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Abstract

Summary

This study proposes a two-layer optimization framework for hybrid energy storage systems integrating batteries and hydrogen, aimed at supporting the design of resilient and low-carbon renewable energy communities (RECs). By combining particle swarm optimization for system sizing with rolling-horizon mixed-integer linear programming for operational scheduling, the model effectively addresses the challenges of renewable intermittency. A key feature is the incorporation of battery degradation and piecewise affine modeling of component efficiencies, enabling accurate and computationally efficient optimization.

The analysis compares grid-connected and off-grid configurations, demonstrating that hybrid storage enhances flexibility and self-sufficiency. It also evaluates the influence of rolling-horizon lengths, showing that different forecast windows significantly affect both capacity sizing and dispatch strategies. Daily horizons lead to overdimensioned hydrogen systems, while monthly horizons enable more strategic and cost-effective operations.

Moreover, a sensitivity analysis on the optimization objective—cost, emissions, or a trade-off—reveals how design choices shift depending on sustainability priorities. The inclusion of a self-consumption incentive further encourages local renewable use. Overall, results emphasize the importance of integrated planning tools in balancing economic viability, system resilience, and environmental impact, while highlighting the role of time granularity and policy context in shaping optimal solutions for RECs.

© EAGE Publications BV
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/content/papers/10.3997/2214-4609.202521194
2025-10-27
2026-01-22

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References

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/content/papers/10.3997/2214-4609.202521194
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Hybrid Energy Storage for Renewable Integration: Optimizing Hydrogen Systems for Flexible and Resilient Energy Supply
Conference Proceedings 2025, 1 (2025); https://doi.org/10.3997/2214-4609.202521194
/content/papers/10.3997/2214-4609.202521194
/content/papers/10.3997/2214-4609.202521194
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