To address the challenges of voltage violations and limited photovoltaic (PV) accommodation caused by large-scale PV integration into distribution networks, and in line with the development trend toward hierarchical and distributed control in modern active distribution networks (ADNs), this paper proposes a fast time-varying optimization algorithm based on voltage feedforward prediction. This algorithm is positioned within the edge-level control layer, acting as a rapid, underlying supplement to upper-level global dispatch. By incorporating the power and voltage constraints of the distribution network alongside the regulation of flexible resources such as PV and energy storage systems (ESS), a time-varying optimization model is established for distribution networks with high PV penetration. Specifically, a time-varying optimization method driven by voltage feedforward prediction is designed. This method calculates the system’s augmented Lagrangian gradient based on voltage feedforward predictions and sensitivity values. This gradient information is then utilized to iteratively optimize the output of distributed energy resources. While strictly satisfying power flow constraints, the optimization achieves the dual objectives of minimizing system operating costs and ensuring a high level of PV accommodation. The superiority and effectiveness of the proposed algorithm are validated through a case study on a 129-node medium- and low-voltage distribution network. The analytical results demonstrate that, even at a minute-level sampling resolution, the algorithm effectively mitigates voltage fluctuations and PV curtailment caused by the unstable active power output of PV systems. Furthermore, the proposed approach exhibits strong robustness, significantly alleviates the over-reliance on high-frequency measurement devices, and fulfills the requirements for the secure and economic operation of distribution networks.
Zhu et al. (Thu,) studied this question.