Two-stage distributionally robust bidding strategy of virtual power plants participating in electricity markets considering WT-PV complementarity and multiple uncertainties

To address the challenges of multi-uncertainties and multi-market coordination faced by VPP under high renewable energy penetration, this paper proposes an Internal and External Coordinated Two-Stage Distributionally Robust Optimization model (TSDRO-IE). This study first elucidates the internal and external coordinated bidding process for VPP participation in multi-market environments. Building on this framework, the TSDRO-IE model is established by integrating Information Gap Decision Theory with Distributionally Robust Optimization (IDRO). In the first stage, under deterministic electricity price forecasting conditions, an optimal baseline strategy is generated by combining a hierarchical uncertainty handling method with a coordinated structure that couples inner-level multi-market bidding with outer-level resource scheduling. In the second stage, based on the optimal decisions from the first stage and the electricity price fuzzy sets defined by IDRO, the VPP adjusts the baseline strategy to enhance its robustness against market price fluctuations. Finally, a hybrid multi-objective evolutionary strategy fusing an Improved Lattice Genetic Algorithm and an Enhanced Beluga Whale Optimization algorithm is employed in conjunction with the Gurobi solver to optimize the model. Simulation results demonstrate that the proposed framework effectively enhances the VPP’s market competitiveness and economic robustness in multi-uncertainty environments, achieving synergistic gains within the system. Specifically, through the coordinated regulation of ESS and Loads, the average deviation penalty cost is reduced by 2477 yuan, and the level of renewable energy accommodation is significantly improved. Furthermore, compared with traditional models, the proposed method effectively enhances power supply reliability, reducing the average load factor by 8.55%, and ensures the system’s revenue floor under worst-case scenarios.