A method for determining a rotating reserve capacity based on a new energy station

Abstract

The application provides a kind of based on the determination method of rotating reserve capacity of new energy station, belong to data processing technical field, specifically include: according to the load prediction result of the power user in demarcated area and the power prediction result of different new energy station, determine the rotating reserve reference capacity of demarcated area, according to the data anomaly probability of different new energy station in different predicted power interval and power prediction result, determine the reliability of prediction result and comprehensive reliability in different predicted power interval, the comprehensive reliability of new energy station that does not meet the requirement is determined by adjusting factor through comprehensive reliability, and the rotating reserve capacity of demarcated area is determined based on adjustment factor and rotating reserve reference capacity, to further improve the reliability of operation of power system.

Description

Technical Field

[0001] This invention belongs to the field of data processing technology, and in particular relates to a method for determining the spinning reserve capacity of renewable energy power plants. Background Technology

[0002] With the large-scale construction and grid connection of wind and solar power, the randomness, volatility, and intermittency of wind and solar power have brought severe challenges to the safe and stable operation of the power grid. Accurately predicting the distribution characteristics and uncertainty range of wind and solar power across multiple temporal and spatial scales, and proposing safe and economical power grid operation strategies under high-proportion wind and solar power integration, has become an urgent problem to be solved in building a new power system dominated by new energy sources. Therefore, how to clean and process the raw wind and solar power data of different new energy power plants within a certain area has become a pressing technical issue.

[0003] To address the aforementioned technical problems, existing solutions are often limited to cleaning the raw data and predicting the power generation of a single renewable energy power plant. Specifically, invention patent CN201611193694.0, "A Method and Device for Real-Time Prediction of Power Generation in a Photovoltaic System," improves the accuracy of load prediction by cleaning and processing the raw data of the photovoltaic power plant. However, analysis reveals the following technical problems in all of these solutions: When performing load dispatching within a certain area, the data quality of different wind and solar power plants directly affects the accurate prediction of their power generation. Therefore, relying on the data quality of a single wind and solar power plant cannot provide a high reference value for accurately determining the spinning reserve capacity and load dispatching within a certain area.

[0004] To address the aforementioned technical problems, this invention provides a method for determining the spinning reserve capacity of renewable energy power plants. Summary of the Invention

[0005] To achieve the objectives of this invention, the following technical solution is adopted: According to one aspect of the present invention, a method for determining the spinning reserve capacity of renewable energy power plants is provided.

[0006] A method for determining the spinning reserve capacity of renewable energy power plants, characterized in that it specifically includes: S1 determines the base capacity of the spinning reserve in the designated area based on the load forecast results of the power users in the designated area and the power forecast results of different new energy power plants. S2 determines the anomaly probability of different historical power output data of different new energy power plants based on the correlation factors between historical power output data and different power output impact data. The anomaly probability is used to determine the data anomaly probability of different new energy power plants. When there are new energy power plants whose data anomaly probability does not meet the requirements, proceed to the next step. S3 divides the new energy power stations into different predicted power intervals based on the power prediction results of different new energy power stations, and determines the reliability of the prediction results and the overall reliability in different predicted power intervals based on the data anomaly probability of different new energy power stations in different predicted power intervals and the power prediction results. When there are new energy power stations whose overall reliability does not meet the requirements, proceed to the next step. S4 determines the adjustment factor based on the overall reliability of new energy power stations that do not meet the requirements, and determines the spinning reserve capacity of the designated area based on the adjustment factor and the spinning reserve baseline capacity.

[0007] A further technical solution is that the load forecast result of the power user is determined based on the historical power load data of the power user.

[0008] A further technical solution is that the new energy power station includes a photovoltaic power station and a wind power station.

[0009] A further technical solution is that the power prediction result of the new energy power station is determined based on the historical output data of the new energy power station.

[0010] A further technical solution is that the method for determining the rotating reserve reference capacity of the designated area is as follows: The minimum capacity of the spinning reserve for the designated area is determined based on the deviation between the load forecast results and the power forecast results. The number of new energy power stations in the defined area and the power prediction results of different new energy power stations are obtained, and the fluctuation compensation factor is determined based on the number of new energy power stations and the power prediction results of different new energy power stations. The base capacity of the rotating reserve for the designated area is determined by the fluctuation compensation factor and the minimum capacity of the rotating reserve for the designated area.

[0011] A further technical solution is that the power output influencing data includes temperature, wind speed, irradiance, and irradiance duration.

[0012] A further technical solution is that, when there are no new energy power stations whose data anomaly probability does not meet the requirements, the rotating reserve benchmark capacity is used as the rotating reserve capacity of the designated area.

[0013] A further technical solution is that, when the overall reliability meets the requirements, the rotating reserve reference capacity is used as the rotating reserve capacity of the designated area.

[0014] A further technical solution is that the adjustment factor of the spinning reserve reference capacity is determined based on the weighted sum of the comprehensive reliability of the new energy power stations whose comprehensive reliability does not meet the requirements. The larger the weighted sum of the comprehensive reliability of the new energy power stations whose comprehensive reliability does not meet the requirements, the larger the adjustment factor of the spinning reserve reference capacity.

[0015] On the other hand, the present invention provides a computer device, comprising: a memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, characterized in that: when the processor runs the computer program, it executes the above-described method for determining the spinning reserve capacity of a new energy power station.

[0016] The beneficial effects of this invention are as follows: By determining the anomaly probability of different historical output data for different renewable energy power plants based on the correlation factors between historical output data and different output impact data, the system can screen historically processed data with deviations based on the correlation of output impact data. Furthermore, by evaluating the anomaly probability through comprehensive correlation factors, the system can improve the accurate identification of historically processed data with anomalies. This also lays the foundation for determining the spinning reserve capacity in a differentiated manner and ensuring the operational reliability of the power system.

[0017] The spinning reserve capacity of the designated area is determined based on the adjustment factor and the spinning reserve baseline capacity, which avoids the original technical problem of inaccurate determination of spinning reserve capacity due to inaccurate historical output data of renewable energy power plants. Taking into account the impact of inaccurate forecasts of renewable energy power plants, the reliability of the determination of spinning reserve capacity is improved.

[0018] Other features and advantages will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained through the structures particularly pointed out in the description and the drawings.

[0019] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0020] The above and other features and advantages of the present invention will become more apparent from a detailed description of exemplary embodiments thereof with reference to the accompanying drawings.

[0021] Figure 1This is a flowchart of a method for determining the spinning reserve capacity of renewable energy power plants; Figure 2 This is a flowchart illustrating the method for determining the rotating reserve baseline capacity of a designated area; Figure 3 This is a flowchart illustrating the method for determining the reliability of prediction results within a predicted power range. Detailed Implementation

[0022] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the embodiments set forth herein; rather, they are provided so that the invention will be thorough and complete, and the concept of the exemplary embodiments will be fully conveyed to those skilled in the art. The same reference numerals in the drawings denote the same or similar structures, and therefore their detailed description will be omitted.

[0023] The terms “a,” “one,” “the,” and “the” are used to indicate the existence of one or more elements / components / etc.; the terms “including” and “having” are used to indicate an open-ended meaning of inclusion and that other elements / components / etc. may exist in addition to the listed elements / components / etc. Example

[0024] To solve the above problems, according to one aspect of the present invention, such as Figure 1 As shown, according to one aspect of the present invention, a method for determining the spinning reserve capacity of a renewable energy power station is provided, characterized in that it specifically includes: S1 determines the base capacity of the spinning reserve in the designated area based on the load forecast results of the power users in the designated area and the power forecast results of different new energy power plants. Specifically, the load forecast results for the power users are determined based on the historical power load data of the power users.

[0025] Furthermore, the new energy power stations include photovoltaic power stations and wind power stations.

[0026] It is understood that the power prediction results of the new energy power station are determined based on the historical output data of the new energy power station.

[0027] Specific examples, such as Figure 2 As shown, the method for determining the rotating reserve reference capacity of the designated area is as follows: The minimum capacity of the spinning reserve for the designated area is determined based on the deviation between the load forecast results and the power forecast results. The number of new energy power stations in the designated area and the power prediction results of different new energy power stations are obtained, and the fluctuation compensation factor is determined based on the number of new energy power stations and the power prediction results of different new energy power stations. The base capacity of the rotating reserve for the designated area is determined by the fluctuation compensation factor and the minimum capacity of the rotating reserve for the designated area.

[0028] S2 determines the anomaly probability of different historical power output data of different new energy power plants based on the correlation factors between historical power output data and different power output impact data. The anomaly probability is used to determine the data anomaly probability of different new energy power plants. When there are new energy power plants whose data anomaly probability does not meet the requirements, proceed to the next step. It is understood that the output impact data includes temperature, wind speed, light intensity, and duration of sunlight.

[0029] Furthermore, the correlation coefficient between the historical output data and different output impact data is used to determine the correlation factor based on the correlation factors between the historical output data and different output impact data. A further technical solution is that the correlation coefficient is determined using principal component analysis.

[0030] In one possible embodiment, the method for determining the anomaly probability of the historical output data is as follows: Obtain the correlation factors between the historical output data and different output impact data, and determine the anomaly probability of the historical output data based on the correlation factors with the different output impact data and the number of output impact data.

[0031] Furthermore, the abnormal probability ranges from 0 to 1, where the higher the abnormal probability, the greater the probability that the historical output data is abnormal.

[0032] In another possible embodiment, the method for determining the anomaly probability of the historical output data is as follows: Obtain the correlation factors between the historical power output data and different power output impact data. When there are correlation factors with abnormal power output impact data: Output impact data with abnormal correlation factors are considered as abnormal impact data. When the number of abnormal impact data types exceeds the preset number of types, the abnormal probability of the historical output data is determined by the types of abnormal impact data. When the number of types of abnormal impact data is no greater than the preset number of types, the abnormal probability of the historical output data is determined by the types of abnormal impact data and the correlation factors of different abnormal impact data. When there is no correlation factor that causes abnormal output impact data: The probability of anomalies in the historical output data is determined based on the correlation factor with the different output impact data and the number of output impact data.

[0033] In one possible embodiment, the method for determining the probability of data anomalies at the new energy power station is as follows: Based on the anomaly probability, abnormal output data of the new energy power station under different date categories are collected for different dates, and the dates with abnormal output data are taken as problem data dates. The date data anomaly probability of different problem data dates is determined according to the amount of abnormal output data of different problem data dates and the anomaly probability of different abnormal output data. The overall data anomaly probability for different date categories is determined by the number of problematic data dates under different date categories and the date data anomaly probability for different problematic data dates. The probability of data anomalies at the new energy power station is determined based on the comprehensive data anomaly probability of different date types and the number of dates in each date type.

[0034] It is understood that the date categories are determined based on the amount of sunlight and wind speed on different days. The specific steps for classifying the date categories are as follows: The weather impact values ​​for different dates are determined by measuring the amount of sunlight and wind speed on different dates, and the dates are classified into different date categories based on the range in which the weather impact values ​​fall.

[0035] In another possible embodiment, the method for determining the probability of data anomalies at the new energy power station is as follows: Based on the aforementioned anomaly probability, abnormal power output data of the new energy power station is collected for different dates under different date categories. When the amount of abnormal power output data is within a preset data volume range: The abnormal probability assessment value of the new energy power station is determined based on the amount of abnormal output data of the new energy power station and the abnormal probability of different abnormal output data. When the abnormal probability assessment value of the new energy power station meets the requirements, it is determined that the data abnormal probability of the new energy power station meets the requirements. When the amount of abnormal power output data is outside the preset data range or when the abnormal probability assessment of the new energy power station does not meet the requirements: Dates with abnormal power output data are designated as problematic data dates. If the number of problematic data dates is less than the preset number of dates, then the data anomaly probability of the new energy power station is determined to meet the requirements. When the number of problematic data dates is not less than the preset number of dates, the abnormal probability of the date data for different problematic data dates is determined based on the amount of abnormal output data for different problematic data dates and the abnormal probability of different abnormal output data. The comprehensive data anomaly probability for different date types is determined by the number of problematic data dates under different date types and the date data anomaly probability for different problematic data dates. When the comprehensive data anomaly probability for different date types meets the requirements, it is determined that the data anomaly probability of the new energy power station meets the requirements. When there are date types whose overall data anomaly probability does not meet the requirements, the data anomaly probability of the new energy power station is determined based on the overall data anomaly probability of different date types and the number of dates in different date types.

[0036] In another possible embodiment, the method for determining the probability of data anomalies at the new energy power station is as follows: Based on the anomaly probability, abnormal output data of the new energy power station under different date categories are collected for different dates. The anomaly probability assessment quantity of the new energy power station is determined according to the data volume of the abnormal output data and the anomaly probability of different abnormal output data. It is then determined whether the anomaly probability assessment quantity of the new energy power station does not meet the requirements. If not, it is determined that the data anomaly probability of the new energy power station meets the requirements. If so, proceed to the next step. Dates with abnormal power output data are designated as problematic data dates. It is then determined whether there are any date types where the number of problematic data dates does not meet the requirements. If not, it is determined that the data anomaly probability of the new energy power station meets the requirements. If so, proceed to the next step. Based on the amount of abnormal output data for different problem data dates and the abnormal probability of different abnormal output data, the date data abnormality probability for different problem data dates is determined. Based on the number of problem data dates under different date types and the date data abnormality probability for different problem data dates, the comprehensive data abnormality probability for different date types is determined. It is then determined whether there are any date types whose comprehensive data abnormality probability does not meet the requirements. If so, proceed to the next step; otherwise, it is determined that the data abnormality probability of the new energy power station meets the requirements. The probability of data anomalies at the new energy power station is determined based on the comprehensive data anomaly probability of different date types and the number of dates in each date type.

[0037] Furthermore, when there are no new energy power stations whose data anomaly probability does not meet the requirements, the spinning reserve baseline capacity is used as the spinning reserve capacity of the designated area.

[0038] S3 divides the new energy power stations into different predicted power intervals based on the power prediction results of different new energy power stations, and determines the reliability of the prediction results and the overall reliability in different predicted power intervals based on the data anomaly probability of different new energy power stations in different predicted power intervals and the power prediction results. When there are new energy power stations whose overall reliability does not meet the requirements, proceed to the next step. In one possible embodiment, such as Figure 3 As shown, the method for determining the reliability of the prediction results within the predicted power range is as follows: The output reliability coefficient of different new energy power stations is determined based on the data anomaly probability and power prediction results. The reliability of the prediction results within the predicted power range is determined based on the number of new energy power plants within the predicted power range and the output reliability coefficient of different new energy power plants.

[0039] Specifically, the method for determining the overall reliability is as follows: The comprehensive power prediction amount for different power prediction ranges is determined by the number of new energy power stations in different power prediction ranges and the power prediction results of different new energy power stations. The overall reliability of the new energy power station is determined based on the comprehensive power prediction amount of different predicted power ranges and the reliability of the prediction results within the predicted power ranges.

[0040] In another possible embodiment, the method for determining the overall reliability is as follows: Based on the reliability of the prediction results in different prediction power regions, determine whether there are prediction power regions where the reliability of the prediction results does not meet the requirements. If so, proceed to the next step; otherwise, determine the comprehensive reliability by the reliability of the prediction results in different prediction power regions. The predicted power ranges whose reliability does not meet the requirements are designated as unreliable power ranges. It is then determined whether the number of unreliable power ranges is greater than the preset number of ranges. If so, it is determined that the overall reliability does not meet the requirements. If not, proceed to the next step. The comprehensive power prediction amount for different power prediction ranges is determined by the number of new energy power stations in different power prediction ranges and the power prediction results of different new energy power stations. It is then determined whether the comprehensive power prediction amount of the unreliable power range is within the preset power range. If it is, proceed to the next step; otherwise, it is determined that the comprehensive reliability does not meet the requirements. The number of unreliable power intervals and the comprehensive power prediction of different unreliable power intervals are obtained. An unreliable assessment quantity is obtained by using the number of unreliable power intervals, the comprehensive power prediction of different unreliable power intervals and the reliability of the prediction results. It is then determined whether the unreliable assessment quantity meets the requirements. If yes, proceed to the next step; otherwise, it is determined that the comprehensive reliability does not meet the requirements. The output reliability coefficients for different predicted power ranges are determined based on the comprehensive power prediction quantities for different predicted power ranges and the reliability of the prediction results within those ranges. The comprehensive reliability of the new energy power station is then determined in conjunction with the unreliable assessment quantities.

[0041] Furthermore, when the overall reliability meets the requirements, the rotating reserve reference capacity is used as the rotating reserve capacity of the designated area.

[0042] S4 determines the adjustment factor based on the overall reliability of new energy power stations that do not meet the requirements, and determines the spinning reserve capacity of the designated area based on the adjustment factor and the spinning reserve baseline capacity.

[0043] It is understood that the adjustment factor of the spinning reserve baseline capacity is determined based on the weighted sum of the comprehensive reliability of the new energy power plants that do not meet the comprehensive reliability requirements. The larger the weighted sum of the comprehensive reliability of the new energy power plants that do not meet the comprehensive reliability requirements, the larger the adjustment factor of the spinning reserve baseline capacity. Example

[0044] On the other hand, the present invention provides a computer device, comprising: a memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, characterized in that: when the processor runs the computer program, it executes the above-described method for determining the spinning reserve capacity of a new energy power station.

[0045] Through the above embodiments, this application achieves the following technical effects: By determining the anomaly probability of different historical output data for different renewable energy power plants based on the correlation factors between historical output data and different output impact data, the system can screen historically processed data with deviations based on the correlation of output impact data. Furthermore, by evaluating the anomaly probability through comprehensive correlation factors, the system can improve the accurate identification of historically processed data with anomalies. This also lays the foundation for determining the spinning reserve capacity in a differentiated manner and ensuring the operational reliability of the power system.

[0046] The spinning reserve capacity of the designated area is determined based on the adjustment factor and the spinning reserve baseline capacity, avoiding the technical problem of inaccurate determination of spinning reserve capacity caused by the inaccuracy of historical output data of renewable energy power plants. Taking into account the impact of inaccurate forecasts of renewable energy power plants, the reliability of the determination of spinning reserve capacity is improved.

[0047] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, devices, and non-volatile computer storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0048] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0049] The above description is merely one or more embodiments of this specification and is not intended to limit this specification. Various modifications and variations can be made to the one or more embodiments of this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of one or more embodiments of this specification should be included within the scope of the claims of this specification.

Claims

1. A method for determining the spinning reserve capacity of renewable energy power plants, characterized in that, Specifically, it includes: The base capacity of the spinning reserve in the designated area is determined based on the load forecast results of power users within the designated area and the power forecast results of different new energy power plants. Based on the correlation factors between historical power output data and different power output impact data, the anomaly probability of different historical power output data of different new energy power plants is determined. The anomaly probability is used to determine the data anomaly probability of different new energy power plants. When there are new energy power plants whose data anomaly probability does not meet the requirements, the process proceeds to the next step. Based on the power prediction results of different new energy power stations, the new energy power stations are divided into different prediction power intervals. The reliability of the prediction results and the overall reliability within the different prediction power intervals are determined according to the data anomaly probability of different new energy power stations within the different prediction power intervals and the power prediction results. When there are new energy power stations whose overall reliability does not meet the requirements, proceed to the next step. The adjustment factor is determined by the overall reliability of new energy power stations that do not meet the requirements, and the spinning reserve capacity of the designated area is determined based on the adjustment factor and the spinning reserve benchmark capacity. The method for determining the rotating reserve reference capacity of the designated area is as follows: The minimum capacity of the spinning reserve for the designated area is determined based on the deviation between the load forecast results and the power forecast results. The number of new energy power stations in the designated area and the power prediction results of different new energy power stations are obtained, and the fluctuation compensation factor is determined based on the number of new energy power stations and the power prediction results of different new energy power stations. The base capacity of the rotating reserve of the designated area is determined by the fluctuation compensation factor and the minimum capacity of the rotating reserve of the designated area. The method for determining the anomaly probability of the historical output data is as follows: Obtain the correlation factors between the historical power output data and different power output impact data. When there are correlation factors with abnormal power output impact data: Output impact data with abnormal correlation factors are considered as abnormal impact data. When the number of abnormal impact data types exceeds the preset number of types, the abnormal probability of the historical output data is determined by the types of abnormal impact data. When the number of types of abnormal impact data is no greater than the preset number of types, the abnormal probability of the historical output data is determined by the types of abnormal impact data and the correlation factors of different abnormal impact data. When there is no correlation factor that causes abnormal output impact data: The probability of anomalies in the historical output data is determined based on the correlation factors with the different output impact data and the number of output impact data. The method for determining the reliability of the prediction results within the predicted power range is as follows: The output reliability coefficient of different new energy power stations is determined based on the data anomaly probability and power prediction results. The reliability of the prediction results within the predicted power range is determined based on the number of new energy power stations within the predicted power range and the output reliability coefficient of different new energy power stations. The method for determining the probability of data anomalies at the aforementioned new energy power stations is as follows: Based on the anomaly probability, abnormal output data of the new energy power station under different date categories are collected for different dates, and the dates with abnormal output data are taken as problem data dates. The date data anomaly probability of different problem data dates is determined according to the amount of abnormal output data of different problem data dates and the anomaly probability of different abnormal output data. The overall data anomaly probability for different date categories is determined by the number of problematic data dates under different date categories and the date data anomaly probability for different problematic data dates. The probability of data anomalies at the new energy power station is determined based on the comprehensive data anomaly probability of different date types and the number of dates in each date type. The date categories are determined based on the amount of sunlight and wind speed on different days. The specific steps for classifying the date categories are as follows: The weather impact values ​​for different dates are determined by measuring the amount of sunlight and wind speed on different dates, and the dates are classified into different date categories based on the range in which the weather impact values ​​fall.

2. The method for determining the spinning reserve capacity of renewable energy power plants as described in claim 1, characterized in that, The load forecast results for the power users are determined based on the historical power load data of the power users.

3. The method for determining the spinning reserve capacity of renewable energy power plants as described in claim 1, characterized in that, The new energy power stations include photovoltaic power stations and wind power stations.

4. The method for determining the spinning reserve capacity of renewable energy power plants as described in claim 1, characterized in that, The output impact data includes temperature, wind speed, irradiance, and duration of sunlight.

5. The method for determining the spinning reserve capacity of renewable energy power plants as described in claim 1, characterized in that, The correlation coefficient between the historical output data and different output impact data is used to determine the correlation factor based on the historical output data and different output impact data.

6. The method for determining the spinning reserve capacity of renewable energy power plants as described in claim 1, characterized in that, When there are no new energy power stations whose data anomaly probability does not meet the requirements, the spinning reserve baseline capacity shall be used as the spinning reserve capacity of the designated area.

7. A computer device, comprising: A memory and processor connected by communication, and a computer program stored in the memory and capable of running on the processor, characterized in that: when the processor runs the computer program, it executes a method for determining the spinning reserve capacity of a new energy power station as described in any one of claims 1-6.

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