Decision-Oriented Learning for Future Power System Decision-Making under Uncertainty
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866914743050043392 |
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| author | Li, Ran Zhang, Haipeng Sun, Mingyang Teng, Fei Wan, Can Pineda, Salvador Kariniotakis, Georges |
| author_facet | Li, Ran Zhang, Haipeng Sun, Mingyang Teng, Fei Wan, Can Pineda, Salvador Kariniotakis, Georges |
| contents | Better forecasts may not lead to better decision-making. To address this challenge, decision-oriented learning (DOL) has been proposed as a new branch of machine learning that replaces traditional statistical loss with a decision loss to form an end-to-end model. Applications of DOL in power systems have been developed in recent years. For renewable-rich power systems, uncertainties propagate through sequential tasks, where traditional statistical-based approaches focus on minimizing statistical errors at intermediate stages but may fail to provide optimal decisions at the final stage. This paper first elaborates on the mismatch between more accurate forecasts and more optimal decisions in the power system caused by statistical-based learning (SBL) and explains how DOL resolves this problem. Secondly, this paper extensively reviews DOL techniques and their applications in power systems while highlighting their pros and cons in relation to SBL. Finally, this paper identifies the challenges to adopt DOL in the energy sector and presents future research directions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_03680 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Decision-Oriented Learning for Future Power System Decision-Making under Uncertainty Li, Ran Zhang, Haipeng Sun, Mingyang Teng, Fei Wan, Can Pineda, Salvador Kariniotakis, Georges Systems and Control Better forecasts may not lead to better decision-making. To address this challenge, decision-oriented learning (DOL) has been proposed as a new branch of machine learning that replaces traditional statistical loss with a decision loss to form an end-to-end model. Applications of DOL in power systems have been developed in recent years. For renewable-rich power systems, uncertainties propagate through sequential tasks, where traditional statistical-based approaches focus on minimizing statistical errors at intermediate stages but may fail to provide optimal decisions at the final stage. This paper first elaborates on the mismatch between more accurate forecasts and more optimal decisions in the power system caused by statistical-based learning (SBL) and explains how DOL resolves this problem. Secondly, this paper extensively reviews DOL techniques and their applications in power systems while highlighting their pros and cons in relation to SBL. Finally, this paper identifies the challenges to adopt DOL in the energy sector and presents future research directions. |
| title | Decision-Oriented Learning for Future Power System Decision-Making under Uncertainty |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2401.03680 |