Abstract
Modern power and energy systems are becoming more complicated and uncertain as distributed energy resources (DERs), flexible loads, and other developing technologies become more integrated. This brings great challenges to the operation and control. Furthermore, the deployment of modern sensor and smart metres generates a considerable amount of data, which opens the door to fresh data-driven ways for dealing with complex operation and control difficulties. One of the most commonly touted strategies for control and optimization problems is reinforcement learning (RL). Designing a fuzzy Q-learning power energy system using RL technique will control and reduce the problems arranging in the energy system.
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