【目的】在可再生能源大规模并网与电力市场深化改革的背景下，为应对风光出力不确定性、电氢多能互补复杂性及多层级市场协同挑战，构建了多虚拟电厂（multi-virtual power plant，MVPP）与虚拟电厂调度中心（virtual power plant operator，VPPO）协同优化新架构。【方法】首先，采用Wasserstein距离构建风光出力模糊集，结合两阶段分布鲁棒优化模型量化预测误差风险；然后，建立Stackelberg-Nash双层博弈框架，VPPO作为领导者动态制定电能/氢能交易价格与资源分配策略，MVPP作为跟随者优化购售电计划、氢能调度及灵活负荷响应；最后，采用多智能体双延迟深度确定性策略梯度（multi-agent twin delayed deep deterministic policy gradient，MATD3）算法，通过集中训练分散执行机制与双重Critic网络抑制策略偏差，高效求解高维非凸问题。【结果】算例结果表明，所提策略成功实现VPPO跨市场套利收益与MVPP运行成本协同优化，显著提升算法收敛效率与策略稳定性，增强系统应对风光波动的鲁棒性及电氢协同灵活性，同时通过灵活负荷调峰服务优化资源时空分布。【结论】所提方法融合鲁棒优化与博弈理论，有效协调了电氢耦合与多市场交互矛盾，为高比例新能源并网下虚拟电厂集群的经济、鲁棒运行提供了创新解决方案。

[Objective] In the context of large-scale grid integration of renewable energy and deepening reforms in the electricity market， a new collaborative optimization framework consisting of multi-virtual power plants （MVPP） and an aggregate-level virtual power plant operator （VPPO） is established to address the challenges posed by the uncertainty of wind and solar power output， the complexity of electricity-hydrogen energy complementarity， and multi-level market coordination. [Methods] First， the Wasserstein distance is employed to construct a fuzzy set for wind and solar power output， and a two-stage distribution robust optimization model is combined to quantify the risk of prediction errors. Subsequently， a Stackelberg-Nash bi-level game framework is established， where VPPO， as the leader， dynamically formulates strategies for electricity/hydrogen energy trading prices and resource allocation， while MVPP， as the follower， optimizes electricity purchase and sale plans， hydrogen scheduling， and flexible load response. Finally， the multi-agent twin delayed deep deterministic policy gradient （MATD3） algorithm is adopted to efficiently solve high-dimensional non-convex problems through the utilization of a centralized training and decentralized execution mechanism and a dual Critic network to mitigate strategy bias. [Results] The numerical results demonstrate that the proposed strategy successfully optimizes the cross-market arbitrage profits of VPPO and the operational costs of MVPP， significantly enhancing the convergence efficiency and strategy stability of the algorithm. It also bolsters the system's robustness against fluctuations in wind and solar power and the flexibility of electricity-hydrogen coordination， while optimizing the spatiotemporal distribution of resources through flexible peak load shaving services. [Conclusions] By integrating robust optimization and game theory， this study effectively coordinates electricity-hydrogen coupling with multi-market interactions， providing an innovative solution to the economic and robust operation of MVPP under high penetration of integrated renewable energy.