论文标题
为城市地区设计优化的电动汽车充电站基础设施:印度尼西亚的案例研究
Designing an Optimized Electric Vehicle Charging Station Infrastructure for Urban Area: A Case study from Indonesia
论文作者
论文摘要
电动汽车(EV)技术的快速发展有望更清洁的空气和更有效的运输系统,尤其是针对被污染和拥挤的城市地区。为了利用这一潜力,印尼政府任命了其最大的国有电力提供商PLN加速印度尼西亚EV基础设施的准备。该公司的任务是在全国范围内提供可靠,可访问且具有成本效益的电动汽车充电站基础设施,该公司正在制定一个位置优化的模型,以模拟其基础架构设计如何满足客户,满足需求并产生收入。在这项工作中,我们研究了PLN如何通过采用最大覆盖位置模型在城市地区最佳地放置EV充电站来最大化利润。在我们的实验中,我们使用来自印度尼西亚苏拉巴亚的数据,并考虑两种主要的运输模式,以供当地人充电:电动摩托车和电动汽车。数值实验表明,鉴于PLN获得的充电技术,只需要四个充电站来覆盖整个城市。但是,消费者的旅行时间异常高(约35分钟),这可能导致消费者服务不良和对EV技术的障碍。灵敏度分析表明,建造更多充电站可以减少时间,但由于设施的额外安装而导致的成本更高。在停电或其他干扰方面增加冗余层也会增加更高的成本,但可能是设计更可靠和蓬勃发展的EV基础架构的一种吸引人的选择。该模型可以为决策者提供见解,以设计最可靠,最具成本效益的基础设施设计,以支持电动汽车的部署和更高级的智能运输系统。
The rapid development of electric vehicle (EV) technologies promises cleaner air and more efficient transportation systems, especially for polluted and congested urban areas. To capitalize on this potential, the Indonesian government has appointed PLN, its largest state-owned electricity provider, to accelerate the preparation of Indonesia's EV infrastructure. With a mission of providing reliable, accessible, and cost-effective EV charging station infrastructure throughout the country, the company is prototyping a location-optimized model to simulate how well its infrastructure design reaches customers, fulfills demands, and generates revenue. In this work, we study how PLN could maximize profit by optimally placing EV charging stations in urban areas by adopting a maximal covering location model. In our experiments, we use data from Surabaya, Indonesia, and consider the two main transportation modes for the locals to charge: electric motorcycles and electric cars. Numerical experiments show that only four charging stations are needed to cover the whole city, given the charging technology that PLN has acquired. However, consumers' time-to-travel is exceptionally high (about 35 minutes), which could lead to poor consumer service and hindrance toward EV technologies. Sensitivity analysis reveals that building more charging stations could reduce the time but comes with higher costs due to extra facility installations. Adding layers of redundancy to buffer against outages or other disruptions also incurs higher costs but could be an appealing option to design a more reliable and thriving EV infrastructure. The model can provide insights to decision-makers to devise the most reliable and cost-effective infrastructure designs to support the deployment of electric vehicles and much more advanced intelligent transportation systems in the near future.