This paper proposes a Risk-Aware Hosting-Capacity-Constrained Optimal Power Flow (RA-HC-OPF) framework for renewable-integrated electric vehicle (EV) charging distribution networks. The proposed method is designed to move hosting-capacity analysis from a passive post-power-flow assessment to an active optimization process, where power-based EV and photovoltaic (PV) hosting capacity, network security, and operational risk are optimized within a unified OPF formulation. Our proposed framework integrates four key design layers: stochastic spatial–temporal modeling of PV generation, base-load demand, and EV charging uncertainty; coordinated dispatch of PV generation, EV charging, vehicle-to-grid (V2G) support, and battery energy storage systems (BESSs); a composite hosting-capacity security margin that jointly captures voltage stress, feeder loading, transformer loading, reverse power flow, PV curtailment, and EV charging feasibility; and risk-aware penalty terms for voltage violation, overload, transformer stress, and reverse-power-flow exposure. The RA-HC-OPF implementation follows a scenario-based optimization workflow in which representative PV-load-EV scenarios are generated, controllable resources are scheduled, hosting-security margins are evaluated, and benchmark operating cases are compared. The proposed framework is validated on IEEE 33-bus and IEEE 69-bus distribution systems using seven benchmark cases. Compared with uncontrolled EV and PV operation, RA-HC-OPF increases EV hosting capacity by 66.1% and 63.5%, improves PV hosting capacity by 44.0% and 45.1%, reduces PV curtailment by 77.5% and 75.8%, and decreases network losses by 40.7% and 41.3% for the IEEE 33-bus and IEEE 69-bus systems, respectively. It also eliminates voltage and overload violations in both systems and improves the expected hosting-capacity security margin by 51.7% and 55.4%. These outcomes confirm that the proposed RA-HC-OPF framework provides a technically robust and scalable planning-operation tool for expanding secure EV and renewable hosting capacity under uncertainty.
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Gu et al. (2026) studied this question.
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