Plug-in electric vehicles (PEVs) represent a significant opportunity for governments to reduce emissions of both air pollutants and greenhouse gases, in addition to reduce their dependency on foreign sources of energy. Comprehensive planning analysis prior to the rollout of electric vehicle charging stations (EVCS) can ensure that charging stations are effectively sited, providing the best returns on investment while also meeting critical service requirements. We present a detailed description of the PEV infrastructure (PEVI) model, a spatially explicit agent-based microsimulation model that represents charging infrastructure, charging behavior, competition for scarce EVCS, and driver adaptation. A differential evolution and a heuristic optimization scheme are compared in their ability to find a cost-effective distribution of EVCS. In addition, several key assumptions of the model are tested for their impact on critical outcomes. Results are presented from a case study in Delhi, India, that highlight the spatial distribution of chargers of different levels, the impact of several technical and policy trends on the need for EVCS, the spatiotemporal distribution of charging demand, and the technical potential for load shifting PEV demand.
BT - IEEE Transactions on Transportation Electrification DA - 03/2016 DO - 10.1109/TTE.2016.2540663 IS - 2 LA - eng N2 -Plug-in electric vehicles (PEVs) represent a significant opportunity for governments to reduce emissions of both air pollutants and greenhouse gases, in addition to reduce their dependency on foreign sources of energy. Comprehensive planning analysis prior to the rollout of electric vehicle charging stations (EVCS) can ensure that charging stations are effectively sited, providing the best returns on investment while also meeting critical service requirements. We present a detailed description of the PEV infrastructure (PEVI) model, a spatially explicit agent-based microsimulation model that represents charging infrastructure, charging behavior, competition for scarce EVCS, and driver adaptation. A differential evolution and a heuristic optimization scheme are compared in their ability to find a cost-effective distribution of EVCS. In addition, several key assumptions of the model are tested for their impact on critical outcomes. Results are presented from a case study in Delhi, India, that highlight the spatial distribution of chargers of different levels, the impact of several technical and policy trends on the need for EVCS, the spatiotemporal distribution of charging demand, and the technical potential for load shifting PEV demand.
PY - 2016 SP - 174 EP - 189 ST - IEEE Trans. Transp. Electrific. T2 - IEEE Transactions on Transportation Electrification TI - Cost-Effective Siting of Electric Vehicle Charging Infrastructure With Agent-Based Modeling VL - 2 ER -