On the Verification of Deep Reinforcement Learning Solution for Intelligent Operation of Distribution Grids

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dc.contributor.authorHosseini, Mohammad Mehdi
dc.contributor.authorParvania, Masood
dc.date.accessioned2021-12-24T17:49:59Z
dc.date.available2021-12-24T17:49:59Z
dc.date.issued2022-01-04
dc.description.abstractCapabilities of deep reinforcement learning (DRL) in obtaining fast decision policies in high dimensional and stochastic environments have led to its extensive use in operational research, including the operation of distribution grids with high penetration of distributed energy resources (DER). However, the feasibility and robustness of DRL solutions are not guaranteed for the system operator, and hence, those solutions may be of limited practical value. This paper proposes an analytical method to find feasibility ellipsoids that represent the range of multi-dimensional system states in which the DRL solution is guaranteed to be feasible. Empirical studies and stochastic sampling determine the ratio of the discovered to the actual feasible space as a function of the sample size. In addition, the performance of logarithmic, linear, and exponential penalization of infeasibility during the DRL training are studied and compared in order to reduce the number of infeasible solutions.
dc.format.extent9 pages
dc.identifier.doi10.24251/HICSS.2022.426
dc.identifier.isbn978-0-9981331-5-7
dc.identifier.urihttp://hdl.handle.net/10125/79761
dc.language.isoeng
dc.relation.ispartofProceedings of the 55th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectMonitoring, Control, and Protection
dc.subjectdeep reinforcement learning
dc.subjectdistributed energy resources
dc.subjectpower distribution system
dc.subjectsolution verification
dc.titleOn the Verification of Deep Reinforcement Learning Solution for Intelligent Operation of Distribution Grids
dc.type.dcmitext

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