@article{HUO2026, 
author = {Feifan HUO and You LÜ and Helu TIAN and Conglin LIAO},
title = {A Multi-Timescale Adaptive Dispatch Method for Virtual Power Plants Based on Multi-Source Uncertainty and Online Parameter Correction},
year = {2026},
journal = {Distributed Energy},
volume = {11},
number = {3},
pages = {32-44},
keywords = {virtual power plant, multi-source uncertainty, online parameter correction, multi-time scale scheduling, robust optimization, quantum genetic algorithm},
url = {https://www.sciopen.com/article/10.16513/j.2096-2185.DE.25100518},
doi = {10.16513/j.2096-2185.DE.25100518},
abstract = {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.}
}