A power optimization scheduling method and system for a virtual power plant

Abstract

The present invention relates to the technical field of power dispatching, and particularly relates to a method and system for power optimization dispatching of a virtual power plant. The method includes: obtaining the daily power consumption load data of users by using the virtual power plant; determining the fluctuation trend of the power consumption load data at each moment within each day; determining the similarity of the power consumption load data of each day with that of the other days; determining the target sequence and regularity of each day according to the similarity; predicting the average load of the day to be predicted; and regulating the start and stop of the generator set according to the average load to complete the power optimization dispatching of the virtual power plant. By considering the fluctuation trend of the daily power consumption load data of users and combining similarity and regularity analysis, the present invention can more accurately predict the power consumption load of the target day, making the load prediction closer to the actual power consumption behavior and reducing the error caused by the failure to fully consider the fluctuation factors in the traditional prediction method.

Description

Technical Field

[0001] The present invention relates to the technical field of power dispatching, and in particular to a method and system for optimizing power dispatching of a virtual power plant. Background Art

[0002] With the continuous increase in global energy demand and the increase in the proportion of renewable energy, traditional power systems are facing more and more challenges, especially in terms of imbalance in power supply and demand, peak-valley compound differences, energy waste, environmental pollution, etc. The limitations of traditional power grids in scheduling optimization are becoming increasingly apparent. Therefore, Virtual Power Plant (VPP), as an emerging method for optimizing power system scheduling, has received much attention and research.

[0003] The patent document with announcement number CN108037667B discloses a base station power optimization scheduling method based on a virtual power plant, including the following steps: (1) firstly establishing an interaction model between a microgrid containing a base station and a virtual power plant and a traditional large power grid; (2) establishing an optimization scheduling model with the purpose of maximizing the scheduling profit of the virtual power plant containing the base station; (3) predicting wind and solar power with the purpose of minimizing the abandoned energy cost caused by the uncertainty of clean energy and establishing a model; (4) using a robust optimization method to handle the output uncertainty of clean energy for constraints and setting prediction coefficients and robust coefficients; (5) finally solving the optimal scheduling result using computer software.

[0004] However, due to the fluctuations in user electricity consumption behavior, when users are active during monitoring of their electricity consumption behavior, the electricity load will fluctuate. Traditional prediction methods use the same prediction weight for all samples, which will lead to inaccurate prediction results, thereby affecting the power dispatch results and causing grid overload or energy waste.

[0005] However, due to certain fluctuations in user electricity consumption behavior, especially during peak periods and special events (such as seasonal changes, holidays or emergencies), traditional forecasting methods often cannot fully consider these complex factors, resulting in increased errors in power load forecasting; when users are active, especially concentrated in certain specific time periods, load fluctuations are often more drastic, which makes it impossible for simple forecasting models based on historical data to accurately capture the dynamic changes in electricity demand; traditional forecasting methods usually assume that all samples have the same prediction weight, but this assumption does not take into account the differences in electricity consumption behavior in each time period or user group. For example, the electricity consumption patterns of some user groups (such as industrial users or commercial users) are quite different from those of household users, and their electricity demand may be affected by factors such as production plans, working hours or weather, which are not fully reflected in traditional forecasting models; therefore, traditional forecasting methods will show greater inaccuracy when dealing with high volatility or abnormal electricity consumption behavior, thereby affecting the optimization effect of power dispatch, and further leading to grid load overload or energy waste. Summary of the invention

[0006] In order to solve the problem that due to fluctuations in users' electricity consumption behavior, traditional prediction methods often cannot fully consider these complex factors, thereby increasing the error of power load prediction, affecting the optimization effect of power dispatch, and further leading to grid overload or energy waste, the present invention provides a power optimization dispatching method and system for a virtual power plant.

[0007] In a first aspect, the present invention provides a method for optimizing power dispatching of a virtual power plant, which adopts the following technical solution:

[0008] A power optimization scheduling method for a virtual power plant, comprising: obtaining the daily electricity load data of users by using the virtual power plant; recording the electricity load data at the target moment within the target day of each day of the user as the target electricity load data, taking the target electricity load data as the center of a preset fluctuation window, and determining the fluctuation trend of the electricity load data at the target moment within the target day according to the difference between the target electricity load data and the electricity load data at the remaining moments in the fluctuation window; the normalized result of the correlation coefficient of the fluctuation trends of the electricity load data at all corresponding moments of the target day and the remaining days is used as the similarity of the electricity load data of the target day and the remaining days; according to the magnitude of the similarity, determining a target sequence including multiple dates, obtaining the difference value of the number of days between all adjacent two days in the target sequence, and determining the regularity of the target day according to the difference value and the similarity of the electricity load data of the target day and the remaining days in the target sequence; obtaining the regularity and the average electricity load data of each day within a preset prediction window, predicting the average load of the day to be predicted according to the regularity and the average electricity load data; and regulating the start and stop of the generator set according to the average load to complete the power optimization scheduling of the virtual power plant.

[0009] The beneficial effects are as follows: By considering the fluctuation trend of the daily electricity load data of users and combining similarity and regularity analysis, the electricity load of the target day can be predicted more accurately, making the load prediction closer to the actual electricity consumption behavior and reducing the error caused by the failure to fully consider the fluctuation factors in the traditional prediction method; Based on the analysis of the similarity of the load data between the target day and other dates, the system can identify similar electricity load patterns and further improve the prediction accuracy through regularity analysis, which helps to ensure the stability and efficiency of power supply; By accurately predicting the electricity load, more scientific and reasonable power scheduling can be achieved. By regulating the start and stop of the generator set, unnecessary power generation can be reduced, the energy use efficiency can be optimized, thus avoiding the risk of load overloading in the power grid and reducing energy waste; By dynamically obtaining the daily load data, fluctuation trend and regularity analysis, the scheduling strategy of the virtual power plant can be adjusted in time to adapt to the changes in electricity consumption demand in different time periods and seasons, and improve the overall response ability of the power system.

[0010] Further, the fluctuation trend satisfies:

[0011] ; where is the fluctuation trend of the electricity load data at the th moment within the th day, is the number of moments within the preset fluctuation window, is the electricity load data at the th moment within the th day, For the th moment within the th preset fluctuation window corresponding to the th moment, the electricity load data at that moment, is the standard normalization function, and

[0012] The beneficial effects are as follows: It can accurately identify the instantaneous fluctuations in the electricity load changes. By comparing the differences between the current moment and the data of other moments within the fluctuation window, the fluctuation intensity of the electricity load can be judged; through normalization processing, the fluctuation trends can be compared on the same scale, avoiding the deviation caused by different data scales and making the analysis of the fluctuation trends more accurate.

[0013] Furthermore, the correlation coefficient is the Pearson correlation coefficient.

[0014] Furthermore, the normalization method is extreme value normalization.

[0015] Furthermore, determining the target sequence of the target day including multiple dates according to the magnitude of the similarity includes: in response to the target day and the other days corresponding to the similarity greater than the preset similarity threshold being sorted according to the date, obtaining the target sequence.

[0016] The beneficial effects are as follows: By sorting according to the similarity between the target day and other days, other days with a stronger correlation with the target day can be more accurately identified, and more accurate prediction or judgment can be provided; through the setting of the similarity threshold and sorting, the interference of irrelevant or low-similarity data is reduced, thereby improving the efficiency of data processing. When dealing with a large amount of historical data, valuable parts can be more quickly screened out, avoiding unnecessary calculations and redundant information.

[0017] Furthermore, the regularity satisfies:

[0018] ; where is the regularity of the th day, is the total number of days in the target sequence of the th day, is the th day difference value in the target sequence of the th day, is the average value of all the day difference values in the target sequence of the th day, is the th day and the th day, the average value of the similarity of the electricity load data of the other days in the target sequence, is the natural exponential function, is a standard normalization function.

[0019] The beneficial effects are as follows: By integrating multiple factors such as the variance of difference values, mean, similarity, and normalization, the regularity can more accurately reflect the strength of the regularity of a target sequence; through the design of the exponential function, the model can more sensitively identify data with low regularity or large fluctuations.

[0020] Further, the average load satisfies:

[0021] ; where the average load of the day to be predicted , is the regularity of the th day within the prediction window corresponding to the day to be predicted , is the number of days within the preset prediction window , is the ordinal number of the th day within the prediction window corresponding to the day to be predicted , is the ordinal number of the day to be predicted , is the average power consumption load data of the th day within the prediction window corresponding to the day to be predicted is the natural exponential function.

[0022] The beneficial effects are as follows: By utilizing the regularity of each day in the historical load data and the time series relationship, a more accurate prediction of the load of the day to be predicted can be made; through the weighting of the natural exponential function, the time difference between the historical load data and the day to be predicted can be considered, ensuring that the historical data closer to the current time difference has a greater impact on the prediction result, improving the timeliness; by using the regularity parameter, the impact of periodic or seasonal changes on the load can be captured, thereby improving the prediction accuracy.

[0023] In the second aspect, the present invention provides a power optimization dispatching system for a virtual power plant, adopting the following technical solution:

[0024] A power optimization dispatching system for a virtual power plant includes: a processor and a memory, and the memory stores computer program instructions, which implement the above-mentioned power optimization dispatching method for a virtual power plant when executed by the processor.

[0025] By adopting the above technical solution, the above-mentioned power optimization dispatching method for a virtual power plant is generated into a computer program and stored in the memory, so as to be loaded and executed by the processor, thereby manufacturing a terminal device according to the memory and the processor for convenient use.

[0026] The present invention has the following technical effects:

[0027] By centering on the target power consumption load data and combining the fluctuation window to determine the fluctuation trend of the power consumption load data, the fluctuation characteristics of the user's power consumption behavior can be captured. Compared with traditional prediction methods, it comprehensively considers complex power consumption factors; using the normalization result of the correlation coefficient of the fluctuation trends of the power consumption load data at corresponding times between the target day and the other days to measure the similarity can more accurately reflect the correlation degree between the power consumption loads on different dates, providing a reliable basis for determining the target sequence subsequently; determining the target sequence based on the similarity and combining the day difference value and the similarity to determine the regularity of the target day helps to discover the potential rules of the user's power consumption in the time series, making the prediction more in line with the actual power consumption situation; predicting the average load of the day to be predicted based on the regularity and average power consumption load data of each day within the preset prediction window, comprehensively considering various factors, can effectively reduce the power load prediction error and improve the prediction accuracy compared with traditional prediction methods; regulating the start and stop of the generator set according to the accurately predicted average load helps to avoid problems such as grid load overloading or energy waste, realize the optimal dispatching of the virtual power plant power, and improve the power resource utilization efficiency and grid operation stability. Description of the Drawings

[0028] By reading the following detailed description with reference to the drawings, the above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the drawings, several embodiments of the present invention are shown in an exemplary rather than restrictive manner, and the same or corresponding reference numerals are for the same or corresponding parts.

[0029] Figure 1 It is the flowchart of a method for optimizing the power dispatching of a virtual power plant in an embodiment of the present invention. Detailed Embodiments

[0030] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts fall within the protection scope of the present invention.

[0031] It should be understood that when the claims, the specification and the drawings of the present invention use terms such as "first", "second", etc., they are only used to distinguish different objects and not to describe a specific order. The terms "including" and "comprising" used in the specification and claims of the present invention indicate the presence of the described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or their combinations.

[0032] The embodiment of the present invention discloses a method for optimizing power dispatching of a virtual power plant, referring to Figure 1 , comprising steps S1 to S6:

[0033] S1: Use virtual power plants to obtain users’ daily electricity load data.

[0034] It should be noted that the virtual power plant combines distributed energy (such as solar energy, wind energy, energy storage equipment, demand response equipment, etc.) with smart grids through information and digital means to form an integrated power supply system consisting of multiple independent power generation equipment and consumers; it enables these distributed power resources to participate in the power supply dispatch of the power grid like traditional large power plants through centralized management and optimized dispatch, thereby maximizing energy utilization, reducing electricity costs, and improving the stability and flexibility of power grid operation. The user's power load data refers to the data on power usage obtained from various end users (households, commercial buildings, industrial users, etc.). These data are usually collected using monitoring equipment, such as smart meters, sensors and other monitoring equipment, and then transmitted to the virtual power plant system for optimizing power dispatch and management. The user referred to in this invention is any one of the various end users.

[0035] Implementers can set the acquisition frequency according to specific implementation conditions, for example, 1 Hz.

[0036] S2: Determine the fluctuation trend of power load data at each moment of each day.

[0037] It should be noted that the degree of fluctuation of electricity load during the low-peak period and peak period of electricity consumption of users is different. For example, during the low-peak period (small load value), the fluctuation range of electricity load data is small, and the electricity load fluctuates within a small range; while during the peak period (large load value), the user's electricity load data shows a significant upward or downward trend as a whole; that is, the performance of electricity load data is different during the low-peak period and peak period of electricity consumption of users. If it is not distinguished, it is easy to make a misjudgment, which further leads to errors in power dispatching. Therefore, this step obtains the fluctuation trend of the electricity load data of a certain user at any time in any day based on the user's daily electricity load data.

[0038] The power load data at the target time within the target day of each day of the user is recorded as the target power load data, and the target power load data is taken as the center of the preset fluctuation window. The fluctuation trend of the power load data at the target time within the target day is determined according to the difference between the target power load data and the power load data at other times in the fluctuation window.

[0039] The implementer can set the size of the fluctuation window according to the specific implementation situation. For example, 1×51.

[0040] Specifically, the fluctuation trend satisfies:

[0041] ;

[0042] In the formula, is the fluctuation trend of the electricity consumption load data at the th moment on the th day, is the number of moments within the preset fluctuation window, is the electricity consumption load data at the th moment on the th day, is the electricity consumption load data at the th moment corresponding to the th moment within the preset fluctuation window on the th day, is the standard normalization function, is the absolute value symbol.

[0043] Among them, the greater the fluctuation trend, the greater the possibility that the electricity consumption load data at this moment is in the peak electricity consumption period; represents the relative difference between the remaining electricity consumption load data within its corresponding fluctuation window and the electricity consumption load data at this moment for the electricity consumption load data at this moment. The larger its value, the greater the difference, that is, the greater the fluctuation trend of the electricity consumption load data at this moment; the fluctuation trend of the electricity consumption load data at this moment is measured by calculating the average cumulative relative change difference.

[0044] S3: Determine the similarity between the electricity consumption load data of each day and that of the other days.

[0045] It should be noted that due to the influence of users' daily behavior habits, users' daily electricity consumption usually shows a periodic pattern. In order to avoid directly calculating through the electricity consumption load data, resulting in inaccurate calculation results, therefore, in this step, the similarity of the electricity consumption load data of the two days is calculated by using the fluctuation trends of the electricity consumption load data at the corresponding moments of any two days. Through the similarity, the similarity of the electricity consumption patterns can be more effectively identified, avoiding misjudgment caused by simply relying on the difference between the electricity consumption load data.

[0046] The normalized result of the correlation coefficient of the fluctuation trends of the electricity consumption load data at all corresponding moments between the target day and the other days is used as the similarity between the electricity consumption load data of the target day and that of the other days.

[0047] Specifically, the correlation coefficient is the Pearson correlation coefficient.

[0048] Specifically, the normalization method is extreme value normalization.

[0049] In another embodiment, the normalization method is standard normalization.

[0050] S4: Determine the target sequence and regularity for each day according to the similarity.

[0051] It should be noted that usually, the user's electricity consumption pattern has regularity. If sudden situations occur, such as holding activities, entertaining guests, etc., it will cause a sudden change in the electricity consumption situation. When predicting the user's electricity consumption data, this type of data will have a reverse impact on the prediction result. Therefore, it is necessary to calculate the regularity of the electricity load data for any day to facilitate subsequent prediction based on the result of the regularity of the electricity load data and make the prediction result more accurate.

[0052] Determine the target sequence of the target day including multiple dates according to the magnitude of the similarity.

[0053] Specifically, the determining of the target sequence of the target day including multiple dates according to the magnitude of the similarity includes:

[0054] In response to the target day and the other days corresponding to the similarity greater than the preset similarity threshold being sorted according to the date, a target sequence is obtained.

[0055] The implementer can set the similarity threshold according to the specific implementation situation. For example, 0.8.

[0056] Obtain the difference value of the number of days between all adjacent two days in the target sequence (the numerical value of the number of days between adjacent two days in the target sequence), and determine the regularity of the target day according to the difference value of the number of days and the similarity of the electricity load data between the target day and the other days in the target sequence.

[0057] Specifically, the regularity satisfies:

[0058] ;

[0059] In the formula, is the regularity of the th day, is the total number of days in the target sequence of the th day, is the th difference value of the number of days in the target sequence of the th day, is the average value of all difference values of the number of days in the target sequence of the th day, is the th day and the The mean of the similarities of the electricity consumption load data for the remaining days in the target sequence of the target day, is the natural exponential function, and is the standard normalization function.

[0060] Among them, represents the difference of all the day difference values in the target sequence of the target day. The smaller its value, the smaller the difference, that is, the closer the day difference values are, the stronger the regularity of the target day; the numerator uses for limitation to avoid inaccurate calculation results due to the small number of days in the target sequence of the target day; the mean of the similarities , the larger its value, the more similar the target day is to all the days in its target sequence, and the stronger the regularity of the target day.

[0061] S5: Predict the average load of the day to be predicted.

[0062] It should be noted that for the electricity consumption load data of any day, when using it as a data sample for prediction, if the regularity of the electricity consumption load data of this day is stronger, the prediction weight corresponding to this day is larger, avoiding using a unified weight and making the electricity consumption load data corresponding to a day with poor regularity affect the prediction result.

[0063] Obtain the regularity and average electricity consumption load data of each day within the preset prediction window, and predict the average load of the day to be predicted according to the regularity and average electricity consumption load data.

[0064] Specifically, the average load satisfies:

[0065] ;

[0066] In the formula, the average load of the day to be predicted, is the regularity of the th day in the prediction window corresponding to the day to be predicted, is the number of days in the preset prediction window, is the day ordinal number of the th day in the prediction window corresponding to the day to be predicted, is the day ordinal number of the day to be predicted, is the average electricity consumption load data of the th day in the prediction window corresponding to the day to be predicted, is the natural exponential function. is the day ordinal number of the day to be predicted, is the day ordinal number of the th day in the prediction window corresponding to the day to be predicted, is the average electricity consumption load data of the th day in the prediction window corresponding to the day to be predicted,

[0067] Implementers can set the size of the prediction window according to the specific implementation situation. For example, it can be 1×30.

[0068] Among them, for any day within the prediction window corresponding to the day to be predicted, if the regularity of this day is stronger and it is closer to the day to be predicted, then the prediction weight of this day is greater.

[0069] S6: Regulate the start and stop of the generator set according to the average load to complete the power optimization dispatch of the virtual power plant.

[0070] Obtain the sum of the average loads of all end-users on the day to be predicted to get the final prediction result. According to the prediction result, regulate the start and stop plan of the generator set, and determine the standby generator set. For example, in high-load periods, high-efficiency but costly generator sets can be enabled to ensure electricity demand; in low-load periods, some high-load generator sets can be shut down to reduce resource waste; it is also possible to combine energy storage systems (such as pumped storage power stations, lithium battery energy storage, etc.), charge during low-load periods according to the prediction result, and discharge during high-load periods to balance load fluctuations and improve the flexibility of the power system to complete the power optimization dispatch of the virtual power plant.

[0071] An embodiment of the present invention also discloses a power optimization dispatch system for a virtual power plant, including a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, a power optimization dispatch method for a virtual power plant according to the present invention is implemented.

[0072] The above system also includes other components well-known to those skilled in the art such as a communication bus and a communication interface. Their settings and functions are known in the art, so they will not be elaborated here.

[0073] In the present invention, the aforementioned memory can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, device, or component. For example, a computer-readable storage medium can be any suitable magnetic storage medium or magneto-optical storage medium, or any other medium that can be used to store the required information and can be accessed by an application program, module, or both. Any such computer storage medium can be part of the device or accessible or connectable to the device.

[0074] Although this specification has shown and described multiple embodiments of the present invention, it is obvious to those skilled in the art that such embodiments are provided only by way of example. Those skilled in the art will think of many changes, alterations, and alternative ways without departing from the spirit and idea of the present invention. It should be understood that various alternative solutions to the embodiments of the present invention described herein can be adopted in the process of practicing the present invention.

[0075] The above are all preferred embodiments of the present invention, and the protection scope of the present invention is not limited thereby. Therefore, all equivalent changes made according to the structure, shape and principle of the present invention shall be covered within the protection scope of the present invention.

Claims

1. A power optimization dispatching method for a virtual power plant, characterized in that Including: Obtaining the daily electricity load data of users by using a virtual power plant; Taking the electricity load data at the target moment within the target day of each day of the user as the target electricity load data, using the target electricity load data as the center of a preset fluctuation window, and determining the fluctuation trend of the electricity load data at the target moment within the target day according to the difference between the target electricity load data and the electricity load data at the remaining moments in the fluctuation window; Taking the normalized result of the correlation coefficient of the fluctuation trends of the electricity load data at all corresponding moments between the target day and the other days as the similarity of the electricity load data between the target day and the other days; Determining the target sequence of the target day including multiple dates according to the magnitude of the similarity, obtaining the difference values of the number of days between all adjacent two days in the target sequence, and determining the regularity of the target day according to the difference values of the number of days and the similarity of the electricity load data between the target day and the other days in the target sequence; Obtaining the regularity and the average electricity load data of each day within a preset prediction window, and predicting the average load of the day to be predicted according to the regularity and the average electricity load data; Regulating the start and stop of the generator set according to the average load to complete the power optimization dispatching of the virtual power plant; The fluctuation trend satisfies: ； Wherein, is the fluctuation trend of the electricity load data at the -th moment within the -th day, is the number of moments within the preset fluctuation window, is the electricity load data at the -th moment within the -th day, is the electricity load data at the -th moment within the -th day corresponding to the -th moment within the preset fluctuation window, is the standard normalization function, is the absolute value symbol.

2. The power optimization scheduling method of a virtual power plant according to claim 1, characterized in that The correlation coefficient is the Pearson correlation coefficient.

3. The power optimization scheduling method of a virtual power plant according to claim 1, characterized in that, The normalization method is extreme value normalization.

4. A power optimization scheduling method for a virtual power plant according to claim 1, characterized in that The determining the target sequence of the target day including multiple dates according to the magnitude of the similarity includes: Responding to the other days corresponding to the target day with the similarity greater than a preset similarity threshold and sorting them according to the date to obtain the target sequence.

5. The power optimization scheduling method of a virtual power plant according to claim 1, wherein The regularity satisfies: ； Wherein, is the regularity of the th day, is the total number of days in the target sequence of the th day, is the th difference value of the number of days in the target sequence of the th day, is the average value of all the difference values of the number of days in the target sequence of the th day, is the average value of the similarities of the electricity load data of the remaining days in the target sequence of the th day and the th day, is the natural exponential function, is the standard normalization function.​ 6. The power optimization scheduling method of a virtual power plant according to claim 1, wherein The average load satisfies: ； In the formula, Average load of the day to be predicted, is the regularity of the day corresponding to the day within the preset prediction window, is the number of days within the preset prediction window, is the day number ordinal of the day corresponding to the day within the preset prediction window, is the day number ordinal of the day to be predicted, is the average power consumption load data of the day corresponding to the day within the preset prediction window, is the natural exponential function.

7. A power optimization scheduling system for a virtual power plant, characterized in that, Including: A processor and a memory, where the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a power optimization dispatching method of a virtual power plant according to any one of claims 1-6 is implemented.

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