Dual-Layer Q-Learning Strategy for Energy Management of Battery Storage in Grid-Connected Microgrids

Ali, Khurshid; Abusara, Mohammad; Tahir, Asif Ali; Das, Saptarshi

doi:10.3390/en16031334

Cited by 3 publications

(1 citation statement)

References 23 publications

(36 reference statements)

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“…Maintenance and Optimization: The solar-hydrogen storage system is maintained and optimized on a regular basis to guarantee its efficiency and dependability. To enhance system performance over time, this may entail checking storage tanks, cleaning solar panels, examining electrolyzer components, and upgrading control algorithms [151][152][153].…”

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confidence: 99%

Solar–Hydrogen Storage System: Architecture and Integration Design of University Energy Management Systems

Joshua,

Yeon,

Park

et al. 2024

Applied Sciences

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As a case study on sustainable energy use in educational institutions, this study examines the design and integration of a solar–hydrogen storage system within the energy management framework of Kangwon National University’s Samcheok Campus. This paper provides an extensive analysis of the architecture and integrated design of such a system, which is necessary given the increasing focus on renewable energy sources and the requirement for effective energy management. This study starts with a survey of the literature on hydrogen storage techniques, solar energy storage technologies, and current university energy management systems. In order to pinpoint areas in need of improvement and chances for progress, it also looks at earlier research on solar–hydrogen storage systems. This study’s methodology describes the system architecture, which includes fuel cell integration, electrolysis for hydrogen production, solar energy harvesting, hydrogen storage, and an energy management system customized for the needs of the university. This research explores the energy consumption characteristics of the Samcheok Campus of Kangwon National University and provides recommendations for the scalability and scale of the suggested system by designing three architecture systems of microgrids with EMS Optimization for solar–hydrogen, hybrid solar–hydrogen, and energy storage. To guarantee effective and safe functioning, control strategies and safety considerations are also covered. Prototype creation, testing, and validation are all part of the implementation process, which ends with a thorough case study of the solar–hydrogen storage system’s integration into the university’s energy grid. The effectiveness of the system, its effect on campus energy consumption patterns, its financial sustainability, and comparisons with conventional energy management systems are all assessed in the findings and discussion section. Problems that arise during implementation are addressed along with suggested fixes, and directions for further research—such as scalability issues and technology developments—are indicated. This study sheds important light on the viability and efficiency of solar–hydrogen storage systems in academic environments, particularly with regard to accomplishing sustainable energy objectives.

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confidence: 99%

Solar–Hydrogen Storage System: Architecture and Integration Design of University Energy Management Systems

Joshua,

Yeon,

Park

et al. 2024

Applied Sciences

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Application of Improved Reinforcement Learning Technology for Real Time Operation and Scheduling Optimization of Virtual Power Plant

Chao,

Ying,

2023

2023 IEEE Sustainable Power and Energy Conference (iSPEC)

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Optimal Electric Vehicle Battery Management Using Q-learning for Sustainability

Suanpang,

Jamjuntr

2024

Sustainability

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This paper presents a comprehensive study on the optimization of electric vehicle (EV) battery management using Q-learning, a powerful reinforcement learning technique. As the demand for electric vehicles continues to grow, there is an increasing need for efficient battery-management strategies to extend battery life, enhance performance, and minimize operating costs. The primary objective of this research is to develop and assess a Q-learning-based approach to address the intricate challenges associated with EV battery management. This paper starts by elucidating the key challenges inherent in EV battery management and discusses the potential advantages of incorporating Q-learning into the optimization process. Leveraging Q-learning’s capacity to make dynamic decisions based on past experiences, we introduce a framework that considers state-of-charge, state-of-health, charging infrastructure, and driving patterns as critical state variables. The methodology is detailed, encompassing the selection of state, action, reward, and policy, with the training process informed by real-world data. Our experimental results underscore the efficacy of the Q-learning approach in optimizing battery management. Through the utilization of Q-learning, we achieve substantial enhancements in battery performance, energy efficiency, and overall EV sustainability. A comparative analysis with traditional battery-management strategies is presented to highlight the superior performance of our approach. A comparative analysis with traditional battery-management strategies is presented to highlight the superior performance of our approach, demonstrating compelling results. Our Q-learning-based method achieves a significant 15% improvement in energy efficiency compared to conventional methods, translating into substantial savings in operational costs and reduced environmental impact. Moreover, we observe a remarkable 20% increase in battery lifespan, showcasing the effectiveness of our approach in enhancing long-term sustainability and user satisfaction. This paper significantly enriches the body of knowledge on EV battery management by introducing an innovative, data-driven approach. It provides a comprehensive comparative analysis and applies novel methodologies for practical implementation. The implications of this research extend beyond the academic sphere to practical applications, fostering the broader adoption of electric vehicles and contributing to a reduction in environmental impact while enhancing user satisfaction.

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Dual-Layer Q-Learning Strategy for Energy Management of Battery Storage in Grid-Connected Microgrids

Cited by 3 publications

References 23 publications

Solar–Hydrogen Storage System: Architecture and Integration Design of University Energy Management Systems

Solar–Hydrogen Storage System: Architecture and Integration Design of University Energy Management Systems

Application of Improved Reinforcement Learning Technology for Real Time Operation and Scheduling Optimization of Virtual Power Plant

Optimal Electric Vehicle Battery Management Using Q-learning for Sustainability

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