Method and device for evaluating demand response capability of adjustable load and computer equipment

Abstract

The invention relates to a load-adjustable demand response capability evaluation method and device and computer equipment. The method comprises the steps of determining a load change trend of power load data according to the power load data of the adjustable load and a preset load prediction model, determining an acquisition frequency and acquisition precision according to the load change trend, and obtaining multi-source environment data influencing the demand response capability of the adjustable load based on the acquisition frequency and the acquisition precision. And evaluating the demand response capability of the adjustable load according to the multi-source environment data to obtain a demand response capability evaluation result of the adjustable load. In the embodiment of the invention, the acquisition frequency and the acquisition precision are dynamically adjusted based on the load change trend, so that the fluctuation of the load is accurately captured, the resource waste is reduced, further, the multi-source environment data is acquired based on the acquisition frequency and the acquisition precision, and the demand response capability of the adjustable load is evaluated in combination with the multi-source environment data. And the comprehensiveness of demand response capability evaluation of the adjustable load is improved.

Description

Technical Field

[0001] The present application relates to the field of smart grid technology, and in particular to a method, device and computer equipment for evaluating demand response capability of adjustable loads. Background Art

[0002] At present, the total installed capacity of renewable energy such as solar power and wind power continues to grow nationwide. However, a large number of renewable energy sources are intermittent, volatile and random, which leads to a widening of the peak-to-valley difference of the power grid. A large peak-to-valley difference means that the power system needs to provide more power during high-load periods, but may face the problem of excess power during low-load periods, which directly affects the stable operation of the power system and the reliability of power supply.

[0003] The large number of adjustable loads in the power system have gradually become important demand-side resources due to their large adjustable capacity, strong flexibility and high reliability. The use of adjustable loads can more effectively respond to changes in power supply and demand in the power system, help balance the grid load, and reduce power supply instability caused by power shortages or surpluses.

[0004] Therefore, a method for evaluating the demand response capability of adjustable loads is urgently needed. Summary of the invention

[0005] Based on this, it is necessary to provide a method, device and computer equipment for evaluating the demand response capability of an adjustable load, which can evaluate the demand response capability of the adjustable load, in order to address the above technical problems.

[0006] In a first aspect, the present application provides a method for evaluating demand response capability of an adjustable load, comprising:

[0007] Determining a load change trend of the power load data according to the power load data of the adjustable load and a preset load prediction model;

[0008] Determine the acquisition frequency and acquisition accuracy according to the load change trend;

[0009] Based on the acquisition frequency and the acquisition accuracy, acquiring multi-source environmental data that affects the demand response capability of the adjustable load;

[0010] The demand response capability of the adjustable load is evaluated according to the multi-source environmental data to obtain an evaluation result of the demand response capability of the adjustable load.

[0011] In one embodiment, evaluating the demand response capability of the adjustable load according to the multi-source environmental data to obtain the demand response capability evaluation result of the adjustable load includes:

[0012] Performing standardization processing on the multi-source environmental data to obtain standardized multi-source environmental data;

[0013] Performing correlation analysis on the standardized multi-source environmental data to determine different types of data to be evaluated;

[0014] The demand response capability of the adjustable load is evaluated according to each of the data to be evaluated, and an evaluation result of the demand response capability of the adjustable load is obtained.

[0015] In one embodiment, the performing correlation analysis on the standardized multi-source environmental data to determine different types of data to be evaluated includes:

[0016] Performing a correlation analysis on two types of environmental data in the standardized multi-source environmental data to obtain a first correlation coefficient between the two types of environmental data;

[0017] According to each of the first correlation coefficients and the first preset correlation coefficient, target environmental data is determined from the multi-source environmental data, and the target environmental data and the power load data are used as the data to be evaluated.

[0018] In one embodiment, determining target environment data from the multi-source environment data according to each of the first correlation coefficients and the first preset correlation coefficient includes:

[0019] Determining candidate environmental data from the multi-source environmental data according to the plurality of first correlation coefficients and a first preset correlation coefficient;

[0020] Performing correlation analysis on each type of environmental data in the candidate environmental data and the power load data to obtain second correlation coefficients;

[0021] The target environment data is determined from the candidate environment data according to the second correlation coefficient and the second preset correlation coefficient.

[0022] In one embodiment, the step of evaluating the demand response capability of the adjustable load according to each of the data to be evaluated to obtain an evaluation result of the demand response capability of the adjustable load includes:

[0023] For each type of data to be evaluated, according to a preset spatiotemporal dynamic model and the type of data to be evaluated, determining the temporal characteristics and spatial characteristics of the data to be evaluated;

[0024] According to the temporal characteristics and spatial characteristics of the data to be evaluated, weight coefficients of the data to be evaluated of the type at different time scales are obtained;

[0025] According to the weight coefficients of the types of data to be evaluated at different time scales and the data to be evaluated, the demand response capability of the adjustable load is evaluated to obtain an evaluation result of the demand response capability of the adjustable load.

[0026] In one embodiment, the method further comprises:

[0027] Determining each load to be controlled in the adjustable load according to the demand response capability evaluation result of the adjustable load;

[0028] Based on the demand response capability evaluation result of each load to be controlled, each load to be controlled is dynamically controlled.

[0029] In a second aspect, the present application also provides a device for evaluating demand response capability of an adjustable load, comprising:

[0030] A first determination module, configured to determine a load change trend of the power load data according to the power load data of the adjustable load and a preset load prediction model;

[0031] A second determination module is used to determine the collection frequency and collection accuracy according to the load change trend;

[0032] An acquisition module, configured to acquire multi-source environmental data that affects the demand response capability of the adjustable load based on the acquisition frequency and the acquisition accuracy;

[0033] An evaluation module is used to evaluate the demand response capability of the adjustable load according to the multi-source environmental data to obtain an evaluation result of the demand response capability of the adjustable load.

[0034] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the method steps provided in the first aspect when executing the computer program.

[0035] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method steps provided in the first aspect.

[0036] In a fifth aspect, the present application also provides a computer program product, including a computer program, which implements the method steps provided in the first aspect when executed by a processor.

[0037] The above-mentioned method, device and computer equipment for evaluating the demand response capability of the adjustable load determine the load change trend of the power load data according to the power load data of the adjustable load and the preset load forecasting model, determine the acquisition frequency and acquisition accuracy according to the load change trend, obtain multi-source environmental data that affects the demand response capability of the adjustable load based on the acquisition frequency and acquisition accuracy, evaluate the demand response capability of the adjustable load based on the multi-source environmental data, and obtain the evaluation result of the demand response capability of the adjustable load. In the embodiment of the present application, the time series prediction model is used to predict the load change trend in real time, and the acquisition frequency and acquisition accuracy can be dynamically adjusted based on the load change trend, so as to accurately capture the load fluctuation and reduce resource waste. Compared with the traditional fixed frequency acquisition method, the timeliness and pertinence of data acquisition are significantly improved. Furthermore, based on the acquisition frequency and acquisition accuracy, multi-source environmental data are obtained, and the demand response capability of the adjustable load is evaluated in combination with the multi-source environmental data, thereby improving the comprehensiveness of the demand response capability evaluation of the adjustable load. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions 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 related drawings can be obtained based on these drawings without paying creative work.

[0039] Figure 1 A diagram of an application environment of a method for evaluating demand response capability of an adjustable load in one embodiment;

[0040] Figure 2 A schematic flow chart of a method for evaluating demand response capability of an adjustable load in one embodiment;

[0041] Figure 3 A flowchart of a method for determining a demand response capability evaluation result in one embodiment;

[0042] Figure 4 is a flow chart of a method for determining data to be evaluated in one embodiment;

[0043] Figure 5 A schematic diagram of a flow chart of a method for determining target environment data in one embodiment;

[0044] Figure 6 A flowchart of a demand response capability evaluation result in another embodiment;

[0045] Figure 7 It is a structural block diagram of a device for evaluating demand response capability of an adjustable load in one embodiment. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0047] The method for evaluating the demand response capability of an adjustable load provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown in FIG. 1 , a computer device may be a server, and its internal structure diagram may be as shown in FIG. Figure 1 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store relevant data for evaluating the demand response capability of adjustable loads. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for evaluating the demand response capability of an adjustable load is implemented. The server can be an independent physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0048] Those skilled in the art will understand that Figure 1 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0049] In an exemplary embodiment, Figure 2 As shown in the figure, a method for evaluating the demand response capability of adjustable load is provided. Figure 1 The computer device in the example is used to illustrate, including the following S201 to S204. Among them:

[0050] S201, determining a load change trend of the power load data according to the power load data of the adjustable load and a preset load prediction model.

[0051] Optionally, the power load data includes active power, reactive power, power factor, voltage and current, load curve, peak load and load rate, etc.

[0052] In the embodiment of the present application, the power load data can be input into the load forecasting model to obtain the load change trend of the power load data. The load change trend can be a stable state or a significant fluctuation; the load change trend can also be the tilt degree of the power load data.

[0053] In a possible implementation, redundant data in the power load data may be processed first, and then the processed data may be input into a load prediction model to obtain a load change trend of the power load data.

[0054] Optionally, the load forecasting model can be a deep learning network or a support vector machine, etc.

[0055] S202, determining the collection frequency and collection accuracy according to the load change trend.

[0056] In the embodiment of the present application, the greater the fluctuation degree of the load change trend, the higher the acquisition frequency and acquisition accuracy. For example, when the load change trend indicates that the load may fluctuate significantly, the acquisition frequency and acquisition accuracy are increased to capture key changes; when the load change trend indicates that the load is relatively stable, the acquisition frequency and acquisition accuracy can be reduced to reduce unnecessary resource consumption and data storage costs.

[0057] Optionally, the collection frequency may be real-time data collection, minute-level data collection, hour-level data collection, event-driven data collection, daily, monthly, and annual data collection.

[0058] Optionally, the acquisition accuracy can be analog signal acquisition, digital signal acquisition, high-precision intelligent power load management data acquisition terminal, etc.

[0059] S203, based on the acquisition frequency and the acquisition accuracy, obtain multi-source environmental data that affects the demand response capability of the adjustable load.

[0060] Optionally, the multi-source environmental data may include meteorological data, electricity market prices, user behavior patterns, etc. Meteorological data is one type of the multi-source environmental data, electricity market prices is another type, and user behavior patterns is another type.

[0061] S204, evaluating the demand response capability of the adjustable load according to the multi-source environmental data to obtain an evaluation result of the demand response capability of the adjustable load.

[0062] In an embodiment of the present application, multi-source environmental data can be standardized to obtain standardized multi-source environmental data, correlation analysis can be performed on the standardized multi-source environmental data to determine different types of data to be evaluated, and the demand response capability of the adjustable load can be evaluated based on each data to be evaluated to obtain an evaluation result of the demand response capability of the adjustable load.

[0063] In one possible implementation, different weight coefficients may be set for the multi-source environmental data and the power load data, respectively, and a weighted sum may be taken based on the multi-source environmental data, the power load data and the corresponding weight coefficients to obtain an evaluation result of the demand response capability of the adjustable load.

[0064] In the above-mentioned method for evaluating the demand response capability of an adjustable load, the load change trend of the power load data is determined based on the power load data of the adjustable load and a preset load forecasting model, and the acquisition frequency and acquisition accuracy are determined based on the load change trend. Based on the acquisition frequency and acquisition accuracy, multi-source environmental data that affects the demand response capability of the adjustable load is obtained, and the demand response capability of the adjustable load is evaluated based on the multi-source environmental data to obtain an evaluation result of the demand response capability of the adjustable load. In an embodiment of the present application, a time series prediction model is used to predict the load change trend in real time, and the acquisition frequency and acquisition accuracy can be dynamically adjusted based on the load change trend, thereby accurately capturing load fluctuations and reducing resource waste. Compared with the traditional fixed frequency acquisition method, the timeliness and pertinence of data acquisition are significantly improved. Furthermore, multi-source environmental data is acquired based on the acquisition frequency and acquisition accuracy, and the demand response capability of the adjustable load is evaluated in combination with the multi-source environmental data, thereby improving the comprehensiveness of the evaluation of the demand response capability of the adjustable load.

[0065] Figure 3 FIG. 1 is a flow chart of a method for determining a demand response capability evaluation result in an embodiment. Figure 3 As shown, the embodiment of the present application relates to a possible implementation method of how to evaluate the demand response capability of an adjustable load according to multi-source environmental data to obtain an evaluation result of the demand response capability of the adjustable load, including the following steps:

[0066] S301, performing standardization processing on multi-source environmental data to obtain standardized multi-source environmental data.

[0067] In an embodiment of the present application, for each type of environmental data in the multi-source environmental data, the maximum value and the minimum value in the environmental data of that type are obtained, and the environmental data of that type is standardized according to the maximum value and the minimum value to obtain the standardized environmental data.

[0068] S302, performing correlation analysis on the standardized multi-source environmental data to determine different types of data to be evaluated.

[0069] In an embodiment of the present application, for any two types of environmental data in the standardized multi-source environmental data, a correlation analysis is performed on the two types of environmental data to obtain a first correlation coefficient between the two types of environmental data, and based on each first correlation coefficient and a first preset correlation coefficient, target environmental data is determined from the multi-source environmental data, and the target environmental data and power load data are used as data to be evaluated.

[0070] In one possible implementation, a correlation analysis can be performed on two types of environmental data in the standardized multi-source environmental data to obtain a first correlation coefficient between the two types of environmental data. Based on each first correlation coefficient and a first preset correlation coefficient, target environmental data can be determined from the multi-source environmental data, and the target environmental data can be used as data to be evaluated.

[0071] S303, evaluating the demand response capability of the adjustable load according to each data to be evaluated, and obtaining an evaluation result of the demand response capability of the adjustable load.

[0072] In an embodiment of the present application, for each type of data to be evaluated, the time characteristics and spatial characteristics of the data to be evaluated can be determined according to a preset spatiotemporal dynamic model and the type of data to be evaluated, and based on the time characteristics and spatial characteristics of the data to be evaluated, the weight coefficient of the type of data to be evaluated on different time scales can be obtained. Based on the weight coefficient of each type of data to be evaluated on different time scales and the data to be evaluated, the demand response capability of the adjustable load is evaluated to obtain an evaluation result of the demand response capability of the adjustable load.

[0073] In a possible implementation, different weight coefficients may be preset for each type of data to be evaluated, and weighted sums are performed on each type of data to be evaluated and the corresponding weight coefficients to obtain an evaluation result of the demand response capability of the adjustable load.

[0074] In an embodiment of the present application, the multi-source environmental data is standardized to obtain standardized multi-source environmental data; the standardized multi-source environmental data is subjected to correlation analysis to determine different types of data to be evaluated; the demand response capability of the adjustable load is evaluated according to each data to be evaluated to obtain an evaluation result of the demand response capability of the adjustable load. The embodiment of the present application combines multi-source environmental data, and by standardizing and analyzing the correlation of the multi-source environmental data, the collection priority and data source can be automatically adjusted according to real-time environmental changes, so that the demand response system can accurately optimize the evaluation of demand response capability. This method breaks through the limitations of traditional single data source or static collection strategies, significantly improves the adaptability and responsiveness of the system, and improves the adaptability of the demand response system to external environmental changes.

[0075] Figure 4 FIG. 1 is a flow chart of a method for determining data to be evaluated in one embodiment. Figure 4 As shown, the embodiment of the present application relates to a possible implementation method of how to perform correlation analysis on standardized multi-source environmental data to determine different types of data to be evaluated, including the following steps:

[0076] S401, performing correlation analysis on two types of environmental data in the standardized multi-source environmental data to obtain a first correlation coefficient between the two types of environmental data.

[0077] In an embodiment of the present application, two types of environmental data in the standardized multi-source environmental data can be subjected to correlation analysis based on an empirical mode decomposition method, a Pearson correlation coefficient method, a Spearman rank correlation coefficient method, etc., to obtain a first correlation coefficient between the two types of environmental data. For example, the standardized multi-source environmental data includes three types of environmental data, namely, meteorological data, electricity market prices, and user behavior patterns. The first correlation coefficient between meteorological data and electricity market prices, the first correlation coefficient between electricity market prices and user behavior patterns, and the first correlation coefficient between meteorological data and user behavior patterns are obtained respectively.

[0078] S402, determining target environmental data from the multi-source environmental data according to each first correlation coefficient and the first preset correlation coefficient, and using the target environmental data and the power load data as data to be evaluated.

[0079] In the embodiment of the present application, each first correlation coefficient can be compared with the first preset correlation coefficient. If the first correlation coefficient is greater than the first preset correlation coefficient, the environmental data corresponding to the first correlation coefficient is used as the candidate environmental data. Correlation analysis is performed on various types of environmental data and power load data in the candidate environmental data to obtain each second correlation coefficient. According to each second correlation coefficient and the second preset correlation coefficient, the target environmental data is determined from the candidate environmental data, and the target environmental data and power load data are used as the data to be evaluated.

[0080] In a possible implementation, after the candidate environment data is obtained, the candidate environment data is directly used as the target environment data, and the target environment data and the power load data are used as the data to be evaluated.

[0081] In an embodiment of the present application, a correlation analysis is performed on two types of environmental data in the standardized multi-source environmental data to obtain a first correlation coefficient between the two types of environmental data, and the target environmental data is determined from the multi-source environmental data according to each first correlation coefficient and the first preset correlation coefficient, and the target environmental data and the power load data are used as data to be evaluated. In an embodiment of the present application, the multi-source environmental data is standardized, and the standardized multi-source environmental data is correlated and analyzed to obtain the data to be evaluated, and the data source of the collected data to be evaluated can be dynamically adjusted.

[0082] Figure 5 FIG. 1 is a flow chart of a method for determining target environment data in an embodiment. Figure 5 As shown, the embodiment of the present application relates to a possible implementation method of how to determine target environment data from multi-source environment data according to each first correlation coefficient and a first preset correlation coefficient, including the following steps:

[0083] S501 : Determine candidate environmental data from multi-source environmental data according to a plurality of first correlation coefficients and a first preset correlation coefficient.

[0084] In an embodiment of the present application, environmental data corresponding to a first correlation coefficient greater than a first preset correlation coefficient is determined as candidate environmental data. For example, among the first correlation coefficient between meteorological data and electricity market prices, the first correlation coefficient between electricity market prices and user behavior patterns, and the first correlation coefficient between meteorological data and user behavior patterns, if the first correlation coefficient between the first correlation coefficient between meteorological data and electricity market prices is greater than the first preset correlation coefficient, then the candidate environmental data is meteorological data and electricity market prices; if the first correlation coefficient between the first correlation coefficient between meteorological data and electricity market prices is greater than the first preset correlation coefficient, and the first correlation coefficient between meteorological data and user behavior patterns is greater than the first preset correlation coefficient, then the selected environmental data is meteorological data, electricity market prices, and user behavior patterns.

[0085] S502: performing correlation analysis on various types of environmental data and power load data in the candidate environmental data to obtain second correlation coefficients.

[0086] In the embodiment of the present application, for the candidate environmental data, each type of environmental data in the candidate environmental data is subjected to correlation analysis with the power load data to obtain a second correlation coefficient between each type of environmental data in the candidate environmental data and the power load data. For example, if the candidate environmental data includes meteorological data and electricity market prices, the second correlation coefficient between the meteorological data and the power load data and the second correlation coefficient between the electricity market prices and the power load data are obtained.

[0087] S503: Determine target environment data from candidate environment data according to the second correlation coefficients and the second preset correlation coefficient.

[0088] In the embodiment of the present application, each second correlation coefficient is compared with the second preset correlation coefficient, and the candidate environmental data corresponding to the second correlation coefficient greater than the second preset correlation coefficient is used as the target environmental data. For example, among the second correlation coefficient between meteorological data and power load data and the second correlation coefficient between the power market electricity price and power load data, if the second correlation coefficient between meteorological data and power load data is greater than the second preset correlation coefficient, the meteorological data is used as the target environmental data.

[0089] In the embodiment of the present application, candidate environmental data is determined from multi-source environmental data according to multiple first correlation coefficients and first preset correlation coefficients, and correlation analysis is performed on various types of environmental data and power load data in the candidate environmental data to obtain each second correlation coefficient, and target environmental data is determined from the candidate environmental data according to each second correlation coefficient and the second preset correlation coefficient. In the embodiment of the present application, by analyzing the correlation between the candidate environmental data and the power load data, environmental data related to the demand response capability can be intelligently selected, thereby improving the accuracy of determining the target environmental data.

[0090] Figure 6 FIG. 1 is a flow chart of the demand response capability evaluation result in another embodiment. Figure 6 As shown, the embodiment of the present application relates to a possible implementation method of how to evaluate the demand response capability of an adjustable load according to each data to be evaluated, and obtain an evaluation result of the demand response capability of the adjustable load, including the following steps:

[0091] S601 : for each type of data to be evaluated, according to a preset spatiotemporal dynamic model and the type of data to be evaluated, determining the time characteristics and spatial characteristics of the data to be evaluated.

[0092] In an embodiment of the present application, each type of data to be evaluated is input into a spatiotemporal dynamic model, which uses a time series analysis method to extract the temporal characteristics of the data to be evaluated, and uses a spatial statistical method to extract the spatial characteristics of the data to be evaluated.

[0093] Among them, the temporal characteristics mainly capture the periodic fluctuations and trend changes of the adjustable load, thereby identifying the response mode of the adjustable load; the spatial characteristics mainly analyze the heterogeneity of the adjustable load response capacity between different regions.

[0094] In one possible implementation, before extracting the time features and spatial features, the noise and outliers in the data to be evaluated can be identified and filtered out through data preprocessing techniques such as adaptive filtering (such as Kalman filter) and wavelet transform, ensuring that only high-quality valid data is used for subsequent calculations, thereby improving the accuracy and efficiency of extracting time features and determining spatial features.

[0095] S602: Determine weight coefficients of different types of data to be evaluated at different time scales according to the time characteristics and spatial characteristics of the data to be evaluated.

[0096] In an embodiment of the present application, the evaluation process of the data to be evaluated is divided into multiple time scales. At a short time scale (such as hourly or daily), an adaptive neural network is used to extract the fluctuation characteristics of the adjustable load, and the weight is adjusted in combination with a real-time feedback mechanism to obtain a weight coefficient at a short time scale, thereby ensuring a rapid response to instantaneous fluctuations of the adjustable load.

[0097] On long time scales (such as seasonal changes or interannual changes), recurrent neural networks are used to capture the long-term trend changes of adjustable loads, and the weight distribution is optimized based on historical data to obtain the weight coefficient corresponding to the long time scale, ensuring accurate prediction and evaluation of adjustable load patterns on long time scales.

[0098] When obtaining weight coefficients at different time scales, the multi-objective optimization algorithm can be used to balance the timeliness and flexibility at different time scales, so as to achieve the best weight distribution between short-term load response and long-term development trend, ensuring dual optimization in timeliness and prediction accuracy. And by introducing a real-time data feedback mechanism, the weight coefficient can be dynamically optimized, and the adaptability and accuracy of the adjustable load response capacity assessment can be significantly improved based on the historical response behavior and changes in external environmental factors.

[0099] S603, evaluating the demand response capability of the adjustable load according to the weight coefficients of various types of data to be evaluated at different time scales and the data to be evaluated, and obtaining an evaluation result of the demand response capability of the adjustable load.

[0100] In the embodiment of the present application, the weight coefficients of various types of data to be evaluated at different time scales are weighted and summed with the corresponding data to be evaluated, so as to obtain the evaluation results of the demand response capacity of the adjustable load in different time periods.

[0101] In the embodiment of the present application, for each type of data to be evaluated, according to the preset spatiotemporal dynamic model and the type of data to be evaluated, the time period characteristics of the adjustable load change are determined; according to the time period characteristics of the adjustable load change, the time weight coefficient and space weight coefficient of the type of data to be evaluated are obtained; according to the time weight coefficient and space weight coefficient of the data to be evaluated and the data to be evaluated, the demand response capacity of the adjustable load is evaluated to obtain the evaluation result of the demand response capacity of the adjustable load. The embodiment of the present application adopts a spatiotemporal dynamic model to extract the time characteristics and space characteristics of the data to be evaluated respectively, and obtain the time period characteristics of the adjustable load change. On this basis, weight functions on different time scales are constructed based on the third correlation coefficient, that is, the spatiotemporal dynamic evaluation model and the multi-time scale adaptive weighted optimization mechanism further improve the evaluation accuracy and flexibility of the demand response capability, and significantly improve the adaptability and accuracy of the demand response capability evaluation.

[0102] In one embodiment, the method further includes: determining each load to be controlled in the adjustable load according to the demand response capability evaluation result of the adjustable load; and dynamically controlling each load to be controlled based on the demand response capability evaluation result of each load to be controlled.

[0103] In the embodiment of the present application, according to the demand response capability evaluation results, the scene perception technology is used to dynamically analyze the adjustability of the load in the power grid and generate a preliminary control load. The preliminary control load is dynamically optimized based on the deep reinforcement learning algorithm, and the optimal dynamic control target, i.e., each load to be controlled, is generated by introducing the Pareto optimal solution and weighing multi-dimensional objectives (such as economic benefits, load balance, and user comfort). By adjusting the allocation strategy of each load to be controlled, the balance and optimization of each load to be controlled are achieved.

[0104] When generating control strategies for each load to be controlled, multi-time scale prediction models are used, including short-term, medium-term and long-term prediction models. By integrating the time series prediction model, the trends of the loads to be controlled at different time scales are accurately predicted to ensure that the dynamic generation of control strategies can cope with the changes of various loads to be controlled. At the same time, according to the urgency of the response tasks of the loads to be controlled, user comfort and grid balance requirements, the execution order of the response tasks of each load to be controlled is dynamically adjusted. For example, non-essential loads to be controlled during peak hours are adjusted first, and the continuous power supply of necessary primary and secondary loads to be controlled is guaranteed. Through this dynamic scheduling, the optimal configuration of grid resources can be achieved.

[0105] The execution of the control strategy of each load to be controlled is realized by a distributed autonomous control system (computer equipment), which allows each load node to be controlled (such as temperature control load, electric vehicle, energy storage system, etc.) to independently execute load response tasks. This decentralized control method avoids the single point failure problem in traditional centralized control and ensures the security and real-time performance of data transmission through blockchain technology.

[0106] Optionally, a fuzzy control system is introduced to fuzzily infer the effect of the current control strategy based on the feedback information and automatically adjust the control strategy. For example, when the feedback information shows that the operation of the user equipment deviates from the expected value, the fuzzy control system can automatically identify the anomaly and make optimization adjustments to ensure that the control strategy is always close to the optimal state.

[0107] Optionally, based on the dynamic changes of the real-time response of the load to be controlled, the distributed autonomous control system can dynamically adjust the execution strength and scope of the control strategy. For example, priority is given to controlling load 1 and load 2 to be controlled. When load 1 and load 2 to be controlled exceed expectations, the distributed autonomous control system will automatically expand the control scope, increase the participation of more loads to be controlled, or formulate a control strategy that better meets the actual control needs to achieve precise load management.

[0108] It should be understood that, although the steps in the flowcharts involved in the above embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0109] Based on the same inventive concept, the embodiment of the present application also provides an adjustable load demand response capability assessment device for implementing the above-mentioned adjustable load demand response capability assessment method. The implementation solution provided by the device to solve the problem is similar to the implementation solution recorded in the above-mentioned method, so the specific limitations in the embodiments of one or more adjustable load demand response capability assessment devices provided below can refer to the limitations of the adjustable load demand response capability assessment method above, and will not be repeated here.

[0110] In an exemplary embodiment, Figure 7As shown, a device for evaluating the demand response capability of an adjustable load is provided, comprising: a first determining module 11, a second determining module 12, an acquiring module 13 and an evaluating module 14, wherein:

[0111] A first determination module 11 is used to determine a load change trend of the power load data according to the power load data of the adjustable load and a preset load prediction model;

[0112] The second determination module 12 is used to determine the acquisition frequency and acquisition accuracy according to the load change trend;

[0113] An acquisition module 13, configured to acquire multi-source environmental data affecting the demand response capability of the adjustable load based on acquisition frequency and acquisition accuracy;

[0114] The evaluation module 14 is used to evaluate the demand response capability of the adjustable load according to the multi-source environmental data to obtain an evaluation result of the demand response capability of the adjustable load.

[0115] In one embodiment, the evaluation module 14 is specifically used to perform standardization processing on multi-source environmental data to obtain standardized multi-source environmental data; perform correlation analysis on the standardized multi-source environmental data to determine different types of data to be evaluated; and evaluate the demand response capability of the adjustable load according to each data to be evaluated to obtain an evaluation result of the demand response capability of the adjustable load.

[0116] In one embodiment, the evaluation module 14 is specifically used to perform a correlation analysis on two types of environmental data in the standardized multi-source environmental data to obtain a first correlation coefficient between the two types of environmental data; based on each first correlation coefficient and a first preset correlation coefficient, the target environmental data is determined from the multi-source environmental data, and the target environmental data and the power load data are used as data to be evaluated.

[0117] In one embodiment, the evaluation module 14 is specifically used to determine candidate environmental data from multi-source environmental data based on multiple first correlation coefficients and a first preset correlation coefficient; perform correlation analysis on various types of environmental data and power load data in the candidate environmental data to obtain each second correlation coefficient; and determine the target environmental data from the candidate environmental data based on each second correlation coefficient and the second preset correlation coefficient.

[0118] In one embodiment, the evaluation module 14 is specifically used to determine the time characteristics and spatial characteristics of the data to be evaluated for each type of data to be evaluated according to a preset spatiotemporal dynamic model and the type of data to be evaluated; obtain the weight coefficient of the type of data to be evaluated on different time scales according to the time characteristics and spatial characteristics of the data to be evaluated; evaluate the demand response capability of the adjustable load according to the weight coefficient of the type of data to be evaluated on different time scales and the data to be evaluated, and obtain an evaluation result of the demand response capability of the adjustable load.

[0119] In one embodiment, the apparatus further comprises:

[0120] A third determination module is used to determine each load to be controlled in the adjustable load according to the demand response capability evaluation result of the adjustable load at the same time scale;

[0121] The control module is used to dynamically control each load to be controlled based on the demand response capability evaluation result of each load to be controlled.

[0122] Each module in the above-mentioned adjustable load demand response capability assessment device can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each of the above modules.

[0123] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps of any of the above method embodiments when executing the computer program.

[0124] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above method embodiments are implemented.

[0125] In one embodiment, a computer program product is provided, including a computer program, which implements the steps of any of the above method embodiments when executed by a processor.

[0126] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0127] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access 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 and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0128] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0129] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for evaluating the demand response capability of an adjustable load, characterized in that: The method comprises: Determining a load change trend of the power load data according to the power load data of the adjustable load and a preset load prediction model; Determine the acquisition frequency and acquisition accuracy according to the load change trend; Based on the acquisition frequency and the acquisition accuracy, acquiring multi-source environmental data that affects the demand response capability of the adjustable load; The demand response capability of the adjustable load is evaluated according to the multi-source environmental data to obtain an evaluation result of the demand response capability of the adjustable load.

2. The method according to claim 1, characterized in that The step of evaluating the demand response capability of the adjustable load according to the multi-source environmental data to obtain an evaluation result of the demand response capability of the adjustable load includes: Performing standardization processing on the multi-source environmental data to obtain standardized multi-source environmental data; Performing correlation analysis on the standardized multi-source environmental data to determine different types of data to be evaluated; The demand response capability of the adjustable load is evaluated according to each of the data to be evaluated, and an evaluation result of the demand response capability of the adjustable load is obtained.

3. The method according to claim 2, characterized in that The performing correlation analysis on the standardized multi-source environmental data to determine different types of data to be evaluated includes: Performing a correlation analysis on two types of environmental data in the standardized multi-source environmental data to obtain a first correlation coefficient between the two types of environmental data; According to each of the first correlation coefficients and the first preset correlation coefficient, target environmental data is determined from the multi-source environmental data, and the target environmental data and the power load data are used as the data to be evaluated.

4. The method according to claim 3, characterized in that: The determining target environment data from the multi-source environment data according to each of the first correlation coefficients and the first preset correlation coefficient includes: Determining candidate environmental data from the multi-source environmental data according to the plurality of first correlation coefficients and a first preset correlation coefficient; Performing correlation analysis on each type of environmental data in the candidate environmental data and the power load data to obtain second correlation coefficients; The target environment data is determined from the candidate environment data according to each of the second correlation coefficient and the second preset correlation coefficient.

5. The method according to claim 2, characterized in that: The step of evaluating the demand response capability of the adjustable load according to each of the data to be evaluated to obtain an evaluation result of the demand response capability of the adjustable load includes: For each type of data to be evaluated, according to a preset spatiotemporal dynamic model and the type of data to be evaluated, determining the temporal characteristics and spatial characteristics of the data to be evaluated; According to the temporal characteristics and spatial characteristics of the data to be evaluated, weight coefficients of the data to be evaluated of the type at different time scales are obtained; According to the weight coefficients of the types of data to be evaluated at different time scales and the data to be evaluated, the demand response capability of the adjustable load is evaluated to obtain an evaluation result of the demand response capability of the adjustable load.

6. The method according to any one of claims 1 to 5, characterized in that: The method further comprises: Determining each load to be controlled in the adjustable load according to the demand response capability evaluation result of the adjustable load; Based on the demand response capability evaluation result of each load to be controlled, each load to be controlled is dynamically controlled.

7. A device for evaluating the demand response capability of an adjustable load, characterized in that: The device comprises: A first determination module, configured to determine a load change trend of the power load data according to the power load data of the adjustable load and a preset load prediction model; A second determination module is used to determine the collection frequency and collection accuracy according to the load change trend; An acquisition module, configured to acquire multi-source environmental data that affects the demand response capability of the adjustable load based on the acquisition frequency and the acquisition accuracy; An evaluation module is used to evaluate the demand response capability of the adjustable load according to the multi-source environmental data to obtain an evaluation result of the demand response capability of the adjustable load.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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