This chapter proposes an energy storage solution controlled by Deep Reinforcement Learning (DRL) to address fluctuating electricity costs in the smart grid (SG). . In an era where energy efficiency and sustainability are paramount, smart grid energy storage systems have emerged as a cornerstone of modern energy infrastructure. These systems are not just about storing energy; they represent a paradigm shift in how energy is managed, distributed, and consumed. The deep Q-network (DQN) method is employed to optimize the capacity configuration and operation strategy of the ESS. In this study, an isolated microgrid on a small island is selected as the research subject. It optimizes electricity trading in a variable tariff setting, yielding consumer savings averaging 20. 91% annually without altering consumption habits.
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