A Robust and Efficient Optimization Model for Electric Vehicle Charging Stations in Developing Countries under Electricity Uncertainty
Mansur Arief, Yan Akhra, Iwan Vanany

TL;DR
This paper introduces a robust, simulation-based optimization model for EV charging station placement in developing countries, effectively handling electricity disruptions and improving infrastructure resilience.
Contribution
It develops a novel simulation-based optimization approach with variance reduction for designing reliable EV charging infrastructure under electricity uncertainty.
Findings
Achieves 13% higher objective value than non-robust methods
Reduces simulation sample size by up to 10 times using CV technique
Provides a scalable solution for developing countries with unreliable electricity
Abstract
The rising demand for electric vehicles (EVs) worldwide necessitates the development of robust and accessible charging infrastructure, particularly in developing countries where electricity disruptions pose a significant challenge. Earlier charging infrastructure optimization studies do not rigorously address such service disruption characteristics, resulting in suboptimal infrastructure designs. To address this issue, we propose an efficient simulation-based optimization model that estimates candidate stations' service reliability and incorporates it into the objective function and constraints. We employ the control variates (CV) variance reduction technique to enhance simulation efficiency. Our model provides a highly robust solution that buffers against uncertain electricity disruptions, even when candidate station service reliability is subject to underestimation or overestimation.…
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Taxonomy
TopicsElectric Vehicles and Infrastructure · Advanced Battery Technologies Research · Transportation and Mobility Innovations
