<p dir="ltr">The rising demand for EVs, motivated by their environmental benefits, is generating increased need for EV charging infrastructure. Also, it has been recognized that the adequacy of such infrastructure helps promote EV use. Therefore, to facilitate EV adoption, governments seek guidance on continued investments in EV charging infrastructure development. The high cost of these investments motivates governments to seek optimal decisions on EV-related investments including EV charging infrastructure, and such decisions include locations, capacities, and deployment scheduling of such infrastructure. Additionally, uncertainties in travel demand prediction and EV driving range constraints need to be considered in EV infrastructure investment planning. To help address these questions, this thesis developed a framework to establish optimal schedules and locations for new charging stations and for decommissioning gasoline refueling stations for any given network over a long-term planning horizon, considering uncertainties in travel demand forecasts and EV driving-range heterogeneity. To address the uncertainties, the proposed framework is formulated as a robust mathematical model that minimizes the worst-case total system travel cost and the total penalty for unused charging station capacity. This study uses an adaption of the cutting-plane method to solve the proposed model. In the numerical analyses, the performance of the robust framework and its deterministic counterpart are compared. The results show that the optimal robust plan outperforms the deterministic plan by yielding savings in the costs of travel and electricity charging. The thesis also investigates the effects of investment budget levels of robust planning. The numerical results throw light on the relationships between higher investment levels and electric charging station deployment levels and consequently, the savings in travel costs and impacts on unused charging capacity. The outcomes of this thesis can help road agencies and related private sector entities enhance preparations towards infrastructure investments to support electric charging stations in an efficient manner.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/24878361 |
| Date | 20 December 2023 |
| Creators | Mohammadhosein Pourgholamali Davarani (17683734) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/Robust_Design_of_Electric_Charging_Infrastructure_Locations_under_Travel_Demand_Uncertainty_and_Driving_Range_Heterogeneity/24878361 |
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