Resource allocation method and system based on virtual power plant

Abstract

The invention relates to a resource allocation method and system based on a virtual power plant, and belongs to the technical field of electric power, and the method comprises the steps: respectively obtaining electric power basic data corresponding to an area covered by the virtual power plant in different historical time periods; analyzing the power basic data to obtain power analysis data corresponding to the power basic data in different historical time periods; constructing an initial power prediction model according to the power analysis data; acquiring a fluctuation coefficient corresponding to the electric power prediction model to correct the initial electric power prediction model to obtain a target electric power prediction model; and performing supply-demand balance prediction based on the target power prediction model and the power data corresponding to the current time period, and performing power resource allocation according to a prediction result. According to the invention, the electric power resources can be effectively scheduled and allocated.

Description

Technical Field

[0001] The present invention relates to the field of electric power technology, and in particular to a resource allocation method and system based on a virtual power plant. Background Art

[0002] With the massive growth of business data of various virtual power plants, due to the lack of large-scale data processing capabilities, traditional business routing and network resource allocation methods are less efficient in solving the scheduling of massive data resources. In addition, the distribution network side of the virtual power plant is connected to a large number of user-side distributed adjustable flexible resources, such as distributed photovoltaic power generation, distributed wind power generation, electric vehicle charging piles and commercial building loads. At present, the unified control method for distributed flexible resources is generally simulation control, which adopts the following method: simulating the power generation curves of various flexible resources (distributed photovoltaic power generation, distributed wind power generation, electric vehicle charging piles and commercial building loads). However, the above method usually fails to establish corresponding power resource models for different flexible resources, resulting in inaccurate simulated power information, which is not conducive to the scheduling of power resources.

[0003] Therefore, there is an urgent need to propose a resource allocation method and system based on a virtual power plant that can effectively dispatch power resources. Summary of the invention

[0004] Based on this, it is necessary to provide a resource allocation method and system based on a virtual power plant that can effectively dispatch power resources in response to the above technical problems.

[0005] In a first aspect, a resource allocation method based on a virtual power plant is provided, the method comprising: respectively obtaining basic power data corresponding to the area covered by the virtual power plant in different historical time periods, the basic power data at least including power demand data and power supply data; analyzing the basic power data to obtain power analysis data corresponding to the basic power data in different historical time periods; constructing an initial power forecasting model based on the power analysis data, wherein the power forecasting model at least includes a power demand forecasting model and a power supply forecasting model; obtaining a fluctuation coefficient corresponding to the power forecasting model to correct the initial power forecasting model to obtain a target power forecasting model, wherein the fluctuation coefficients respectively include a power demand side fluctuation coefficient and a power supply side fluctuation coefficient; performing supply and demand balance forecasting based on the target power forecasting model and the power data corresponding to the current time period, and allocating power resources according to the forecasting results.

[0006] Optionally, analyzing the power basic data to obtain power analysis data corresponding to the power basic data in different historical time periods includes: performing data cleaning on the power basic data, the data cleaning process at least including processing missing values, outlier detection and consistency check on the power basic data; normalizing the data after data cleaning to obtain normalized power basic data; analyzing the normalized power basic data to obtain power analysis data corresponding to the power basic data in different historical time periods.

[0007] Optionally, analyzing the normalized power basic data to obtain power analysis data corresponding to the power basic data in different historical time periods includes: based on an objective function, respectively calculating and determining target values ​​corresponding to the normalized power demand data and power supply data, wherein the objective function includes: ; in, represents the target value, Indicates the number of data elements. Indicates The weight coefficient of each data element, Indicates The normalized value of the data element, represents a constant coefficient; defining the target value corresponding to the power demand data greater than a first preset value as a first target value, and defining the target value corresponding to the power supply data greater than a second preset value as a second target value; marking the historical time periods corresponding to the first target value and the second target value once respectively; in response to detecting that the first target value and the second target value exist simultaneously in the target historical time period marked once, marking the target historical time period twice; selecting the associated indicators corresponding to the power demand data and the power supply data in the target historical time period respectively, and calculating the associated value based on the associated indicators, wherein the calculation formula of the associated value includes: ; ; in, Represents the associated value, Represents the associated index , The correlation coefficient, Indicates time, Indicates the time coefficient corresponding to the target historical time period, Represents the associated index The rate of change, Represents the associated index In response to detecting that the associated value is greater than a third preset value, the target historical time period is marked three times, and the target historical time period after the three markings, as well as the associated value and the target value corresponding to the target historical time period are mapped to generate the power analysis data and save it.

[0008] Optionally, based on the power analysis data, constructing an initial power forecasting model includes: constructing a first power demand forecasting model and a first power supply forecasting model; dividing the power analysis data into a training set and a verification set, and training and verifying the first power demand forecasting model and the first power supply forecasting model, respectively; in response to detecting that the verification result reaches a preset standard, outputting a second power demand forecasting model and a second power supply forecasting model, and defining the second power demand forecasting model and the second power supply forecasting model as the initial power forecasting model.

[0009] Optionally, the expression of the second power demand prediction model includes: ; in, represents the demand forecast value, Indicates the number of time periods in a time period, Indicates time period The probability of demand increasing. Indicates the ratio of the associated value corresponding to the power demand data to the target value, Represents the correction function of the power demand forecasting model.

[0010] Optionally, the expression of the second power supply prediction model includes: ; in, represents the supply forecast value, Indicates time period The probability of a supply increase occurring, Indicates the ratio of the associated value corresponding to the power supply data to the target value, Represents the correction function of the power supply prediction model.

[0011] Optionally, obtaining the fluctuation coefficient corresponding to the power forecasting model to correct the initial power forecasting model to obtain the target power forecasting model includes: obtaining relevant parameters of the renewable energy power station in the area covered by the virtual power plant, and determining the fluctuation coefficient of the second power supply forecasting model based on the relevant parameters of the renewable energy power station; based on the fluctuation coefficient of the second power supply forecasting model, determining a correction function of the power supply forecasting model to correct the second power supply forecasting model, wherein the correction function of the power supply forecasting model includes: ; in, represents the number of renewable energy power stations, Indicates The probability of a renewable energy power station generating a power generation error, represents the weight coefficient, represents a correction coefficient; obtaining power consumption parameters of the user side in different time periods in the area covered by the virtual power plant, and determining the fluctuation coefficient of the second power demand forecasting model based on the power consumption parameters of the user side in different time periods; based on the fluctuation coefficient of the second power demand forecasting model, determining a correction function of the power demand forecasting model to correct the second power demand forecasting model, wherein the correction function of the power demand forecasting model includes: ; in, Represents the seasonal attribute. Indicates The probability of electricity consumption deviation in each season, represents the weight coefficient, represents the correction coefficient; defines the corrected second power demand forecasting model and the second power supply forecasting model as the target power forecasting model.

[0012] Optionally, a supply and demand balance prediction is performed based on the target power prediction model and the power data corresponding to the current time period, and power resources are allocated according to the prediction result, including: obtaining the power data of the current time period, and inputting the power data into the target power prediction model to obtain a demand prediction value and a supply prediction value; in response to detecting that the difference between the demand prediction value and the supply prediction value is greater than a fourth preset value, determining that the current supply and demand is unbalanced, and supplying power based on stored power resources; in response to detecting that the difference between the demand prediction value and the supply prediction value is less than a fourth preset value, determining that the current supply and demand is unbalanced, and storing power resources.

[0013] In a second aspect, a resource allocation system based on a virtual power plant is provided, and the device includes: a data acquisition module, which is used to respectively obtain the basic power data corresponding to the area covered by the virtual power plant in different historical time periods, and the basic power data at least includes power demand data and power supply data; a data analysis module, which is used to analyze the basic power data to obtain power analysis data corresponding to the basic power data in different historical time periods; a model construction module, which is used to construct an initial power forecasting model according to the power analysis data, wherein the power forecasting model at least includes a power demand forecasting model and a power supply forecasting model; a model correction module, which is used to obtain the fluctuation coefficient corresponding to the power forecasting model to correct the initial power forecasting model to obtain a target power forecasting model, wherein the fluctuation coefficients respectively include a power demand side fluctuation coefficient and a power supply side fluctuation coefficient; a resource allocation module, which is used to perform supply and demand balance prediction based on the target power forecasting model and the power data corresponding to the current time period, and to allocate power resources according to the prediction results.

[0014] Optionally, analyzing the normalized power basic data to obtain power analysis data corresponding to the power basic data in different historical time periods includes: based on an objective function, respectively calculating and determining target values ​​corresponding to the normalized power demand data and power supply data, wherein the objective function includes: ; in, represents the target value, Indicates the number of data elements. Indicates The weight coefficient of each data element, Indicates The normalized value of the data element, represents a constant coefficient; defining the target value corresponding to the power demand data greater than a first preset value as a first target value, and defining the target value corresponding to the power supply data greater than a second preset value as a second target value; marking the historical time periods corresponding to the first target value and the second target value once respectively; in response to detecting that the first target value and the second target value exist simultaneously in the target historical time period marked once, marking the target historical time period twice; selecting the associated indicators corresponding to the power demand data and the power supply data in the target historical time period respectively, and calculating the associated value based on the associated indicators, wherein the calculation formula of the associated value includes: ; ; in, Represents the associated value, Represents the associated index , The correlation coefficient, Indicates time, Indicates the time coefficient corresponding to the target historical time period, Represents the associated index The rate of change, Represents the associated index In response to detecting that the associated value is greater than a third preset value, the target historical time period is marked three times, and the target historical time period after the three markings, as well as the associated value and the target value corresponding to the target historical time period are mapped to generate the power analysis data and save it.

[0015] According to a third aspect, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program: respectively obtaining basic power data corresponding to the area covered by the virtual power plant in different historical time periods, wherein the basic power data at least include power demand data and power supply data; analyzing the basic power data to obtain power analysis data corresponding to the basic power data in different historical time periods; constructing an initial power forecasting model based on the power analysis data, wherein the power forecasting model at least includes a power demand forecasting model and a power supply forecasting model; obtaining a fluctuation coefficient corresponding to the power forecasting model to correct the initial power forecasting model to obtain a target power forecasting model, wherein the fluctuation coefficients respectively include a power demand side fluctuation coefficient and a power supply side fluctuation coefficient; performing supply and demand balance forecasting based on the target power forecasting model and the power data corresponding to the current time period, and allocating power resources according to the forecasting results.

[0016] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented: respectively obtaining basic power data corresponding to the area covered by the virtual power plant in different historical time periods, the basic power data at least including power demand data and power supply data; analyzing the basic power data to obtain power analysis data corresponding to the basic power data in different historical time periods; constructing an initial power forecasting model based on the power analysis data, wherein the power forecasting model at least includes a power demand forecasting model and a power supply forecasting model; obtaining the fluctuation coefficient corresponding to the power forecasting model to correct the initial power forecasting model to obtain a target power forecasting model, wherein the fluctuation coefficients respectively include a power demand side fluctuation coefficient and a power supply side fluctuation coefficient; performing supply and demand balance forecasting based on the target power forecasting model and the power data corresponding to the current time period, and allocating power resources according to the forecasting results.

[0017] In a fifth aspect, a computer program product is provided, which includes a computer program, and when the computer program is executed by a processor, the following steps are implemented: respectively obtaining basic power data corresponding to the area covered by the virtual power plant in different historical time periods, the basic power data at least including power demand data and power supply data; analyzing the basic power data to obtain power analysis data corresponding to the basic power data in different historical time periods; constructing an initial power forecasting model based on the power analysis data, wherein the power forecasting model at least includes a power demand forecasting model and a power supply forecasting model; obtaining the fluctuation coefficient corresponding to the power forecasting model to correct the initial power forecasting model to obtain a target power forecasting model, wherein the fluctuation coefficients respectively include a power demand side fluctuation coefficient and a power supply side fluctuation coefficient; performing supply and demand balance forecasting based on the target power forecasting model and the power data corresponding to the current time period, and allocating power resources according to the forecasting results.

[0018] The above-mentioned resource allocation method and system based on virtual power plant, the method includes: respectively obtaining the basic power data corresponding to the area covered by the virtual power plant in different historical time periods, the basic power data at least including power demand data and power supply data; analyzing the basic power data to obtain power analysis data corresponding to the basic power data in different historical time periods; constructing an initial power forecasting model based on the power analysis data, wherein the power forecasting model at least includes a power demand forecasting model and a power supply forecasting model; obtaining the fluctuation coefficient corresponding to the power forecasting model to correct the initial power forecasting model to obtain a target power forecasting model, wherein the fluctuation coefficients respectively include a power demand side fluctuation coefficient and a power supply side fluctuation coefficient; based on the target power forecasting model and the power data corresponding to the current time period, a supply and demand balance forecast is made, and power resources are allocated according to the forecast results. The present invention is based on constructing relevant power resource models, which can improve the power data processing efficiency and the accuracy of supply and demand balance forecasts, and thus can effectively schedule and allocate power resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is an application environment diagram of a resource allocation method based on a virtual power plant in one embodiment; Figure 2 is a flow chart of a resource allocation method based on a virtual power plant in one embodiment; Figure 3 is a structural block diagram of a resource allocation system based on a virtual power plant in one embodiment; Figure 4 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is only a part of the embodiment of the present invention, not all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0021] It should be understood that in the description of the present invention, unless the context clearly requires otherwise, words such as “include”, “including” and the like throughout the specification should be interpreted as including rather than being exclusive or exhaustive; that is, as including but not limited to “including”.

[0022] It should also be understood that the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, in the description of the present invention, unless otherwise specified, "plurality" means two or more.

[0023] It should be noted that the terms "S1", "S2", etc. are only used for the purpose of describing the steps, and do not specifically refer to the order or sequence, nor are they used to limit the present invention. They are only for the convenience of describing the method of the present invention, and cannot be understood as indicating the order of the steps. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0024] The resource allocation method based on virtual power plant provided by the present invention can be applied to Figure 1 In the application environment shown, the terminal 102 communicates with the data processing platform set on the server 104 through the network, wherein the terminal 102 can be but not limited to various personal computers, laptops, smart phones, tablet computers and portable wearable devices, and the server 104 can be implemented by an independent server or a server cluster composed of multiple servers.

[0025] In one embodiment, Figure 2 As shown in FIG. 1 , a resource allocation method based on a virtual power plant is provided. The method is applied to Figure 1 The terminal in is used as an example to illustrate, including the following steps: S1: Obtain the basic power data corresponding to the area covered by the virtual power plant in different historical time periods respectively, and the basic power data at least includes power demand data and power supply data.

[0026] It should be noted that different historical time periods can be different time periods within a historical time cycle, and the historical time cycle can be one year, half a year or one month, and the time period can be one day or one week in a year, etc., which can be selected according to actual needs; a virtual power plant refers to a power supply coordination management system that uses information and communication technology and software systems to achieve the aggregation and coordinated optimization of DER (distributed energy resources) such as energy storage systems, controllable loads, and electric vehicles, so as to participate in the power market and power grid operation as a special power plant; basic power data mainly include: 1. Data of power generation equipment, such as parameters such as power, voltage, current, frequency of the unit, and the number and attributes of power generation equipment, including wind power generation, solar power generation, and coal power generation; 2. Energy market environment data, such as the market electricity price of the day, load forecast, etc.; 3. Weather and meteorological data, such as parameters such as temperature, humidity, wind speed and wind direction; 4. User load data, such as user power consumption, power consumption time, etc. The present invention mainly uses user load data related to power demand data and power generation equipment related data related to power supply data; the above data are mainly obtained from the historical database.

[0027] S2: Analyze the basic power data to obtain power analysis data corresponding to the basic power data in different historical time periods.

[0028] It should be noted that the analysis of basic power data includes dedimensionalizing the data and calculating the corresponding target values ​​and associated values ​​to improve the robustness of subsequent model training.

[0029] In some specific implementations, analyzing the basic power data to obtain power analysis data corresponding to the basic power data in different historical time periods includes: performing data cleaning on the basic power data, wherein the data cleaning process at least includes processing missing values, detecting abnormal values, and performing consistency checks on the basic power data, and the cleaning process is a common method and will not be described in detail here; normalizing the data after data cleaning to obtain the normalized basic power data, wherein the normalization process is a common method and will not be described in detail here; analyzing the normalized basic power data to obtain the power analysis data corresponding to the basic power data in different historical time periods includes: based on the objective function, respectively calculating and determining the target values ​​corresponding to the normalized power demand data and the power supply data, and the objective function includes: ; in, represents the target value, Indicates the number of data elements. Indicates The weight coefficient of each data element, Indicates The normalized value of the data element, represents a constant coefficient.

[0030] The target value corresponding to the power demand data greater than the first preset value is defined as the first target value, and the target value corresponding to the power supply data greater than the second preset value is defined as the second target value, wherein the target function can be used to calculate the target values ​​corresponding to the power demand data and the power supply data respectively, and the operation can be performed through two threads, and the first preset value and the second preset value can be set according to actual needs; the historical time periods corresponding to the first target value and the second target value are marked once respectively, and classified and stored, wherein the marker can be set according to actual needs, such as "1-first target value-time period", "1-second target value-time period", etc.; in response to detecting that the first target value and the second target value exist at the same time in the target historical time period marked once, the target historical time period is marked twice, that is, if the historical time period corresponding to the first target value and the historical time period corresponding to the second target value are both marked and the historical time periods of the two belong to the same time period, the time period is marked twice; the associated indicators corresponding to the power demand data and the power supply data in the target historical time period are selected respectively, and the associated value is calculated based on the associated indicators, wherein the associated indicators can be the power generation of the unit, the power consumption of the user, etc., and the corresponding associated indicators can be selected according to actual needs. The calculation formula of the associated value includes: ; ; in, Represents the associated value, Represents the associated index , The correlation coefficient, Indicates time, Indicates the time coefficient corresponding to the target historical time period, Represents the associated index The rate of change, Represents the associated index In response to detecting that the associated value is greater than a third preset value, the target historical time period is marked three times, and the target historical time period after the three markings, as well as the associated value and target value corresponding to the target historical time period are mapped to generate the power analysis data and save it, wherein the third preset value can be set according to actual needs.

[0031] S3: constructing an initial power prediction model based on the power analysis data, wherein the power prediction model at least includes a power demand prediction model and a power supply prediction model.

[0032] It should be noted that the initial models corresponding to the power demand forecasting model and the power supply forecasting model are the second power demand forecasting model and the second power supply forecasting model respectively.

[0033] In some specific embodiments, based on the power analysis data, constructing an initial power forecasting model includes: constructing a first power demand forecasting model and a first power supply forecasting model; dividing the power analysis data into a training set and a validation set, and training and verifying the first power demand forecasting model and the first power supply forecasting model, respectively, wherein the division ratio of the validation set and the training set may be 7:3; in response to detecting that the verification result reaches a preset standard, outputting a second power demand forecasting model and a second power supply forecasting model, and defining the second power demand forecasting model and the second power supply forecasting model as the initial power forecasting model, wherein the preset standard may be that the prediction accuracy reaches a set value, or that the number of training times reaches a set value, etc.

[0034] Specifically, the expression of the second power demand prediction model includes: ; in, represents the demand forecast value, Indicates the number of time periods in a time period, Indicates time period The probability of demand increasing. Indicates the ratio of the associated value corresponding to the power demand data to the target value, Represents the correction function of the power demand forecasting model.

[0035] And, the expression of the second power supply prediction model includes: ; in, represents the supply forecast value, Indicates time period The probability of a supply increase occurring, Indicates the ratio of the associated value corresponding to the power supply data to the target value, Represents the correction function of the power supply prediction model.

[0036] Among them, during the initial training, the values ​​corresponding to the above correction functions are all constant values ​​set according to previous data.

[0037] S4: Obtaining the fluctuation coefficient corresponding to the power forecasting model to correct the initial power forecasting model to obtain a target power forecasting model, wherein the fluctuation coefficients include the fluctuation coefficient on the power demand side and the fluctuation coefficient on the power supply side.

[0038] It should be noted that the electricity demand side may have peaks and valleys or seasonal changes, thereby generating corresponding fluctuation coefficients. The electricity supply side may generate corresponding fluctuation coefficients due to the intermittent or random characteristics of some renewable energy power stations (such as wind power stations and photovoltaic power stations), resulting in prediction errors. Based on this, using the fluctuation coefficient to correct the model can improve the accuracy of model predictions.

[0039] In some specific embodiments, obtaining the fluctuation coefficient corresponding to the power forecasting model to correct the initial power forecasting model to obtain the target power forecasting model includes: obtaining relevant parameters of the renewable energy power station in the area covered by the virtual power plant, and determining the fluctuation coefficient of the second power supply forecasting model based on the relevant parameters of the renewable energy power station, wherein the relevant parameters may include the number of power stations, the probability of each power station generating a power generation error, etc., and the relevant parameters are defined as the corresponding fluctuation coefficient; based on the fluctuation coefficient of the second power supply forecasting model, determining a correction function of the power supply forecasting model to correct the second power supply forecasting model, wherein the correction function of the power supply forecasting model includes: ; in, represents the number of renewable energy power stations, Indicates The probability of a renewable energy power station generating a power generation error, represents the weight coefficient, Indicates the correction factor.

[0040] Obtain the power consumption parameters of the user side in different time periods in the area covered by the virtual power plant, and determine the fluctuation coefficient of the second power demand forecasting model based on the power consumption parameters of the user side in different time periods, wherein the power consumption parameters may include the season (such as spring, etc.) in which the power consumption data is collected, the probability of power consumption deviation in each season, etc., and define the power consumption parameters as the corresponding fluctuation coefficient; based on the fluctuation coefficient of the second power demand forecasting model, determine the correction function of the power demand forecasting model to correct the second power demand forecasting model, wherein the correction function of the power demand forecasting model includes: ; in, Represents the seasonal attribute. Indicates The probability of electricity consumption deviation in each season, represents the weight coefficient, represents the correction coefficient; defines the corrected second power demand forecasting model and the second power supply forecasting model as the target power forecasting model.

[0041] S5: Perform supply and demand balance prediction based on the target power prediction model and the power data corresponding to the current time period, and allocate power resources according to the prediction results.

[0042] It should be noted that the power data corresponding to the current time period may be data of the same type as the above-mentioned basic power data, which is acquired in real time through collection devices such as sensors.

[0043] In some specific embodiments, a supply and demand balance prediction is performed based on the target power prediction model and the power data corresponding to the current time period, and power resources are allocated according to the prediction result, including: obtaining the power data of the current time period, and inputting the power data into the target power prediction model to obtain a demand prediction value and a supply prediction value; in response to detecting that the difference between the demand prediction value and the supply prediction value is greater than a fourth preset value, determining that the current supply and demand is unbalanced, and supplying power based on stored power resources; in response to detecting that the difference between the demand prediction value and the supply prediction value is less than a fourth preset value, determining that the current supply and demand is unbalanced, and storing power resources.

[0044] Among them, the difference between the demand forecast value and the supply forecast value refers to the difference obtained by subtracting the supply forecast value from the demand forecast value. The fourth preset value can be set according to actual demand. When the difference is greater than the fourth preset value, it can be determined that the current real-time power generation is insufficient to meet the user-side demand, and it is necessary to supply the user side based on the electric energy pre-stored in the energy storage power station. When the difference is less than the fourth preset value, it can be determined that the current user-side demand is less than the real-time power generation. At this time, the excess electric energy (such as electric energy generated by new energy such as photovoltaics) is stored in the energy storage power station for subsequent use.

[0045] In the above-mentioned resource allocation method based on virtual power plant, the method includes: respectively obtaining the basic power data corresponding to the area covered by the virtual power plant in different historical time periods, the basic power data at least including power demand data and power supply data; analyzing the basic power data to obtain power analysis data corresponding to the basic power data in different historical time periods; constructing an initial power forecasting model based on the power analysis data, wherein the power forecasting model at least includes a power demand forecasting model and a power supply forecasting model; obtaining the fluctuation coefficient corresponding to the power forecasting model to correct the initial power forecasting model to obtain a target power forecasting model, wherein the fluctuation coefficients respectively include a power demand side fluctuation coefficient and a power supply side fluctuation coefficient; based on the target power forecasting model and the power data corresponding to the current time period, a supply and demand balance forecast is made, and power resources are allocated according to the forecast results. The present invention is based on constructing relevant power resource models, which can improve the power data processing efficiency and the accuracy of supply and demand balance forecasts, and thus can effectively schedule and allocate power resources.

[0046] It should be understood that although Figure 2 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 2 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0047] In one embodiment, Figure 3 As shown, a resource allocation system based on a virtual power plant is provided, comprising: a data acquisition module, a data analysis module, a model construction module, a model correction module and a resource allocation module, wherein: the data acquisition module is used to respectively obtain the basic power data corresponding to the area covered by the virtual power plant in different historical time periods, and the basic power data at least includes power demand data and power supply data; the data analysis module is used to analyze the basic power data to obtain power analysis data corresponding to the basic power data in different historical time periods; the model construction module is used to construct an initial power forecasting model according to the power analysis data, wherein the power forecasting model at least includes a power demand forecasting model and a power supply forecasting model; the model correction module is used to obtain the fluctuation coefficient corresponding to the power forecasting model to correct the initial power forecasting model to obtain a target power forecasting model, wherein the fluctuation coefficients respectively include a power demand side fluctuation coefficient and a power supply side fluctuation coefficient; the resource allocation module is used to perform supply and demand balance prediction based on the target power forecasting model and the power data corresponding to the current time period, and to allocate power resources according to the prediction results.

[0048] As a preferred implementation manner, in an embodiment of the present invention, the data analysis module is specifically used to: perform data cleaning on the power basic data, and the data cleaning process at least includes processing missing values, detecting outliers and performing consistency checks on the power basic data; normalizing the data after data cleaning to obtain normalized power basic data; analyzing the normalized power basic data to obtain power analysis data corresponding to the power basic data in different historical time periods.

[0049] As a preferred implementation, in the embodiment of the present invention, the data analysis module is further used to: calculate and determine the target values ​​corresponding to the normalized power demand data and power supply data based on the objective function, and the objective function includes: ; in, represents the target value, Indicates the number of data elements. Indicates The weight coefficient of each data element, Indicates The normalized value of the data element, represents a constant coefficient; defining the target value corresponding to the power demand data greater than a first preset value as a first target value, and defining the target value corresponding to the power supply data greater than a second preset value as a second target value; marking the historical time periods corresponding to the first target value and the second target value once respectively; in response to detecting that the first target value and the second target value exist simultaneously in the target historical time period marked once, marking the target historical time period twice; selecting the associated indicators corresponding to the power demand data and the power supply data in the target historical time period respectively, and calculating the associated value based on the associated indicators, wherein the calculation formula of the associated value includes: ; ; in, Represents the associated value, Represents the associated index , The correlation coefficient, Indicates time, Indicates the time coefficient corresponding to the target historical time period, Represents the associated index The rate of change, Represents the associated index In response to detecting that the associated value is greater than a third preset value, the target historical time period is marked three times, and the target historical time period after the three markings, as well as the associated value and the target value corresponding to the target historical time period are mapped to generate the power analysis data and save it.

[0050] As a preferred implementation, in an embodiment of the present invention, the model building module is specifically used to: construct a first power demand forecasting model and a first power supply forecasting model; divide the power analysis data into a training set and a verification set, and respectively train and verify the first power demand forecasting model and the first power supply forecasting model; in response to detecting that the verification result reaches a preset standard, output a second power demand forecasting model and a second power supply forecasting model, and define the second power demand forecasting model and the second power supply forecasting model as the initial power forecasting model, wherein the expression of the second power demand forecasting model includes: ; in, represents the demand forecast value, Indicates the number of time periods in a time period, Indicates time period The probability of demand increasing. Indicates the ratio of the associated value corresponding to the power demand data to the target value, Represents a correction function of the power demand forecasting model; the expression of the second power supply forecasting model includes: ; in, represents the supply forecast value, Indicates time period The probability of a supply increase occurring, Indicates the ratio of the associated value corresponding to the power supply data to the target value, Represents the correction function of the power supply prediction model.

[0051] As a preferred implementation, in an embodiment of the present invention, the model correction module is specifically used to: obtain relevant parameters of the renewable energy power station in the area covered by the virtual power plant, and determine the fluctuation coefficient of the second power supply prediction model based on the relevant parameters of the renewable energy power station; based on the fluctuation coefficient of the second power supply prediction model, determine the correction function of the power supply prediction model to correct the second power supply prediction model, wherein the correction function of the power supply prediction model includes: ; in, represents the number of renewable energy power stations, Indicates The probability of a renewable energy power station generating a power generation error, represents the weight coefficient, represents a correction coefficient; obtaining power consumption parameters of the user side in different time periods in the area covered by the virtual power plant, and determining the fluctuation coefficient of the second power demand forecasting model based on the power consumption parameters of the user side in different time periods; based on the fluctuation coefficient of the second power demand forecasting model, determining a correction function of the power demand forecasting model to correct the second power demand forecasting model, wherein the correction function of the power demand forecasting model includes: ; in, Represents the seasonal attribute. Indicates The probability of electricity consumption deviation in each season, represents the weight coefficient, represents the correction coefficient; defines the corrected second power demand forecasting model and the second power supply forecasting model as the target power forecasting model.

[0052] As a preferred implementation mode, in an embodiment of the present invention, the resource allocation module is specifically used to: obtain power data for the current time period, and input the power data into the target power forecasting model to obtain a demand forecast value and a supply forecast value; in response to detecting that the difference between the demand forecast value and the supply forecast value is greater than a fourth preset value, determine that the current supply and demand are unbalanced, and supply is performed based on stored power resources; in response to detecting that the difference between the demand forecast value and the supply forecast value is less than a fourth preset value, determine that the current supply and demand are unbalanced, and store power resources.

[0053] For the specific limitations of the resource allocation system based on the virtual power plant, please refer to the limitations of the resource allocation method based on the virtual power plant above, which will not be repeated here. Each module in the above-mentioned resource allocation system based on the virtual power plant can be implemented in whole or in part through software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0054] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 4As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a resource allocation method based on a virtual power plant is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0055] Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0056] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program: S1: respectively obtaining basic power data corresponding to the area covered by the virtual power plant in different historical time periods, wherein the basic power data at least includes power demand data and power supply data; S2: analyzing the basic power data to obtain power analysis data corresponding to the basic power data in different historical time periods; S3: constructing an initial power forecasting model based on the power analysis data, wherein the power forecasting model at least includes a power demand forecasting model and a power supply forecasting model; S4: obtaining a fluctuation coefficient corresponding to the power forecasting model to correct the initial power forecasting model to obtain a target power forecasting model, wherein the fluctuation coefficients respectively include a power demand side fluctuation coefficient and a power supply side fluctuation coefficient; S5: performing supply and demand balance forecasting based on the target power forecasting model and the power data corresponding to the current time period, and allocating power resources according to the forecasting results.

[0057] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented: S1: respectively obtaining basic power data corresponding to the area covered by the virtual power plant in different historical time periods, the basic power data at least including power demand data and power supply data; S2: analyzing the basic power data to obtain power analysis data corresponding to the basic power data in different historical time periods; S3: constructing an initial power forecasting model based on the power analysis data, wherein the power forecasting model at least includes a power demand forecasting model and a power supply forecasting model; S4: obtaining a fluctuation coefficient corresponding to the power forecasting model to correct the initial power forecasting model to obtain a target power forecasting model, wherein the fluctuation coefficients respectively include a power demand side fluctuation coefficient and a power supply side fluctuation coefficient; S5: performing supply and demand balance forecasting based on the target power forecasting model and the power data corresponding to the current time period, and allocating power resources according to the forecasting results.

[0058] In one embodiment, a computer program product is provided, which includes a computer program, and when the computer program is executed by a processor, the following steps are implemented: S1: respectively obtaining basic power data corresponding to the area covered by the virtual power plant in different historical time periods, the basic power data at least including power demand data and power supply data; S2: analyzing the basic power data to obtain power analysis data corresponding to the basic power data in different historical time periods; S3: constructing an initial power forecasting model based on the power analysis data, wherein the power forecasting model at least includes a power demand forecasting model and a power supply forecasting model; S4: obtaining a fluctuation coefficient corresponding to the power forecasting model to correct the initial power forecasting model to obtain a target power forecasting model, wherein the fluctuation coefficients respectively include a power demand side fluctuation coefficient and a power supply side fluctuation coefficient; S5: performing supply and demand balance forecasting based on the target power forecasting model and the power data corresponding to the current time period, and allocating power resources according to the forecasting results.

[0059] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0060] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0061] The above-mentioned embodiments only express several implementation methods of the present invention, and the description is relatively specific and detailed, but it cannot be understood as limiting the scope of the invention patent. It should be pointed out that for ordinary technicians in this field, several modifications and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention.

Claims

1. A resource allocation method based on a virtual power plant, characterized in that: The method comprises: Respectively obtain basic power data corresponding to different historical time periods in the areas covered by the virtual power plant, wherein the basic power data at least includes power demand data and power supply data; Analyze the basic power data to obtain power analysis data corresponding to the basic power data in different historical time periods; According to the power analysis data, construct an initial power forecasting model, wherein the power forecasting model at least includes a power demand forecasting model and a power supply forecasting model; Obtaining a fluctuation coefficient corresponding to the power forecasting model to correct the initial power forecasting model and obtain a target power forecasting model, wherein the fluctuation coefficients include a power demand side fluctuation coefficient and a power supply side fluctuation coefficient; A supply-demand balance forecast is performed based on the target power forecast model and the power data corresponding to the current time period, and power resources are allocated according to the forecast results.

2. The resource allocation method based on a virtual power plant according to claim 1, characterized in that: The power basic data is analyzed to obtain power analysis data corresponding to the power basic data in different historical time periods, including: Performing data cleaning on the basic power data, wherein the data cleaning process at least includes processing missing values, detecting abnormal values, and performing consistency checks on the basic power data; Normalize the cleaned data to obtain normalized basic power data; The normalized power basic data is analyzed to obtain power analysis data corresponding to the power basic data in different historical time periods.

3. The resource allocation method based on virtual power plant according to claim 2, characterized in that: The normalized power basic data is analyzed to obtain power analysis data corresponding to the power basic data in different historical time periods, including: Based on the objective function, the target values ​​corresponding to the normalized power demand data and power supply data are calculated and determined respectively, and the objective function includes: ； in, represents the target value, Indicates the number of data elements. Indicates The weight coefficient of each data element, Indicates The normalized value of the data element, represents a constant coefficient; The target value corresponding to the power demand data greater than the first preset value is defined as a first target value, and the target value corresponding to the power supply data greater than the second preset value is defined as a second target value; Marking the historical time periods corresponding to the first target value and the second target value respectively; In response to detecting that a first target value and a second target value exist simultaneously within a target historical time period marked once, marking the target historical time period twice; Select the correlation indicators corresponding to the power demand data and the power supply data in the target historical time period respectively, and calculate the correlation value based on the correlation indicators. The calculation formula of the correlation value includes: ； ； in, Represents the associated value, Represents the associated index , The correlation coefficient, Indicates time, Indicates the time coefficient corresponding to the target historical time period, Represents the associated index The rate of change, Represents the associated index The rate of change of In response to detecting that the associated value is greater than a third preset value, the target historical time period is marked three times, and the target historical time period after the three markings, as well as the associated value and target value corresponding to the target historical time period are mapped to generate the power analysis data and save it.

4. The resource allocation method based on virtual power plant according to claim 3 is characterized in that: According to the power analysis data, constructing an initial power prediction model includes: Constructing a first power demand forecasting model and a first power supply forecasting model; Dividing the power analysis data into a training set and a validation set, and respectively training and validating the first power demand prediction model and the first power supply prediction model; In response to detecting that the verification result reaches a preset standard, a second power demand forecasting model and a second power supply forecasting model are output, and the second power demand forecasting model and the second power supply forecasting model are defined as the initial power forecasting model.

5. The resource allocation method based on virtual power plant according to claim 4, characterized in that: The expression of the second power demand prediction model includes: ； in, represents the demand forecast value, Indicates the number of time periods in a time period, Indicates time period The probability of demand increasing. Indicates the ratio of the associated value corresponding to the power demand data to the target value, Represents the correction function of the power demand forecasting model.

6. The resource allocation method based on virtual power plant according to claim 5, characterized in that: The expression of the second power supply prediction model includes: ； in, represents the supply forecast value, Indicates time period The probability of a supply increase occurring, Indicates the ratio of the associated value corresponding to the power supply data to the target value, Represents the correction function of the power supply prediction model.

7. The resource allocation method based on virtual power plant according to claim 6, characterized in that: Obtaining the fluctuation coefficient corresponding to the power forecast model to correct the initial power forecast model, and obtaining the target power forecast model includes: Acquire relevant parameters of renewable energy power stations in the area covered by the virtual power plant, and determine the fluctuation coefficient of the second power supply prediction model based on the relevant parameters of the renewable energy power stations; Based on the fluctuation coefficient of the second power supply prediction model, a correction function of the power supply prediction model is determined to correct the second power supply prediction model, wherein the correction function of the power supply prediction model includes: ； in, represents the number of renewable energy power stations, Indicates The probability of a renewable energy power station generating a power generation error, represents the weight coefficient, represents the correction factor; Obtaining power consumption parameters of the user side in different time periods in the area covered by the virtual power plant, and determining the fluctuation coefficient of the second power demand prediction model based on the power consumption parameters of the user side in different time periods; Based on the fluctuation coefficient of the second power demand forecasting model, a correction function of the power demand forecasting model is determined to correct the second power demand forecasting model, wherein the correction function of the power demand forecasting model includes: ； in, Represents the seasonal attribute. Indicates The probability of electricity consumption deviation in each season, represents the weight coefficient, represents the correction factor; The modified second power demand forecasting model and the second power supply forecasting model are defined as the target power forecasting model.

8. The resource allocation method based on virtual power plant according to claim 7, characterized in that: Providing supply and demand balance prediction based on the target power prediction model and power data corresponding to the current time period, and allocating power resources according to the prediction results includes: Acquire power data for the current time period, and input the power data into the target power forecasting model to obtain a demand forecast value and a supply forecast value; In response to detecting that the difference between the demand prediction value and the supply prediction value is greater than a fourth preset value, determining that the current supply and demand are unbalanced, and supplying based on the stored power resources; In response to detecting that the difference between the demand prediction value and the supply prediction value is less than a fourth preset value, it is determined that the current supply and demand are unbalanced, and the power resources are stored.

9. A resource allocation system based on a virtual power plant, characterized in that: The system comprises: A data acquisition module is used to respectively obtain the basic power data corresponding to the area covered by the virtual power plant in different historical time periods, wherein the basic power data at least includes power demand data and power supply data; A data analysis module, used to analyze the basic power data to obtain power analysis data corresponding to the basic power data in different historical time periods; A model building module, used to build an initial power forecasting model according to the power analysis data, wherein the power forecasting model at least includes a power demand forecasting model and a power supply forecasting model; A model correction module, used to obtain the fluctuation coefficient corresponding to the power forecasting model, so as to correct the initial power forecasting model and obtain a target power forecasting model, wherein the fluctuation coefficients include the fluctuation coefficient of the power demand side and the fluctuation coefficient of the power supply side respectively; The resource allocation module is used to make supply and demand balance prediction based on the target power prediction model and the power data corresponding to the current time period, and to allocate power resources according to the prediction results.

10. The resource allocation system based on virtual power plant according to claim 9, characterized in that: The normalized basic power data is analyzed to obtain power analysis data corresponding to the basic power data in different historical time periods, including: Based on the objective function, the target values ​​corresponding to the normalized power demand data and power supply data are calculated and determined respectively, and the objective function includes: ； in, represents the target value, Indicates the number of data elements. Indicates The weight coefficient of each data element, Indicates The normalized value of the data element, represents a constant coefficient; The target value corresponding to the power demand data greater than the first preset value is defined as a first target value, and the target value corresponding to the power supply data greater than the second preset value is defined as a second target value; Marking the historical time periods corresponding to the first target value and the second target value respectively; In response to detecting that a first target value and a second target value exist simultaneously within a target historical time period marked once, marking the target historical time period twice; Select the correlation indicators corresponding to the power demand data and the power supply data in the target historical time period respectively, and calculate the correlation value based on the correlation indicators. The calculation formula of the correlation value includes: ； ； in, Represents the associated value, Represents the associated index , The correlation coefficient, Indicates time, Indicates the time coefficient corresponding to the target historical time period, Represents the associated index The rate of change, Represents the associated index The rate of change of In response to detecting that the associated value is greater than a third preset value, the target historical time period is marked three times, and the target historical time period after the three markings, as well as the associated value and target value corresponding to the target historical time period are mapped to generate the power analysis data and save it.

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