Geographic information system-based closed-loop co-optimization of site-capacity-operation for multi-energy complementary bases

The efficient planning of multi-energy complementary bases necessitates coordinated decision-making across geographical siting, capacity allocation, and dispatch. Prevailing approaches often treat geospatial suitability as a static, upfront constraint, lacking a closed-loop mechanism to dynamically refine siting and capacity decisions based on operational feedback. To bridge this gap, this study proposes a geographic information system-based closed-loop co-optimization framework. The framework seamlessly integrates high-resolution spatial analysis with system-level techno-economic optimization. Specifically, geographic information system-derived multi-criteria site evaluations are quantified and directly embedded as weighting factors in the capacity allocation model, ensuring optimal spatial resource distribution. Subsequently, a hybrid energy storage model co-optimizes net present value and loss of power supply probability. Crucially, the actual loss of power supply probability obtained from the hourly operational simulation is fed back to iteratively adjust the upper-layer capacity configuration, establishing a robust planning-operation feedback loop. A case study of a wind-solar-hydro-coal-storage system in Bortala, China, shows that the framework achieves a 4.58% higher net present value and a 42.27% lower initial investment than conventional open-loop planning, while ensuring 100% renewable accommodation and a 1.64% loss of power supply probability. This work highlights the indispensable role of deeply coupling dynamic operational feedback with granular spatial intelligence for designing high-performance multi-energy systems.