Comparative analysis of Q-learning, SARSA, and deep Q-network for microgrid energy management
Abstract 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 a...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-83625-8 |
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| author | Sreyas Ramesh Sukanth B N Sri Jaswanth Sathyavarapu Vishwash Sharma Nippun Kumaar A. A. Manju Khanna |
| author_facet | Sreyas Ramesh Sukanth B N Sri Jaswanth Sathyavarapu Vishwash Sharma Nippun Kumaar A. A. Manju Khanna |
| author_sort | Sreyas Ramesh |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-bb71be7507f041f8a398aca079347f2d |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-bb71be7507f041f8a398aca079347f2d2025-01-05T12:19:30ZengNature PortfolioScientific Reports2045-23222025-01-0115111110.1038/s41598-024-83625-8Comparative analysis of Q-learning, SARSA, and deep Q-network for microgrid energy managementSreyas Ramesh0Sukanth B N1Sri Jaswanth Sathyavarapu2Vishwash Sharma3Nippun Kumaar A. A.4Manju Khanna5Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa VidyapeethamDepartment of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa VidyapeethamDepartment of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa VidyapeethamDepartment of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa VidyapeethamDepartment of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa VidyapeethamDepartment of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa VidyapeethamAbstract 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.https://doi.org/10.1038/s41598-024-83625-8MicrogridQ-learningSARSADeep Q-networkPyMGridModel predictive control |
| spellingShingle | Sreyas Ramesh Sukanth B N Sri Jaswanth Sathyavarapu Vishwash Sharma Nippun Kumaar A. A. Manju Khanna Comparative analysis of Q-learning, SARSA, and deep Q-network for microgrid energy management Scientific Reports Microgrid Q-learning SARSA Deep Q-network PyMGrid Model predictive control |
| title | Comparative analysis of Q-learning, SARSA, and deep Q-network for microgrid energy management |
| title_full | Comparative analysis of Q-learning, SARSA, and deep Q-network for microgrid energy management |
| title_fullStr | Comparative analysis of Q-learning, SARSA, and deep Q-network for microgrid energy management |
| title_full_unstemmed | Comparative analysis of Q-learning, SARSA, and deep Q-network for microgrid energy management |
| title_short | Comparative analysis of Q-learning, SARSA, and deep Q-network for microgrid energy management |
| title_sort | comparative analysis of q learning sarsa and deep q network for microgrid energy management |
| topic | Microgrid Q-learning SARSA Deep Q-network PyMGrid Model predictive control |
| url | https://doi.org/10.1038/s41598-024-83625-8 |
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