Smart energy management system framework for population dynamics modelling and suitable energy trajectories identification in islanded micro-grids

In an increasingly electrified and connected world, renewable energy production and robust distribution as well as sobriety paradigm, both for the individual and the society, will most likely play a central role regarding global systems stability. Consequently, while being able to conceive efficient storage systems coupled with robust energy management strategies present significant interests, a number of related studies often consider the human behaviour factor separately. While not decisive in large industrial factories, human demeanor impact cannot be overlooked in residential areas. As such, this work proposes an innovative and flexible dynamic population model, inspired from epidemiological methods, that allows simulation of a vast spectrum of social scenarios. By pairing this formalization with a smart energy management strategy, a complete framework is proposed. In particular, beyond the theoretical identification of sustainable parameters in a wide diversity of configurations, our experiments demonstrate the relevance of reinforcement learning agents as efficient energy management policies. Depending on the scenario, the trained agent enables an increase of the sustainability areas over baseline strategies up to 200%, thus hinting at ultimately softer societal impact.