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

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. In this work, a scenario-adaptive hierarchical optimisation framework is developed for the design of hybrid energy storage systems for industrial parks. It improves renewable use, cuts energy costs and carbon emissions, and offers a scalable pathway for the low-carbon energy transition of these parks.