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. Using a dataset from Surabaya, Indonesia, our numerical experiment demonstrates that the proposed model achieves a 13% higher average objective value compared to the non-robust solution. Furthermore, the CV technique successfully reduces the simulation sample size up to 10 times compared to Monte Carlo, allowing the model to solve efficiently using a standard MIP solver. Our study provides a robust and efficient solution for designing EV charging infrastructure that can thrive even in developing countries with uncertain electricity disruptions.

翻译：随着全球电动汽车需求的不断增长，亟需开发可靠且便捷的充电基础设施，尤其是在电力中断构成重大挑战的发展中国家。早期的充电基础设施优化研究未能严谨地解决此类服务中断特性，导致基础设施设计方案欠优。为解决这一问题，我们提出了一种基于仿真的高效优化模型，该模型能够估计候选站点的服务可靠性，并将其纳入目标函数和约束条件中。我们采用控制变量（CV）方差缩减技术来提高仿真效率。我们的模型提供了高度鲁棒的解决方案，能够缓冲不确定的电力中断影响，即使在候选站点服务可靠性被低估或高估的情况下仍有效。基于印度尼西亚泗水市的数据集进行的数值实验表明，与不鲁棒的方案相比，所提模型的平均目标值提高了13%。此外，与蒙特卡罗方法相比，CV技术成功地将仿真样本量减少了最多10倍，使得模型能够使用标准的MIP求解器高效求解。本研究为设计即使在电力供应不确定的发展中国家也能稳定运行的电动汽车充电基础设施，提供了一种鲁棒且高效的解决方案。