An AI Data-driven simulation framework for an electric transit system and its integration with the power distribution network

人工智能数据驱动的电动交通系统仿真框架及其与配电网络的集成

• 批准号: 575639-2022
• 负责人: Yassine, AbdulsalamA
• 金额: $ 3.28万
• 依托单位: Lakehead University
• 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=765613
• 关键词: AI; Data; driven; simulation; framework

The Government of Canada plans to switch public transit systems to cleaner electric power, including supporting the purchase of 5000 zero-emission buses in the next five years. Such plans will create green transportation in Canadian cities that does not emit toxic gasses which impact the environment and human health. On the other hand, Canadian municipalities and utility companies need tools that provide them with a sound analysis of the technical, economic, and environmental challenges that might affect the implementation of clean transit systems. First, the driving range of an electric bus is much shorter than the diesel counterpart, therefore, several charging stations must be carefully placed to ensure uninterrupted operation of the transit service. However, it is not trivial to decide on the number and location of these charging stations without estimating the energy consumption of an electric bus. Second, the introduction of electric buses adds an extra load to the electric distribution grid and will change the power flow in the grid; and hence, requires mechanisms to analyze the impact on the grid and recommend measures to respond to the increased load. To address the above challenges, in this research project we propose to work with Synergy North, BluWave-ai, and the City of Thunder Bay to design a state-of-art AI data-driven simulation platform that provides comprehensive decision support that eventually will accelerate the adoption of the green transit system in Thunder Bay. Concerning social benefits, the adoption of an electric transit system in Northwestern Ontario will not only reduce green gas emissions (~ 40%) but also improve local air quality and associated health benefits. Furthermore, the cost reduction (~50%) of the operation and maintenance of electric buses compared to diesel buses will benefit the City's economy. These savings can be invested back locally to drive municipal economic development and growth over time leading to the creation of more job opportunities and a fast recovery from the COVID-19 pandemic.

加拿大政府计划将公共交通系统改用更清洁的电力，包括在未来五年内支持购买5000辆零排放公交车。此类计划将为加拿大城市打造绿色交通，不会排放影响环境和人类健康的有毒气体。另一方面，加拿大市政当局和公用事业公司需要工具来对可能影响清洁交通系统实施的技术、经济和环境挑战进行合理的分析。首先，电动公交车的续驶里程比柴油公交车短得多，因此必须精心布置多个充电站，以确保公交服务不间断运行。然而，在不估算电动公交车的能耗的情况下决定这些充电站的数量和位置并非易事。其次，电动公交车的引入给配电网增加了额外的负载，将改变电网的潮流；因此，需要建立机制来分析对电网的影响并提出应对增加的负荷的措施建议。为了应对上述挑战，在这个研究项目中，我们建议与 Synergy North、BluWave-ai 和桑德贝市合作，设计一个最先进的人工智能数据驱动模拟平台，提供全面的决策支持，最终将加快桑德贝绿色交通系统的采用。在社会效益方面，安大略省西北部采用电动交通系统不仅可以减少绿色气体排放（约40%），而且可以改善当地的空气质量和相关的健康效益。此外，与柴油公交车相比，电动公交车的运营和维护成本降低（约 50%），将有利于城市经济。这些节省的资金可以投资回当地，以随着时间的推移推动城市经济的发展和增长，从而创造更多的就业机会并从 COVID-19 大流行中快速恢复。