Robust machine-learned algorithms for efficient grid operation
Increasing penetration of variable and intermittent renewable energy resources on the energy grid poses a challenge for reliable and efficient grid operation, necessitating the development of algorithms that are robust to this uncertainty. However, standard algorithms incorporating uncertainty for g...
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| Vydané v: | Environmental Data Science Ročník 4 |
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| Hlavní autori: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Cambridge University Press
22.04.2025
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| ISSN: | 2634-4602, 2634-4602 |
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| Abstract | Increasing penetration of variable and intermittent renewable energy resources on the energy grid poses a challenge for reliable and efficient grid operation, necessitating the development of algorithms that are robust to this uncertainty. However, standard algorithms incorporating uncertainty for generation dispatch are computationally intractable when costs are nonconvex, and machine learning-based approaches lack worst-case guarantees on their performance. In this work, we propose a learning-augmented algorithm, R obust ML, that exploits the good average-case performance of a machine-learned algorithm for minimizing dispatch and ramping costs of dispatchable generation resources while providing provable worst-case guarantees on cost. We evaluate the algorithm on a realistic model of a combined cycle cogeneration plant, where it exhibits robustness to distribution shift while enabling improved efficiency as renewables penetration increases. |
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| AbstractList | Increasing penetration of variable and intermittent renewable energy resources on the energy grid poses a challenge for reliable and efficient grid operation, necessitating the development of algorithms that are robust to this uncertainty. However, standard algorithms incorporating uncertainty for generation dispatch are computationally intractable when costs are nonconvex, and machine learning-based approaches lack worst-case guarantees on their performance. In this work, we propose a learning-augmented algorithm, R obust ML, that exploits the good average-case performance of a machine-learned algorithm for minimizing dispatch and ramping costs of dispatchable generation resources while providing provable worst-case guarantees on cost. We evaluate the algorithm on a realistic model of a combined cycle cogeneration plant, where it exhibits robustness to distribution shift while enabling improved efficiency as renewables penetration increases. Increasing penetration of variable and intermittent renewable energy resources on the energy grid poses a challenge for reliable and efficient grid operation, necessitating the development of algorithms that are robust to this uncertainty. However, standard algorithms incorporating uncertainty for generation dispatch are computationally intractable when costs are nonconvex, and machine learning-based approaches lack worst-case guarantees on their performance. In this work, we propose a learning-augmented algorithm, RobustML, that exploits the good average-case performance of a machine-learned algorithm for minimizing dispatch and ramping costs of dispatchable generation resources while providing provable worst-case guarantees on cost. We evaluate the algorithm on a realistic model of a combined cycle cogeneration plant, where it exhibits robustness to distribution shift while enabling improved efficiency as renewables penetration increases. |
| ArticleNumber | e24 |
| Author | Wierman, Adam Torabi Rad, Mahdi Yeh, Christopher Li, Tongxin Christianson, Nicolas Hosseini, Mehdi Golmohammadi, Azarang |
| Author_xml | – sequence: 1 givenname: Nicolas orcidid: 0000-0001-8330-8964 surname: Christianson fullname: Christianson, Nicolas – sequence: 2 givenname: Christopher surname: Yeh fullname: Yeh, Christopher – sequence: 3 givenname: Tongxin surname: Li fullname: Li, Tongxin – sequence: 4 givenname: Mehdi surname: Hosseini fullname: Hosseini, Mehdi – sequence: 5 givenname: Mahdi surname: Torabi Rad fullname: Torabi Rad, Mahdi – sequence: 6 givenname: Azarang surname: Golmohammadi fullname: Golmohammadi, Azarang – sequence: 7 givenname: Adam surname: Wierman fullname: Wierman, Adam |
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