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<p>The increasing interdependence between power distribution networks and transportation systems introduces unprecedented operational complexity under high renewable penetration and stochastic electric vehicle (EV) mobility. This study proposes a spatiotemporally coupled distributionally robust topology–dispatch co-optimization framework to coordinate network reconfiguration, dispatch scheduling, and EV charging operations under dual-layer uncertainty from renewable generation and transportation flows. A Wasserstein metric–based distributionally robust optimization (DRO) model is developed to capture ambiguity in the joint probability distributions of wind–solar availability and EV traffic intensity, ensuring robust feasibility against distributional shifts. The upper-level problem minimizes the expected operational cost, switching losses, and travel-related energy cost, while the lower-level subproblem represents the worst-case realization of uncertain parameters within a data-driven ambiguity set. The resulting min–max structure is decomposed through a column-and-constraint generation algorithm augmented by Benders cuts, enabling tractable convergence for large-scale mixed-integer nonlinear decision spaces. Case studies on a 10-node distribution network coupled with 20-route EV mobility scenarios demonstrate that the proposed framework achieves 12.4% lower operational cost variance and 45% voltage deviation reduction compared with deterministic optimization. The Wasserstein ambiguity radius <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\varepsilon\)</EquationSource> </InlineEquation> is shown to critically shape the robustness–efficiency trade-off, with <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\varepsilon\)</EquationSource> </InlineEquation> = 0.05 yielding a near-optimal balance– improving out-of-sample reliability by 39% with only a 12.7% cost penalty. Moreover, scenario-wise analyses reveal adaptive reconfiguration patterns that dynamically align switching actions with renewable curtailment and traffic congestion, leading to coordinated mitigation of spatiotemporal imbalances. These findings confirm that integrating topology flexibility with distributionally robust dispatch and mobility coordination can substantially enhance the resilience and economic efficiency of renewable-dominated urban energy systems.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月27日

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  • Liang Xu,
  • Jianjian Jiang,
  • Shizhao Hu,
  • Tianlin Wang,
  • Jiadu Dong

摘要

The increasing interdependence between power distribution networks and transportation systems introduces unprecedented operational complexity under high renewable penetration and stochastic electric vehicle (EV) mobility. This study proposes a spatiotemporally coupled distributionally robust topology–dispatch co-optimization framework to coordinate network reconfiguration, dispatch scheduling, and EV charging operations under dual-layer uncertainty from renewable generation and transportation flows. A Wasserstein metric–based distributionally robust optimization (DRO) model is developed to capture ambiguity in the joint probability distributions of wind–solar availability and EV traffic intensity, ensuring robust feasibility against distributional shifts. The upper-level problem minimizes the expected operational cost, switching losses, and travel-related energy cost, while the lower-level subproblem represents the worst-case realization of uncertain parameters within a data-driven ambiguity set. The resulting min–max structure is decomposed through a column-and-constraint generation algorithm augmented by Benders cuts, enabling tractable convergence for large-scale mixed-integer nonlinear decision spaces. Case studies on a 10-node distribution network coupled with 20-route EV mobility scenarios demonstrate that the proposed framework achieves 12.4% lower operational cost variance and 45% voltage deviation reduction compared with deterministic optimization. The Wasserstein ambiguity radius \(\varepsilon\) is shown to critically shape the robustness–efficiency trade-off, with \(\varepsilon\) = 0.05 yielding a near-optimal balance– improving out-of-sample reliability by 39% with only a 12.7% cost penalty. Moreover, scenario-wise analyses reveal adaptive reconfiguration patterns that dynamically align switching actions with renewable curtailment and traffic congestion, leading to coordinated mitigation of spatiotemporal imbalances. These findings confirm that integrating topology flexibility with distributionally robust dispatch and mobility coordination can substantially enhance the resilience and economic efficiency of renewable-dominated urban energy systems.