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• W4360764867 abstract "Future power systems will rely heavily on micro grids with a high share of decentralised renewable energy sources and energy storage systems. The high complexity and uncertainty in this context might make conventional power dispatch strategies infeasible. Reinforcement-learning-based (RL) controllers can address this challenge, however, cannot themselves provide safety guarantees, preventing their deployment in practice. To overcome this limitation, we propose a formally validated RL controller for economic dispatch. We extend conventional constraints by a time-dependent constraint encoding the islanding contingency. The contingency constraint is computed using set-based backwards reachability analysis, and actions of the RL agent are verified through a safety layer. Unsafe actions are projected into the safe action space while leveraging constrained zonotope set representations for computational efficiency. The developed approach is demonstrated on a residential use case considering real-world measurements." @default.
• W4360764867 created "2023-03-25" @default.
• W4360764867 creator A5005383495 @default.
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• W4360764867 date "2022-12-01" @default.
• W4360764867 modified "2023-09-27" @default.
• W4360764867 title "Contingency-constrained economic dispatch with safe reinforcement learning" @default.
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• W4360764867 doi "https://doi.org/10.1109/icmla55696.2022.00103" @default.
• W4360764867 hasPublicationYear "2022" @default.
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