Robust topology and dispatch optimization for renewable distribution networks with electric vehicle mobility uncertainty.

Xu, L; Jiang, J; Hu, S; Wang, T; Dong, J · 2026 · PubMed Central

DOI: 10.1038/s41598-026-41486-3

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Summary

This study addresses the operational complexity of modern distribution networks characterized by high renewable energy penetration and the stochastic mobility of electric vehicles (EVs). The authors identify a critical gap in existing literature: conventional optimization models often treat network topology as static and assume known probability distributions for uncertainty, failing to capture the bidirectional coupling between power grids and transportation systems. To resolve this, the paper proposes a spatiotemporally coupled distributionally robust optimization (DRO) framework that co-optimizes network topology reconfiguration, energy dispatch, and EV charging operations under dual-layer uncertainty from renewable generation and traffic flows. The methodology employs a Wasserstein metric-based DRO model to handle ambiguity in the joint probability distributions of wind-solar availability and EV traffic intensity. The problem is formulated as a hierarchical min-max structure: the upper-level objective minimizes total operational costs, including generation, switching losses, and EV travel-related energy costs, while the lower-level subproblem identifies the worst-case realization of uncertain parameters within a data-driven ambiguity set. The model integrates AC power flow constraints, radial topology conditions, and EV state-of-charge dynamics. To solve this large-scale mixed-integer nonlinear problem, the authors utilize a column-and-constraint generation algorithm augmented by Benders cuts, which decomposes the min-max structure into tractable subproblems. Case studies on a 10-node distribution network coupled with 20-route EV mobility scenarios demonstrate the framework’s effectiveness. The proposed method achieved a 12.4% reduction in operational cost variance and a 45% reduction in voltage deviation compared to deterministic optimization approaches. Sensitivity analysis revealed that the Wasserstein ambiguity radius ($\epsilon$) critically influences the robustness-efficiency trade-off; specifically, $\epsilon = 0.05$ provided a near-optimal balance, improving out-of-sample reliability by 39% with only a 12.7% cost penalty. Furthermore, scenario-wise analyses showed that the framework enables adaptive reconfiguration, dynamically aligning switching actions with renewable curtailment and traffic congestion to mitigate spatiotemporal imbalances. The significance of this work lies in its unified mathematical foundation for operating renewable-dominated urban energy systems. By integrating topology flexibility with distributionally robust dispatch and mobility coordination, the study confirms that such an approach substantially enhances both the resilience and economic efficiency of distribution networks. The findings suggest that moving beyond static topologies and fixed distribution assumptions is essential for managing the interdependent uncertainties of future energy-mobility ecosystems.

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