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

Area of Science:

• Energy Systems Engineering
• Renewable Energy Integration
• Sustainable Industrial Processes

Background:

• Increasing renewable energy penetration causes generation-demand mismatches, leading to clean energy underutilization.
• Industrial parks face challenges with overlapping building and process loads, exacerbating renewable energy underutilization.
• Existing solutions for hybrid energy storage systems lack scalability and generalizability.

Purpose of the Study:

• To propose a general and scenario-adaptive design framework for hybrid energy storage systems (HESS).
• To address the lack of scalable and generalizable design frameworks for integrating electrical, thermal, and cooling storage.
• To enhance flexibility and renewable energy utilization in industrial sectors.

Main Methods:

• Developed a five-stage framework: demand analysis, energy storage selection, energy system modeling, optimization design, and performance evaluation.
• Implemented a hierarchical optimization method for joint equipment configuration and operation scheduling.
• Utilized iterative feedback between optimization layers for enhanced scalability and robustness.

Main Results:

• The framework was evaluated across diverse industrial parks, considering different climate zones and energy demand levels.
• Achieved significant energy cost savings of 43.7%.
• Reduced carbon emissions by 69.9%.

Conclusions:

• The proposed framework offers a practical and transferable solution for deploying HESS in carbon-intensive industrial sectors.
• This work facilitates the low-carbon transition of industrial sectors by improving renewable energy utilization.
• The hierarchical optimization approach demonstrates superior scalability and robustness compared to existing methods.