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] In modern power systems integrating renewable energy sources like solar PV and wind, ensuring high-quality power delivery is essential. This article addresses the challenge of enhancing power quality in Hybrid Sustainable Energy Systems connected to the. . The landscape of grid-connected photovoltaic (PV) power systems is evolving rapidly, driven by technological advances, policy shifts, and increasing demand for renewable energy. As the sector matures, choosing the right vendor becomes crucial for project success and long-term sustainability. With. . While renewable energy systems are capable of powering houses and small businesses without any connection to the electricity grid, many people prefer the advantages that grid-connection offers.
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