Optimal Energy Storage Allocation Strategy for Smart Distribution Networks with High‐Penetration Distributed Photovoltaics

High‐penetration distributed photovoltaic (PV) generation introduces significant voltage deviations and net‐load fluctuations in distribution networks. This paper proposes a two‐stage planning framework for battery energy storage system (BESS) allocation. The cluster‐based two‐stage framework is adopted as a decomposition strategy under centralized planning to reduce the decision space and improve computational scalability, while aligning with the area‐based operation and maintenance characteristics of practical distribution networks. First, the network is partitioned into clusters using a multi‐dimensional clustering index that integrates electrical distance, active/reactive power balance, and cluster coupling, solved by an improved Gold Rush Optimizer (GRO) with chaotic initialization and opposition‐based learning. Second, a multi‐objective BESS siting and sizing model is formulated to minimize (i) net‐load fluctuation, (ii) node‐voltage fluctuation, and (iii) installed BESS energy capacity, and is solved by an improved multi‐objective Northern Goshawk Optimization (NGO) algorithm. Entropy‐weighted TOPSIS is adopted to select a representative compromise solution from the Pareto set. Case studies on a practical distribution network in Gansu Province demonstrate that the proposed method can effectively reduce load fluctuation and voltage fluctuation compared with baseline methods while obtaining a cost‐effective BESS planning solution.