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3294. Optimizing Charging Station Infrastructure Deployment: A Multi-Objective Stochastic Programming Approach
Invited abstract in session TD-37: Applications of Multiobjective Optimization, stream Multiobjective Optimization.
Tuesday, 14:30-16:00Room: 33 (building: 306)
Authors (first author is the speaker)
| 1. | Büşra Alptekin
|
| Industrial Engineering PhD program, Istanbul Technical University | |
| 2. | Sule Itir Satoglu
|
| Industrial Engineering, ITU |
Abstract
Electromobility plays a pivotal role in achieving Net Zero emissions targets globally, with transportation accounting for 15% of emissions, 72% of which stem from road transportation. However, the widespread adoption of electric vehicles (EVs) poses significant challenges. EV penetration and CS network establishment are intertwined, resembling a chicken-and-egg problem. If EV demand stagnates, CS utilization remains low, discouraging investment from charging point operators (CPOs). Conversely, inadequate CS infrastructure worsens user concerns like range anxiety, hindering electrification. While existing studies often focus on one-time CS location decisions, developing a well-distributed CS infrastructure requires a holistic approach in decision-making. To address this, our objective is to formulate a multi-period CS network deployment plan focused on the most frequented highways. We propose a multi-objective stochastic programming approach to optimize sustainability dimensions and identify new CS locations to augment the existing network. This model embraces the Triple Bottom Line (TBL) approach, considering economic, ecological, and social aspects from the perspectives of charging point operators (CPOs), government entities, and EV users, respectively. Factors such as location, charger quantity, load capacity, grid expansion needs, and energy source types—be they transformer upgrades or renewable installations—are assessed across various EV penetration scenarios.
Keywords
- Mathematical Programming
- Location
- Optimization Modeling
Status: accepted
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