A data-driven load forecasting method for incentive demand response
The participation of incentive demand response (IDR) can improve power grid flexibility, and reduce peak shaving pressure. However, the further development of incentive demand response services will be limited by the uncertainty of user response behavior. To tackle this problem, this paper proposes...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-07-01
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| Series: | Energy Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484722002323 |
| _version_ | 1798037678567981056 |
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| author | Haixin Wang Jiahui Yuan Guanqiu Qi Yanzhen Li Junyou Yang Henan Dong Yiming Ma |
| author_facet | Haixin Wang Jiahui Yuan Guanqiu Qi Yanzhen Li Junyou Yang Henan Dong Yiming Ma |
| author_sort | Haixin Wang |
| collection | DOAJ |
| description | The participation of incentive demand response (IDR) can improve power grid flexibility, and reduce peak shaving pressure. However, the further development of incentive demand response services will be limited by the uncertainty of user response behavior. To tackle this problem, this paper proposes a data-driven load forecasting method for IDR, which considers consumer behavior. Firstly, we describe the power market auxiliary service operation mechanism for load aggregators, through which the ability of demand-side resources to respond to auxiliary services is improved. Then, add an attention layer to improve the traditional long and short memory (LSTM) model, and propose an IDR load forecasting method based on this model, which considers the model’s learning of historical data of user behavior. Finally, establish a simulation model to verify the effectiveness of the improved LSTM model forecasting. The proposed method is simulated and compared with the traditional LSTM method and k-nearest neighbor prediction method. The results show that compared with the other two methods, the mean error, root means square error, and mean absolute percentage error in the improved LSTM method are reduced by 7.31 MW, 8.32 MW, 2.06% and 10.62 MW, 13.46 MW and 3.09%, respectively, which effectively improves the accuracy of forecasting and increase the participation of demand-side resources in the power auxiliary service market. |
| first_indexed | 2024-04-11T21:29:48Z |
| format | Article |
| id | doaj.art-c3b4e34eef3842b7a334677c1dbde3ba |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-04-11T21:29:48Z |
| publishDate | 2022-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-c3b4e34eef3842b7a334677c1dbde3ba2022-12-22T04:02:15ZengElsevierEnergy Reports2352-48472022-07-01810131019A data-driven load forecasting method for incentive demand responseHaixin Wang0Jiahui Yuan1Guanqiu Qi2Yanzhen Li3Junyou Yang4Henan Dong5Yiming Ma6Shenyang University of Technology, Shenyang 110870, ChinaShenyang University of Technology, Shenyang 110870, ChinaInner Mongolia Power(Group) Co., Ltd., Hohhot 010020, ChinaShenyang University of Technology, Shenyang 110870, ChinaShenyang University of Technology, Shenyang 110870, China; Corresponding author.Electric Power Research Institute of State Grid Liaoning Electric Power Co., Ltd., Shenyang 110000, ChinaShenyang University of Technology, Shenyang 110870, ChinaThe participation of incentive demand response (IDR) can improve power grid flexibility, and reduce peak shaving pressure. However, the further development of incentive demand response services will be limited by the uncertainty of user response behavior. To tackle this problem, this paper proposes a data-driven load forecasting method for IDR, which considers consumer behavior. Firstly, we describe the power market auxiliary service operation mechanism for load aggregators, through which the ability of demand-side resources to respond to auxiliary services is improved. Then, add an attention layer to improve the traditional long and short memory (LSTM) model, and propose an IDR load forecasting method based on this model, which considers the model’s learning of historical data of user behavior. Finally, establish a simulation model to verify the effectiveness of the improved LSTM model forecasting. The proposed method is simulated and compared with the traditional LSTM method and k-nearest neighbor prediction method. The results show that compared with the other two methods, the mean error, root means square error, and mean absolute percentage error in the improved LSTM method are reduced by 7.31 MW, 8.32 MW, 2.06% and 10.62 MW, 13.46 MW and 3.09%, respectively, which effectively improves the accuracy of forecasting and increase the participation of demand-side resources in the power auxiliary service market.http://www.sciencedirect.com/science/article/pii/S2352484722002323User behavior forecastingPower big dataData-drivenIncentive demand responsePower market |
| spellingShingle | Haixin Wang Jiahui Yuan Guanqiu Qi Yanzhen Li Junyou Yang Henan Dong Yiming Ma A data-driven load forecasting method for incentive demand response Energy Reports User behavior forecasting Power big data Data-driven Incentive demand response Power market |
| title | A data-driven load forecasting method for incentive demand response |
| title_full | A data-driven load forecasting method for incentive demand response |
| title_fullStr | A data-driven load forecasting method for incentive demand response |
| title_full_unstemmed | A data-driven load forecasting method for incentive demand response |
| title_short | A data-driven load forecasting method for incentive demand response |
| title_sort | data driven load forecasting method for incentive demand response |
| topic | User behavior forecasting Power big data Data-driven Incentive demand response Power market |
| url | http://www.sciencedirect.com/science/article/pii/S2352484722002323 |
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