Hierarchical coordinated planning of urban distribution networks with high PV-ES penetration considering spatiotemporal source-load correlation and energy storage lifetime degradation

The rapid growth of distributed photovoltaics (PVs) generation in urban distribution networks introduces significant volatility and uncertainty, leading to voltage instability, power congestion, and suboptimal utilization of network assets. Traditional single-layer planning methods fail to capture the coupling between long-term investment decisions and short-term operational dynamics, especially under the combined effects of spatiotemporal source–load correlations and storage degradation. To address these challenges, this paper develops a two-layer source–grid–storage collaborative planning framework for urban distribution networks. The proposed two-layer model is formulated as a mixed-integer linear programming (MILP) problem. The upper layer aims to minimize total investment cost, including the siting and sizing of PV units, ESS, and distribution network expansion components. The lower layer simultaneously minimizes operating cost, voltage stability index, and line capacity margin. To capture the joint stochastic characteristics of PV output and load demand, a spatiotemporal source–load correlation probability model is established using Copula theory. Moreover, to account for the lifecycle impact of energy storage, a degradation model considering both depth of discharge and cycle number is proposed. The Benders decomposition method is employed to decouple and iteratively solve the investment and operation subproblems. The effectiveness of the proposed model is validated using an improved IEEE 33-node test system. The results demonstrate that the proposed approach significantly outperforms the single-layer planning scheme in reducing operating cost, enhancing network hosting capacity, and improving voltage quality.