Lifelong control of off-grid microgrid with model-based reinforcement learning

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• dc.contributor.author Totaro, Simone
• dc.contributor.author Boukas, Ioannis
• dc.contributor.author Jonsson, Anders, 1973-
• dc.contributor.author Cornélusse, Bertrand
• dc.date.accessioned 2023-03-01T07:23:40Z
• dc.date.issued 2021
• dc.description.abstract Off-grid microgrids are receiving a growing interest for rural electrification purposes in developing countries due to their ability to ensure affordable, sustainable and reliable energy services. Off-grid microgrids rely on renewable energy sources (RES) coupled with storage systems to supply the electrical consumption. The inherent uncertainty introduced by RES as well as the stochastic nature of the electrical demand in rural contexts pose significant challenges to the efficient control of off-grid microgrids throughout their entire life span. In this paper, we address the lifelong control problem of an isolated microgrid. We categorize the set of changes that may occur over its life span in progressive and abrupt changes. We propose a novel model-based reinforcement learning algorithm that is able to address both types of changes. In particular, the proposed algorithm demonstrates generalisation properties, transfer capabilities and better robustness in case of fast-changing system dynamics. The proposed algorithm is compared against a rule-based policy and a model predictive controller with look-ahead. The results show that the trained agent is able to outperform both benchmarks in the lifelong setting where the system dynamics are changing over time.
• dc.description.sponsorship This research is carried out in the framework of the project Dynamically Evolving Long-Term Autonomy (DELTA), a European research project funded under the CHIST-ERA scheme (http://www.chistera.eu/). Anders Jonsson is partially supported by the grants TIN2015-67959 and PCIN-2017-082 of the Spanish Ministry of Science. The authors would like to thank Sergio Balderrama for the provision of measured data from the “El Espino” microgrid in Bolivia and Alessandro Davide Ialongo for the fruitful discussion.
• dc.format.mimetype application/pdf
• dc.identifier.citation Totaro S, Boukas I, Jonsson A, Cornélusse B. Lifelong control of off-grid microgrid with model-based reinforcement learning. Energy. 2021;232:121035. DOI: 10.1016/j.energy.2021.121035
• dc.identifier.doi http://dx.doi.org/10.1016/j.energy.2021.121035
• dc.identifier.issn 0360-5442
• dc.identifier.uri http://hdl.handle.net/10230/55975
• dc.language.iso eng
• dc.publisher Elsevier
• dc.relation.ispartof Energy. 2021;232:121035.
• dc.relation.projectID info:eu-repo/grantAgreement/ES/1PE/TIN2015-67959
• dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/PCIN-2017-082
• dc.rights © Elsevier http://dx.doi.org/10.1016/j.energy.2021.121035
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.subject.keyword Microgrid control
• dc.subject.keyword Optimization
• dc.subject.keyword Reinforcement learning
• dc.title Lifelong control of off-grid microgrid with model-based reinforcement learning
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/acceptedVersion