Bi-Level Capacity Planning of Park Integrated Energy Systems Considering Energy Supply Reliability
This study addresses the capacity planning of park integrated energy systems (IES) with explicit consideration of energy supply reliability. A bi-level optimization framework is proposed: the upper level minimizes total system cost to determine optimal source and storage capacities, while the lower level minimizes operational cost through scheduling. A genetic algorithm solves the coupled problem. The model incorporates wind and photovoltaic generation, energy storage, combined heat and power, gas boilers, and electric chillers. Flexible load control is introduced to reduce peak-valley differences and alleviate peak energy pressure. The methodology is validated using a real urban park case study. Results demonstrate that the proposed approach ensures economic efficiency while satisfying load demand under reliability constraints. The integration of demand-side flexible load scheduling and energy storage reduces operational costs and improves system flexibility. The study contributes a practical planning tool for park-level IES that balances economic and reliability objectives, offering a reference for low-carbon energy system design.