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A Concise Review of Energy Management Strategies for Hybrid Energy Storage Systems

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  • Bassey Etim Nyong-Bassey

Abstract

In this work, relevant literature with regards to energy management strategies was reviewed and discussed. The energy management strategies were grouped into forecast/historical, heuristic logic, ANN-fuzzy logic, and reinforcement learning (machine learning) based methods. From the literature, it is clear that energy management strategies are imperative if the optimal operation of hybrid energy storage systems and assets is to adequately counteract uncertainty due to intermittent renewable energy sources. The Reinforcement learning-based algorithm which uses an agent-based approach to optimally control the system offers an optimal solution for energy management.

Suggested Citation

  • Bassey Etim Nyong-Bassey, 2022. "A Concise Review of Energy Management Strategies for Hybrid Energy Storage Systems," European Journal of Engineering and Technology Research, European Open Science, vol. 7(3), pages 77-81, May.
    • Handle: RePEc:epw:ejeng0:v:7:y:2022:i:3:id:62815
    DOI: 10.24018/ejeng.2022.7.3.2815
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    References listed on IDEAS

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    1. Nyong-Bassey, Bassey Etim & Giaouris, Damian & Patsios, Charalampos & Papadopoulou, Simira & Papadopoulos, Athanasios I. & Walker, Sara & Voutetakis, Spyros & Seferlis, Panos & Gadoue, Shady, 2020. "Reinforcement learning based adaptive power pinch analysis for energy management of stand-alone hybrid energy storage systems considering uncertainty," Energy, Elsevier, vol. 193(C).
    2. Cai, Mengting & Huang, Guohe & Chen, Jiapei & Li, Yunhuan & Fan, Yurui, 2018. "A generalized fuzzy chance-constrained energy systems planning model for Guangzhou, China," Energy, Elsevier, vol. 165(PA), pages 191-204.
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