Robust Optimization of Hospital Regional Integrated Energy Systems Based on Multi-Scenario Weight Scanning

Regional Integrated Energy Systems (RIESs) are pivotal in the low-carbon transition of energy-intensive hospital campuses. However, traditional multi-objective optimization for RIES planning often suffers from the subjective selection of weights, leading to configurations that lack robustness against varying decision-maker preferences. To address this, this paper proposes a robust optimization methodology integrating shadow cost theory and multi-scenario weight scanning. A high-fidelity dynamic simulation model of a hospital in Beijing was constructed using TRNSYS. By monetizing environmental externalities into shadow costs, a comprehensive objective function, including annual cost savings rate, primary energy savings rate, and environmental shadow cost savings rate, was established, and the Hooke–Jeeves algorithm was employed to scan ten distinct weight scenarios, ranging from profit-driven to eco-centric preferences. The results reveal that solar collectors lack economic competitiveness under current boundary conditions due to cooling–heating coupling constraints. Instead, a configuration featuring a large-capacity gas turbine (2790 kW) coupled with a moderate GSHP was identified as the optimal solution in over 80% of the scenarios, demonstrating high preference robustness. Crucially, this configuration achieves net-negative emissions by maximizing clean power exports to displace carbon-intensive grid electricity. Compared to the reference system, the optimized RIES reduces primary energy consumption by 82.7% and achieves substantial environmental benefits, subject to grid emission factors. These findings confirm that prioritizing clean power export is a resilient pathway for hospitals to balance economic feasibility with environmental goals under current policy frameworks, providing scientific guidance for policymakers and engineers.