Intelligent use of energy is one of the keys to success for an energy transition. With growing use of renewable energy sources and growing tariffs for electricity production, the need for optimization in small scale grids is omnipresent. This paper evaluates two approaches i.e., Reinforcement Learning and mathematical optimization to optimize the usage and production of energy in small communities. We compare and evaluate the strengths and weaknesses of both models in the context of energy flow optimization. For this purpose, both approaches are used to optimize the energy flows in a small grid with single-family households, that receive electricity from battery storage, photovoltaic (PV), and the public electricity grid. The main objective is to reduce the cost of electricity while considering local grid restrictions. The results show each model has its own strengths and weaknesses and further improvements and investigations in both models are needed.
Naqvi et al. (Mon,) studied this question.
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