An improved spatial upscaling method for producing day‐ahead power forecasts for wind farm clusters
Abstract Large‐scale day‐ahead wind power forecasting (WPF) for wind farm clusters (WFCs) can enable dispatching agencies to formulate scientifically sound power generation plans and enhance the robustness of power grids. Most available WPF methods for WFCs only involve mathematical models and rarel...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-10-01
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| Series: | IET Generation, Transmission & Distribution |
| Online Access: | https://doi.org/10.1049/gtd2.12569 |
| _version_ | 1828345344488898560 |
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| author | Mao Yang Qi Yan Bozhi Dai Xinxin Chen Miaomiao Ma Xin Su |
| author_facet | Mao Yang Qi Yan Bozhi Dai Xinxin Chen Miaomiao Ma Xin Su |
| author_sort | Mao Yang |
| collection | DOAJ |
| description | Abstract Large‐scale day‐ahead wind power forecasting (WPF) for wind farm clusters (WFCs) can enable dispatching agencies to formulate scientifically sound power generation plans and enhance the robustness of power grids. Most available WPF methods for WFCs only involve mathematical models and rarely consider spatial correlation factors. This necessitates further improvements to forecasting systems. In this study, to increase the day‐ahead WPF accuracy for WFCs, fractal transform theory is introduced to optimize the process of WPF for WFCs through spatial upscaling and establish a day‐ahead WPF model for WFCs based on an improved spatial upscaling method. First, a WFC is partitioned into subclusters. Then, using fractal transform theory, an affine relation is established between the local output of each subcluster and the overall output of the WFC. Finally, a day‐ahead power forecast is produced for the WFC through spatial upscaling of the forecast for the subcluster with the highest grey relational grade with the output of the WFC. The applicability of the proposed forecasting model is examined using historical measured data for a large WFC in north‐eastern China. The case study results show that the proposed forecasting model outperforms the summation method and the statistical upscaling method in terms of forecasting accuracy. |
| first_indexed | 2024-04-14T00:09:27Z |
| format | Article |
| id | doaj.art-518bcca280c8458abe5e9edbce4be589 |
| institution | Directory Open Access Journal |
| issn | 1751-8687 1751-8695 |
| language | English |
| last_indexed | 2024-04-14T00:09:27Z |
| publishDate | 2022-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Generation, Transmission & Distribution |
| spelling | doaj.art-518bcca280c8458abe5e9edbce4be5892022-12-22T02:23:23ZengWileyIET Generation, Transmission & Distribution1751-86871751-86952022-10-0116193860387310.1049/gtd2.12569An improved spatial upscaling method for producing day‐ahead power forecasts for wind farm clustersMao Yang0Qi Yan1Bozhi Dai2Xinxin Chen3Miaomiao Ma4Xin Su5Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education (Northeast Electric Power University) Jilin ChinaKey Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education (Northeast Electric Power University) Jilin ChinaZhongshan Power Supply Bureau of Guangdong Power Grid Co. Ltd. of China Southern Power Grid Company Limited Zhongshan ChinaPingdingshan Power Supply Company State Grid Henan Power Company No. 6, South Section of Xinhua Road Shareholding Pingdingshan ChinaKey Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education (Northeast Electric Power University) Jilin ChinaKey Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education (Northeast Electric Power University) Jilin ChinaAbstract Large‐scale day‐ahead wind power forecasting (WPF) for wind farm clusters (WFCs) can enable dispatching agencies to formulate scientifically sound power generation plans and enhance the robustness of power grids. Most available WPF methods for WFCs only involve mathematical models and rarely consider spatial correlation factors. This necessitates further improvements to forecasting systems. In this study, to increase the day‐ahead WPF accuracy for WFCs, fractal transform theory is introduced to optimize the process of WPF for WFCs through spatial upscaling and establish a day‐ahead WPF model for WFCs based on an improved spatial upscaling method. First, a WFC is partitioned into subclusters. Then, using fractal transform theory, an affine relation is established between the local output of each subcluster and the overall output of the WFC. Finally, a day‐ahead power forecast is produced for the WFC through spatial upscaling of the forecast for the subcluster with the highest grey relational grade with the output of the WFC. The applicability of the proposed forecasting model is examined using historical measured data for a large WFC in north‐eastern China. The case study results show that the proposed forecasting model outperforms the summation method and the statistical upscaling method in terms of forecasting accuracy.https://doi.org/10.1049/gtd2.12569 |
| spellingShingle | Mao Yang Qi Yan Bozhi Dai Xinxin Chen Miaomiao Ma Xin Su An improved spatial upscaling method for producing day‐ahead power forecasts for wind farm clusters IET Generation, Transmission & Distribution |
| title | An improved spatial upscaling method for producing day‐ahead power forecasts for wind farm clusters |
| title_full | An improved spatial upscaling method for producing day‐ahead power forecasts for wind farm clusters |
| title_fullStr | An improved spatial upscaling method for producing day‐ahead power forecasts for wind farm clusters |
| title_full_unstemmed | An improved spatial upscaling method for producing day‐ahead power forecasts for wind farm clusters |
| title_short | An improved spatial upscaling method for producing day‐ahead power forecasts for wind farm clusters |
| title_sort | improved spatial upscaling method for producing day ahead power forecasts for wind farm clusters |
| url | https://doi.org/10.1049/gtd2.12569 |
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