Demand response evaluation method, device and equipment of virtual power plant and storage medium

Abstract

The invention provides a demand response evaluation method and device for a virtual power plant, equipment and a storage medium. The method comprises the following steps: acquiring daily load curve data of a user; processing the user daily load curve data based on a clustering algorithm to obtain a user category and a clustering center load curve of each category of users in the user category; calculating the load rate and the time elasticity coefficient of the clustering center load curve of the target class user; determining a self-demand elastic coefficient and a cross elastic coefficient of the target type user according to the electricity consumption data and the electricity price data before and after the target type user executes the time-of-use electricity price; and performing demand response evaluation on the load rate, the time elastic coefficient, the self-demand elastic coefficient and the cross elastic coefficient based on an entropy weight method to obtain demand response evaluation scores of the target users. According to the method, multiple indexes of different clustering center load curves are synthesized based on the entropy weight method, demand response potential evaluation of different types of users is carried out, and thus the demand response strategy is optimized.

Description

Technical Field

[0001] The present application relates to the field of electric power technology, and in particular to a demand response evaluation method, device, equipment and storage medium for a virtual power plant. Background Art

[0002] With the growth of energy demand and the transformation of energy structure, virtual power plants have gradually attracted attention as a new energy management model. By integrating distributed energy resources and flexible loads, they provide a more flexible, efficient and sustainable solution for the power system.

[0003] In the operation of virtual power plants, demand response is an important technology and strategy. Virtual power plants can achieve flexible dispatch of power systems by adjusting loads and energy storage, thereby improving the operating efficiency and stability of power systems. However, current demand response potential assessment methods have their own advantages and disadvantages, and there are certain limitations in practical applications. Summary of the invention

[0004] In view of this, the purpose of this application is to propose a demand response evaluation method, device, equipment and storage medium for a virtual power plant.

[0005] As one aspect of the present application, a demand response evaluation method for a virtual power plant is provided, comprising: Obtain user daily load curve data; Processing the user daily load curve data based on a clustering algorithm to obtain user categories and cluster center load curves of each category of users in the user categories; Calculating the load rate and time elasticity coefficient of the cluster center load curve of the target class user; Determine the self-demand elasticity coefficient and cross elasticity coefficient of the target user according to the power consumption data and electricity price data before and after the target user implements the time-of-use electricity price; Based on the entropy weight method, the load rate, the time elasticity coefficient, the self-demand elasticity coefficient and the cross elasticity coefficient are evaluated for demand response to obtain a demand response evaluation score for the target user.

[0006] Optionally, based on the load rate and a first threshold, a response behavior of the target user is determined; the response behavior includes participating in demand response and not participating in demand response.

[0007] Optionally, based on the time elasticity coefficient and the second threshold, a response behavior of the target class user is determined; the response behavior includes participating in demand response and not participating in demand response.

[0008] Optionally, the user daily load curve includes the user's power load data; The calculating of the time elasticity coefficient of the cluster center load curve of the target class user includes: Determine the daily average load of the target class of users; According to the daily average load, per-unit processing is performed on the power load data of the target type of users in the target period to obtain the per-unit value of the target type of users in the target period; Determining the load mean square error of the target class users according to the per-unit value; The time elastic coefficient is obtained according to the load mean square error.

[0009] Optionally, the per-unit value satisfies the following expression: ; ; in, For the number of days, is the total number of days, ; The 24 hours in a day are divided into hours. ; For the User Class day The power load during the period; For the User Class day Average daily load for the time period; For the Class User The per-unit value of the time period.

[0010] Optionally, the demand response evaluation score of the target class user satisfies the following expression: ; in, is the demand response evaluation score of the target class users, is the weight of the load rate obtained based on information entropy, is the load rate, is the weight of the time elasticity coefficient obtained based on information entropy, is the time elastic coefficient, is the weight of the self-demand elasticity coefficient based on information entropy, is the self-demand elasticity coefficient, is the weight of the cross elasticity coefficient based on information entropy, is the cross elastic coefficient.

[0011] Optionally, storing the demand response evaluation score corresponding to each type of user; Generate a priority queue based on the demand response evaluation scores corresponding to each type of user in descending order; In response to the category of the target class user being greater than the user category, a user invocation strategy is determined based on the priority queue.

[0012] As a second aspect of the present application, a demand response evaluation device for a virtual power plant is provided, comprising: an acquisition module, a processing module, a calculation module, and a determination module; The acquisition module is used to obtain user daily load curve data; The processing module is used to process the user daily load curve data based on a clustering algorithm to obtain user categories and cluster center load curves of each category of users in the user categories; The calculation module is used to calculate the load rate and time elasticity coefficient of the cluster center load curve of the target class user; The determination module is used to determine the self-demand elasticity coefficient and the cross elasticity coefficient of the target user according to the power consumption data and electricity price data of the target user before and after the implementation of the time-of-use electricity price; The processing module is also used to perform demand response evaluation on the load rate, the time elasticity coefficient, the self-demand elasticity coefficient and the cross elasticity coefficient based on the entropy weight method to obtain the demand response evaluation score of the target class user.

[0013] Optionally, the determination module is further used to determine the response behavior of the target class user based on the load rate and the first threshold; the response behavior includes participating in demand response and not participating in demand response.

[0014] Optionally, the determination module is further used to determine the response behavior of the target class user based on the time elasticity coefficient and the second threshold; the response behavior includes participating in demand response and not participating in demand response.

[0015] Optionally, the user daily load curve includes the user's power load data; The calculation module is specifically used to determine the daily average load of the target type of users; based on the daily average load, normalize the power load data of the target type of users in the target time period to obtain the per-unit value of the target type of users in the target time period; based on the per-unit value, determine the load mean square deviation of the target type of users; based on the load mean square deviation, obtain the time elasticity coefficient.

[0016] Optionally, the per-unit value satisfies the following expression: ; ; in, For the number of days, is the total number of days, ; The 24 hours in a day are divided into hours. ; For the Class User day The power load during the period; For the Class User day Average daily load for the time period; For the Class User The per-unit value of the time period.

[0017] Optionally, the demand response evaluation score of the target class user satisfies the following expression: ; in, is the demand response evaluation score of the target class users, is the weight of the load rate obtained based on information entropy, is the load rate, is the weight of the time elasticity coefficient obtained based on information entropy, is the time elastic coefficient, is the weight of the self-demand elasticity coefficient based on information entropy, is the self-demand elasticity coefficient, is the weight of the cross elasticity coefficient based on information entropy, is the cross elastic coefficient.

[0018] Optionally, the demand response evaluation device of the virtual power plant further includes a storage module; The storage module is used to store the demand response evaluation score corresponding to each type of user; The processing module is further used to generate a priority queue according to the demand response evaluation scores corresponding to each type of user in descending order; The determination module is further configured to, in response to the category of the target class user being greater than the user category, determine a user call strategy based on the priority queue.

[0019] As the third aspect of the present application, an electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned demand response evaluation method for a virtual power plant when executing the computer program.

[0020] As the fourth aspect of the present application, a non-transitory computer-readable storage medium is provided, which stores computer instructions, and the computer instructions are used to enable the computer to execute the above-mentioned virtual power plant demand response evaluation method provided in the present application.

[0021] As can be seen from the above, the demand response evaluation method, device, equipment and storage medium of the virtual power plant provided by the present application can classify users with similar electricity consumption behavior patterns into one category by processing the user's daily load curve data through a clustering algorithm. This helps to identify different types of user groups and provide a basis for subsequent differentiated demand response strategies. Then, by calculating the load rate and time elasticity coefficient of the cluster center load curve of each type of user, the electricity consumption characteristics of different user groups can be understood, which provides an important reference for formulating targeted demand response measures. Further, the electricity consumption data and electricity price data before and after the target user implements the time-of-use electricity price are analyzed to determine the self-demand elasticity coefficient and the cross-elasticity coefficient. These coefficients reflect the changes in the user's electricity consumption under different electricity prices, which helps to evaluate the user's sensitivity to changes in electricity prices. Finally, based on the entropy weight method, a comprehensive evaluation of multiple indicators is performed to obtain the demand response evaluation score of the target user, which improves the accuracy of the evaluation and provides a scientific basis for the virtual power plant to select user categories with higher response potential when implementing demand response, thereby optimizing the demand response strategy and improving the effect of demand response. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the present application or related technologies, the drawings required for use in the embodiments or related technical descriptions are briefly introduced below. Obviously, the drawings described below are only embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 A flowchart of a demand response evaluation method for a virtual power plant provided in an embodiment of the present application; Figure 2 A flow chart of another method for evaluating demand response of a virtual power plant provided in an embodiment of the present application; Figure 3 A flowchart of another method for evaluating demand response of a virtual power plant provided in an embodiment of the present application; Figure 4 A flowchart of another method for evaluating demand response of a virtual power plant provided in an embodiment of the present application; Figure 5 A schematic diagram of the composition of a demand response evaluation device for a virtual power plant provided in an embodiment of the present application; Figure 6A schematic diagram of the composition of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below in combination with specific embodiments and with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0025] It should be noted that, in the embodiments of the present application, words such as "exemplarily" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplarily" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplarily" or "for example" is intended to present related concepts in a concrete way. Unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present application should have the usual meaning understood by people with ordinary skills in the field to which the present application belongs. SUMMARY OF THE INVENTION Among the related technologies, in the field of demand response potential assessment, the current mainstream methods cover load curve analysis, load models and simulations, user surveys and interviews, as well as statistical analysis and data mining. Load curve analysis can reveal potential demand response capabilities and patterns through in-depth analysis of historical load data, providing valuable reference information for decision makers. Load models and simulation technologies can further accurately evaluate the actual effects of different demand response strategies by building sophisticated models and simulation environments, providing strong support for strategy optimization. User surveys and interviews directly obtain first-hand information from users and energy consumers to ensure the authenticity and reliability of the evaluation results. Statistical analysis and data mining technologies use the power of big data and statistics to deeply explore the potential laws and demand response opportunities in load patterns, providing a new perspective for exploring demand response potential.

[0027] The inventors of the present application have found that each of the above-mentioned technologies has some defects. Although load curve analysis is simple and easy, it fails to fully consider the impact of time factors on demand response potential, resulting in limited ability to predict future potential. Although load models and simulation technologies are highly accurate, the model establishment and verification process is cumbersome and requires a large amount of data, time and cost investment, which may be unaffordable for organizations with limited resources. Although user surveys and interviews can obtain real demand response potential information, users' subjective wishes and answer deviations will lead to data distortion, and the cost of data collection is relatively high. Although statistical analysis and data mining techniques can discover potential laws in load patterns, the amount of data required is huge, and the requirements for data quality are extremely strict, which increases the difficulty of data acquisition and processing.

[0028] In order to solve the above problems, the present application provides a demand response evaluation method for a virtual power plant. By processing the user's daily load curve data through a clustering algorithm, users with similar electricity consumption behavior patterns can be classified into one category. This helps to identify different types of user groups and provide a basis for subsequent differentiated demand response strategies. Then, by calculating the load rate and time elasticity coefficient of the cluster center load curve of each type of user, the electricity consumption characteristics of different user groups can be understood, which provides an important reference for formulating targeted demand response measures. Further, the electricity consumption data and electricity price data of the target class users before and after the implementation of the time-of-use electricity price are analyzed to determine the self-demand elasticity coefficient and the cross-elasticity coefficient. These coefficients reflect the changes in the user's electricity consumption under different electricity prices, which helps to evaluate the user's sensitivity to changes in electricity prices. Finally, based on the entropy weight method, a comprehensive evaluation of multiple indicators is performed to obtain the demand response evaluation score of the target class users, which improves the accuracy of the evaluation and provides a scientific basis for the virtual power plant to select user categories with higher response potential when implementing demand response, thereby optimizing the demand response strategy and improving the effect of demand response.

[0029] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application are described in detail below.

[0030] Figure 1 A flowchart of a demand response evaluation method for a virtual power plant provided in an embodiment of the present application. Figure 1 As shown, the demand response evaluation method of the virtual power plant provided in this application specifically includes the following steps: S101. Obtain user daily load curve data.

[0031] Among them, the user's daily load curve data is the daily load curve data of users participating in demand response within the virtual power plant on historical dates. The daily load curve data refers to the curve that records the changes in the user's power consumption in each period of a day (for example, every hour). These data are presented in the form of a time series and can reflect the user's electricity consumption behavior and habits.

[0032] In some embodiments, the method of obtaining user daily load curve data may include smart meter collection, user active reporting, or third-party data provider provision. Smart meters are the most common data source, which can record the user's power consumption in real time and transmit the data to the data center through a remote communication system. Obtaining accurate user daily load curve data is the basis for subsequent analysis, which helps to understand the user's power consumption behavior, identify peak and valley periods of power consumption, and provide a basis for formulating effective demand response strategies.

[0033] For example, smart meters, power monitoring equipment, etc. collect power load data of users and organize them into daily load curves in time series. For example, the load data of users in the past 30 days can be collected, and the load value can be recorded once every hour to form 30 daily load curves.

[0034] S102: Processing user daily load curve data based on a clustering algorithm to obtain user categories and cluster center load curves of each category of users in the user categories.

[0035] In some embodiments, the clustering algorithm is an unsupervised learning method used to group similar objects into one category. In the user's daily load curve data, the clustering algorithm can classify users into different categories based on their electricity consumption behavior. Each category represents a group of users with similar electricity consumption patterns, which facilitates the subsequent formulation of differentiated demand response strategies for different types of users. The cluster center load curve is the average or median of the load curves of all users in the category, representing the typical electricity consumption behavior of users in this category.

[0036] In some embodiments, clustering algorithms include K-means, hierarchical clustering, DBSCAN, etc. Taking K-means clustering as an example, first randomly select K initial cluster centers, then calculate the distance between each sample and the cluster center, divide the sample into the category where the nearest cluster center is located, then recalculate the cluster center of each category, and repeat the above process until the cluster center no longer changes or reaches a preset number of iterations. For example, after normalizing the user's daily load curve data, use the K-means clustering algorithm to divide the users into 5 categories, and obtain the cluster center load curve of each category of users.

[0037] S103: Calculate the load rate and time elasticity coefficient of the cluster center load curve of the target class users.

[0038] The load rate refers to the ratio of the average load to the maximum load of a user in a certain period of time, reflecting the user's power efficiency. The time elasticity coefficient is used to measure the user's response to time changes, that is, the flexibility of load changes in different time periods. The larger the value, the higher the time elasticity.

[0039] In some embodiments, based on the load rate and the first threshold, the response behavior of the target user is determined; the response behavior includes participating in demand response and not participating in demand response. For example, if the load rate is greater than the first threshold, it means that the time elasticity of this type of user is low, and this type of user within the virtual power plant does not participate in demand response.

[0040] In some embodiments, based on the time elasticity coefficient and the second threshold, the response behavior of the target user is determined; the response behavior includes participating in demand response and not participating in demand response. For example, if the time elasticity coefficient is less than the second threshold, it means that the time elasticity of this type of user is low, and this type of user within the virtual power plant does not participate in demand response.

[0041] In some embodiments, the user's daily load profile includes the user's power load data, such as Figure 2 As shown, S103 can be specifically implemented as follows S1031-S1034: S1031. Determine the daily average load of target users.

[0042] In some embodiments, the daily average load refers to the average power consumption of the target class of users in all periods of a day, reflecting the overall power consumption level of this type of users. The daily average load is obtained by calculating the sum of the power load data of the target class of users in a day and then dividing it by the number of data points (i.e., the number of time intervals). The daily average load is an important indicator for measuring the user's power demand, which helps to understand the user's power consumption habits and load characteristics, provides a benchmark for subsequent normalization processing, and eliminates the impact of differences in user power consumption scales.

[0043] S1032. Based on the daily average load, normalize the power load data of the target users in the target period to obtain the normalized value of the target users in the target period.

[0044] In some embodiments, according to n Before user pricing D The daily load curve of the day takes the daily average load as the base value of the load for that day. n Class User d sky t The load of the time period is normalized and then D The load value of the corresponding period of each day is normalized to get n Class User t The per-unit value of the time period satisfies the following expression: ; ; in, For the number of days, is the total number of days, ; The 24 hours in a day are divided into hours. ; For the Class User day The power load during the period; For the Class User day Average daily load for the time period; For the Class User The per-unit value of the time period.

[0045] In some embodiments, per-unit normalization converts the power load data into a ratio relative to the daily average load, eliminating the absolute dimension and highlighting the time series fluctuation characteristics. The per-unit normalization process makes the load data more intuitive and easier to compare, helps to identify the load change trend and fluctuation, and helps to identify the power consumption pattern and abnormal situation.

[0046] S1033: Determine the load mean square error of the target class users according to the per-unit value.

[0047] In some embodiments, the mean square error is calculated based on the per-unit value to obtain the mean square error value of the load in each period of the day. The mean square error of the load of the target user satisfies the following expression: .

[0048] In some embodiments, mean squared deviation (MSD) is a statistic that measures the degree of data fluctuation, which represents the average of the squared deviations of each data point from the mean. The larger the mean squared deviation, the higher the dispersion of the load data and the greater the load fluctuation. The mean squared deviation provides insight into the volatility of user loads and helps identify the stability and predictability of loads, which is very important for the scheduling and planning of power systems.

[0049] S1034. Obtain a time elastic coefficient according to the load mean square error.

[0050] In some embodiments, the average value of the mean square deviation of the load of the n-th type of users in each period of a day is calculated according to the mean square deviation of the load, and then the average value of the mean square deviation of the load of the n-th type of users in each period of a day is obtained according to the average value of the mean square deviation of the load of the n-th type of users in each period of a day. n The time elasticity coefficient of the class user load curve. The time elasticity coefficient satisfies the following expression: ; ; in, is the average value of the mean square error of the load of the nth type of user in each period of a day, is the time elasticity coefficient.

[0051] In some embodiments, the time elasticity coefficient helps identify which user groups are more sensitive to price changes by quantifying the discreteness of user load changes, thereby designing demand response strategies more targetedly.

[0052] S104. Determine the self-demand elasticity coefficient and cross elasticity coefficient of the target user based on the power consumption data and electricity price data of the target user before and after the implementation of the time-of-use electricity price.

[0053] Among them, the self-demand elasticity coefficient reflects the user's own sensitivity to electricity price changes in a certain period of time. The cross elasticity coefficient reflects the impact of electricity price changes in other periods on electricity consumption in this period. Time-of-use electricity prices indicate that different electricity prices are allocated to peak and low electricity consumption periods based on the power supply situation.

[0054] In some embodiments, the self-demand elasticity coefficient of the target user is determined based on the power consumption data and power price data before and after the target user implements the time-of-use power price. n The self-demand elasticity coefficient of this type of user satisfies the following expression: ; in, For the n The period before and after the time-of-use electricity price is implemented for this type of user t The change in electricity consumption, For the n User-like time period t The power consumption, For the n The period before and after the time-of-use electricity price is implemented for this type of user t The change in electricity price, For the n User-like time period t electricity price.

[0055] In some embodiments, the cross elasticity coefficient of the target user is determined based on the power consumption data and power price data before and after the target user implements the time-of-use power price. n The cross elasticity coefficient of class users satisfies the following expression: ; in, For the n The period before and after the time-of-use electricity price is implemented for this type of user k The change in electricity price, For the n User-like time period k electricity price.

[0056] It should be understood that the calculation of the self-demand elasticity coefficient and the cross-elasticity coefficient can quantify the changes in users' electricity consumption under different electricity prices and evaluate the degree of users' responsiveness to changes in electricity prices.

[0057] S105. Based on the entropy weight method, the load rate, time elasticity coefficient, self-demand elasticity coefficient and cross elasticity coefficient are evaluated for demand response to obtain the demand response evaluation score of the target users.

[0058] Among them, the entropy weight method is an objective weighting method, which determines the weight of each indicator according to its information entropy. The larger the information entropy, the less information the indicator contains, and the lower its contribution to the evaluation result; conversely, the smaller the information entropy, the more information the indicator contains, and the higher its contribution to the evaluation result.

[0059] In some embodiments, the target class user's demand response evaluation score satisfies the following expression: ; in, is the demand response evaluation score of the target user, is the weight of the load rate based on information entropy, is the load rate, is the weight of the time elasticity coefficient based on information entropy, is the time elasticity coefficient, is the weight obtained from the demand elasticity coefficient based on information entropy, is the elasticity coefficient of self-demand, is the weight of the cross elasticity coefficient based on information entropy, is the cross elastic coefficient.

[0060] In some embodiments, through the demand response evaluation based on the entropy weight method, an evaluation score that comprehensively reflects the user's demand response potential can be obtained. This score helps to identify user categories with greater demand response potential and provides a basis for formulating differentiated demand response strategies. At the same time, as an objective weighting method, the entropy weight method can avoid the bias and uncertainty that may be caused by subjective weighting, and improve the accuracy and reliability of the evaluation results.

[0061] In some embodiments, Figure 3 As shown, the demand response evaluation method for a virtual power plant provided in this application also includes the following S201-S203: S201. Store the demand response evaluation score corresponding to each type of user.

[0062] In some embodiments, the demand response evaluation scores corresponding to each type of user can be stored in a local server, distributed database or cloud storage in the form of a database table or key-value pair. For example, each record contains fields: user category, score, and timestamp. These scores reflect the user's response and can be used for subsequent user classification management and priority setting. At the same time, the stored demand response evaluation scores can be used multiple times to avoid repeated calculations and improve efficiency.

[0063] S202: Generate a priority queue based on the demand response evaluation scores corresponding to each type of user in descending order.

[0064] In some embodiments, a sorting algorithm (such as quick sort, merge sort, etc.) is used to sort the demand response evaluation scores, and a priority queue is generated based on the sorting results. The user categories in the queue are arranged from high to low according to the demand response evaluation scores. Through the priority queue, resource allocation can be optimized to ensure that resources are preferentially allocated to user categories with greater demand response potential, thereby improving resource utilization efficiency.

[0065] S203: In response to the target class user's category being greater than the user category, determine a user call strategy based on a priority queue.

[0066] In some embodiments, after the users in the user category are traversed, the virtual power plant will first call the user category with a high demand response evaluation score to participate in the demand response. If the demand response target is still not met after calling a certain user category once, the virtual power plant will continue to call the next user category with a higher demand response evaluation score until the demand response requirements are met.

[0067] The demand response evaluation method of the virtual power plant provided in this application processes the user's daily load curve data through a clustering algorithm, and can classify users with similar electricity consumption behavior patterns into one category. This helps to identify different types of user groups and provide a basis for subsequent differentiated demand response strategies. Then, by calculating the load rate and time elasticity coefficient of the cluster center load curve of each type of user, the electricity consumption characteristics of different user groups can be understood, which provides an important reference for formulating targeted demand response measures. Furthermore, the electricity consumption data and electricity price data of the target users before and after the implementation of the time-of-use electricity price are analyzed to determine the self-demand elasticity coefficient and the cross-elasticity coefficient. These coefficients reflect the changes in the user's electricity consumption under different electricity prices, which helps to evaluate the user's sensitivity to changes in electricity prices. Finally, based on the entropy weight method, a comprehensive evaluation of multiple indicators is performed to obtain the demand response evaluation score of the target user, which improves the accuracy of the evaluation and provides a scientific basis for the virtual power plant to select user categories with higher response potential when implementing demand response, thereby optimizing the demand response strategy and improving the effect of demand response.

[0068] like Figure 4 As shown, an embodiment of the present application provides a flow chart of a demand response evaluation method for a virtual power plant. First, cluster analysis is performed on the load curve data of users within the virtual power plant and the users are divided into N categories. For a specific description, please refer to the relevant description of S101-S102 above, which will not be described in detail in this application. Next, starting from the first category of users, the load rate and time elasticity coefficient are calculated for each category. When the load rate is less than or equal to the first threshold, the load mean square error is calculated to obtain the time elasticity coefficient. When the time elasticity coefficient is greater than or equal to the second threshold, the self-elasticity coefficient and the cross-elasticity coefficient are calculated, and the entropy weight method is used to evaluate the demand response potential. For a specific description, please refer to the relevant description of S103-S104 above, which will not be described in detail in this application. After the evaluation, the category counter n is incremented by 1, and the next category of users is processed until all N categories of users are processed. Finally, the virtual power plant prioritizes user categories with high scores to participate in demand response based on the demand response evaluation scores. For specific descriptions, please refer to the relevant descriptions of S105 and S201-S203 above, which will not be repeated in this application. This will optimize the demand response strategy, provide strong support for the planning and operation of the power industry, optimize resource allocation, and improve the operating efficiency of the power grid.

[0069] It should be noted that the method of the embodiment of the present application can be performed by a single device, such as a computer or server. The method of this embodiment can also be applied to a distributed scenario and completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only perform one or more steps in the method of the embodiment of the present application, and the multiple devices will interact with each other to complete the described method.

[0070] It should be noted that the above describes some embodiments of the present application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the above embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0071] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present application also provides a demand response evaluation device for a virtual power plant.

[0072] refer to Figure 5 , the demand response evaluation device of the virtual power plant includes: an acquisition module 501, a processing module 502, a calculation module 503 and a determination module 504; The acquisition module 501 is used to acquire user daily load curve data; The processing module 502 is used to process the user daily load curve data based on a clustering algorithm to obtain user categories and cluster center load curves of each category of users in the user category; The calculation module 503 is used to calculate the load rate and time elasticity coefficient of the cluster center load curve of the target class user; The determination module 504 is used to determine the self-demand elasticity coefficient and the cross elasticity coefficient of the target user according to the power consumption data and electricity price data of the target user before and after the implementation of the time-of-use electricity price; The processing module 502 is also used to perform demand response evaluation on the load rate, the time elasticity coefficient, the self-demand elasticity coefficient and the cross elasticity coefficient based on the entropy weight method to obtain the demand response evaluation score of the target user.

[0073] In some embodiments, the determination module 504 is further used to determine the response behavior of the target user based on the load rate and the first threshold; the response behavior includes participating in demand response and not participating in demand response.

[0074] In some embodiments, the determination module 504 is further used to determine the response behavior of the target class user based on the time elasticity coefficient and the second threshold; the response behavior includes participating in demand response and not participating in demand response.

[0075] In some embodiments, the user daily load curve includes the user's power load data; The calculation module 503 is specifically used to determine the daily average load of the target type of users; based on the daily average load, normalize the power load data of the target type of users in the target time period to obtain the normalized value of the target type of users in the target time period; based on the normalized value, determine the load mean square deviation of the target type of users; based on the load mean square deviation, obtain the time elasticity coefficient.

[0076] In some embodiments, the per-unit value satisfies the following expression: ; ; in, For the number of days, is the total number of days, ; The 24 hours in a day are divided into hours. ; For the User Class day The power load during the period; For the User Class day Average daily load for the time period; For the Class User The per-unit value of the time period.

[0077] In some embodiments, the target user's demand response evaluation score satisfies the following expression: ; in, is the demand response evaluation score of the target class users, is the weight of the load rate obtained based on information entropy, is the load rate, is the weight of the time elasticity coefficient obtained based on information entropy, is the time elastic coefficient, is the weight of the self-demand elasticity coefficient based on information entropy, is the self-demand elasticity coefficient, is the weight of the cross elasticity coefficient based on information entropy, is the cross elastic coefficient.

[0078] In some embodiments, the demand response evaluation device of the virtual power plant further includes a storage module 505; The storage module 505 is used to store the demand response evaluation score corresponding to each type of user; The processing module 502 is further used to generate a priority queue according to the demand response evaluation scores corresponding to each type of user in descending order; The determination module 504 is further configured to, in response to the category of the target class user being greater than the user category, determine a user invocation strategy based on the priority queue.

[0079] For the convenience of description, the above device is described in terms of functions divided into various modules. Of course, when implementing the present application, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0080] The device of the above embodiment is used to implement the demand response evaluation method of the corresponding virtual power plant in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0081] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the demand response evaluation method for a virtual power plant described in any of the above embodiments is implemented.

[0082] Figure 6 A more specific schematic diagram of the hardware structure of an electronic device provided in this embodiment is shown, and the device may include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040 are connected to each other through the bus 1050 in the device.

[0083] The processor 1010 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0084] The memory 1020 may be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 may store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program codes are stored in the memory 1020 and are called and executed by the processor 1010.

[0085] The input / output interface 1030 is used to connect the input / output module to realize information input and output. The input / output module can be configured in the device as a component (not shown in the figure), or it can be externally connected to the device to provide corresponding functions. The input device may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device may include a display, a speaker, a vibrator, an indicator light, etc.

[0086] The communication interface 1040 is used to connect a communication module (not shown) to realize communication interaction between the device and other devices. The communication module can realize communication through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0087] The bus 1050 includes a path that transmits information between the various components of the device (eg, the processor 1010 , the memory 1020 , the input / output interface 1030 , and the communication interface 1040 ).

[0088] It should be noted that, although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040 and the bus 1050, in the specific implementation process, the device may also include other components necessary for normal operation. In addition, it can be understood by those skilled in the art that the above device may also only include the components necessary for implementing the embodiments of the present specification, and does not necessarily include all the components shown in the figure.

[0089] The electronic device of the above-mentioned embodiment is used to implement the demand response evaluation method of the corresponding virtual power plant in any of the above-mentioned embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0090] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present application also provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the demand response evaluation method of the virtual power plant as described in any of the above embodiments.

[0091] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0092] The computer instructions stored in the storage medium of the above embodiment are used to enable the computer to execute the demand response evaluation method of the virtual power plant as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0093] Based on the same inventive concept, corresponding to the demand response evaluation method for a virtual power plant described in any of the above embodiments, the present disclosure also provides a computer program product, which includes a computer program. In some embodiments, the computer program is executable by one or more processors so that the processor executes the demand response evaluation method for a virtual power plant. Corresponding to the execution subject corresponding to each step in each embodiment of the demand response evaluation method for a virtual power plant, the processor that executes the corresponding step may belong to the corresponding execution subject.

[0094] The computer program product of the above embodiment is used to enable the processor to execute the demand response evaluation method of the virtual power plant as described in any of the above embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0095] A person skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present application (including the claims) is limited to these examples. In line with the concept of the present application, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present application as described above, which are not provided in detail for the sake of simplicity.

[0096] In addition, to simplify the description and discussion, and in order not to make the embodiments of the present application difficult to understand, the well-known power / ground connections to the integrated circuit (IC) chip and other components may or may not be shown in the provided drawings. In addition, the device may be shown in the form of a block diagram to avoid making the embodiments of the present application difficult to understand, and this also takes into account the fact that the details of the implementation of these block diagram devices are highly dependent on the platform on which the embodiments of the present application are to be implemented (that is, these details should be fully within the scope of understanding of those skilled in the art). Where specific details (e.g., circuits) are set forth to describe exemplary embodiments of the present application, it is obvious to those skilled in the art that the embodiments of the present application can be implemented without these specific details or with changes in these specific details. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0097] Although the present application has been described in conjunction with specific embodiments of the present application, many alternatives, modifications and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the discussed embodiments.

[0098] The embodiments of the present application are intended to cover all such substitutions, modifications and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present application should be included in the scope of protection of the present application.

Claims

1. A demand response evaluation method for a virtual power plant, characterized in that: The method comprises: Obtain user daily load curve data; Processing the user daily load curve data based on a clustering algorithm to obtain user categories and cluster center load curves of each category of users in the user categories; Calculating the load rate and time elasticity coefficient of the cluster center load curve of the target class user; Determine the self-demand elasticity coefficient and cross elasticity coefficient of the target user according to the power consumption data and electricity price data before and after the target user implements the time-of-use electricity price; Based on the entropy weight method, the load rate, the time elasticity coefficient, the self-demand elasticity coefficient and the cross elasticity coefficient are evaluated for demand response to obtain a demand response evaluation score for the target user.

2. The method according to claim 1, characterized in that The method further comprises: Based on the load rate and the first threshold, a response behavior of the target user is determined; the response behavior includes participating in demand response and not participating in demand response.

3. The method according to claim 1, characterized in that The method further comprises: Based on the time elasticity coefficient and the second threshold, a response behavior of the target user is determined; the response behavior includes participating in demand response and not participating in demand response.

4. The method according to claim 1, characterized in that: The user daily load curve includes the user's power load data; The calculating of the time elasticity coefficient of the cluster center load curve of the target class user includes: Determine the daily average load of the target class of users; According to the daily average load, per-unit processing is performed on the power load data of the target type of users in the target period to obtain the per-unit value of the target type of users in the target period; Determining the load mean square error of the target class users according to the per-unit value; The time elastic coefficient is obtained according to the load mean square error.

5. The method according to claim 4, characterized in that The per-unit value satisfies the following expression: ； ； in, For the number of days, is the total number of days, ; The 24 hours in a day are divided into hours. ; For the Class User day The power load during the period; For the Class User day Average daily load for the time period; For the Class User The per-unit value of the time period.

6. The method according to claim 1, characterized in that The target user's demand response evaluation score satisfies the following expression: ； in, is the demand response evaluation score of the target class users, is the weight of the load rate obtained based on information entropy, is the load rate, is the weight of the time elasticity coefficient obtained based on information entropy, is the time elastic coefficient, is the weight of the self-demand elasticity coefficient based on information entropy, is the self-demand elasticity coefficient, is the weight of the cross elasticity coefficient based on information entropy, is the cross elastic coefficient.

7. The method according to claim 1, characterized in that The method further comprises: Storing the demand response evaluation score corresponding to each type of user; Generate a priority queue based on the demand response evaluation scores corresponding to each type of user in descending order; In response to the category of the target class user being greater than the user category, a user invocation strategy is determined based on the priority queue.

8. A demand response evaluation device for a virtual power plant, characterized in that: The device comprises: an acquisition module, a processing module, a calculation module and a determination module; The acquisition module is used to obtain user daily load curve data; The processing module is used to process the user daily load curve data based on a clustering algorithm to obtain user categories and cluster center load curves of each category of users in the user categories; The calculation module is used to calculate the load rate and time elasticity coefficient of the cluster center load curve of the target class user; The determination module is used to determine the self-demand elasticity coefficient and the cross elasticity coefficient of the target user according to the power consumption data and electricity price data of the target user before and after the implementation of the time-of-use electricity price; The processing module is also used to perform demand response evaluation on the load rate, the time elasticity coefficient, the self-demand elasticity coefficient and the cross elasticity coefficient based on the entropy weight method to obtain the demand response evaluation score of the target class user.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 7 when executing the computer program. 10 . A non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to execute the method according to claim 1 .

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