Green Technologies: Reinforcement Learning for Renewable Energy Management

Abstract

The increasing integration of renewable energy sources within modern power systems presents complex challenges in balancing generation, storage and demand due to their inherent intermittency and uncertainty. Reinforcement Learning (RL), as an adaptive and data-driven optimization framework, has emerged as a promising approach for autonomous and sustainable energy management. This paper explores the design and implementation of RL-based strategies for optimizing energy flows in smart grids, microgrids and building energy systems. By employing deep and multi-agent RL architecture, the proposed framework enables real-time decision-making for demand response, distributed generation scheduling and battery storage optimization. The study demonstrates that RL agents can learn dynamic control policies that minimize operational costs, reduce carbon emissions and enhance grid resilience without requiring explicit system models. Comparative evaluations against traditional rule-based and predictive control methods show superior adaptability and energy efficiency. Furthermore, the paper discusses algorithmic advancements, including policy gradient methods and actor–critic architectures, that facilitate stable convergence in complex renewable environments. Overall, reinforcement learning provides a scalable pathway toward intelligent, self-optimizing and sustainable energy systems capable of driving the next generation of green technologies.