基于多源不确定性与参数在线校正的虚拟电厂多时间尺度自适应调度方法OA

To address the issues of dispatch failure and economic losses caused by multi-source uncertainties—including the randomness of wind and solar power generation,load fluctuations,and parameter deviations—during the aggregation of distributed energy resources in virtual power plants(VPP),this paper proposes a multi-time scale adaptive dispatching framework embedded with multi-source uncertainty modeling and an online parameter correction mechanism.Based on two-stage robust optimization and an improved quantum genetic algorithm(QGA),a pre-dispatch scheme is generated via robust optimization during the day-ahead stage.During the intraday stage,a state feedback mechanism is introduced to rolling-correct key parameters using the improved QGA,thereby establishing a closed-loop dispatching structure.Simulation results demonstrate that under significant prediction deviations in wind/solar generation and electric/thermal loads,the actual operational revenue of the proposed method increases by approximately 3.2%compared to traditional deterministic dispatching.Furthermore,the online parameter correction strategy significantly reduces the system balancing cost in most periods,with a reduction margin approaching 90%.The proposed method effectively coordinates the robustness,economics,and adaptability of the dispatching scheme,providing a technical pathway for the secure and economic operation of VPP in highly uncertain environments.