Robust Energy Management System with Safe Reinforcement Learning Using Short-Horizon Forecasts
In this letter, we study an energy management system (EMS) with an inconsistent energy supply that aims to minimize energy costs while avoiding failing to satisfy energy demands. To this end, we propose a robust EMS algorithm based on safe reinforcement learning which can effectively exploit short-h...
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| Veröffentlicht in: | IEEE transactions on smart grid 2023-05, Vol.14 (3), p.1-1 |
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| creator | Hong, Seong-Hyun Lee, Hyun-Suk |
| description | In this letter, we study an energy management system (EMS) with an inconsistent energy supply that aims to minimize energy costs while avoiding failing to satisfy energy demands. To this end, we propose a robust EMS algorithm based on safe reinforcement learning which can effectively exploit short-horizon forecasts on system uncertainties. We show via experimental results using real datasets that our robust EMS algorithm outperforms other state-of-the-art algorithms in terms of both robustness and cost-efficiency thanks to its capability of utilizing short-horizon forecasts. |
| doi_str_mv | 10.1109/TSG.2023.3240588 |
| format | Article |
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| ispartof | IEEE transactions on smart grid, 2023-05, Vol.14 (3), p.1-1 |
| issn | 1949-3053 1949-3061 |
| language | eng |
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| source | IEEE Electronic Library (IEL) |
| subjects | Algorithms Batteries Costs Energy costs Energy management Energy management system forecasting Horizon Reinforcement learning Renewable energy sources robust scheduling Robustness safe reinforcement learning Uncertainty |
| title | Robust Energy Management System with Safe Reinforcement Learning Using Short-Horizon Forecasts |
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