Ramesh Sreyas, N Sukanth B, Sathyavarapu Sri Jaswanth, Sharma Vishwash, A A Nippun Kumaar, Khanna Manju
Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Bengaluru, India.
Sci Rep. 2025 Jan 3;15(1):694. doi: 10.1038/s41598-024-83625-8.
The growing integration of renewable energy sources within microgrids necessitates innovative approaches to optimize energy management. While microgrids offer advantages in energy distribution, reliability, efficiency, and sustainability, the variable nature of renewable energy generation and fluctuating demand pose significant challenges for optimizing energy flow. This research presents a novel application of Reinforcement Learning (RL) algorithms-specifically Q-Learning, SARSA, and Deep Q-Network (DQN)-for optimal energy management in microgrids. Utilizing the PyMGrid simulation framework, this study not only develops intelligent control strategies but also integrates advanced mathematical control techniques, such as Model Predictive Control (MPC) and Kalman filters, within the Markov Decision Process (MDP) framework. The innovative aspect of this research lies in its comparative analysis of these RL algorithms, demonstrating that DQN outperforms Q-Learning and SARSA by 12% and 30%, respectively, while achieving a remarkable 92% improvement over scenarios without an RL agent. This study addresses the unique challenges of energy management in microgrids and provides practical insights into the application of RL techniques, thereby contributing to the advancement of sustainable energy solutions.
可再生能源在微电网中的日益融合，需要创新方法来优化能源管理。虽然微电网在能源分配、可靠性、效率和可持续性方面具有优势，但可再生能源发电的多变性和需求的波动给优化能源流动带来了重大挑战。本研究提出了强化学习（RL）算法的一种新应用，即具体的Q学习、SARSA和深度Q网络（DQN），用于微电网中的最优能源管理。利用PyMGrid仿真框架，本研究不仅开发了智能控制策略，还在马尔可夫决策过程（MDP）框架内集成了先进的数学控制技术，如模型预测控制（MPC）和卡尔曼滤波器。本研究的创新之处在于对这些RL算法进行了比较分析，结果表明DQN的性能分别比Q学习和SARSA高出12%和30%，同时与没有RL智能体的情况相比有显著的92%的提升。本研究解决了微电网能源管理的独特挑战，并为RL技术的应用提供了实际见解，从而有助于推进可持续能源解决方案。
