Optimizing Energy Storage Systems Using Deep Reinforcement Learning in Smart Grids
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With the progress of IT technology, it has become possible to reduce consumer costs in the power market byusing energy storage systems (ESS) in smart grids. Traditional algorithms proposed to solve optimization ofESS problems are difficult to apply to dynamic situations, hence adaptable and relatively simple designs suchas deep reinforcement learning (DRL) techniques have begun to be used instead. In this study, a Markovdecision process is designed to determine the charging and discharging amounts within a certain range to extendthe lifespan of the ESS. Furthermore, DRL techniques such as deep deterministic policy gradient (DDPG), twindelayed deep deterministic policy gradient (TD3), and soft actor-critic (SAC) were trained, and their performanceswere compared for analysis.
Abstract
With the progress of IT technology, it has become possible to reduce consumer costs in the power market byusing energy storage systems (ESS) in smart grids. Traditional algorithms proposed to solve optimization ofESS problems are difficult to apply to dynamic situations, hence adaptable and relatively simple designs suchas deep reinforcement learning (DRL) techniques have begun to be used instead. In this study, a Markovdecision process is designed to determine the charging and discharging amounts within a certain range to extendthe lifespan of the ESS. Furthermore, DRL techniques such as deep deterministic policy gradient (DDPG), twindelayed deep deterministic policy gradient (TD3), and soft actor-critic (SAC) were trained, and their performanceswere compared for analysis.