A virtual power plant load end resource optimization scheduling method, device and medium

Abstract

The present application relates to a method, device and medium for optimizing and scheduling resources at the load end of a virtual power plant, which is applied to the field of data processing. The method includes: obtaining the historical total power consumption corresponding to each of the multiple historical periods of the energy supply range of the virtual power plant; estimating the first total power consumption required for the next period based on the historical total power consumption corresponding to each of the multiple historical periods of the energy supply range of the virtual power plant; for each region, estimating the maximum probability estimated power consumption corresponding to the region based on the probability distribution model corresponding to the region and the first total power consumption; and performing power scheduling based on the maximum probability estimated power consumption corresponding to each region. In the present application, after the overall estimation, each region can be estimated in detail based on the first total power consumption after the overall estimation, not only the overall data but also the estimated data of each region are considered, which effectively improves the accuracy and reliability of the power estimation.

Description

Technical Field

[0001] The present application relates to a field, and in particular to a method, device and medium for optimizing and scheduling load-side resources of a virtual power plant. Background Art

[0002] As an important direction for the deep integration of energy and information technology, virtual power plants effectively improve the system flexibility and the management level of load-side resources by power grid dispatching agencies by aggregating one or more controllable resources such as adjustable loads, energy storage, microgrids, electric vehicles, distributed power sources, etc. in different spaces, thereby promoting the supply and demand balance of the power grid.

[0003] In related technologies, the energy supply range of a virtual power plant is divided into multiple regions. The power consumption for the next cycle is estimated based on historical data for each region. The total power consumption within the energy supply range of the virtual power plant is then estimated to facilitate power scheduling for the next cycle based on this total power consumption. However, estimating regional power consumption based solely on historical data can lead to biased estimates, resulting in inaccurate regional estimates. Summary of the Invention

[0004] The purpose of this application is to provide a virtual power plant load-side resource optimization scheduling method, equipment and medium, which can improve the accuracy and reliability of power estimation.

[0005] In a first aspect, a method for optimizing and scheduling load-side resources of a virtual power plant is provided, comprising:

[0006] Obtain the historical total electricity consumption corresponding to multiple historical periods of the energy supply range of the virtual power plant;

[0007] estimating a first total power consumption required for a next cycle based on the historical total power consumption corresponding to each of multiple historical cycles within the energy supply range of the virtual power plant;

[0008] Obtaining a probability distribution model of electricity consumption for each region within the energy supply range, the probability distribution model being obtained based on an analysis of historical electricity consumption for each historical period of the region and the historical total electricity consumption for the energy supply range within each historical period;

[0009] For each region, based on the probability distribution model corresponding to the region and the first total power consumption, estimate the maximum probability power consumption corresponding to the region;

[0010] Power dispatch is performed based on the estimated power consumption with the maximum probability corresponding to each area.

[0011] Through the above technical solution, by analyzing the total electricity consumption data of multiple historical periods, the first total electricity consumption of the next period is estimated; combined with the probability distribution model of electricity consumption in each region, the estimate is further refined, and the estimated electricity consumption with the maximum probability is allocated to each region. After the overall estimate, a detailed estimate can be made for each region based on the first total electricity consumption after the overall estimate. In this solution, not only the overall data is taken into account, but also the estimated data of each region, which effectively improves the accuracy and reliability of the electricity estimate.

[0012] In one achievable manner, after estimating the maximum probability estimated power consumption corresponding to each region based on the probability distribution model corresponding to the region and the first total power consumption, the method further includes:

[0013] Summarize the estimated power consumption with the maximum probability corresponding to each area to obtain the second total power consumption;

[0014] determining whether a difference between the first total power consumption and the second total power consumption is within a preset difference range;

[0015] If yes, then executing the step of performing power dispatch according to the estimated power consumption with the maximum probability corresponding to each area;

[0016] If not, determining the effective historical total electricity consumption in the historical total electricity consumption corresponding to each of the multiple historical periods of the energy supply range of the virtual power plant, and determining the effective historical electricity consumption in each historical period of each area of ​​the energy supply range;

[0017] Based on the effective historical total power consumption, a new first total power consumption is estimated.

[0018] Construct a new probability distribution model of electricity consumption in each region based on the effective historical electricity consumption in each historical period of each region within the energy supply range;

[0019] For each region, based on the new probability distribution model corresponding to the region and the new first total power consumption, estimate the new maximum probability estimated power consumption corresponding to the region until a preset condition is met, thereby obtaining the maximum probability estimated power consumption corresponding to each region;

[0020] The preset condition is that the number of iterations reaches a preset threshold, or the difference between the first total power consumption and the second total power consumption is within a preset difference range.

[0021] The above technical solution, after initially estimating each region's electricity consumption, determines the rationality of the estimate by comparing the difference between the estimated total electricity consumption and the actual estimated target (the first total electricity consumption). If the difference exceeds a preset range, an iterative process is automatically triggered, using the filtered valid historical data to rebuild the probability distribution model and perform a new round of estimates based on this data. This process repeats until the preset conditions are met (such as the number of iterations reaching a threshold or the difference being within an acceptable range), thereby ensuring the accuracy and adaptability of the electricity estimate and improving the rationality of the scheduling plan.

[0022] In one achievable manner, determining the effective historical total electricity consumption in the historical total electricity consumption corresponding to each of the multiple historical periods within the energy supply range of the virtual power plant, and determining the effective historical electricity consumption in each historical period for each region within the energy supply range, includes:

[0023] Determine a first difference in historical total electricity consumption between adjacent periods;

[0024] Determining a first change moment corresponding to the total power consumption according to the first difference;

[0025] Determine the historical total power consumption corresponding to each period after the first change moment as the effective historical total power consumption;

[0026] For each region, it is determined whether there is a first change moment. If there is a first change moment, the historical power consumption of the region in each historical period after the first change moment is determined as the effective historical power consumption.

[0027] Through the above technical solution, during the iteration process, by identifying the changing moments of the historical total electricity consumption, based on the changing moments, valid historical data that reflects the changes and conforms to the current situation is retained, so as to build a more accurate and stable probability distribution model and improve the accuracy of electricity estimation.

[0028] In one achievable method, the present invention further includes:

[0029] determining whether the number of cycles after the first change moment is greater than a preset cycle number threshold;

[0030] If not, then correct the historical total power consumption before the first change moment according to the first difference in the historical total power consumption of adjacent cycles corresponding to the first change moment, and use the corrected historical total power consumption as the supplementary effective historical total power consumption;

[0031] For the area with the first change moment, the historical power consumption of the area before the first change moment is corrected according to the first difference in historical power consumption of the area in the adjacent periods corresponding to the first change moment, and the corrected historical power consumption is used as the supplementary effective historical power consumption.

[0032] Through the above technical solution, when faced with insufficient historical data or sudden changes, the continuity and stability of power estimation are ensured by correcting historical data or adjusting the identification method of the sudden change moment, making the power estimation system more robust.

[0033] In one achievable manner, determining the first change moment of total power consumption according to the first difference includes:

[0034] Determining an initial first change moment of total power consumption according to the first difference;

[0035] For each region, determining a second difference in historical power consumption of the region in adjacent periods; determining a second change time of the region based on the second difference;

[0036] determining whether the second change moment is the same as the first change moment;

[0037] If not, the initial first change moment is adjusted according to the second change moment to obtain the first change moment.

[0038] Through the above technical solution, when determining the mutation moment of total electricity consumption, not only the changing trend of total electricity consumption is taken into account, but also the changes in electricity consumption in each region are combined. By comprehensively comparing the mutation moments of the two, the first change moment is corrected, and the accurate identification of the mutation moment is achieved, further improving the accuracy and reliability of mutation identification.

[0039] In one achievable manner, the power dispatching according to the estimated power consumption with the maximum probability corresponding to each area includes:

[0040] Determine the first priority of each area based on the area information of each area;

[0041] Determine the second priority for each area based on the estimated electricity consumption of each area;

[0042] Determine the priority of each area based on the first and second priorities;

[0043] Power is allocated according to the priority of each area.

[0044] Through the above technical solution, this solution establishes an electricity dispatch priority system that comprehensively considers multiple factors. During the electricity dispatch process, not only the second priority of the estimated electricity consumption of each region is considered, but also the first priority determined by combining regional information (such as subject type, impact index, environmental information, etc.). Through a comprehensive evaluation method of multiple factors, the priority is determined, and then electricity is allocated based on the priority, making the electricity dispatch more reasonable.

[0045] In one achievable manner, the regional information includes: subject type, impact index, and environmental information;

[0046] Based on the regional information of each area, determine the first priority of each area, including:

[0047] Determine the first sub-priority for each area based on the subject type;

[0048] Determine the initial second sub-priority for each region based on the impact index;

[0049] According to the environmental information, the initial second sub-priority of each area is modified to obtain the second sub-priority;

[0050] A weighted calculation is performed based on the first sub-priority and the second sub-priority to obtain the first priority of each area.

[0051] Through the above technical solution, when determining the first priority of a region, not only the basic factor of the subject type is taken into consideration, but also the impact index and environmental information are introduced as adjustment factors. By quantifying the impact of these factors on regional priorities and performing weighted calculations, a more accurate and detailed regional priority ranking is obtained.

[0052] In one achievable manner, after performing weighted calculation based on the first sub-priority and the second sub-priority to obtain the first priority of each region, the method further includes:

[0053] After receiving the urgent need index, revising the first priority according to the urgent need index and the first priority;

[0054] Accordingly, determining the priority of each area according to the first priority and the second priority includes:

[0055] Determine the priority of each area based on the revised first priority and second priority.

[0056] Through the above technical solution, the emergency demand index is introduced as a correction factor, which enables flexible response to emergencies or urgent needs. By receiving and analyzing the emergency demand index in real time, the priority ranking of each area can be quickly adjusted. This dynamic adjustment mechanism ensures that power resources can be quickly and accurately allocated to where they are most needed in an emergency, thereby improving the flexibility and response speed of power dispatch.

[0057] In a second aspect, an electronic device is provided, the electronic device comprising:

[0058] one or more processors;

[0059] Memory;

[0060] One or more applications, wherein one or more applications are stored in a memory and configured to be executed by one or more processors, and the one or more programs are configured to: perform operations corresponding to the method shown in any possible implementation of the first aspect.

[0061] In a third aspect, a computer-readable storage medium is provided, wherein the storage medium stores at least one instruction, at least one program, code set or instruction set, and the at least one instruction, at least one program, code set or instruction set is loaded and executed by a processor to implement the method as shown in any possible implementation method of the first aspect.

[0062] In a fourth aspect, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by a processor, the method as shown in any possible implementation manner in the first aspect is implemented.

[0063] In summary, this application includes at least one of the following beneficial technical effects:

[0064] 1. By analyzing the total electricity consumption data of multiple historical periods, the first total electricity consumption of the next period is estimated. Combined with the probability distribution model of electricity consumption in each region, the estimate is further refined, and the estimated electricity consumption with the highest probability is assigned to each region. After the overall estimate, a detailed estimate can be made for each region based on the first total electricity consumption after the overall estimate. In this solution, not only the overall data but also the estimated data of each region are taken into account, effectively improving the accuracy and reliability of the electricity estimate.

[0065] 2. After initially estimating electricity consumption for each region, the rationality of the estimate is determined by comparing the difference between the estimated total electricity consumption and the actual estimated target (the first total electricity consumption). If the difference exceeds a preset range, an iterative process is automatically triggered. The probability distribution model is rebuilt using the filtered valid historical data, and a new round of estimates is performed based on this. This process repeats until the preset conditions are met (such as the number of iterations reaching a threshold or the difference is within an acceptable range). This ensures the accuracy and adaptability of the electricity estimate and improves the rationality of the scheduling plan. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 This is a structural diagram of a virtual power plant load-side resource optimization and scheduling system provided by an embodiment of the present application;

[0067] Figure 2 This is a flow chart of a method for optimizing resource scheduling at the load end of a virtual power plant provided in an embodiment of the present application;

[0068] Figure 3 This is a flow chart of a data screening process provided by an embodiment of the present application;

[0069] Figure 4 This is a flowchart of a priority determination process provided by an embodiment of the present application;

[0070] Figure 5 This is a structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0071] This specific embodiment is merely an explanation of the present application and is not a limitation of the present application. After reading this specification, those skilled in the art may make non-creative modifications to the present embodiment as needed, but as long as they are within the scope of the claims of the present application, they are protected by the patent law.

[0072] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0073] In this document, the term "and / or" simply describes a relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document, unless otherwise specified, generally indicates an "or" relationship between the related objects.

[0074] The virtual power plant (VPP), a cutting-edge energy management concept and technology practice, transcends the traditional physical power plant. Leveraging advanced information and communications technologies and intelligent software systems, it cleverly weaves diverse distributed energy resources (DERs), including distributed generators (DG), energy storage devices, controllable loads, and electric vehicles, into a highly integrated virtual energy network. This network, while non-physical, can flexibly participate in electricity market transactions and daily grid operations like a unified entity, promoting intelligent power dispatch and refined management. By continuously monitoring grid supply and demand dynamics, the virtual power plant utilizes advanced algorithms and models to deeply mine and analyze massive amounts of data, accurately predicting power demand trends and formulating optimized power supply and dispatch plans. This process not only significantly improves the overall efficiency of energy utilization but also effectively enhances the resilience and stability of the power system, providing a solid foundation for the stable operation of the power market.

[0075] In actual operation, refer to Figure 1The virtual power plant collects real-time operating data from various DERs; processes and analyzes this data in combination with the areas requiring power supply to formulate the optimal power dispatch strategy; and sends dispatch instructions to each DER to achieve accurate dispatch and optimal configuration of power.

[0076] In an embodiment of the present application, a virtual power plant load-side resource optimization scheduling method is provided, which can accurately predict the next cycle of electricity within the energy supply range of the virtual power plant to facilitate accurate scheduling of electricity.

[0077] Specifically, the embodiment of the present application provides a method for optimizing and scheduling load resources of a virtual power plant, such as Figure 2 As shown, the method provided in the embodiments of the present application can be executed by an electronic device, which can be a server or a terminal device, wherein 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. The terminal device can be a smartphone, a tablet computer, a laptop computer, a desktop computer, etc., but is not limited to these. The terminal device and the server can be directly or indirectly connected via wired or wireless communication, which is not limited in the embodiments of the present application. The method includes:

[0078] S101. Obtaining the historical total electricity consumption corresponding to each of multiple historical periods of the energy supply range of the virtual power plant;

[0079] Each virtual power plant has a corresponding energy supply scope and corresponding distributed energy resources. The energy supply scope refers to the geographic area or customer group to which the virtual power plant can provide power services. A historical period represents a period of time within the past, divided into specific time intervals (such as days, weeks, or months). Historical total electricity consumption represents the total electricity consumed by all users or devices within the virtual power plant's energy supply scope during the historical period.

[0080] Specifically, if the energy supply range of the virtual power plant needs to be determined, the energy supply range corresponding to the current virtual power plant can be determined by querying the pre-stored mapping relationship between virtual power plants and energy supply ranges.

[0081] In one possible scenario, the selection of cycles can be multiple recent cycles, or multiple cycles in the same period of each year, or other cycles. This embodiment of the present application does not limit this, and users can set it according to actual needs as long as the purpose of this embodiment can be achieved.

[0082] In some embodiments, electricity consumption data for all users within the energy supply range of the virtual power plant is obtained from a database; this data is cleaned and organized to remove outliers and duplicate data; the data is grouped and aggregated according to selected historical periods, and the total electricity consumption for each period is calculated. It is understood that other implementation methods can also be used, such as obtaining electricity consumption data from a third-party data service provider or using Internet of Things technology to achieve real-time monitoring and aggregation of electricity consumption. This is not limited here, and the specific implementation method can be flexibly selected based on actual conditions and needs.

[0083] S102: estimating a first total power consumption required for a next cycle based on the historical total power consumption corresponding to each of multiple historical cycles within the energy supply range of the virtual power plant;

[0084] The first total electricity consumption required for the next cycle refers to the total electricity consumption that may be required within the energy supply scope of the virtual power plant in a certain future cycle, estimated based on historical data and prediction models.

[0085] In some embodiments, a time series analysis method (such as an ARIMA model) can be used to analyze the historical total electricity consumption corresponding to multiple historical periods, identify seasonal patterns and trends in electricity consumption changes, and obtain the first total electricity consumption required for the next period.

[0086] Specifically, when predicting the next cycle based on the time series analysis method, the entire cycle can be used for prediction, and a local combination method can also be used for prediction. For example, the cycle is divided into multiple time periods according to a preset number of divisions, and then all historical cycles are divided according to time periods to obtain multiple sub-historical time periods. For example, each cycle is divided into 5 time periods, and the first time period of each historical cycle is used as the first data group, the second time period is used as the second data group, and so on, to obtain 5 data groups; for the data group corresponding to each sub-historical time period, a prediction is made according to the time series analysis method to obtain the sub-total power consumption of each sub-historical time period corresponding to the next cycle, and then all sub-total power consumptions are summarized to obtain the first total power consumption.

[0087] Furthermore, after obtaining the first total electricity consumption, the first total electricity consumption is adjusted based on external factors such as the current market environment and weather conditions as adjustment parameters to obtain the first total electricity consumption required for the next cycle, thereby ensuring the accuracy and reliability of the prediction results.

[0088] Of course, other methods can also be used for prediction, which are not limited in the embodiments of the present application, and users can set them according to actual needs.

[0089] S103. Obtain a probability distribution model of electricity consumption in each region within the energy supply range, where the probability distribution model is obtained by analyzing the historical electricity consumption of the region in each historical period and the historical total electricity consumption of the energy supply range in each historical period;

[0090] S104: For each region, estimate the maximum probability estimated power consumption corresponding to the region based on the probability distribution model corresponding to the region and the first total power consumption;

[0091] Each energy supply direction is divided into multiple areas. When dividing the energy supply range into regions, it can be divided based on geographical proximity, grid connection conditions, or user type (residential electricity, commercial electricity, factory electricity).

[0092] The probability distribution model is built based on historical data and can reflect the changing patterns and trends of power consumption.

[0093] In some embodiments, this step can be implemented in a variety of ways:

[0094] Method 1: Select an appropriate probability distribution model (such as normal distribution, Poisson distribution, or Weibull distribution) to divide the historical total electricity consumption into total intervals, with the data in each total interval being similar. Then, for each total interval, estimate the electricity consumption and obtain the probability distribution of each region in multiple intervals as the probability distribution model.

[0095] The second approach uses machine learning algorithms (such as random forests and neural networks) to construct a probability distribution model for electricity consumption. This method automatically learns the characteristics and patterns of electricity consumption from data, eliminating the need for manual model specification. Historical total electricity consumption data is fed into a training model to obtain the probabilities corresponding to multiple intervals. The machine learning model is then iteratively trained using intervals corresponding to the region's historical electricity consumption. Model performance is evaluated through methods such as cross-validation, and model parameters are adjusted to improve prediction accuracy, resulting in a final model. The trained model serves as the probability distribution model for the region.

[0096] It is understandable that this step can also be implemented in other ways, such as prediction methods based on expert systems, fuzzy reasoning methods, etc., which are not limited here.

[0097] The maximum probability estimated electricity consumption is the most likely electricity consumption forecast for a region, calculated based on the probability distribution model and the first total electricity consumption. This value corresponds to the highest probability density or the greatest likelihood in the probability distribution.

[0098] Furthermore, if the probability distribution model is obtained using Method 1, the probability distribution model corresponding to the first total power consumption can be selected to analyze the region and obtain the estimated power consumption corresponding to the maximum probability. Of course, if the current date or climate is special, the estimated power consumption can be adjusted accordingly after it is obtained, providing accurate data support for subsequent energy management and grid scheduling.

[0099] When the probability distribution model is obtained through method 2, advanced machine learning or deep learning algorithms are used to directly predict regional electricity consumption and obtain the estimated electricity consumption with the highest probability. This method can automatically learn complex patterns and relationships in the data and improve the accuracy and efficiency of the prediction.

[0100] S105: Perform power dispatch according to the estimated power consumption with the maximum probability corresponding to each area.

[0101] After obtaining the estimated electricity consumption, the total electricity consumption can be summarized and then electricity scheduling can be carried out. The estimated electricity consumption of each area can also be used for electricity scheduling separately, aiming to meet the electricity demand of each area while ensuring the stable operation of the power system.

[0102] It can be seen that in the embodiment of the present application, by analyzing the total electricity consumption data of multiple historical periods, the first total electricity consumption of the next period is estimated; combined with the probability distribution model of electricity consumption in each region, the estimate is further refined, and the estimated electricity consumption with the maximum probability is allocated to each region. After the overall estimate, based on the first total electricity consumption after the overall estimate, a detailed estimate can be made for each region, taking into account not only the overall data but also the estimated data of each region, thereby effectively improving the accuracy and reliability of the electricity estimate.

[0103] Further, refer to Figure 3 In order to improve the accuracy of determining the estimated power consumption, in an embodiment of the present application, for each region, after estimating the estimated power consumption with the maximum probability corresponding to the region based on the probability distribution model corresponding to the region and the first total power consumption, the method further includes:

[0104] S201. Summarize the estimated power consumption with the highest probability corresponding to each area to obtain a second total power consumption;

[0105] The estimated power consumption with the maximum probability corresponding to all areas is summed up to obtain the second total power consumption.

[0106] S202: Determine whether the difference between the first total power consumption and the second total power consumption is within a preset difference range;

[0107] The preset difference range is a custom setting. When it is within this range, it means that the estimation of the area is accurate. When it is no longer within this range, it means that there is an error in the estimation and further verification and adjustment are required.

[0108] S203: If yes, then executing the step of performing power dispatching according to the estimated power consumption with the maximum probability corresponding to each area;

[0109] S204: If not, determining the effective historical total electricity consumption in the historical total electricity consumption corresponding to each of the multiple historical periods within the energy supply range of the virtual power plant, and determining the effective historical electricity consumption for each historical period in each region within the energy supply range;

[0110] Effective historical total electricity consumption: refers to the historical total electricity consumption data that can truly reflect the historical electricity consumption situation under the current state.

[0111] Effective historical electricity consumption: refers to the actual electricity consumption data of each area within the energy supply range of the virtual power plant that can truly reflect the current state during a specific historical period.

[0112] Specifically, the effective historical total electricity consumption in the historical total electricity consumption corresponding to each of the multiple historical periods within the energy supply range of the virtual power plant is determined, and the effective historical electricity consumption in each historical period of each area within the energy supply range is determined, including SA1-SA4 (not shown in the drawings), wherein:

[0113] SA1. Determine a first difference in the total historical electricity consumption of adjacent cycles;

[0114] Adjacent cycles refer to two consecutive and adjacent time periods in a time series, which are used to compare and analyze the changing trends of electricity consumption.

[0115] SA2. Determine a first change time corresponding to the total power consumption based on the first difference;

[0116] The first change moment refers to the moment when the electricity consumption changes significantly based on the change of the first difference. The change needs to be continuous rather than sporadic. This moment may mark some important shift in electricity demand or supply, indicating that the status of the area may have changed.

[0117] A curve graph is drawn according to the first difference, and a change moment closest to the current moment is determined according to the curve graph as the first change moment.

[0118] If there is a change, the first change moment is obtained;

[0119] If there is no change, determine whether there is a change moment for each area. If there is a change moment in a certain area, the most recent change moment is selected as the effective change moment, and the historical electricity consumption data after the change moment of the area with the effective change moment is used as the effective historical electricity consumption data, and the other areas remain unchanged.

[0120] SA3. Determine the historical total power consumption corresponding to each period after the first change moment as the effective historical total power consumption;

[0121] SA4. For each region, determine whether there is a first change moment. If so, determine the historical power consumption of the region in each historical period after the first change moment as the effective historical power consumption.

[0122] Each region has its own electricity consumption patterns. If an area is an industrial production area, the electricity consumption may be relatively stable and will not change. Some areas are living areas and may change due to environmental influences.

[0123] For each region, determine whether there is a change moment based on the region's historical electricity consumption;

[0124] If so, further determine whether the change moment includes the first change moment. If so, determine the historical electricity consumption of the region in each historical period after the first change moment as the effective historical electricity consumption. If not, determine the historical electricity consumption of the region in each historical period after the most recent change as the effective historical electricity consumption.

[0125] If it does not exist, all historical electricity consumption will be regarded as effective historical electricity consumption.

[0126] Through the above method, effective power consumption information can be obtained through determination.

[0127] It can be seen that in the embodiment of the present application, during the iteration process, by identifying the changing moments of the historical total electricity consumption, based on the changing moments, valid historical data reflecting the changes and in line with the current situation is retained, so as to build a more accurate and stable probability distribution model and improve the accuracy of electricity estimation.

[0128] S205: estimating a new first total power consumption based on the effective historical total power consumption;

[0129] S206: Construct a new probability distribution model of electricity consumption in each region based on the effective historical electricity consumption in each historical period of each region within the energy supply range;

[0130] S207. For each region, estimate the new maximum probability estimated power consumption corresponding to the region based on the new probability distribution model corresponding to the region and the new first total power consumption, until a preset condition is met, thereby obtaining the maximum probability estimated power consumption corresponding to each region;

[0131] The preset condition is that the number of iterations reaches a preset threshold, or the difference between the first total power consumption and the second total power consumption is within a preset difference range.

[0132] Iterative estimation: If the number of repeated estimations reaches the preset number, the cycle is stopped and the final power consumption is used as the estimated power consumption, or the technicians revise the estimated power consumption based on experience.

[0133] As can be seen, in the embodiment of the present application, after initially estimating the electricity consumption of each region, the rationality of the estimate is determined by comparing the difference between the estimated total electricity consumption and the actual estimated target (the first total electricity consumption). If the difference exceeds a preset range, an iterative process is automatically triggered, and the probability distribution model is rebuilt using the filtered valid historical data, and a new round of estimates is performed based on this. This process is repeated until the preset conditions are met (such as the number of iterations reaches a threshold or the difference is within an acceptable range), thereby ensuring the accuracy and adaptability of the electricity estimate and improving the rationality of the scheduling plan.

[0134] Furthermore, if the number of cycles after the first change moment is small, the accuracy of the model may be affected. To further improve the accuracy, the method further includes:

[0135] determining whether the number of cycles after the first change moment is greater than a preset cycle number threshold;

[0136] If not, then correct the historical total power consumption before the first change moment according to the first difference in the historical total power consumption of adjacent cycles corresponding to the first change moment, and use the corrected historical total power consumption as the supplementary effective historical total power consumption;

[0137] For the area with the first change moment, the historical power consumption of the area before the first change moment is corrected according to the first difference in historical power consumption of the area in the adjacent periods corresponding to the first change moment, and the corrected historical power consumption is used as the supplementary effective historical power consumption.

[0138] The preset cycle number threshold can be set according to actual needs.

[0139] When the number of cycles after the first change moment is not greater than the preset cycle number threshold, it means that the amount of data is small, then the difference in the historical total power consumption of adjacent cycles corresponding to the first change moment is calculated, and the historical total power consumption before the first change moment is corrected according to the first difference in the historical total power consumption of adjacent cycles corresponding to the first change moment. Specifically, the difference + historical total power consumption = corrected historical total power consumption is obtained to supplement the effective historical power consumption, so as to supplement the effective historical charging capacity and increase the data sample.

[0140] If the area with the first change moment also has other change moments, data correction can be performed by combining the differences corresponding to the multiple change moments with the difference corresponding to the first change moment. In one possible scenario, all data can be corrected, or only a preset amount of data can be corrected to supplement a sufficient number of data samples.

[0141] It can be seen that in the embodiment of the present application, when faced with insufficient historical data or sudden changes, the continuity and stability of power estimation are ensured by correcting historical data or adjusting the identification method of the sudden change moment, making the power estimation system more robust.

[0142] A possible implementation of the embodiment of the present application is to determine the first change moment of total power consumption according to the difference, including:

[0143] Determining an initial first change moment of total power consumption according to the first difference;

[0144] For each region, determining a second difference in historical power consumption of the region in adjacent periods; determining a second change time of the region based on the second difference;

[0145] determining whether the second change moment is the same as the first change moment;

[0146] If not, the initial first change moment is adjusted according to the second change moment to obtain the first change moment.

[0147] In an embodiment of the present application, when the change moment determined based on the total power consumption is different from the change moment of the region, the most recent moment among all the second change moments can be selected as the first change moment to make the change moment more accurate.

[0148] It can be seen that in the embodiment of the present application, when determining the mutation moment of the total electricity consumption, not only the changing trend of the total electricity consumption is taken into consideration, but also the changes in electricity consumption in each region are combined. By comprehensively comparing the mutation moments of the two, the first change moment is corrected, and the accurate identification of the mutation moment is achieved, further improving the accuracy and reliability of mutation identification.

[0149] Combine Figure 4When performing power dispatch, different priorities are determined for different areas to achieve priority supply and ensure the stability of power supply in important areas. Specifically, power dispatch is performed based on the maximum probability estimated power consumption corresponding to each area, including: SC1-SC4 (not shown in the figure), where:

[0150] SC1. Determine the first priority of each area based on the area information of each area;

[0151] Regional information includes: subject type, impact index, and environmental information;

[0152] Based on the regional information of each area, determine the first priority of each area, including:

[0153] Determine the first sub-priority for each area based on the subject type;

[0154] Determine the initial second sub-priority for each region based on the impact index;

[0155] According to the environmental information, the initial second sub-priority of each area is modified to obtain the second sub-priority;

[0156] A weighted calculation is performed based on the first sub-priority and the second sub-priority to obtain the first priority of each area.

[0157] Specifically, the subject type is the main electricity consumer in the region, such as residential electricity, commercial electricity, and factory electricity.

[0158] The electronic device is preset with correspondences between different subject types and different priorities, so as to facilitate determination of the first sub-priority level;

[0159] The electronic device is preset with correspondences between impact indices of different ranges and different priorities, so as to facilitate determination of the initial second sub-priority level;

[0160] Environmental information: It represents various conditions and states in the current environment or scenario, including but not limited to temperature, humidity, light intensity, and pedestrian flow. This information is crucial for evaluating the importance or urgency of different areas. Environmental information may have an impact on the power consumption of the area. Through environmental information, the initial second sub-priority is corrected so that the obtained second sub-priority is closer to the actual situation of the current environment and can more accurately guide subsequent resource allocation, task scheduling or decision-making. Different environmental information settings have different modification coefficients or adjustment amounts, so as to obtain the coefficient or adjustment amount corresponding to the current environmental information, correct the initial second sub-priority, and obtain the second sub-priority.

[0161] When determining the first priority of a region, not only the basic factor of the subject type is taken into consideration, but also the impact index and environmental information are introduced as adjustment factors. By quantifying the impact of these factors on regional priorities and performing weighted calculations, a more accurate and detailed regional priority ranking is obtained.

[0162] SC2: Determine the second priority of each area based on the estimated power consumption of each area;

[0163] The more estimated power consumption, the higher the corresponding second priority.

[0164] SC3. Determine the priority of each area based on the first priority and the second priority;

[0165] Perform weighted calculation based on each priority to obtain the priority of the area.

[0166] SC4. Allocate power according to the priority of each area.

[0167] It can be seen that in the embodiment of the present application, a power dispatch priority system that comprehensively considers multiple factors is established. During the power dispatch process, not only the second priority of the estimated power consumption of each area is considered, but also the first priority determined by combining regional information (such as subject type, impact index, environmental information, etc.). Through the method of comprehensive evaluation of multiple factors, the priority is determined, and then power is allocated based on the priority, making the power dispatch more reasonable.

[0168] Furthermore, a possible implementation of the embodiment of the present application, after performing weighted calculation based on the first sub-priority and the second sub-priority to obtain the first priority of each area, further includes:

[0169] After receiving the urgent need index, revising the first priority according to the urgent need index and the first priority;

[0170] Accordingly, the priority of each area is determined based on the first priority and the second priority, including:

[0171] Determine the priority of each area based on the revised first priority and second priority.

[0172] In the embodiment of the present application, the urgent demand index indicates the urgency of a certain demand or task under the current circumstances, and is usually a quantitative indicator used to measure the urgency with which the demand or task needs to be processed.

[0173] After receiving the Urgent Needs Index, if it indicates an urgent need that requires immediate processing or special attention, a comprehensive analysis and assessment will be conducted based on the specific value of the Urgent Needs Index and the first priority level to adjust the first priority level to better respond to the urgent need.

[0174] In some embodiments, the operation of modifying the first priority level according to the emergency demand index can be implemented in multiple ways:

[0175] Optionally, a correspondence between the emergency demand index and the priority adjustment range is preset. When the emergency demand index is received, the specific range of priority adjustment is determined based on the correspondence; finally, the adjustment range is applied to the first priority to obtain a revised new priority.

[0176] Optionally, a dynamic priority adjustment algorithm is used, which can analyze the changes in the emergency demand index T and the first priority P of the current system in real time, automatically calculate the optimal priority adjustment plan, and update the first priority in real time, see Table 1.

[0177] Table 1

[0178]

[0179] Specifically, a first index threshold T1 and a second index threshold T2 are preset, and the first index threshold T1 is smaller than the second index threshold T; a first priority threshold P1 and a second priority threshold P2 are preset, and the first priority threshold P1 is smaller than the second priority threshold P2.

[0180] When the emergency demand index is less than the first index threshold, the first priority remains unchanged;

[0181] When the emergency demand index is greater than the first index threshold, the first priority is adjusted to the maximum priority;

[0182] When the emergency demand index is between the first index threshold and the second index threshold, if the first priority is greater than the second priority threshold, the first priority is adjusted to the maximum priority; if the first priority is less than the first priority threshold, then according to the preset correspondence between the emergency demand index and the priority, the priority corresponding to the current emergency demand index is determined, and the first priority is replaced; if the first priority is between the first priority threshold and the second priority threshold, according to the preset correspondence between the emergency demand index and the priority, the priority corresponding to the current emergency demand index is determined, and the priority and the first priority are weighted to obtain the priority, and the first priority is replaced. When performing weighted calculations, the weight value can be customized by the user. This method can respond to changes in emergency needs more flexibly and intelligently.

[0183] It is understandable that other methods may be used to implement the operation of modifying the first priority according to the emergency demand index, such as decision support based on expert systems, fuzzy logic reasoning, etc., which are not limited here.

[0184] It can be seen that in the embodiment of the present application, the emergency demand index is introduced as a correction factor, which enables flexible response to emergencies or emergency needs. By receiving and analyzing the emergency demand index in real time, the priority ranking of each area can be quickly adjusted; this dynamic adjustment mechanism ensures that power resources can be quickly and accurately allocated to where they are most needed in an emergency, thereby improving the flexibility and response speed of power dispatch.

[0185] A possible implementation of the embodiment of the present application allocates power according to regional priorities, including:

[0186] Obtaining energy information corresponding to multiple power supply energy sources of the virtual grid, including power supply stability and available power;

[0187] According to the priority of the region and the energy information corresponding to the multiple power supply energy sources, power supply energy and the power supply amount corresponding to the power supply energy are allocated to each region.

[0188] Regions are sorted by priority, and the sorted list of regions is then traversed. For each region, one or more energy sources are selected from multiple sources for distribution based on its priority and energy needs. The selection process considers factors such as energy generation, cost, and stability, and an optimization algorithm is used to calculate the optimal power distribution plan.

[0189] An electronic device is provided in an embodiment of the present application, such as Figure 5 As shown, Figure 5 The electronic device 300 shown includes a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, for example, via a bus 302. Optionally, the electronic device 300 may further include a transceiver 304. It should be noted that in actual applications, the number of transceivers 304 is not limited to one, and the structure of the electronic device 300 does not constitute a limitation on the embodiments of the present application.

[0190] Processor 301 can be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic device, transistor logic device, hardware component, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 301 can also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0191] Bus 302 may include a path for transmitting information between the above components. Bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. Bus 302 may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, Figure 5 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0192] The memory 303 may be a ROM (Read Only Memory) or other type of static storage device that can store static information and instructions, a RAM (Random Access Memory) or other type of dynamic storage device that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0193] The memory 303 is used to store application code for executing the solution of the present application, and the execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the content shown in the above method embodiment.

[0194] Among them, electronic devices include but are not limited to: mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 5 The electronic device shown is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0195] An embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer-readable storage medium is run on a computer, the computer can execute the corresponding contents of the aforementioned method embodiment.

[0196] An embodiment of the present application provides a computer program product, including a computer program, which implements the corresponding contents of the aforementioned method embodiment when the computer program is executed by a processor.

[0197] It should be understood that although the steps in the flowcharts of the accompanying drawings are shown in sequence as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the flowcharts of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0198] The above are only some of the implementation methods of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for optimizing and scheduling load-side resources of a virtual power plant, characterized in that: include: Obtain the historical total electricity consumption corresponding to multiple historical periods of the energy supply range of the virtual power plant; Using a time series analysis method, the first total electricity consumption required for the next cycle is estimated based on the historical total electricity consumption corresponding to multiple historical cycles of the virtual power plant's energy supply range. The first total electricity consumption is adjusted based on external factors such as the current market environment and weather conditions as adjustment parameters to obtain the first total electricity consumption required for the next cycle. Obtaining a probability distribution model of electricity consumption for each region within the energy supply range, the probability distribution model being obtained based on an analysis of historical electricity consumption for each historical period of the region and the historical total electricity consumption for the energy supply range within each historical period; For each region, based on the probability distribution model corresponding to the region and the first total power consumption, estimate the maximum probability power consumption corresponding to the region; Determine the first priority for each area based on the subject type, impact index, and environmental information of each area; Determine the second priority of each area based on the estimated power consumption with the highest probability in each area; Determine the priority of each area based on the first and second priorities; Allocate power supply energy and the corresponding power supply amount of each power supply energy to each region based on the priority of the region and the power supply stability and available power of multiple power supply energy sources; Based on the regional information of each area, determine the first priority of each area, including: Determine the first sub-priority for each area based on the subject type; Determine the initial second sub-priority for each region based on the impact index; According to the environmental information, the initial second sub-priority of each area is modified to obtain the second sub-priority; Perform weighted calculation based on the first sub-priority and the second sub-priority to obtain the first priority of each area; After receiving the urgent need index, revising the first priority according to the urgent need index and the first priority; Accordingly, determining the priority of each area according to the first priority and the second priority includes: Determine the priority of each area based on the revised first and second priorities; When the emergency demand index is less than the first index threshold, the first priority remains unchanged; When the emergency demand index is greater than the first index threshold, the first priority is adjusted to the maximum priority; When the emergency demand index is between the first index threshold and the second index threshold, if the first priority is greater than the second priority threshold, the first priority is adjusted to the maximum priority; if the first priority is less than the first priority threshold, the priority corresponding to the current emergency demand index is determined according to the preset correspondence between the emergency demand index and the priority, and the first priority is replaced; if the first priority is between the first priority threshold and the second priority threshold, the priority corresponding to the current emergency demand index is determined according to the preset correspondence between the emergency demand index and the priority, and the priority and the first priority are weighted to obtain the priority, and the first priority is replaced; when performing weighted calculation, the weight value can be customized by the user.

2. The method for optimizing and scheduling load-side resources of a virtual power plant according to claim 1, characterized in that: After estimating the maximum probability estimated power consumption corresponding to each region based on the probability distribution model corresponding to the region and the first total power consumption, the method further includes: Summarize the estimated power consumption with the maximum probability corresponding to each area to obtain the second total power consumption; determining whether a difference between the first total power consumption and the second total power consumption is within a preset difference range; If yes, then executing the step of performing power dispatch according to the estimated power consumption with the maximum probability corresponding to each area; If not, determining the effective historical total electricity consumption in the historical total electricity consumption corresponding to each of the multiple historical periods of the energy supply range of the virtual power plant, and determining the effective historical electricity consumption in each historical period of each area of ​​the energy supply range; Based on the effective historical total power consumption, a new first total power consumption is estimated. Construct a new probability distribution model of electricity consumption in each region based on the effective historical electricity consumption in each historical period of each region within the energy supply range; For each region, based on the new probability distribution model corresponding to the region and the new first total power consumption, estimate the new maximum probability estimated power consumption corresponding to the region until a preset condition is met, thereby obtaining the maximum probability estimated power consumption corresponding to each region; The preset condition is that the number of iterations reaches a preset threshold, or the difference between the first total power consumption and the second total power consumption is within a preset difference range.

3. The method for optimizing and scheduling load-side resources of a virtual power plant according to claim 2, characterized in that: The determining of the effective historical total electricity consumption in the historical total electricity consumption corresponding to each of the multiple historical periods of the energy supply range of the virtual power plant, and determining the effective historical electricity consumption in each historical period of each area of ​​the energy supply range, includes: Determine a first difference in historical total electricity consumption between adjacent periods; Determining a first change moment corresponding to the total power consumption according to the first difference; Determine the historical total power consumption corresponding to each period after the first change moment as the effective historical total power consumption; For each region, it is determined whether there is a first change moment. If there is a first change moment, the historical power consumption of the region in each historical period after the first change moment is determined as the effective historical power consumption.

4. The method for optimizing and scheduling load-side resources of a virtual power plant according to claim 3, characterized in that: Also includes: determining whether the number of cycles after the first change moment is greater than a preset cycle number threshold; If not, then correct the historical total power consumption before the first change moment according to the first difference in the historical total power consumption of adjacent cycles corresponding to the first change moment, and use the corrected historical total power consumption as the supplementary effective historical total power consumption; For the area with the first change moment, the historical power consumption of the area before the first change moment is corrected according to the first difference in historical power consumption of the area in the adjacent periods corresponding to the first change moment, and the corrected historical power consumption is used as the supplementary effective historical power consumption.

5. The method for optimizing and scheduling load-side resources of a virtual power plant according to claim 3, characterized in that: Determining the first change moment of total power consumption according to the first difference includes: Determining an initial first change moment of total power consumption according to the first difference; For each region, determining a second difference in historical power consumption of the region in adjacent periods; determining a second change time of the region based on the second difference; determining whether the second change moment is the same as the first change moment; If not, the initial first change moment is adjusted according to the second change moment to obtain the first change moment.

6. An electronic device, characterized in that: include: one or more processors; Memory; One or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, and the one or more applications are configured to: execute the steps of the virtual power plant load-side resource optimization scheduling method according to any one of claims 1 to 5.

7. A computer-readable storage medium, characterized in that The storage medium stores at least one instruction, at least one program, code set or instruction set, and the at least one instruction, at least one program, code set or instruction set is loaded by the processor and executes the steps of the virtual power plant load-end resource optimization scheduling method according to any one of claims 1 to 5.

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