This repository contains an implementation of a Deep Reinforcement Learning (DRL) algorithm for managing the energy demand and supply of a microgrid. † †thanks: This work was supported by RBC Borealis through the Let's Solve it program. We propose a novel microgrid model that consists of a wind turbine generator, an energy storage system, a. . Microgrids (MGs) provide a promising solution by enabling localized control over energy generation, storage, and distribution. Specifically, we propose an RL agent that learns. . Abstract—The accessibility of real-time operational data along with breakthroughs in processing power have promoted the use of Machine Learning (ML) applications in current power systems. tailored for remote communities.
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[pdf] This paper aims to provide a comprehensive analysis of recent research on microgrid hierarchical control, specifically focusing on the control schemes and the application of machine learning (ML) techniques. . High penetration of Renewable Energy Resources (RESs) introduces numerous challenges into the Microgrids (MG), such as supply–demand imbalance, non-linear loads, voltage instability, etc. Hence, to address these issues, an effective control system is essential. In the event of disturbances, the microgrid disconnects from the. . A microgrid is a small power generation system composed of distributed power sources, energy storage devices capable of bidirectional transmission, efficient energy conversion equipment, associated loads, and monitoring and protection equipment for the operation [7]. 15 minutes, with the goal of minimizing microgrid's operating costs.
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