To address the deep spatiotemporal coupling of source-load dual uncertainties attributed to the high penetration of distributed photovoltaics (PVs) and electric vehicles (EVs) into distribution grids, and the difficulty of analyzing composite disturbances using traditional methods, this paper proposes a voltage quality analysis method that considers spatiotemporal coupling of source-load uncertainty, focusing on steady-state voltage deviation and three-phase imbalance problems. First, a probabilistic model of PV generation is constructed using the beta distribution combined with Monte Carlo-based scenario reduction, and high-precision forecasting of EV charging loads is achieved by an attention-based convolutional neural network and long short-term memory network. Second, multi-scenario spatiotemporal power flow calculations are conducted on the distribution network to analyze the complementary effects of voltage deviation and three-phase imbalance under the hybrid integration of PV and EV. Finally, gray wolf optimization-based variational mode decomposition is introduced to adaptively decompose the source-load power. This reveals the intrinsic mechanisms where low-frequency components dominate the fundamental amplitude variations of bus voltages, while high-frequency components exert a significant impact on voltage quality. Simulation results demonstrate that the proposed method can effectively analyze the spatiotemporal coupling of source-load uncertainties, providing technical support for the comprehensive management of voltage quality in distribution networks.
Zhang et al. (Tue,) studied this question.