Abstract <p>This paper proposes a bidding optimization strategy for virtual power plants (VPPs) with integrated wind–solar–storage systems, addressing the dual uncertainties of renewable energy output and electricity prices during market bidding, by combining information gap decision theory (IGDT) and distributionally robust optimization (DRO). The model seeks to optimize the revenue of the VPP operator by orchestrating involvement in the energy, peak regulation, and carbon trading markets, thus establishing a multi-timescale resilient bidding framework. The methodology adaptively modifies the tolerance for prediction discrepancies in renewable energy production using IGDT and delineates the uncertainty set of electricity pricing utilizing extensive norm restrictions, thereby efficiently reconciling economic efficiency with robustness. Case studies illustrate that the proposed model substantially reduces hazards. In a situation of concurrent engagement in all three markets, the overall revenue of the VPP rises by 48.98% relative to the single electricity market scenario, while the reduction in system reliability is constrained to 1.6%. The study confirms the efficacy of the IGDT–DRO method in managing intricate uncertainties and establishes a theoretical foundation for the resilient operation of VPPs.</p> Graphical Abstract <p></p>