Data-Driven Distributionally Robust Optimization for Solar-Powered EV Charging Under Spatiotemporal Uncertainty in Urban Distribution Networks

Tianhao Wang, Xuejiao Zhang, Xiaolin Zheng, Jian Wang, Shiqian Ma, Jian Chen, Mengyu Liu () and Wei Wei ()
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Tianhao Wang: State Grid Tianjin Electric Power Company, Electric Power Research Institute of China, Tianjin 300384, China
Xuejiao Zhang: State Grid Tianjin Electric Power Company, Tianjin 300010, China
Xiaolin Zheng: State Grid Tianjin Electric Power Company, Electric Power Research Institute of China, Tianjin 300384, China
Jian Wang: State Grid Tianjin Electric Power Company, Tianjin 300010, China
Shiqian Ma: State Grid Tianjin Electric Power Company, Electric Power Research Institute of China, Tianjin 300384, China
Jian Chen: State Grid Tianjin Electric Power Company, Tianjin 300010, China
Mengyu Liu: School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
Wei Wei: Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China

Energies, 2025, vol. 18, issue 15, 1-20

Abstract: The rapid electrification of transportation and the proliferation of rooftop solar photovoltaics (PVs) in urban environments are reshaping the operational dynamics of power distribution networks. However, the inherent uncertainty in electric vehicle (EV) behavior—including arrival times, charging preferences, and state-of-charge—as well as spatially and temporally variable solar generation, presents a profound challenge to existing scheduling frameworks. This paper proposes a novel data-driven distributionally robust optimization (DDRO) framework for solar-powered EV charging coordination under spatiotemporal uncertainty. Leveraging empirical datasets of EV usage and solar irradiance from a smart city deployment, the framework constructs Wasserstein ambiguity sets around historical distributions, enabling worst-case-aware decision-making without requiring the assumption of probability laws. The problem is formulated as a two-stage optimization model. The first stage determines day-ahead charging schedules, solar utilization levels, and grid allocations across an urban-scale distribution feeder. The second stage models real-time recourse actions—such as dynamic curtailment or demand reshaping—after uncertainties are realized. Physical grid constraints are modeled using convexified LinDistFlow equations, while EV behavior is segmented into user classes with individualized uncertainty structures. The model is evaluated on a modified IEEE 123-bus feeder with 52 EV-PV nodes, using 15 min resolution over a 24 h horizon and 12 months of real-world data. Comparative results demonstrate that the proposed DDRO method reduces total operational costs by up to 15%, eliminates voltage violations entirely, and improves EV service satisfaction by more than 30% relative to deterministic and stochastic baselines. This work makes three primary contributions: it introduces a robust, tractable optimization architecture that captures spatiotemporal uncertainty using empirical Wasserstein sets; it integrates behavioral and physical modeling within a unified dispatch framework for urban energy-mobility systems; and it demonstrates the value of robust coordination in simultaneously improving grid resilience, renewable utilization, and EV user satisfaction. The results offer practical insights for city-scale planners seeking to enable the reliable and efficient electrification of mobility infrastructure under uncertainty.

Keywords: electric vehicles; solar photovoltaics; distributionally robust optimization; urban distribution systems; uncertainty modeling; two-stage scheduling (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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