A Climate-Driven Generative Scenario Framework for Optimizing Integrated Energy Systems with Hybrid Storage Solutions

Abstract As renewable energy sources (RES), particularly wind and solar, become increasingly integral to the urban energy mix, optimizing integrated energy systems (IES) is essential to ensuring sustainability and resilience. The inherent variability and intermittency of RES pose significant challenges for urban energy planning, demanding robust methods to simulate local climate patterns and support reliable energy storage strategies. This study presents a generative model based on adversarial learning to produce realistic wind and solar generation scenarios. Embedded within a Monte Carlo-based optimization framework, the model facilitates climate-responsive planning of urban IES. A case study conducted in a high-RES potential region demonstrates the model's effectiveness in supporting informed system design and operational strategies. Results reveal the complementary roles of battery energy storage systems (BESS) and hydrogen storage in addressing short-term variability and longterm balancing needs. Notably, annual renewable generation can deviate by over 40.5% under extreme climate conditions, highlighting the necessity of hybrid storage strategies. The proposed approach significantly enhances system reliability and economic viability under diverse climate scenarios. Key contributions include: (1) the development of a climate-informed generative model for RES scenarios,(2) its integration into a optimization framework for IES, and (3) a hybrid storage strategy that reinforces the resilience and sustainability of urban energy systems. These findings offer practical insights for advancing low-carbon, climate-adaptive urban infrastructure in the transition toward sustainable cities.