Dynamic Transmission Expansion Planning Under Progressive EV Adoption: Quantifying EV-Driven Reinforcement Needs - Guldorum, Hilmi Cihan; Munoz-Delgado, Gregorio; Contreras, Javier; Erdinc, Ozan (IEEE, 2025)
As part of global electrification efforts, the widespread adoption of electric vehicles (EVs) is expected to fundamentally reshape transmission system planning. While the transition supports sustainability goals, it also introduces significant challenges to transmission expansion planning (TEP) due to the spatial and temporal clustering of EV charging demand. This study proposes a dynamic TEP framework formulated as a mixed-integer linear programming (MILP) model to capture the evolving investment needs under progressive EV penetration scenarios. A stage-wise planning approach is adopted, considering a 10-year horizon divided into two 5-year intervals, with EV load projections and load growth factors derived from national forecasts of Turkiye. The model dynamically schedules transmission investments to balance infrastructure expansion and operational cost efficiency across planning stages. To ensure realistic load modeling, representative day clustering based on annual consumption profiles is employed. Simulation results on the IEEE RTS 24-Node test system demonstrate that EV integration significantly escalates both operational and investment costs, with total expenditures rising by up to 32.4% under high EV adoption scenarios relative to a base case representing no EV penetration. Moreover, the expansion needs progressively shift from reinforcing primary backbone corridors to upgrading peripheral and secondary links.
Keywords: Dynamic transmission expansion planning; electric vehicles; mixed-integer linear programming; power system planning; transportation electrification; Energy & Fuels; Engineering; Telecommunications