Multiple Distributed PVs Participating in Active Power Support Under Resource Aggregation and Data Communication Congestion

To achieve low-carbon operation of a distribution network, new energy resources like photovoltaics (PVs) have been extensively integrated into it. However, this integration poses significant challenges to the supply-demand balance. Specifically, the generation of PVs is stochastic, causing power fluctuations. Additionally, the increase in power data and the open nature of the network will cause network congestion and communication disturbances. To address these issues, an active power support (APS) strategy is developed with the following innovations. First, an adaptive mutation-based generation prediction algorithm incorporating a multi-extreme learning mechanism (ELM) is proposed to optimize the prediction model and provide reliable predicted generation data for regulation. Second, a demand-driven path optimization method is proposed to prioritize critical data transmission, ensuring that regulatory service demands are met while mitigating congestion. Third, a hierarchical control strategy utilizing multifactor matching and a sliding mode controller (SMC)-based virtual leader-following consensus algorithm is designed to generate optimal control commands for PVs and suppress disturbances. Finally, adequate simulations demonstrate that the proposed method reduces the prediction error by at least 10.1% compared to existing methods, adjusts transmission paths based on data importance and service needs to mitigate congestion, and suppresses communication disturbances within 1s, thereby enabling effective APS.