 Introducing reinforcement learning to the energy system design processA.T.D. Perera, P.U. Wickramasinghe, Vahid M. Nik and Jean-Louis Scartezzini Applied Energy, 2020, vol. 262, issue C, No S0306261920300921 Abstract: Design optimization of distributed energy systems has become an interest of a wider group of researchers due the capability of these systems to integrate non-dispatchable renewable energy technologies such as solar PV and wind. White box models, using linear and mixed integer linear programing techniques, are often used in their design. However, the increased complexity of energy flow (especially due to cyber-physical interactions) and uncertainties challenge the application of white box models. This is where data driven methodologies become effective, as they demonstrate higher flexibility to adapt to different environments, which enables their use for energy planning at regional and national scale. Keywords: Distributed energy systems; Energy hubs; Reinforcement learning; Optimization; Data driven models; Machine learning (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:262:y:2020:i:c:s0306261920300921 Ordering information: This journal article can be ordered from DOI: 10.1016/j.apenergy.2020.114580 Access Statistics for this article Applied Energy is currently edited by J. Yan More articles in Applied Energy from Elsevier |
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