Distributed resource response event detection method and related device

Abstract

The invention belongs to the technical field of power grid dispatching, and discloses a distributed resource response event detection method and a related device. The distributed resource response event detection method comprises the following steps: acquiring a distributed resource electric power time sequence, defining a mean value calculation window and a change point detection window, and forming an event detection window; according to time sequence transition, based on the event detection window, calculating to obtain a value of a mean value calculation window and a value of a change point detection window; and performing statistics to obtain a numerical value cumulative sum of the distributed resource electric power time sequence change window, comparing the numerical value cumulative sum with a cumulative sum threshold value, and obtaining a detection result of whether a response event occurs in the distributed resource based on a comparison result. According to the technical scheme, whether the distributed resources participate in the power grid interaction event or not can be effectively judged in the environment with incomplete information, and effective support can be provided for efficient aggregation, decision optimization, response settlement and the like of massive distributed resources.

Description

Technical Field

[0001] The present invention belongs to the technical field of power grid dispatching, and in particular relates to a distributed resource response event detection method and related devices. Background Art

[0002] In recent years, driven by the "dual carbon" goals and the goal of building a new energy system, the source, load and storage flexibility resources connected to the low-voltage power grid have shown explosive growth; compared with traditional regulation resources such as large hydro / thermal power units, these new regulation resources have the characteristics of small single capacity, huge quantity and potential, green environmental protection, flexible economy, etc.; in the future, the flexibility resources in the power grid will show a massive development trend.

[0003] Distributed flexible resources are located at the end of the power grid, and the dispatch center cannot fully view and control the massive terminal data, resulting in a contradiction between the dispatch demand and the efficient matching of massive distributed resources; in addition, the privacy protection needs of the distributed resource subjects have brought about the problem of incomplete information, and it is difficult for the dispatch center to learn the detailed operating characteristics of some distributed resources. On the other hand, for load aggregators or virtual power plant operators, although they have signed response contracts with the lower-level loads or distributed resources they aggregate, they cannot grasp the detailed electricity consumption data of each underlying device, making it difficult to judge whether some devices are actually responding in the real-time response stage. In summary, in order to provide effective support for the efficient aggregation, optimized decision-making and response settlement of massive distributed resources, how to correctly detect distributed resource response events in the case of incomplete information is a technical problem that needs to be solved urgently. Summary of the invention

[0004] The purpose of the present invention is to provide a distributed resource response event detection method and related devices to solve one or more of the above-mentioned technical problems. The technical solution disclosed in the present invention is specifically a distributed resource response event detection solution based on a sliding window bilateral cumulative sum algorithm, which can effectively judge whether distributed resources participate in power grid interaction events in an incomplete information environment, and can provide effective support for efficient aggregation, optimized decision-making and response settlement of massive distributed resources.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] In a first aspect, the present invention provides a distributed resource response event detection method, comprising the following steps:

[0007] Obtain the time series of power consumption of distributed resources;

[0008] For the obtained distributed resource power consumption time series, a mean calculation window and a change point detection window are defined; wherein the mean calculation window and the change point detection window are continuous sliding windows and together constitute an event detection window;

[0009] According to the time series, the value of the mean calculation window and the value of the change point detection window are obtained based on the event detection window calculation;

[0010] Based on the calculated values ​​of the mean calculation window and the change point detection window, the numerical cumulative sum of the distributed resource power consumption time series change window is statistically obtained, the numerical cumulative sum is compared with the cumulative sum threshold, and the detection result of whether a response event occurs in the distributed resource is obtained based on the comparison result.

[0011] A further improvement of the method of the present invention is that

[0012] In the step of obtaining the value of the mean calculation window and the value of the change point detection window based on the event detection window according to the time sequence,

[0013] The calculation expression of the value of the mean calculation window is:

[0014]

[0015] Where W m represents the average value of the power consumption of distributed resources in the mean calculation window; t represents the sampling point t; m represents the window length of the mean calculation window; P t represents the power consumption of distributed resources at time t in the power time series;

[0016] The calculation expression of the value of the change point detection window is:

[0017]

[0018] Where W d It represents the average value of the power consumption of distributed resources within the change point detection window; n represents the window length of the change point detection window.

[0019] A further improvement of the method of the present invention is that

[0020] In the step of obtaining the numerical value of the distributed resource power consumption time series change window based on the calculated mean value calculation window and the numerical value of the change point detection window, comparing the numerical cumulative sum with the cumulative sum threshold, and obtaining the detection result of whether the distributed resource has a response event based on the comparison result,

[0021] The numerical cumulative sum is a positive cumulative sum or a negative cumulative sum; wherein,

[0022] For the sampling point t1, the calculation expression of the forward cumulative sum is:

[0023]

[0024] In the formula, Represents the positive cumulative sum at sampling point t1; represents the positive cumulative sum at sampling point t1-1; β represents the power fluctuation level when the distributed resources work in a steady state in the power time series; W d W represents the average value of the power consumption of distributed resources within the change point detection window; m Indicates the average value of the power consumption of distributed resources within the mean calculation window; Represents the positive cumulative sum at sampling point t1=0;

[0025] For sampling point t1, the calculation expression of negative cumulative sum is:

[0026]

[0027] In the formula, Represents the negative cumulative sum at sampling point t1; Indicates the negative cumulative sum at sampling point t1-1; Indicates the negative cumulative sum at sampling point t1=0;

[0028] like or Then a distributed resource response event occurs; where H represents the cumulative sum threshold.

[0029] A further improvement of the method of the present invention is that

[0030] The cumulative sum threshold satisfies the following expression:

[0031]

[0032] Where ΔP represents the change in power consumption before and after the distributed resources participate in the interactive response of the power grid; n represents the window length of the change point detection window.

[0033] A second aspect of the present invention provides a distributed resource response event detection system, comprising:

[0034] A data acquisition module is used to obtain the time series of power consumption of distributed resources;

[0035] A window definition module is used to define a mean calculation window and a change point detection window for the obtained distributed resource power consumption time series; wherein the mean calculation window and the change point detection window are continuous sliding windows and together constitute an event detection window;

[0036] A numerical calculation module, used for obtaining the numerical value of the mean calculation window and the numerical value of the change point detection window based on the event detection window according to the time series transition;

[0037] The statistical judgment module is used to obtain the numerical value of the distributed resource power consumption time series change window based on the calculated mean calculation window and the numerical value of the change point detection window, compare the numerical cumulative sum with the cumulative sum threshold, and obtain the detection result of whether the distributed resource has a response event based on the comparison result.

[0038] A further improvement of the system of the present invention is that:

[0039] The numerical calculation module performs the step of obtaining the numerical value of the mean calculation window and the numerical value of the change point detection window based on the event detection window according to the time sequence,

[0040] The calculation expression of the value of the mean calculation window is:

[0041]

[0042] Where W m represents the average value of the power consumption of distributed resources in the mean calculation window; t represents the sampling point t; m represents the window length of the mean calculation window; P t represents the power consumption of distributed resources at time t in the power time series;

[0043] The calculation expression of the value of the change point detection window is:

[0044]

[0045] Where W d It represents the average value of the power consumption of distributed resources within the change point detection window; n represents the window length of the change point detection window.

[0046] A further improvement of the system of the present invention is that:

[0047] The statistical determination module executes the step of obtaining the numerical value of the distributed resource power consumption time series change window based on the calculated mean value calculation window and the numerical value of the change point detection window, comparing the numerical cumulative sum with the cumulative sum threshold, and obtaining the detection result of whether the distributed resource has a response event based on the comparison result.

[0048] The numerical cumulative sum is a positive cumulative sum or a negative cumulative sum; wherein,

[0049] For the sampling point t1, the calculation expression of the forward cumulative sum is:

[0050]

[0051] In the formula, Represents the positive cumulative sum at sampling point t1; represents the positive cumulative sum at sampling point t1-1; β represents the power fluctuation level when the distributed resources work in a steady state in the power time series; W d W represents the average value of the power consumption of distributed resources within the change point detection window; m Indicates the average value of the power consumption of distributed resources within the mean calculation window; Represents the positive cumulative sum at sampling point t1=0;

[0052] For sampling point t1, the calculation expression of negative cumulative sum is:

[0053]

[0054] In the formula, Represents the negative cumulative sum at sampling point t1; Indicates the negative cumulative sum at sampling point t1-1; Indicates the negative cumulative sum at sampling point t1=0;

[0055] like or Then a distributed resource response event occurs; where H represents the cumulative sum threshold.

[0056] A further improvement of the system of the present invention is that:

[0057] The cumulative sum threshold satisfies the following expression:

[0058]

[0059] Where ΔP represents the change in power consumption before and after the distributed resources participate in the interactive response of the power grid; n represents the window length of the change point detection window.

[0060] According to a third aspect of the present invention, there is provided an electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the distributed resource response event detection method as described in any one of the first aspect of the present invention is implemented.

[0061] According to a fourth aspect of the present invention, a non-transitory computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the distributed resource response event detection method as described in any one of the first aspects of the present invention is implemented.

[0062] Compared with the prior art, the present invention has the following beneficial effects:

[0063] The present invention discloses a distributed resource response event detection method, which includes the steps of event detection window definition, window value calculation, accumulation and statistics, etc. In the case of incomplete information, singularity feature data is identified, and load response event detection is performed based on a sliding window bilateral accumulation and algorithm, which can effectively identify distributed resource response events, and help provide technical support for efficient aggregation, optimized decision-making and response settlement of massive distributed resources. Further explanatory, the detection method proposed in the present invention is suitable for effectively judging whether distributed resources participate in power grid interaction events in an environment with incomplete information. The detection method proposed in the present invention does not involve the internal detailed electricity consumption data of distributed resources, takes into account the privacy protection of users, and provides technical support for efficient aggregation, optimized decision-making and response settlement of massive distributed resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below; obviously, the drawings described below are some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0065] Figure 1 It is a flow chart of a distributed resource response event detection method in an embodiment of the present invention.

[0066] Figure 2 Schematic diagram of a sliding window bilateral cumulative sum algorithm in an embodiment of the present invention.

[0067] Figure 3 It is a schematic diagram of a distributed resource response event detection process in a specific embodiment of the present invention.

[0068] Figure 4 It is a schematic diagram of a distributed resource response event detection system in an embodiment of the present invention. DETAILED DESCRIPTION

[0069] In order to make the purpose, technical solutions and advantages of the present invention clearer, the technical solutions in the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention; it is obvious that the described embodiments and technical solutions are only part of the embodiments of the present invention, not all of the embodiments.

[0070] All other embodiments obtained by those of ordinary skill in the art without creative work based on the technical solutions disclosed in the embodiments of the present invention belong to the scope of protection of the present invention. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device including a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0071] See also Figure 1 The embodiment of the present invention provides a distributed resource response event detection method, comprising the following steps:

[0072] Step 1, obtaining the time series of distributed resource power consumption;

[0073] Step 2: for the distributed resource power consumption time series obtained in step 1, define a mean calculation window and a change point detection window, wherein the mean calculation window and the change point detection window are continuous sliding windows and together constitute an event detection window; explanatory, in the technical solution of the embodiment of the present invention, specifically, a complete event detection window is formed by two continuous sliding windows;

[0074] Step 3, according to the time series, based on the event detection window defined in step 2, the value of the mean calculation window and the value of the change point detection window are calculated;

[0075] Step 4, based on the value calculated in step 3, count the numerical cumulative sum of the distributed resource power consumption time series change window, compare the numerical cumulative sum with the cumulative sum threshold, and obtain the detection result of whether the distributed resource has a response event based on the comparison result.

[0076] In the technical solution disclosed in the embodiment of the present invention, a distributed resource response event detection method based on a sliding window bilateral cumulative sum (CUSUM) algorithm is proposed; the sliding window bilateral cumulative sum algorithm is a commonly used algorithm in the statistical process, and its theoretical basis is the sequential probability ratio test in the sequential analysis principle. Its design idea is to accumulate the statistical characteristics of the sequence data in small continuous offsets in time to improve the sensitivity of the detection process, and the detection effect of non-mutation points is better; therefore, the technical solution of the embodiment of the present invention can effectively judge whether the distributed resources participate in the power grid interaction event in an incomplete information environment, and can provide effective support for the efficient aggregation, optimized decision-making and response settlement of massive distributed resources.

[0077] See also Figure 2In a specific embodiment of the present invention, in step 2, for the time series of the distributed resource power consumption curve obtained in step 1, a mean calculation window and a change point detection window are defined, and the mean calculation window and the change point detection window together constitute an event detection window. An exemplary explanation is as follows:

[0078] For the time series of distributed resource power consumption curve, the time series T is expressed as t represents the sampling point t; two continuous sliding windows are defined in the time series T, namely: the mean calculation window (WM) and the change point detection window (WD), whose window lengths are m and n respectively. The mean calculation window and the change point detection window together constitute an event detection window, as shown in the attached figure. Figure 2 shown.

[0079] In a specific embodiment of the present invention, in step 3, based on the event detection window defined in step 2 and according to the time series shift, respectively calculate the values ​​of the mean calculation window and the change point detection window,

[0080] The calculation expression of the value of the mean calculation window is:

[0081]

[0082] Where W m represents the average value of the power consumption of distributed resources in the mean calculation window; t represents the sampling point t; m represents the window length of the mean calculation window; P t represents the power consumption of distributed resources at time t in the power time series;

[0083] The calculation expression of the value of the change point detection window is:

[0084]

[0085] Where W d represents the average value of the power consumption of distributed resources in the change point detection window; n represents the window length of the change point detection window;

[0086] Explanatory, since the power time series of distributed resources is constantly changing, the values ​​of the mean calculation window and the change point detection window are constantly updated.

[0087] See also Figure 2 , the embodiment of the present invention specifically illustrates that the calculation is divided into four stages, including:

[0088] In stage 1, the change point detection window has not yet slid to the sampling point t1 (the moment when the power consumption of distributed resources in the power time series changes, which means the response event occurs). At this time, the values ​​of the two windows have basically no large fluctuations, which can be expressed as:

[0089] W m =W d (3)

[0090] In the second stage, the change point detection window slides to the sampling point t1, and the calculated value W in the mean calculation window is m remains unchanged, while the calculated value W within the change point detection window d Constantly changing, expressed as:

[0091] P1=P0+ΔP (4)

[0092] t d =t-t1 (5)

[0093] W m =P0 (6)

[0094]

[0095] Where P0 represents the initial power consumption of distributed resources in the power time series, P1 represents the power consumption of distributed resources after participating in the interactive response of the power grid, ΔP represents the change in power consumption before and after the distributed resources participate in the interactive response of the power grid; t represents the current moment when the change point detection window slides to; t1 represents the power change point moment, that is, the moment when the power consumption of distributed resources in the time series changes; t d represents the time difference between the current time t and the power change point time t1; β represents the power fluctuation level when the distributed resources work in a steady state in the power time series; Represents the positive cumulative sum of the event detection window.

[0096] In stage 3, the mean calculation window slides to the sampling point t1, and the calculated value W in the change point detection window d remains unchanged, while the calculated value W within the mean detection window m Constantly changing, expressed as:

[0097] W d =P1 (9)

[0098]

[0099] Where i represents the sampling point i in the power time series.

[0100] In stage 4, both the mean calculation window and the change point detection window slide over the sampling point t1, W m and W d All remain unchanged, and the cumulative sum remains unchanged.

[0101] Further explanation, the cumulative sum threshold is represented by H and needs to be set reasonably; at different stages, W m and W dThe changes are not uniform;

[0102] For example, in stage 4, when the response event occurs, it can be known that W d -W m >ΔP, i.e. (H+β)t d >ΔP.

[0103] Substituting into formulas (6) and (7), we can derive:

[0104]

[0105] Where H is the cumulative sum threshold.

[0106] In a specific embodiment of the present invention, in step 4, based on the value calculated in step 3, the cumulative sum of the window values ​​of the distributed resource power time series change is counted, and compared with the cumulative sum threshold, and the step of obtaining the detection result of whether the distributed resource has a response event based on the comparison result specifically includes:

[0107] When distributed resources participate in the response, the direction of active power change is different, which may be an increase in power or a decrease in power; therefore, the forward cumulative sum of the kth sampling point is defined as and negative cumulative sum respectively detecting active power changes of distributed resource responses;

[0108] For example, for the t1th sampling point:

[0109]

[0110] In the formula, Represents the positive cumulative sum of the t1th sampling point; represents the positive cumulative sum of the sampling point t1-1; β represents the power fluctuation level when the distributed resources work in a steady state in the power time series; Represents the negative cumulative sum of the t1th sampling point; Represents the negative cumulative sum of the t1-1th sampling point; Represents the positive cumulative sum at sampling point t1=0; Indicates the negative cumulative sum at sampling point t1=0;

[0111] For example, for the kth sampling point, when the cumulative sum threshold is represented by H, if or A distributed resource response event occurs.

[0112] In actual operation, considering that relying on sampling at a single moment may cause misjudgment of change points due to random fluctuations in power values, that is, at the kth sampling point, when the data in the change point detection window changes more than β compared to the data in the mean calculation window, that is, W d >W m +β, there may be a change point, but it is impossible to determine whether the change point must exist because there may be or The case where the power increase event generates a positive cumulative sum For example, if Directly determine the existence of a change point; if At this time, the delay factor d is introduced to calculate if At this time, the time when the distributed resource interactive response event occurs is deduced to be the time when the change point is detected minus the window length of the change point detection window, that is, k+dn.

[0113] See also Figure 3 In the embodiment of the present invention, the power increase event generates a positive cumulative sum For example, the event detection process is as follows:

[0114] Step 1: first read a distributed resource power time series of length N; considering that the smart meter data contains a large amount of power pulse noise, power ripple noise, etc., median filtering / mean filtering and other means are used to remove the noise in the power time series to obtain the power time series to be detected after filtering;

[0115] Step 2, set the window length of the mean calculation window to m, the window length of the change point detection window to n, and the sampling point time to k; it can be seen that to detect a time series of length N, the value range of k is: 0<k<Nmn;

[0116] Step 3, set the cumulative sum threshold to H and the delay factor to d. When the sampling point k < Nmn, calculate the forward cumulative sum according to formula (13): like satisfy and When , confirm that a response event occurs; if it satisfies but Then introduce the delay factor d to determine whether it satisfies the condition after k+d time. If satisfied, it is confirmed that a response event has occurred;

[0117] Step 4: Slide the sampling point to time k+1 and determine the cumulative sum of these two moments. and If the change This indicates that the power is still changing. Continue sliding the sampling point until This means that the power consumption of distributed resources no longer changes, that is, the response event enters a steady state;

[0118] Step 5: When k ≥ Nmn, the detection of the entire power time series is completed.

[0119] To sum up, the embodiment of the present invention proposes a distributed resource response event detection method based on a sliding window bilateral cumulative sum algorithm, which can effectively judge whether distributed resources participate in power grid interaction events in an incomplete information environment, and can provide effective support for efficient aggregation, optimized decision-making and response settlement of massive distributed resources; further explanatory, the present invention aims to address the current situation that the upper-level dispatching end (such as a power grid dispatching center or a load aggregator, etc.) is difficult to obtain detailed operating characteristics of the lower-level resource end (such as individual load-side resources, some distributed resources, etc.), and proposes a method for detecting load response events based on a sliding window bilateral cumulative sum algorithm through singularity feature data identification in the case of incomplete information. The present invention is suitable for application in source-grid-load-storage interactive operation control systems and demand-side resource regulation response platforms across the country, including but not limited to source-grid-load-storage interactive operation control systems, virtual power plant operation management platforms, new load management platforms, demand response management platforms, etc., and can effectively identify distributed resource response events, and provide technical support for efficient aggregation, optimized decision-making and response settlement of massive distributed resources.

[0120] The following are device embodiments of the present invention, which can be used to implement the method embodiments of the present invention. For details not disclosed in the device embodiments, please refer to the method embodiments of the present invention.

[0121] See also Figure 4 In an embodiment of the present invention, a distributed resource response event detection system is provided, comprising:

[0122] A data acquisition module is used to obtain the time series of power consumption of distributed resources;

[0123] A window definition module is used to define a mean calculation window and a change point detection window for the obtained distributed resource power consumption time series; wherein the mean calculation window and the change point detection window are continuous sliding windows and together constitute an event detection window;

[0124] A numerical calculation module, used for obtaining the numerical value of the mean calculation window and the numerical value of the change point detection window based on the event detection window according to the time series transition;

[0125] The statistical judgment module is used to obtain the numerical value of the distributed resource power consumption time series change window based on the calculated mean calculation window and the numerical value of the change point detection window, compare the numerical cumulative sum with the cumulative sum threshold, and obtain the detection result of whether the distributed resource has a response event based on the comparison result.

[0126] In one embodiment of the present invention, a computer device is provided, the computer device comprising a processor and a memory, the memory being used to store a computer program, the computer program comprising program instructions, and the processor being used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc., which are the computing core and control core of the terminal, which are suitable for implementing one or more instructions, and are specifically suitable for loading and executing one or more instructions in a computer storage medium to implement a corresponding method flow or a corresponding function; the processor described in the embodiment of the present invention can be used to perform the operation of the distributed resource response event detection method.

[0127] In one embodiment of the present invention, a storage medium is provided, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It is understandable that the computer-readable storage medium here can include both built-in storage media in the computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM (Random Access Memory) memory, or a non-volatile memory (non-volatile memory), such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the distributed resource response event detection method in the above embodiment.

[0128] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, optical storage, etc.) that contain computer-usable program code.

[0129] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0130] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0131] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0132] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A distributed resource response event detection method, characterized in that: The following steps are involved: Obtain the time series of power consumption of distributed resources; For the obtained distributed resource power consumption time series, a mean calculation window and a change point detection window are defined; wherein the mean calculation window and the change point detection window are continuous sliding windows and together constitute an event detection window; According to the time series, the value of the mean calculation window and the value of the change point detection window are obtained based on the event detection window calculation; Based on the calculated values ​​of the mean calculation window and the change point detection window, the numerical cumulative sum of the distributed resource power consumption time series change window is statistically obtained, the numerical cumulative sum is compared with the cumulative sum threshold, and the detection result of whether a response event occurs in the distributed resource is obtained based on the comparison result.

2. A distributed resource response event detection method according to claim 1, characterized in that: In the step of obtaining the value of the mean calculation window and the value of the change point detection window based on the event detection window according to the time sequence, The calculation expression of the value of the mean calculation window is: Where W m represents the average value of the power consumption of distributed resources in the mean calculation window; t represents the sampling point t; m represents the window length of the mean calculation window; P t represents the power consumption of distributed resources at time t in the power time series; The calculation expression of the value of the change point detection window is: Where W d It represents the average value of the power consumption of distributed resources within the change point detection window; n represents the window length of the change point detection window.

3. A distributed resource response event detection method according to claim 1, characterized in that: In the step of obtaining the numerical value of the distributed resource power consumption time series change window based on the calculated mean value calculation window and the numerical value of the change point detection window, comparing the numerical cumulative sum with the cumulative sum threshold, and obtaining the detection result of whether the distributed resource has a response event based on the comparison result, The numerical cumulative sum is a positive cumulative sum or a negative cumulative sum; wherein, For the sampling point t1, the calculation expression of the forward cumulative sum is: In the formula, Represents the positive cumulative sum at sampling point t1; represents the positive cumulative sum at sampling point t1-1; β represents the power fluctuation level when the distributed resources work in a steady state in the power time series; W d W represents the average value of the power consumption of distributed resources within the change point detection window; m Indicates the average value of the power consumption of distributed resources within the mean calculation window; Represents the positive cumulative sum at sampling point t1=0; For sampling point t1, the calculation expression of negative cumulative sum is: In the formula, Represents the negative cumulative sum at sampling point t1; Indicates the negative cumulative sum at sampling point t1-1; Indicates the negative cumulative sum at sampling point t1=0; like or Then a distributed resource response event occurs; where H represents the cumulative sum threshold.

4. A distributed resource response event detection method according to claim 3, characterized in that: The cumulative sum threshold satisfies the following expression: Where ΔP represents the change in power consumption before and after the distributed resources participate in the interactive response of the power grid; n represents the window length of the change point detection window.

5. A distributed resource response event detection system, characterized in that: include: A data acquisition module is used to obtain the time series of power consumption of distributed resources; A window definition module is used to define a mean calculation window and a change point detection window for the obtained distributed resource power consumption time series; wherein the mean calculation window and the change point detection window are continuous sliding windows and together constitute an event detection window; A numerical calculation module, used for obtaining the numerical value of the mean calculation window and the numerical value of the change point detection window based on the event detection window according to the time series transition; The statistical judgment module is used to obtain the numerical value of the distributed resource power consumption time series change window based on the calculated mean calculation window and the numerical value of the change point detection window, compare the numerical cumulative sum with the cumulative sum threshold, and obtain the detection result of whether the distributed resource has a response event based on the comparison result.

6. A distributed resource response event detection system according to claim 5, characterized in that: The numerical calculation module performs the step of obtaining the numerical value of the mean calculation window and the numerical value of the change point detection window based on the event detection window according to the time sequence, The calculation expression of the value of the mean calculation window is: Where W m represents the average value of the power consumption of distributed resources in the mean calculation window; t represents the sampling point t; m represents the window length of the mean calculation window; P t represents the power consumption of distributed resources at time t in the power time series; The calculation expression of the value of the change point detection window is: Where W d It represents the average value of the power consumption of distributed resources within the change point detection window; n represents the window length of the change point detection window.

7. A distributed resource response event detection system according to claim 5, characterized in that: The statistical determination module executes the step of obtaining the numerical value of the distributed resource power consumption time series change window based on the calculated mean value calculation window and the numerical value of the change point detection window, comparing the numerical cumulative sum with the cumulative sum threshold, and obtaining the detection result of whether the distributed resource has a response event based on the comparison result. The numerical cumulative sum is a positive cumulative sum or a negative cumulative sum; wherein, For the sampling point t1, the calculation expression of the forward cumulative sum is: In the formula, Represents the positive cumulative sum at sampling point t1; represents the positive cumulative sum at sampling point t1-1; β represents the power fluctuation level when the distributed resources work in a steady state in the power time series; W d W represents the average value of the power consumption of distributed resources within the change point detection window; m Indicates the average value of the power consumption of distributed resources within the mean calculation window; Represents the positive cumulative sum at sampling point t1=0; For sampling point t1, the calculation expression of negative cumulative sum is: In the formula, Represents the negative cumulative sum at sampling point t1; Indicates the negative cumulative sum at sampling point t1-1; Indicates the negative cumulative sum at sampling point t1=0; like or Then a distributed resource response event occurs; where H represents the cumulative sum threshold.

8. A distributed resource response event detection system according to claim 7, characterized in that: The cumulative sum threshold satisfies the following expression: Where ΔP represents the change in power consumption before and after the distributed resources participate in the interactive response of the power grid; n represents the window length of the change point detection window.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the distributed resource response event detection method according to any one of claims 1 to 4 is implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the distributed resource response event detection method according to any one of claims 1 to 4 is implemented.

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