Two‐stage distributionally robust optimization for the design of hybrid energy‐driven multi‐network processes

The present work focuses on optimizing integrated multi‐network systems that combine hybrid energy supplies, industrial processes, water resources, and carbon reductions. Given the inherent variability of renewable resources and volatility of market pricing and demand, a two‐stage distributionally robust optimization (TSDRO) framework with Wasserstein distance‐based ambiguity sets is proposed. By explicitly considering uncertainties in electricity demand and wind speed, the TSDRO framework simultaneously optimizes long‐term capacity investments and short‐term operational recourse. Case studies reveal that the framework identifies a robust system that prioritizes wind power as the dominant source, leverages photovoltaic and biomass for flexible regulation, and achieves a phased elimination of coal‐fired heating through substitution with biomass technologies. The optimal design not only reduces carbon emissions but also ensures a reliable energy supply while minimizing total cost. These outcomes underscore the distinct advantages of the proposed TSDRO method in balancing economic competitiveness, environmental performance, and operational resilience under uncertainty.