Scenario-adaptive hierarchical optimisation framework for design in hybrid energy storage systems

Abstract

As renewable energy penetration increases, mismatches between generation and demand lead to underutilization of clean energy, particularly in industrial parks with overlapping of building and process loads. To enhance system flexibility and renewable utilization, hybrid energy storage systems integrating electrical, thermal, and cooling storage technologies offer a promising solution. However, a scalable and generalizable design framework for such systems remains lacking. Here, we propose a general and scenario-adaptive design framework for hybrid energy storage systems. The framework encompasses five core stages: demand analysis, energy storage selection, energy system modeling, optimization design, and performance evaluation. We develop a hierarchical optimization method to jointly optimize equipment configuration and operation scheduling through iterative feedback between the two layers, achieving better scalability and robustness than existing collaborative approaches. The proposed framework is systematically evaluated across industrial parks spanning different climate zones and energy demand levels. Results show that the proposed framework significantly enhances energy cost savings (43.7%) and reduces carbon emissions (69.9%). This work provides a practical and transferable pathway for deploying hybrid energy storage systems in carbon-intensive sectors, thereby facilitating the low-carbon transition of industrial sectors.

Affiliated Institutions

• Hunan University CN
• Lawrence Berkeley National Laboratory US
• Hong Kong Polytechnic University HK
• China Academy of Building Research CN
• Hong Kong University of Science and Technology HK
• Shanghai Jiao Tong University CN

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