Grant: Alliance Grants
Lakehead University
$45,000.00
An AI Data-driven simulation framework for an electric transit system and its integration with the power distribution network
EF 2022-23
Résumé
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.
Programme
Alliance Grants
Bénéficiaire
Emplacement
Ontario, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data
Tel que publié
Application id
575639-2022
Name nom
Yassine, Abdulsalam A
Department département
Software Engineering, Dept. of
Organization id
25
Institution établissement
Lakehead University
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2022
Competition year année de concours
2022
Award amount
45000
Program id
ALLRP
Program name en
Alliance Grants
Program name fr
Subventions Alliance
Group en
RESEARCH PARTNERSHIPS
Group fr
PARTENARIATS DE RECHERCHE
Committee code
96
Committee name en
RPP Internal Decision Cttee
Committee name fr
Cté de décision interne - PPR
Area of application code
306
Area of application group en
Energy resources
Area of application group fr
Ressources d'énergie
Area of application en
Energy efficiency
Area of application fr
Efficacité énergétique
Research subject code
2705
Research subject group en
Information technology
Research subject group fr
Technologie de l'information
Research subject en
Software and development
Research subject fr
Logiciels et développement
Installment
inst:1
Partie
1
Nb partie
1
Application title
An AI Data-driven simulation framework for an electric transit system and its integration with the power distribution network
Application summary
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.