Grid expansion planning for Heavy Duty Electric Vehicles charging

Abstract

This paper presents a sequential framework based on a two-stage stochastic programming model for the planning and expansion of distribution networks under the influence of heavy-duty electric vehicles (HDEVs) charging. In the first step, a capacity assignment model determines the spatio-temporal charging demand of HDEVs, where a congestion penalty function is introduced to produce spatially diversified charging demand, preventing vehicle clustering. In the second step, this demand is used as input to a mixed-integer second-order cone programming (MISOCP) model for the joint planning of energy sources and grid reinforcements, which is solved via Benders decomposition. The objective is to minimize total asset investment and operational costs while accounting for uncertainties in electricity demand, renewable energy generation and the arrival of HDEVs at charging stations. The framework is tested across fleet electrification rates and seasonal PV generation conditions to quantify how grid investment scales with HDEV adoption.