Electrified transportation: a collaborative, data-driven approach for a smart city

电气化交通：智慧城市的协作、数据驱动方法

• 批准号: 577247-2022
• 负责人: Darvish, MaryamM
• 金额: $ 3.28万
• 依托单位: Université Laval
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=765328
• 关键词: Electrified; transportation; collaborative; data; driven

Smart cities are at the core of the emerging urban mobility transformation paradigm, where heterogeneous network infrastructure, ubiquitous sensor devices, big-data processing, and intelligent control frameworks are all used to help improve the population's quality of life by providing adapted services. Big-data analytics and operations research are important in delivering these intelligent services to citizens. In this project, we study and propose an electrified transportation system design, including power stations for electric taxis and shared cars and network design for parking lots and charging stations for ride-sharing. The efficient use of the power system is the critical concern of this study.First, we study the location problem for electric taxis and ride-sharing charging stations based on the spatiotemporal demands for battery swapping. We formulate the location planning and capacity allocation of electric taxis and ride-sharing battery swapping stations as a large-scale mixed-integer optimization, whose goal is to minimize the operating costs, subject to the constraint of meeting the drivers' demand. Efficient heuristics will be proposed to solve this optimization problem. The solutions will be studied to provide insight for developing road usage policies, space allocation, and new network design decisions.Second, we investigate the optimal location and capacity for shared car parking spaces and charging stations under uncertain (stochastic) demands. Our goal is to reduce carpooling companies' operating costs while meeting travellers' needs. Sharing mechanisms of parking spaces and charging stations will also be investigated to improve social welfare.The large energy requirements of these systems are the focus of the third aspect of this project. Given the availability of batteries to charge and/or swap, it is necessary to determine when to charge them. With a large number of batteries to be charged, this can place stress on the power grid. The fact that batteries can provide power to the grid can be used as a mitigation mechanism during peak electricity consumption periods, as is often the case in Quebec and Ontario. They are highly distributed, close to consumption points, do not stress the generation system, which could be operating near its limit, and do not impose usage limits on consumers.By investigating these three modules, we design optimal strategies for the exploitation of transportation systems that can be applied to other cities.

智能城市处于新兴的城市移动性转型范式的核心，其中异质网络基础设施、无处不在的传感器设备、大数据处理和智能控制框架都被用来通过提供适配的服务来帮助提高人口的生活质量。大数据分析和运营研究在向市民提供这些智能服务方面非常重要。在本项目中，我们研究并提出了一种电气化交通系统设计，包括电动出租车和共享汽车的发电站和拼车的停车场和充电站的网络设计。电力系统的高效利用是本研究关注的重点。首先，基于电池更换的时空需求，研究了电动出租车和顺风车充电站的选址问题。在满足驾驶员需求的约束下，将电动出租车和顺风车换电站点的选址和容量分配问题描述为大规模的混合整数优化问题，目标是使运营成本最小。将提出有效的启发式算法来解决这一优化问题。其次，我们研究了不确定(随机)需求下共享车位和充电站的最优位置和容量。我们的目标是在满足出行者需求的同时，降低拼车公司的运营成本。还将研究停车位和充电站的共享机制，以改善社会福利。这些系统的大量能源需求是本项目第三个方面的重点。鉴于有电池可供充电和/或更换，因此有必要确定何时充电。由于需要为大量电池充电，这可能会给电网带来压力。电池可以向电网提供电力的事实可以在用电高峰期用作缓解机制，魁北克和安大略省的情况经常如此。通过对这三个模块的研究，设计出可应用于其他城市的最优交通系统开发策略。