Anotace:
To address the suboptimal spatial distribution and low comprehensive utilization of existing electric vehicle (EV) public charging infrastructure, this study proposes an innovative charging station placement and capacity determination methodology integrating K-Means++ clustering with an enhanced RODDPSO variant. Building upon conventional K-Means and RODDPSO frameworks, we develop an improved hybrid algorithm incorporating three critical advancements: 1) an adaptive mutation mechanism within the RODDPSO architecture to enhance global search capabilities and prevent premature convergence; 2) synergistic optimization of K-Means++ cluster centroids through the enhanced RODDPSO operator; and 3) a novel cluster validation metric based on real-world utilization patterns. The proposed methodology effectively resolves the inherent limitations of conventional K-Means approaches, particularly their sensitivity to initial centroid selection and tendency toward local optima. Empirical validation through a case study of Nanjing's charging infrastructure demonstrates the algorithm's superior performance: stations sited using the proposed hybrid method exhibit 63.8% greater spatial correlation with high-utilization zones (>15% operational utilization) compared to baseline K-Means implementations. The advancements provide both methodological contributions to spatial optimization algorithms and practical insights for urban EV infrastructure planning.