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A review on pump‐hydro storage for renewable and hybrid energy systems applications
Author(s) -
Das Pronob,
Das Barun K.,
Mustafi Nirendra N.,
Sakir Md. Takmil
Publication year - 2021
Publication title -
energy storage
Language(s) - English
Resource type - Journals
ISSN - 2578-4862
DOI - 10.1002/est2.223
Subject(s) - renewable energy , energy storage , particle swarm optimization , computer science , reliability (semiconductor) , reliability engineering , grid , metaheuristic , environmental economics , systems engineering , process engineering , risk analysis (engineering) , engineering , business , electrical engineering , power (physics) , physics , geometry , mathematics , quantum mechanics , machine learning , artificial intelligence , economics
The integration of storage technologies into the hybrid energy system (HES) offers significant stability in delivering electricity to a remote community. In addition, the benefits of using storage devices for achieving high renewable energy (RE) contribution to the total energy supply are also paramount. The present study provides a detailed review on the utilization of pump‐hydro storage (PHS) related to the RE‐based stand‐alone and grid‐connected HESs. The PHS‐based HESs have been analyzed considering the technical details, including reliability, cost‐effectiveness, and environmental indicators. The optimization techniques are critically employed to the PHS‐based systems, including metaheuristics intelligent techniques, commercially available optimization and simulation tools, and recent advanced optimization tools. A comparative analysis of PHS integration to the stand‐alone and grid‐connected systems based on techno‐economic and environmental indicators over other storage technologies is also presented. The key technical and environmental challenges related to the PHS‐based HES are identified. Results from the recent research studies indicate that the PHS‐based HESs offer significant cost and environmental benefits over battery storage technologies. The study identifies that the particle swarm optimization is the mostly appreciated optimizing technique for cost‐effective energy supply and environmental aspects followed by hybrid optimization model for electric renewables and genetic algorithm.