Optimal Scheduling of Flexible Resources Under Uncertainty In Multi-Energy Systems

With the increasing penetration of renewable energy and the rising demand for decarbonization, multi-energy systems integrating electricity, heat, and transportation sectors have attracted significant attention. However, the intermittency and uncertainty of renewable generation in integrated energy systems (IES) makes it difficult to ensure cost-effective and coordinated operation across different energy carriers, often leading to suboptimal use of system flexibility and increased operational risk. This paper proposes a robust optimization framework for integrated electric and thermal energy systems with vehicle-togrid (V2G) enabled electric vehicles. This approach employs a linearized model for the distribution network and a constant flow variable temperature (CFVT) methodology for the heating system to balance accuracy and computational tractability. A two-stage formulation captures day-ahead EV charging commitments and real-time adaptive dispatch against wind generation uncertainty. An exact column-and-constraint generation algorithm ensures solution scalability and worst-case feasibility. Simulations on an IEEE 15-bus network and a 5-node district heating system illustrate the cost–robustness trade-off achieved by leveraging EV and thermal flexibility under the proposed robust scheduling approach, compared with stochastic scheduling.