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The lifelong control problem of an off-grid microgrid is composed of twotasks, namely estimation of the condition of the microgrid devices andoperational planning accounting for the uncertainties by forecasting the futureconsumption and the renewable production. The main challenge for the effectivecontrol arises from the various changes that take place over time. In thispaper, we present an open-source reinforcement framework for the modeling of anoff-grid microgrid for rural electrification. The lifelong control problem ofan isolated microgrid is formulated as a Markov Decision Process (MDP). Wecategorize the set of changes that can occur in progressive and abrupt changes.We propose a novel model based reinforcement learning algorithm that is able toaddress both types of changes. In particular the proposed algorithmdemonstrates generalisation properties, transfer capabilities and betterrobustness in case of fast-changing system dynamics. The proposed algorithm iscompared against a rule-based policy and a model predictive controller withlook-ahead. The results show that the trained agent is able to outperform bothbenchmarks in the lifelong setting where the system dynamics are changing overtime.

Lifelong Control of Off-grid Microgrid with Model Based Reinforcement Learning