Integrated energy systems with hybrid renewables, battery storage, and electric vehicles: Uncertainty-aware optimization and grid-supportive management

• Uncertainty-aware optimization integrates design and operation for energy systems. • Grid-friendly flexibility indicator with grid-supportive control at design stage. • Integrated energy system with hybrid renewables, battery storage, and electric vehicles. • Vehicle-to-building compensation mechanism for electric vehicle owners. Integrating hybrid renewables, stationary battery storage, and electric vehicles is critical to enhancing the flexibility and sustainability of future energy systems. However, uncertainties in renewable generation, energy demand, and electric vehicle behaviors, coupled with complex grid interactions, pose significant challenges for system design and operation. To address these issues, this study developed an uncertainty-aware optimization framework that embedded grid-supportive control into the design process, incorporated an incentive-compatible vehicle-to-building compensation mechanism reflecting electric vehicle battery degradation, and introduced a grid-friendly flexibility indicator to assess temporal grid interactions. Multi-objective optimization under stochastic scenarios was conducted to identify Pareto-optimal configurations, followed by entropy-weighted multi-criteria decision-making for final selection. Compared with the conventional strategy without grid-supportive operation, the proposed framework achieved a 26.6 % reduction in CO 2 emissions and 47.3 % decrease in operational costs. It also improved the load match ratio and grid-friendly flexibility by 8.5 % and 122.9 %, respectively, with only a 6.8 % decrease in renewable utilization. Additional simulations verified the reliability and effectiveness of the proposed framework under uncertainties, yielding annual averages of 34.8 % grid-friendly flexibility, 84.7 % renewable utilization, 87.6 % load-match ratio, and 6,822.9 kg CO 2 reduction across all stochastic scenario sets. This study advances beyond existing studies by coupling uncertainty-aware design optimization with a grid-supportive control strategy, offering a practical and sustainable pathway for deploying gird-friendly and reliable integrated energy systems with renewables, storage, and electric vehicles in real-world applications.