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

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  • Totaro, Simone
  • Boukas, Ioannis
  • Jonsson, Anders
  • Cornélusse, Bertrand

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.

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  • Totaro, Simone & Boukas, Ioannis & Jonsson, Anders & Cornélusse, Bertrand, 2021. "Lifelong control of off-grid microgrid with model-based reinforcement learning," Energy, Elsevier, vol. 232(C).
    • Handle: RePEc:eee:energy:v:232:y:2021:i:c:s0360544221012834
    DOI: 10.1016/j.energy.2021.121035
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    References listed on IDEAS

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    Cited by:

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    7. Zhou, Yanting & Ma, Zhongjing & Zhang, Jinhui & Zou, Suli, 2022. "Data-driven stochastic energy management of multi energy system using deep reinforcement learning," Energy, Elsevier, vol. 261(PA).

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