Optimal scheduling of island integrated energy systems considering multi-uncertainties and hydrothermal simultaneous transmission: a deep reinforcement learning approach

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dc.contributor.authorLi, Yang
dc.contributor.authorBu, Fanjin
dc.contributor.authorLi, Yuanzheng
dc.contributor.authorLong, Chao
dc.date.accessioned2023-02-01T16:28:11Z
dc.date.available2023-02-01T16:28:11Z
dc.date.issued2022-12-29
dc.description.abstractMulti-uncertainties from power sources and loads have brought significant challenges to the stable demand supply of various resources at islands. To address these challenges, a comprehensive scheduling framework is proposed by introducing a model-free deep reinforcement learning (DRL) approach based on modeling an island integrated energy system (IES). In response to the shortage of freshwater on islands, in addition to the introduction of seawater desalination systems, a transmission structure of “hydrothermal simultaneous transmission” (HST) is proposed. The essence of the IES scheduling problem is the optimal combination of each unit’s output, which is a typical timing control problem and conforms to the Markov decision-making solution framework of deep reinforcement learning. Deep reinforcement learning adapts to various changes and timely adjusts strategies through the interaction of agents and the environment, avoiding complicated modeling and prediction of multi-uncertainties. The simulation results show that the proposed scheduling framework properly handles multi-uncertainties from power sources and loads, achieves a stable demand supply for various resources, and has better performance than other real-time scheduling methods, especially in terms of computational efficiency. In addition, the HST model constitutes an active exploration to improve the utilization efficiency of island freshwater.en_UK
dc.identifier.citationLi Y, Bu F, Li Y, Long C. (2023) Optimal scheduling of island integrated energy systems considering multi-uncertainties and hydrothermal simultaneous transmission: a deep reinforcement learning approach. Applied Energy, Volume 333, March 2023, Article number 120540en_UK
dc.identifier.issn0306-2619
dc.identifier.urihttps://doi.org/10.1016/j.apenergy.2022.120540
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/19087
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectIsland integrated energy systemen_UK
dc.subjectDeep reinforcement learningen_UK
dc.subjectMulti-uncertaintiesen_UK
dc.subjectDesalinationen_UK
dc.subjectHydrothermal simultaneous transmissionen_UK
dc.subjectOptimal schedulingen_UK
dc.titleOptimal scheduling of island integrated energy systems considering multi-uncertainties and hydrothermal simultaneous transmission: a deep reinforcement learning approachen_UK
dc.typeArticleen_UK

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