Optimized hybrid hydrogen-battery storage planning for Island microgrids: A TSA-THC approach for addressing multi-time-scale imbalances

Qingzhu Zhang, Yunfei Mu, Hongjie Jia, Xiaodan Yu and Kai Hou

Applied Energy, 2025, vol. 398, issue C, No S0306261925011353

Abstract: The high volatility of renewable energy presents significant challenges for electricity balancing in off-grid island microgrids (OGIM) across multiple time scales. Hybrid hydrogen-battery storage (HHBS) offers an effective solution to mitigate electricity imbalances over various time horizons. However, planning HHBS typically requires year-round operational considerations, leading to substantial computational complexity due to the large number of variables. To address this challenge, a novel planning method that integrates time series aggregation (TSA) and time horizon compression (THC) is proposed to optimize computational efficiency without compromising planning accuracy. This method preserves the long operational cycle characteristics of hydrogen storage (HS) while minimizing battery-related variables, thus ensuring a balance between computational feasibility and accuracy. The THC method reduces the battery operation time scale to increase computational efficiency, whereas the TSA guides the operation sequence, ensuring precise battery planning. An HHBS planning model is developed to co-optimize HHBS capacity across different time scales, minimizing combined costs, including investment, operation, maintenance, curtailment, load shedding, and fuel costs. Source-load uncertainty on islands is modelled using intervals, and the uncertain planning model is converted into a deterministic model via the interval optimization (IO) method. Case studies on the OGIM in the South China Sea validate the effectiveness of the proposed method, reducing the computational time by 50.33 % and limiting the HHBS capacity error to no more than 0.87 % compared with the year-round time scale method.

Keywords: Off-grid island microgrid (OGIM); Hybrid hydrogen-battery storage; Time series aggregation (TSA); Time horizon compression (THC); Interval optimization (IO) (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.apenergy.2025.126405

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