Demand response power consumer peak load management method and system and medium

Abstract

The invention relates to a demand response power consumer peak load management method and system and a medium, and the method comprises the steps: collecting hourly power consumption data of a consumer within a certain time range, and carrying out the preprocessing of the data, including abnormal value analysis and missing value filling; determining a target time period in which peak load optimization needs to be performed, performing descending order arrangement on the power data in the time period, and drawing a power duration curve in the time period; on the basis of a data smoothing method, a smoothed power duration curve is obtained, so that the overall change trend of the curve can be known; based on a marginal effect rule, determining an optimal hour number of user peak value optimization; and calculating a user peak reduction proportion corresponding to the optimal optimization hours, and estimating self electric charge which can be saved by the user through participating in demand optimization, thereby realizing economic potential evaluation. According to the method, the optimal peak clipping hours and the peak clipping amount of the user are accurately quantified, and an effective evaluation means is provided for screening demand optimization potential users of a power system.

Description

Technical Field

[0001] The present invention relates to the technical field of power system demand side management, and particularly relates to a demand response power user peak load management method, system and medium. Background Art

[0002] With the continuous growth of the population and the rapid development of economy and technology, the peak social electricity consumption is increasing day by day. At the same time, the strengthening of the global carbon emission reduction consensus has led to an increasing proportion of renewable energy power generation, and the imbalance between supply and demand occurs from time to time, posing a huge challenge to the stability of power grid operation. The traditional source-side regulation method solves the problem of supply-demand imbalance by adding new generating units, but the economic cost is high, which is not conducive to the sustainable development of the power industry. Demand side management changes the load characteristics of the power grid, reduces the maximum power demand of the system, alleviates the power supply pressure of the power grid during peak periods, reduces the power supply and consumption cost ratio, and achieves a win-win situation for the power source and the load. As an important implementation means of demand side management, user peak load management can effectively alleviate the imbalance between energy supply and demand, and improve the flexibility and reliability of the power system. For users, their electricity bills usually consist of two parts: demand charge and energy charge. Participating in demand optimization to achieve peak load reduction can significantly reduce their monthly electricity bills. However, peak load management is a comprehensive process that requires understanding the best peak shaving potential of power users, such as the peak shaving amount that users can participate in and the difficulty of implementing the reduction. Generally speaking, the peak electricity consumption time of power users usually accounts for a relatively small proportion, and the difficulty of completing the demand optimization task will increase as the target reduction amount increases. Determining the best reduction amount is a prerequisite for maximizing the benefits of the demand optimization task, but there is currently no method to quickly and accurately evaluate the best peak load reduction hours and reduction amount of users. Summary of the Invention

[0003] The purpose of the present invention is to provide a demand response power user peak load management method, system and medium, which can accurately quantify the best peak shaving hours and peak shaving amount of users, provide an effective evaluation means for screening users with demand optimization potential in the power system, and is expected to guide and improve users' electricity consumption behavior by publishing the analysis results, further contributing to the stable operation of the power grid and helping users obtain economic benefits.

[0004] To achieve the above purpose, in the first aspect, the present invention provides a demand response power user peak load management method, including the following specific steps:

[0005] Collect the hourly electricity consumption data of users within a certain time range, and preprocess the data, including outlier analysis and missing value filling;

[0006] Determine the target time period for peak load optimization, sort the power data within this time period in descending order, and draw the power duration curve within this time period;

[0007] Based on the data smoothing method, obtain the smoothed power duration curve to facilitate understanding of the overall change trend of the curve;

[0008] Determine the optimal number of hours for user peak optimization based on the marginal effect rule;

[0009] Calculate the user peak reduction ratio corresponding to the optimal optimization hours, estimate the electricity charges that the user can save by participating in demand optimization, and realize the evaluation of economic potential.

[0010] The step of collecting the hourly electricity consumption data of the user within a certain time range and preprocessing the data, including outlier analysis and missing value filling, specifically is,

[0011] The hourly power in the hourly electricity consumption data is the user's historical record data or the predicted power obtained by short-term prediction based on historical data. Outlier analysis is used to screen out abnormal electricity consumption caused by meter failures and a few accidental factors. Outlier analysis adopts the abnormal analysis principle based on statistical principles or the anomaly detection method based on clustering principles. Missing values are supplemented based on the recorded data with similar time using the linear interpolation method.

[0012] The step of obtaining the smoothed power duration curve based on the data smoothing method to facilitate understanding of the overall change trend of the curve, specifically is,

[0013] The data smoothing method uses moving average to smooth the duration curve and eliminate the influence of local random change points on subsequent analysis.

[0014] The step of determining the optimal number of hours for user peak optimization based on the marginal effect rule, specifically is,

[0015] The basic principle of the marginal effect principle is that the reduction of the user's peak load brought by increasing one hour of energy consumption behavior regulation increases with the increase of the input time. At this time, if the reduction is very small, it is not enough to drive the user to implement the regulation behavior, and the user behavior regulation time with the maximum benefit is determined.

[0016] In a second aspect, an embodiment of the present application provides a demand response power user peak load management system, which includes: a memory and a processor. The memory includes a program of the demand response power user peak load management method. When the program of the demand response power user peak load management method is executed by the processor, the steps of the demand response power user peak load management method as described above are implemented.

[0017] In a third aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the demand response power user peak load management method as described above are implemented.

[0018] The beneficial effects of adopting the above embodiments are as follows:

[0019] The method of the present invention can quickly determine the optimal behavior adjustment time for power users to optimize peak load / demand, and can provide technical guidance for power operators to manage user energy consumption behavior and optimize demand response task scheduling;

[0020] The user peak load management method provided by the present invention determines the optimal load reduction hours of users on the basis of considering the marginal effect, realizes an intuitive estimation of the return on investment for adjusting the behavior of users, and can make the demand optimization task more targeted and specific.

[0021] The method of the present invention is only based on user power consumption data, without obtaining the basic information of users, effectively reducing the application difficulty of the method and protecting user privacy to a certain extent. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required to be used in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained according to these drawings without creative efforts.

[0023] Figure 1 is a flowchart of the implementation of a user peak load management method considering marginal effect according to the present invention.

[0024] Figure 2 is a positioning diagram of the optimal optimization hours of the typical monthly peak of users determined by the difference method according to the present invention.

[0025] Figure 3 is a statistical chart of the peak shaving rate and the realized demand charge savings of each month of a typical user considering marginal effect throughout the year according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0026] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0027] It should be noted that in the present invention, the descriptions involving "first", "second", etc. are only for descriptive purposes, and should not be construed as indicating or implying their relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one such feature. Additionally, the technical solutions between various embodiments can be combined with each other, but it must be based on the ability of those of ordinary skill in the art to implement. When the combination of technical solutions results in contradictions 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.

[0028] Figure 1 A user peak load management method considering marginal effect provided for the embodiments of the present application, the specific implementation steps of this method are as follows:

[0029] Step 1, collect the hourly electricity consumption data of a certain user throughout the year through the intelligent power grid terminal. Combine the box plot to locate the outliers, and fill in the missing values based on linear interpolation to complete the data preprocessing and form high-quality data that can be used for subsequent analysis.

[0030] Step 2, considering the demand charge on a monthly basis, divide the data month by month and sort it in descending order, and draw the power duration curve month by month.

[0031] Step 3, smooth the data based on the moving average method to eliminate the influence of local change points on the subsequent inflection point positioning.

[0032] Step 4, calculate the second-order difference of the data before and after in the power curve in sequence, and the point with the largest second-order difference is the inflection point of the curve. As Figure 2 shown, it is the inflection point positioning result of the monthly power duration curve of a certain typical user, and the monthly optimal peak optimization hours of this user is 16h.

[0033] Step 5, based on the inflection point positioning result of Step 4, calculate the peak reduction amount / reduction ratio that can be achieved after the user's behavior adjustment, and estimate the economic benefits that the user can obtain. As Figure 2 can be seen, after this user adjusts its high energy consumption for 16 hours, it can achieve a monthly peak load reduction of 36.6 kW, and the reduction rate reaches 33%. Repeat Steps 2 - 4 to complete the estimation of the monthly peak optimization hours of each month of this user throughout the year, and finally obtain the monthly peak load reduction ratio and the reduction amount of monthly demand charge of each month as Figure 3 shown, where the savings in demand charge is estimated according to the current two-part electricity price of 42 yuan / (kW·month) in Hubei Province. The results show that the economic effect brought by the user's behavior adjustment is obvious, which will effectively promote the enthusiasm of users to participate in the demand optimization project.

[0034] An embodiment of the present application provides a demand response power user peak load management system, which includes: a memory and a processor. The memory includes a program for the demand response power user peak load management method. When the program for the demand response power user peak load management method is executed by the processor, the steps of the demand response power user peak load management method described above are implemented..

[0035] An embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the demand response power user peak load management method described above are implemented.

[0036] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0037] The present application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate for implementing in the process Figure 1 one process or multiple processes and / or blocks Figure 1 a device for the functions specified in one block or multiple blocks.

[0038] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured product including an instruction device, and the instruction device implements in the process Figure 1 one process or multiple processes and / or blocks Figure 1 a device for the functions specified in one block or multiple blocks.

[0039] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide for implementing in the processFigure 1 one or more processes and / or blocks Figure 1 steps of functions specified in one or more blocks

[0040] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0041] The memory may include non-permanent memory in computer-readable media, in the form of random access memory (RAM) and / or non-volatile memory such as read-only memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.

[0042] Computer-readable media includes both permanent and non-permanent, removable and non-removable media implemented by any method or technology for information storage. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transitory media such as modulated data signals and carrier waves.

[0043] Those skilled in the art can implement the present invention with various modification schemes without departing from the scope and essence of the present invention. For example, the features of one embodiment can be used in another embodiment to obtain another embodiment. Any modifications, equivalent replacements, and improvements made within the technical concept of the present invention shall fall within the scope of the rights of the present invention.

Claims

1. A method for peak load management of demand response power users, characterized in that It includes the following specific steps: Collect the hourly electricity consumption data of users within a certain time range, and preprocess the data, including outlier analysis and missing value filling; Determine the target time period for peak load optimization, sort the power data within this time period in descending order, and draw the power duration curve within this time period; Based on the data smoothing method, obtain the smoothed power duration curve to facilitate understanding of the overall change trend of the curve; Determine the optimal number of hours for user peak optimization based on the marginal effect rule; Calculate the user peak reduction ratio corresponding to the optimal number of optimization hours, estimate the electricity bill that the user can save by participating in demand optimization, and realize the evaluation of economic potential.

2. The peak load management method for demand response power users according to claim 1, wherein, The collection of the hourly electricity consumption data of users within a certain time range and the preprocessing of the data, including outlier analysis and missing value filling, specifically are: The hourly power in the hourly electricity consumption data is the user's historical record data or the predicted power obtained by short-term prediction based on historical data. Outlier analysis is used to screen for abnormal electricity consumption caused by meter failures and a few accidental factors. Outlier analysis adopts the abnormal analysis principle based on statistical theory or the anomaly detection method based on clustering theory. Missing values are supplemented based on the recorded data with similar time using the linear interpolation method.

3. A method for peak load management of demand response power users according to claim 1, characterized in that, The obtaining of the smoothed power duration curve based on the data smoothing method to facilitate understanding of the overall change trend of the curve specifically is: The data smoothing method uses moving average to smooth the duration curve and eliminate the influence of local random change points on subsequent analysis.

4. A method for peak load management of demand response power users according to claim 1, characterized in that, The determination of the optimal number of hours for user peak optimization based on the marginal effect rule specifically is: The basic principle of the marginal effect principle is the reduction of the user's peak load brought about by increasing the input of the energy consumption behavior regulation for one hour. As the input time increases, the difficulty of user regulation rises. At this time, if the reduction is very small, it is not enough to drive the user to implement the regulation behavior, and the user behavior regulation time with the maximum benefit is determined.

5. A demand response power user peak load management system, characterized in that, The system includes: a memory and a processor. The memory includes a program for the demand response power user peak load management method. When the program for the demand response power user peak load management method is executed by the processor, the following steps are implemented: Collect the hourly electricity consumption data of users within a certain time range, and preprocess the data, including outlier analysis and missing value filling; Determine the target time period for peak load optimization, sort the power data within this time period in descending order, and draw the power duration curve within this time period; Based on the data smoothing method, obtain the smoothed power duration curve to facilitate understanding of the overall change trend of the curve; Determine the optimal number of hours for user peak optimization based on the marginal effect rule; Calculate the user peak reduction ratio corresponding to the optimal number of optimization hours, estimate the electricity bill that the user can save by participating in demand optimization, and realize the evaluation of economic potential.

6. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium. When the computer program is executed by the processor, the steps of the demand response power user peak load management method as described in any one of claims 1-4 are implemented.

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