<ul><li>The study compares traditional and reinforcement learning methods for energy storage control.</li><li>The comparison is based on a simplified micro-grid model with load component, photovoltaic source, and storage device.</li><li>Three use cases of increasing complexity are examined: ideal storage with convex cost functions, lossy storage devices, and lossy storage devices with convex transmission losses.</li><li>The research aims to promote the principled use of RL methods in energy storage management and provides detailed formulations of each use case along with optimization challenges.</li></ul>

Comparing Traditional and Reinforcement-Learning Methods for Energy Storage Control

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