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• W4310252819 endingPage "106147" @default.
• W4310252819 startingPage "106147" @default.
• W4310252819 abstract "With the advancements in renewable energy and energy storage technologies, the energy hubs (EH) have been emerging in recent years. The scheduling of EH is a challenging task due to the need to incorporate uncertainties at energy supply and load side. The existing model-based optimization approaches proposed for the above purpose have limitations in terms of solution accuracy and computational efficiency, which makes hinders their applications. This paper proposes a model-free, safe deep reinforcement learning (DRL) approach, using primal-dual optimization and imitation learning, for optimal scheduling of an EH that includes a tri-generative advanced adiabatic compressed air energy storage (AA-CAES). First, the operation of an AA-CAES under off-design conditions is modeled and linearized using a mixed-integer linear programming (MILP). Then, a safe DRL approach is proposed with training and testing steps considering a case study. The performance of the proposed approach in reducing operational cost and satisfying constraints is compared to the state-of-the-art DRL algorithms as well as a deterministic MILP approach. In addition, the generalization of the proposed approach is examined on a test-set. Finally, the effect of off-design conditions of a tri-generative AA-CAES on the optimal dispatch strategy is investigated. The results indicate that the proposed approach can effectively reduce the operational cost and satisfy the operational constraints." @default.
• W4310252819 created "2022-11-30" @default.
• W4310252819 creator A5042592800 @default.
• W4310252819 creator A5075519601 @default.
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• W4310252819 date "2023-01-01" @default.
• W4310252819 modified "2023-09-27" @default.
• W4310252819 title "Optimal dispatch of an energy hub with compressed air energy storage: A safe reinforcement learning approach" @default.
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• W4310252819 doi "https://doi.org/10.1016/j.est.2022.106147" @default.
• W4310252819 hasPublicationYear "2023" @default.
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