Intelligent Roadways: Learning-Based Battery Controller Design for Smart Traffic Microgrid

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Intelligent Roadways: Learning-Based Battery Controller Design for Smart Traffic Microgrid

Doost Mohammadi, Farideh / Keshtkar, Hessam / Gendell, Benjamin

Intelligent Roadways are possible future components of Smart City Infrastructure which would help reduce energy spent in traffic and while commuting, along with reducing the peak of the electrical grid’s power consumption and flattening the load profile curve. By optimizing roadways, cars would spend less time on the road, therefore consuming less energy in the form of fossil fuels. Paneled roads would also increase net safety on the roads. Be it highway or city roads, transportation would be safer due to the modular nature of paneled roads. In the inevitable event of damage, paneled roads would be not only easy to repair, they would also have a reduced environmental impact. Additionally, paneled roads could provide utility ranging from statistical data about traffic patterns, to heating roads to remove snow and ice. Finally, smart city infrastructure equipped with intelligent roadways would help with power generation due to solar panels being core to the design. This paper is mainly focused on the advantage of solar roadways on peak shaving. An intelligent battery charging/discharging strategy is proposed to minimize the contribution of the main grid in the solar roadway microgrid system, especially during the peak consumption hours. Feedforward Neural Networks are implemented in the Simulink-based microgrid model in order to predict the photovoltaic (PV) output power. Then, a Particle Swarm Optimization-based (PSO-based) learning algorithm is utilized in the battery control design in order to reduce the power contribution from the main grid during the peak hours. Finally, the effectiveness of the proposed power management method is investigated using MATLAB software.

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Dokumentinformationen

Titel :

Intelligent Roadways: Learning-Based Battery Controller Design for Smart Traffic Microgrid

Weitere Titelangaben:

Advs in Intelligent Syst., Computing

Beteiligte:

Arai, Kohei (Herausgeber:in) / Kapoor, Supriya (Herausgeber:in) / Bhatia, Rahul (Herausgeber:in) / Doost Mohammadi, Farideh (Autor:in) / Keshtkar, Hessam (Autor:in) / Gendell, Benjamin (Autor:in)

Kongress:

Proceedings of the Future Technologies Conference ; 2020 ; San Francisco, CA, USA November 05, 2020 - November 06, 2020

Erschienen in:

Proceedings of the Future Technologies Conference (FTC) 2020, Volume 2 ; Kapitel : 66 ; 998-1011

Advances in Intelligent Systems and Computing ; 1289

Erscheinungsdatum :

2020-11-01

Format / Umfang :

14 pages

ISBN :

978-3-030-63089-8 , 978-3-030-63088-1

ISSN :