0106] In some embodiments, the demand response evaluation index can include a building typical day fluctuation coefficient, and the demand response potential analysis platform can calculate the index value of the building typical day fluctuation coefficient by the above method.

[0107] The building typical day fluctuation coefficient is the variance value of the average load of each typical day, reflecting the load change of the building in different typical days.

[0108] In some embodiments, the demand response evaluation index can include a building environment sensitivity coefficient, and the demand response potential analysis platform can calculate the index value of the building environment sensitivity coefficient by the above method.

[0109] The building environment sensitivity coefficient is the Spearman correlation coefficient of the building load curve data and the temperature, humidity, and electricity price of the day, reflecting the degree to which the building occupants can withstand environmental changes. It is a negative index. The calculation formula of the building environment sensitivity coefficient B 12 is as follows:

[0110] ;

[0111] Wherein, represents the Spearman correlation coefficient of the building load data and the environmental parameters, and B is the type of environmental parameters.

[0112] In some embodiments, the demand response evaluation index can include an energy cost proportion, and the demand response potential analysis platform can calculate the index value of the energy cost proportion by the above method.

[0113] The energy cost proportion is the ratio of building energy consumption expenditure to total building production activity income, reflecting the possibility of the building participating in demand response from an economic perspective.

[0114] In addition to the demand response evaluation indexes that need to be calculated, there are demand response evaluation indexes related to the actual situation of the building or government policy, such as building energy management capability, policy support, building structure, building industry type, and participation willingness, etc. These demand response evaluation indexes can be determined according to the actual index situation.

[0115] For example, the building energy management capability is used to represent whether the building is equipped with energy management equipment such as smart meters, has special energy management personnel, and strict energy management measures to make the use of energy more effective. The policy support can be determined according to whether the actual policy supports it. The policy support can promote the energy management and equipment upgrade of the building, enhance the enthusiasm of the building to participate in demand response, and improve the response capability. The building structure can be determined according to the actual building structure. The structure, floor area, building area, and building height of the building will affect the form of the building connected to the power grid, and then affect the response capability. The building industry type also affects the demand response potential. Different types of buildings built for different purposes have different energy demand and response potential. For example, commercial buildings have high peak power demand and can participate in power demand response during peak hours, while residential buildings may be more suitable for basic load adjustment. The participation willingness of the building owners and users is an important factor in determining whether the building has demand response potential. Active owners and users can transform the building into a sustainable energy use resource and improve its response potential. It can be obtained through a survey questionnaire.

[0116] In the above embodiments, the demand response evaluation indexes and the index calculation methods of each demand response evaluation index can be quickly determined according to the preset demand response potential evaluation system, so as to realize the accuracy and efficiency of the calculation of the value of each demand response evaluation index.

[0117] In some embodiments, as shown in Figure 4 S204, based on the index values of the building with respect to each demand response evaluation index, the subjective and objective weighting of each demand response evaluation index is processed respectively to obtain the subjective weight and the objective weight of each demand response evaluation index, including:

[0118] S402, based on the index values of the building with respect to each demand response evaluation index, the subjective weighting of each demand response evaluation index is processed using the analytic hierarchy process to obtain the subjective weight of each demand response evaluation index.

[0119] Among them, the analytic hierarchy process (AHP) is a decision analysis method, the main feature is to divide the complex problem into multiple levels, including target layer, criterion layer and scheme layer, etc., and then qualitative and quantitative analysis is carried out. The basic principle of analytic hierarchy process is to decompose the decision problem and its related factors according to the hierarchical structure, and then compare each level element with each other to determine their relative importance to the upper level element. This method combines the advantages of qualitative and quantitative decision making, making the decision making process more systematic and quantitative.

[0120] Specifically, the demand response potential analysis platform uses the analytic hierarchy process to respectively perform subjective weighting processing on each demand response evaluation index based on the respective index values of the building with respect to each demand response evaluation index.

[0121] In some embodiments, the demand response potential analysis platform obtains the relative importance relationship between each demand response evaluation index, which is determined by the decision maker, i.e. relevant professionals or experts and scholars. The relative importance relationship can be represented by a numerical value, for example, it can be assigned a value of 1-9. According to the numerical value, a judgment matrix is established between each demand response evaluation index under each criterion layer. The judgment matrix is checked for consistency to determine whether there is a contradiction in the relative importance relationship. If there is a contradiction, the relative importance relationship is reacquired. If there is no contradiction, the global weight of each demand response evaluation index is calculated according to the relative weight of each demand response evaluation index in the layer, and the global weight is determined as the subjective weight of the demand response evaluation index, completing the subjective weighting operation of each demand response evaluation index.

[0122] S404, according to the respective index values of the building with respect to each demand response evaluation index, the index entropy value of each demand response evaluation index is calculated.

[0123] Among them, the index entropy value is used to quantify the uncertainty degree of the information amount contained in the demand response evaluation index.

[0124] Specifically, the demand response potential analysis platform can calculate the index entropy value of each demand response evaluation index according to the respective index values of the building with respect to each demand response evaluation index.

[0125] In some of the embodiments, the number of buildings to be evaluated is m, and the number of demand response evaluation indexes is n. The data is standardized and normalized by the following formula:

[0126]

[0127] ;

[0128] wherein X ij , Y ij , P ij respectively represent the original data, the standardized data, and the normalized data of the i-th building with respect to the j-th demand response evaluation index.

[0129] After obtaining the normalized data, the index entropy value of each demand response evaluation index can be calculated according to the following formula:

[0130]

[0131] wherein, if P ij = 0, then define , E j represents the index entropy value of the j-th demand response evaluation index.

[0132] S406, determine the respective correction weight of each demand response evaluation index based on the index entropy value of each demand response evaluation index.

[0133] wherein the correction weight is an improvement parameter for improving the traditional entropy weight method. When the entropy value of an index is close to 1, a slight change in the entropy value will cause the weight to increase or decrease exponentially when the weight is calculated by the traditional entropy weight method. This will cause the index to be assigned an incorrect weight, so the correction weight is introduced to correct the entropy weight to solve the problem of incorrect weight calculation.

[0134] Specifically, the demand response potential analysis platform can determine the respective correction weight of each demand response evaluation index based on the index entropy value of each demand response evaluation index.

[0135] In some embodiments, the demand response potential analysis platform can calculate the correction weight of the demand response evaluation index by the following formula:

[0136]

[0137]

[0138] wherein I represents the number of indexes after removing the indexes with an entropy value of 1, represents the average value of the index entropy value of the demand response evaluation index after removing the indexes with an entropy value of 1, and W ij is the correction weight of the demand response evaluation index.

[0139] S408, according to the index entropy value and the respective correction weight of each demand response evaluation index, calculate the weight of each demand response evaluation index to obtain the respective objective weight of each demand response evaluation index.

[0140] ​Specifically, after obtaining the respective correction weights of the demand response evaluation indexes, the demand response potential analysis platform can calculate the respective objective weights of the demand response evaluation indexes according to the respective correction weights and the respective entropy values of the demand response evaluation indexes.

[0141] In some embodiments, the demand response potential analysis platform can first calculate the respective initial objective weights of the demand response evaluation indexes, which are the weights calculated by using the traditional entropy weight method.

[0142] The demand response potential analysis platform can calculate the initial objective weights of the demand response evaluation indexes by using the following formula:

[0143]

[0144] wherein n is the number of the demand response evaluation indexes, W j is the initial objective weight calculated by using the traditional entropy weight method.

[0145] Subsequently, the demand response potential analysis platform can calculate the objective weights of the demand response evaluation indexes according to the initial objective weights and the correction weights of the demand response evaluation indexes.

[0146] The demand response potential analysis platform can calculate the objective weights of the demand response evaluation indexes by using the following formula:

[0147] ;

[0148] wherein W oj is the objective weight of the final demand response evaluation index.

[0149] In the above embodiments, the subjective weighting of the demand response evaluation indexes is processed by using the analytic hierarchy process, and the objective weighting of the demand response evaluation indexes is processed by using the improved entropy weight method, which can effectively improve the determination accuracy of the subjective weights and the objective weights of the demand response evaluation indexes and reduce the weight determination error.

[0150] In some embodiments, S206, the weight comprehensive analysis of the demand response evaluation indexes is performed according to the subjective weights and the objective weights to obtain the comprehensive weights of the demand response evaluation indexes, including:

[0151] According to the subjective weights and the objective weights, the index minimum information entropy function of the demand response evaluation indexes is constructed. The index minimum information entropy function is solved by using the Lagrange multiplier to obtain the comprehensive weights of the demand response evaluation indexes.

[0152] To combine the characteristics of subjective and objective weighting methods, a comprehensive algorithm is needed to obtain a combined weight. The demand response potential analysis platform can use the principle of minimum information entropy to calculate the combined weight. The principle of minimum information entropy treats the subjective and objective weights as known probability distributions and calculates the distribution of the unknown combined weight according to the principle of minimizing relative information entropy. Compared to The relative information entropy is defined as The smaller the relative information entropy, the closer the probability distributions of P and Q are. Based on the principle of minimum information entropy, the comprehensive weight can be calculated to make it as close as possible to the subjective and objective weight distributions. Thus, an optimization model can be established, with the objective function being to minimize the sum of the relative information entropies of the comprehensive weight relative to the subjective and objective weights. The constraints are that the sum of the comprehensive weights is 1, and each weight is between 0 and 1.

[0153] Specifically, the demand response potential analysis platform can construct a minimum information entropy function for demand response assessment indicators based on subjective and objective weights. The comprehensive weights of the demand response assessment indicators are then obtained by solving the minimum information entropy function using Lagrange multipliers.

[0154] In some embodiments, the demand response potential analysis platform calculates the comprehensive weight of the demand response assessment metrics using the following formula:

[0155] ;

[0156] Among them, W sj W represents the subjective weight of the j-th demand response evaluation indicator. oj W represents the objective weight of the j-th demand response evaluation indicator. j Let represent the comprehensive weight of the j-th demand response evaluation index. Solving the above optimization problem using Lagrange multipliers yields:

[0157] ;

[0158] In the above embodiments, by using the principle of minimum information entropy to perform a comprehensive weight analysis on the demand response evaluation indicators, the final comprehensive weight can combine the characteristics of subjective weighting and objective weighting methods, taking into account both subjective expert opinions and objective data information, thus effectively improving the accuracy of the comprehensive weight allocation.

[0159] In some embodiments, S208, a demand response potential analysis of the building is performed based on each comprehensive weight to obtain the demand response potential result of the building, including:

[0160] The load closeness degree of each building is evaluated based on the comprehensive weight of each demand response evaluation index, and a load closeness degree evaluation result of each building is obtained.

[0161] The load closeness degree evaluation result is used to represent the load similarity between the power load of each building and the regional power load of the region where the building is located.

[0162] Specifically, the demand response potential analysis platform can evaluate the load closeness degree of each building based on the comprehensive weight of each demand response evaluation index, and obtain the load closeness degree evaluation result of each building. The demand response potential of each building is determined based on the load similarity between the power load of each building and the regional power load of the region where the building is located.

[0163] In some embodiments, the load closeness degree evaluation model is pre-set in the demand response potential analysis platform. After obtaining the comprehensive weight of each demand response evaluation index, the demand response potential analysis platform can call the load closeness degree evaluation model to evaluate the load closeness degree of each building according to the comprehensive weight of each demand response evaluation index, and obtain the load closeness degree evaluation result of each building.

[0164] In some embodiments, the load closeness degree evaluation result can be the load closeness degree of each building. After obtaining the load closeness degree of each building, the demand response potential analysis platform can sort the buildings in descending order based on the size of the load closeness degree, and obtain the sorting result of each building. According to the sorting result and a preset potential result division rule, the demand response potential of each building is determined. For example, if the preset potential result division rule is to determine the top 10% of the sorting result as having strong demand response potential, the middle 50% of the sorting result as having medium demand response potential, and the last 40% of the sorting result as having low demand response potential, the demand response potential analysis platform can determine the demand response potential of each building based on the preset potential result division rule, i.e., the building has strong demand response potential, medium demand response potential, or low demand response potential.

[0165] In other embodiments, the demand response potential analysis platform can directly use the load closeness degree of each building as the demand response potential value of each building, and obtain the demand response potential result of each building.

[0166] In the above embodiments, by evaluating the load closeness degree of each building, the load similarity between the power load of each building and the regional power load of the region where the building is located can be determined. Based on the principle that the higher the load similarity, the greater the potential, the demand response potential of each building is determined according to the load closeness degree evaluation result of each building, which can effectively improve the accuracy of the demand response potential result.

[0167] In some embodiments, as shown in FIG. 4, the building load closeness evaluation is performed based on the comprehensive weights, to obtain a building load closeness evaluation result, including: Figure 5

[0168] S502, using the improved TOPSIS method, determining the absolute ideal solution distance of the building based on the comprehensive weights.

[0169] The improved TOPSIS (ITOPSIS) is a ranking method obtained by optimizing and improving the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method. The optimal reference scheme can be found by defining the positive and negative ideal points, and then the distance between the candidate scheme and the optimal reference scheme is calculated to rank and select the optimal scheme.

[0170] Specifically, the demand response potential analysis platform can use the improved TOPSIS method to determine the absolute ideal solution distance of the building based on the comprehensive weights.

[0171] In some embodiments, the demand response potential analysis platform can first establish a standardized matrix and weight it to obtain a weighted matrix. In the weighted matrix, each row represents a scheme, i.e., a building, and each column represents a demand response evaluation index. Then, the expressions of the positive ideal solution and the negative ideal solution are determined, and the distance between each scheme and the positive and negative ideal solutions, i.e., the absolute ideal solution distance of each building, is calculated based on the weighted matrix. The absolute ideal solution distance includes the positive absolute ideal solution distance and the negative absolute ideal solution distance.

[0172] S504, using the improved gray correlation method, determining the gray correlation coefficient of each demand response evaluation index based on the comprehensive weights.

[0173] The improved gray correlation method (IGC) is an optimization and improvement based on the traditional gray correlation analysis. The gray correlation analysis method is a gray system analysis method that measures the correlation between factors based on the similarity or difference between the development trends of the factors, i.e., the gray correlation degree. The basic idea is to determine the geometric shape similarity of the reference data column and several comparison data columns to judge whether they are closely related.

[0174] Specifically, the demand response potential analysis platform can use the improved gray correlation method to determine the gray correlation coefficient of each demand response evaluation index based on the comprehensive weights. ​

[0175] In some embodiments, the demand response potential analysis platform can calculate the positive grey correlation degree coefficient of each scheme with the positive ideal solution scheme based on the above-mentioned weighted matrix, and calculate the negative grey correlation degree coefficient of each scheme with the negative ideal solution scheme according to the same method, and determine the positive grey correlation degree coefficient and the negative grey correlation degree coefficient as the grey correlation degree coefficient of each building.

[0176] S506, the absolute ideal solution and the grey correlation degree coefficient are standardized and normalized to obtain the processed absolute ideal solution and the grey correlation degree coefficient.

[0177] Specifically, the demand response potential analysis platform can standardize and normalize the absolute ideal solution and the grey correlation degree coefficient to obtain the processed absolute ideal solution and the grey correlation degree coefficient.

[0178] S508, based on the processed absolute ideal solution and the grey correlation degree coefficient, an ideal solution minimum information entropy function is constructed.

[0179] Specifically, the demand response potential analysis platform can construct an ideal solution minimum information entropy function based on the processed absolute ideal solution and the grey correlation degree coefficient.

[0180] In some embodiments, the function expression of the ideal solution minimum information entropy function is as follows:

[0181] ;

[0182] wherein, represents the absolute ideal solution distance of the i th scheme, represents the grey correlation degree coefficient of the i th scheme.

[0183] S510, the ideal solution minimum information entropy function is solved using a Lagrange multiplier to obtain the load closeness degree evaluation result of the building.

[0184] Specifically, the demand response potential analysis platform uses a Lagrange multiplier to solve the ideal solution minimum information entropy function to obtain the load closeness degree evaluation result of the building.

[0185] In some embodiments, the above-mentioned ideal solution minimum information entropy function is obtained by Lagrange multiplier solution:

[0186]

[0187] ;

[0188] wherein, represents the comprehensive closeness degree of the finally obtained positive ideal solution, represents the comprehensive closeness degree of the finally obtained negative ideal solution, denotes the negative absolute ideal solution distance of the i-th scheme, denotes the negative grey correlation coefficient of the i-th scheme.

[0189] Finally, the demand response potential analysis platform can determine the load closeness degree evaluation result of the building based on the comprehensive closeness degree of the positive ideal solution and the comprehensive closeness degree of the negative ideal solution, i.e., the load closeness degree of the building:

[0190] ;

[0191] wherein, I i i.e., the load closeness degree of the building, I i is larger, the higher the demand response capability ranking of the building is.

[0192] In the above embodiment, by using the ITOPSIS-IGC method combining the improved TOPSIS method and the improved grey correlation method, compared with the traditional evaluation method, the model can fully utilize all the data, fuse the building electricity consumption characteristic information contained in different dimension indicators for comprehensive evaluation, and avoid the one-sidedness defect brought by single dimension indicator evaluation.

[0193] In some embodiments, as shown in Figure 6 a building demand response potential determination method is provided, the method comprising the following steps:

[0194] Firstly, the demand response potential analysis platform can obtain building power load information of each type of building, and regional power load information of each building in the region. According to the preset demand response potential evaluation system, determine each demand response evaluation index which has an influence on the demand response potential of the building, and the index calculation method of each demand response evaluation index. For each demand response evaluation index, based on the building power load information and / or the regional power load information of each building, and the index calculation method of the demand response evaluation index, calculate the index value of each building relative to the demand response evaluation index.

[0195] Based on the index value of each building relative to each demand response evaluation index, use the analytic hierarchy process to respectively perform subjective weighting processing on each demand response evaluation index, to obtain the subjective weight of each demand response evaluation index. Use the improved entropy weight method to respectively perform objective weighting processing on each demand response evaluation index, to obtain the objective weight of each demand response evaluation index. According to the subjective weight and the objective weight of each demand response evaluation index, calculate the comprehensive weight of each demand response evaluation index by using the minimum information entropy principle.

[0196] The comprehensive weight of each demand response index is applied to the evaluation method using the minimum information entropy principle combined with ITOPSIS-IGC, and the demand response potential of each type of building is sorted and analyzed to obtain the demand response potential result of each type of building.

[0197] In the above embodiments, by combining the characteristics of the subjective weighting method and the objective weighting method, the minimum information entropy principle is used to calculate the comprehensive weight, so that the weight design takes into account subjective expert opinions and objective data information. At the same time, the ITOPSIS-IGC method combining the improved TOPSIS sorting method and the improved grey correlation degree method is proposed. Compared with the traditional evaluation method, the model can fully utilize all the data, fuse the building power consumption characteristics information contained in different dimension indexes for comprehensive evaluation, and avoid the one-sidedness defect brought by single dimension index evaluation.

[0198] It should be understood that, although each step in the flowchart involved in each embodiment as described above is displayed in sequence according to the arrow, these steps are not necessarily executed in sequence according to the arrow. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other orders. Moreover, at least part of the steps in the flowchart involved in each embodiment as described above can include multiple steps or stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution sequence of these steps or stages is not necessarily sequential, but can be executed in rotation or alternation with at least part of other steps or steps or stages in other steps.

[0199] Based on the same inventive concept, the embodiments of the present application also provide a building demand response potential determination device for implementing the building demand response potential determination method described above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the above method, so the specific limitations in one or more building demand response potential determination device embodiments provided below can refer to the limitations of the building demand response potential determination method in the above text, which will not be repeated here.

[0200] In one embodiment, as shown in Figure 7 a building demand response potential determination device 700 is provided, comprising: an index value determination module 701, a subjective and objective weighting module 702, a weight comprehensive analysis module 703, and a demand response potential determination module 704, wherein:

[0201] The index value determination module 701 is configured to determine the index value of each demand response evaluation index of each building according to the building power load information of each type of building and the regional power load information of the region where each building is located.

[0202] The subjective and objective weighting module 702 is configured to perform subjective and objective weighting processing on each demand response evaluation index based on the index value of each demand response evaluation index of the building, to obtain a subjective weight and an objective weight of each demand response evaluation index.

[0203] The weight comprehensive analysis module 703 is configured to perform weight comprehensive analysis on each demand response evaluation index according to the subjective weight and the objective weight, to obtain a comprehensive weight of the demand response evaluation index.

[0204] The demand response potential determination module 704 is configured to perform demand response potential analysis on the building based on the comprehensive weight, to obtain a demand response potential result of the building.

[0205] In some embodiments, the index value determination module 701 is configured to: obtain building power load information of each type of building, and regional power load information of a region in which each building is located; determine, according to a preset demand response potential evaluation system, each demand response evaluation index that has an influence on the demand response potential of the building, and an index calculation manner of each demand response evaluation index; and for each demand response evaluation index, calculate an index value of each building with respect to the demand response evaluation index based on the building power load information and / or the regional power load information of each building, and the index calculation manner of the demand response evaluation index.

[0206] In some embodiments, the subjective and objective weighting module 702 is configured to: perform subjective weighting processing on each demand response evaluation index using an analytic hierarchy process based on the index value of each demand response evaluation index of the building, to obtain a subjective weight of each demand response evaluation index; calculate an index entropy value of each demand response evaluation index according to the index value of each demand response evaluation index of the building; determine a correction weight of each demand response evaluation index based on the index entropy value; and perform weight calculation on each demand response evaluation index according to the index entropy value and the correction weight of each demand response evaluation index, to obtain an objective weight of each demand response evaluation index.

[0207] In some embodiments, the weight comprehensive analysis module 703 is configured to: construct an index minimum information entropy function of the demand response evaluation index according to the subjective weight and the objective weight; and solve the index minimum information entropy function using a Lagrange multiplier, to obtain a comprehensive weight of the demand response evaluation index.

[0208] In some embodiments, the demand response potential determination module 704 is used to: perform load proximity assessment on buildings based on each comprehensive weight to obtain the load proximity assessment result of the buildings; the load proximity assessment result is used to characterize the load similarity between the building's own power load and the regional power load of the area where it is located; and determine the demand response potential result of each building based on the load proximity assessment result of each building.

[0209] In some embodiments, the demand response potential determination module 704 is further configured to: determine the absolute ideal solution distance of a building based on each comprehensive weight using an improved approximation ideal solution ranking method; determine the gray relational coefficient of a building based on each comprehensive weight using an improved gray relational degree method; standardize and normalize the absolute ideal solution and gray relational coefficient to obtain the processed absolute ideal solution and gray relational coefficient; construct the minimum information entropy function of the ideal solution based on the processed absolute ideal solution and gray relational coefficient; and solve the minimum information entropy function of the ideal solution using Lagrange multipliers to obtain the building's load proximity assessment result.

[0210] The modules in the aforementioned building demand response potential determination device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the operations corresponding to each module.

[0211] In one embodiment, a computer device is provided, which may be a terminal or server integrating a demand response potential analysis platform, and its internal structure diagram may be as follows: Figure 8 As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores data such as building power load information, regional power load information, individual index values ​​of various demand response assessment indicators, subjective and objective weights of the demand response assessment indicators, comprehensive weights of the demand response assessment indicators, and building demand response potential results. The network interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for determining building demand response potential.

[0212] Those skilled in the art will understand that Figure 8The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0213] In one embodiment, a computer device is provided, comprising a memory and a processor, the memory storing a computer program, and the processor implementing the specific implementation steps of the building demand response potential determination method when executing the computer program.

[0214] In one embodiment, a computer readable storage medium is provided, which stores a computer program, and the computer program implements the specific implementation steps of the building demand response potential determination method when executed by a processor.

[0215] In one embodiment, a computer program product is provided, comprising a computer program, and the computer program implements the specific implementation steps of the building demand response potential determination method when executed by a processor.

[0216] It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties. And the acquisition, storage, processing, transmission, etc. of the data comply with the relevant provisions of laws and regulations.

[0217] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when the computer program is executed, the processes of the above-mentioned embodiments of the methods can be included. Any reference to memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0218] Any combination of the technical features of the above embodiments can be made. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combination of the technical features does not exist, it should be considered as the scope of the present application.

[0219] The above embodiments only express several implementation manners of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent of the present application. It should be pointed out that for ordinary skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are within the scope of protection of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A method for determining the demand response potential of a building, characterized in that, The method includes: Based on the building power load information of each type of building and the regional power load information of the area where each building is located, determine the index value of each building relative to each demand response assessment index; For each building, based on the building's index value relative to each demand response assessment index, subjective and objective weighting processes are applied to each demand response assessment index to obtain the subjective weight and objective weight of each demand response assessment index. For each of the aforementioned demand response evaluation indicators, a comprehensive weight analysis is performed on the demand response evaluation indicators based on the subjective weight and the objective weight to obtain the comprehensive weight of the demand response evaluation indicators. An improved method for ranking approximate ideal solutions is used to determine the absolute ideal solution distance of the building based on the comprehensive weights of each solution. The improved grey relational analysis method is used to determine the grey relational coefficient of the building based on the comprehensive weights of each building. The absolute ideal solution and the gray relational coefficient are standardized and normalized to obtain the processed absolute ideal solution and gray relational coefficient. Based on the processed absolute ideal solution and the grey relational coefficient, a minimum information entropy function for the ideal solution is constructed. The minimum information entropy function of the ideal solution is solved using Lagrange multipliers to obtain the load proximity assessment result of the building; the load proximity assessment result is used to characterize the degree of similarity between the building's own power load and the regional power load of the area where it is located; Based on the load proximity assessment results of each building, the demand response potential of each building is determined.

2. The method according to claim 1, characterized in that, The step of determining the respective index values ​​of each building relative to each demand response assessment index based on the building power load information of each type of building and the regional power load information of the area where each building is located includes: Obtain building power load information for each type of building, as well as regional power load information for the area where each building is located; Based on the pre-set demand response potential evaluation system, determine the demand response assessment indicators that affect the demand response potential of the building, as well as the calculation method of each of the aforementioned demand response assessment indicators. For each demand response assessment indicator, based on the building power load information and / or regional power load information of each building, and the indicator calculation method of the demand response assessment indicator, the indicator value of each building relative to the demand response assessment indicator is calculated.

3. The method according to claim 1, characterized in that, Based on the building's index value relative to each of the demand response assessment indicators, subjective and objective weighting processes are applied to each demand response assessment indicator to obtain the subjective weight and objective weight of each demand response assessment indicator, including: Based on the building's index value relative to each of the demand response assessment indicators, the Analytic Hierarchy Process (AHP) is used to subjectively assign weights to each of the demand response assessment indicators to obtain the subjective weights of each demand response assessment indicator. Based on the index values ​​of the building relative to each of the respective demand response assessment indicators, calculate the index entropy value of each of the respective demand response assessment indicators; The corrected weights for each of the aforementioned demand response assessment indicators are determined based on their entropy values. Based on the entropy value of each indicator and the corrected weight of each demand response evaluation indicator, the weights of each demand response evaluation indicator are calculated to obtain the objective weights of each demand response evaluation indicator.

4. The method according to any one of claims 1 to 3, characterized in that, The step of performing a comprehensive weight analysis on the demand response evaluation indicators based on the subjective weights and the objective weights to obtain the comprehensive weights of the demand response evaluation indicators includes: Based on the subjective weights and the objective weights, construct the minimum information entropy function of the demand response evaluation index; The minimum information entropy function of the index is solved using Lagrange multipliers to obtain the comprehensive weight of the demand response evaluation index.

5. A device for determining building demand response potential, characterized in that, The device includes: The indicator value determination module is used to determine the indicator value of each building relative to each demand response assessment indicator based on the building power load information of each type of building and the regional power load information of the area where each building is located. The subjective and objective weighting module is used to perform subjective and objective weighting on each of the demand response assessment indicators for each building, based on the indicator values ​​of the building relative to each demand response assessment indicator, to obtain the subjective weight and objective weight of each demand response assessment indicator. The weighting comprehensive analysis module is used to perform a weighting comprehensive analysis on each of the demand response evaluation indicators based on the subjective weight and the objective weight, so as to obtain the comprehensive weight of the demand response evaluation indicator. The demand response potential determination module is used to determine the absolute ideal solution distance of a building based on the comprehensive weights using an improved approximation ideal solution ranking method; to determine the grey relational coefficient of the building based on the comprehensive weights using an improved grey relational method; to standardize and normalize the absolute ideal solution and the grey relational coefficient to obtain the processed absolute ideal solution and grey relational coefficient; to construct the minimum information entropy function of the ideal solution based on the processed absolute ideal solution and grey relational coefficient; to solve the minimum information entropy function of the ideal solution using Lagrange multipliers to obtain the load proximity assessment result of the building; the load proximity assessment result is used to characterize the load similarity between the building's own power load and the regional power load of the area where it is located; and to determine the demand response potential result of each building based on its own load proximity assessment result.

6. The apparatus according to claim 5, characterized in that, The indicator value determination module is used to: acquire building power load information of various types of buildings, and regional power load information of the area where each building is located; determine, according to a preset demand response potential evaluation system, various demand response assessment indicators that affect the demand response potential of buildings, and the respective indicator calculation methods of each demand response assessment indicator; and, for each demand response assessment indicator, calculate the indicator value of each building relative to the demand response assessment indicator based on the building power load information and / or regional power load information of each building, and the indicator calculation method of the demand response assessment indicator.

7. The apparatus according to claim 5, characterized in that, The subjective and objective weighting module is used to: subjectively weight each demand response assessment indicator based on the indicator value of the building relative to each demand response assessment indicator, using the analytic hierarchy process to obtain the subjective weight of each demand response assessment indicator; calculate the indicator entropy value of each demand response assessment indicator based on the indicator value of the building relative to each demand response assessment indicator; and determine the corrected weight of each demand response assessment indicator based on the indicator entropy value. Based on the entropy value of each indicator and the corrected weight of each demand response evaluation indicator, the weights of each demand response evaluation indicator are calculated to obtain the objective weights of each demand response evaluation indicator.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 4.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 4.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 4.

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