This chapter proposes a risk-averse energy optimization and control method for integrated energy service station (IESS) aggregators participating in electricity markets, considering heterogeneous vehicle types and extreme scenario uncertainties. First, a boundedly rational heterogeneous user equilibrium (BRHUE) model is developed to characterize the spatiotemporal energy demand of electric vehicles, hydrogen fuel cell vehicles, natural gas vehicles, and conventional vehicles. Then, a multi-energy coordination model for IESSs is constructed, integrating electricity, hydrogen, and natural gas systems with on-site renewables to provide one-stop refueling services. To manage operational risks under uncertainties in travel demand, renewable generation, and electricity prices, a conditional value-at-risk (CVaR)-based risk-averse optimization approach is introduced. This enables the IESS aggregator to jointly optimize day-ahead bidding and real-time multi-energy dispatch while balancing expected profit and tail-end losses. Case studies validate the effectiveness of the proposed method, showing that the risk-averse strategy reduces extreme-scenario profit loss by 21.8% with only a 0.3% decrease in overall expected profit. The results also highlight the economic potential of synthetic natural gas production and carbon capture, as well as the impact of user bounded rationality on load distribution and system complexity. The proposed framework offers theoretical and practical support for low-carbon, multi-energy-coordinated transportation systems.