Scheduling Strategy for Virtual Power Plants Based on Electricity-Carbon Coupling and Multi-Time-Scale Distributionally Robust Optimization

To address the coordinated challenge of economy, low-carbon performance, and robustness in the scheduling of virtual power plants (VPPs) under high-proportion renewable energy grid integration, this paper proposes a VPP optimization scheduling strategy based on electricity-carbon coupling and multi-time-scale distributionally robust optimization. Firstly, a joint electricity-carbon market framework integrating stepwise carbon trading and green certificate trading is constructed, which stimulates the low-carbon scheduling potential of VPPs through price signal linkage. Secondly, a three-level day-ahead-intraday-real-time optimization architecture is designed, with the core of formulating global plans, correcting prediction errors, and eliminating instantaneous deviations respectively, to achieve accurate scheduling across different time scales. Finally, in response to the uncertainties of wind and solar power output and market prices, a distributionally robust optimization method is adopted to build a Wasserstein ambiguity set model, which dynamically adjusts robust parameters to balance risks and benefits. The case study results show that the total cost of the proposed strategy is reduced to 12,300 yuan per day, a decrease of 32.4% compared with the traditional strategy; the renewable energy consumption rate reaches 99.5%, the carbon cost-benefit ratio is increased to 1.8, and the robustness compliance rate is as high as 98.2%. At the same time, the energy storage charge-discharge efficiency and the optimization effect of market revenue structure are significant. This strategy achieves an all-round improvement in economy, low-carbon performance, robustness, and energy utilization efficiency, providing an effective technical path for the optimal operation of VPPs in a multi-market environment.