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Investment-based optimisation of energy storage design parameters in a grid-connected hybrid renewable energy system

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  • Farah, Sleiman
  • Andresen, Gorm Bruun

Abstract

Grid-connected hybrid renewable power systems with energy storage can reduce the intermittency of renewable power supply. However, emerging energy storage technologies need improvement to compete with lithium-ion batteries and reduce the cost of energy. Identifying and optimising the most valuable improvement path of these technologies is challenging due to the non-linearity of the energy system model when considering parameters as independent variables. To overcome this challenge, a novel investment-based optimisation method is proposed. The method involves linear optimisation of the hybrid renewable energy system and subsequent investment optimisation, accounting for diminishing improvements per investment. The results from applying the investment-based optimisation to thermal energy, pumped thermal energy, molten salt, and adiabatic compressed air energy storage technologies, show that improving the discharge efficiency is the most valuable for all technologies. The second most important parameters are the costs of discharge capacity and energy storage capacity, and the least important parameters are the charge capacity cost and charge efficiency. The study provides detailed improvement pathways for each technology under various operational conditions, assisting developers in resource allocation. Overall, the investment-based optimisation method and findings contribute to enhancing the competitiveness of emerging energy storage technologies and reducing reliance on batteries in renewable energy systems.

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  • Farah, Sleiman & Andresen, Gorm Bruun, 2024. "Investment-based optimisation of energy storage design parameters in a grid-connected hybrid renewable energy system," Applied Energy, Elsevier, vol. 355(C).
    • Handle: RePEc:eee:appene:v:355:y:2024:i:c:s0306261923017488
    DOI: 10.1016/j.apenergy.2023.122384
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    References listed on IDEAS

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    Cited by:

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    2. Jian Zhang & Jianzhou Wen & Zhen Lu & Jiang Qian & Ning Wei, 2025. "Investment Allocation Method for Distribution Networks Based on a Panel Data Model and an Incentive–Penalty Mechanism," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 27(69), pages 656-656, April.
    3. Li, Ling-Ling & Ji, Bing-Xiang & Li, Zhong-Tao & Lim, Ming K. & Sethanan, Kanchana & Tseng, Ming-Lang, 2025. "Microgrid energy management system with degradation cost and carbon trading mechanism: A multi-objective artificial hummingbird algorithm," Applied Energy, Elsevier, vol. 378(PA).
    4. Fang, Juan & Yang, Miaomiao & Sui, Junpeng & Luo, Tengqi & Yu, Yinsheng & Ao, Yunjin & Dou, Ruifeng & Zhou, Wenning & Li, Wei & Liu, Xunliang & Zhao, Kai, 2024. "Enhancing solar-powered hydrogen production efficiency by spectral beam splitting and integrated chemical energy storage," Applied Energy, Elsevier, vol. 372(C).

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