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Techno-economic appraisal of electric vehicle charging stations integrated with on-grid photovoltaics on existing fuel stations: A multicity study framework

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  • Shah, Talha Hussain
  • Shabbir, Altamash
  • Waqas, Adeel
  • Janjua, Abdul Kashif
  • Shahzad, Nadia
  • Pervaiz, Hina
  • Shakir, Sehar

Abstract

The affordable transportation industry has been severely compromised in 2022 as a consequence of the unexpected surge in the price of fossil fuels. By 2050, the transportation industry is forecasted to expand twice, leading to a variety of issues, such as fossil fuel depletion and environmental emissions. As a result, worldwide trend toward electrifying transport, with energy production originating from both traditional and carbon-free sources. Therefore, consumers of electric vehicles require convenient access to the services of charging stations around the country. This research investigates existing fuel station rooftops for the deployment of the grid-connected photovoltaic (GCPV) system electric vehicle charging stations (EVCS), design, and performance analysis of Pakistan's cities. A mathematical model of the GCPV EVCS under consideration has been described. Three cities in Pakistan: Multan, Lahore, and Islamabad-EVCS powered by photovoltaics and the utility grid have been designed and their comparison of the techno-economic performance indicators have been done through System Advisor Model (SAM) software. To comprehend the impact of uncertainty on the functionality of the GCPV EVCS, sensitivity analyses were conducted at all three designated cities in Pakistan. With this strategy, Pakistan will be able to reduce its greenhouse gas emissions to the level required by the Paris Agreement.

Suggested Citation

  • Shah, Talha Hussain & Shabbir, Altamash & Waqas, Adeel & Janjua, Abdul Kashif & Shahzad, Nadia & Pervaiz, Hina & Shakir, Sehar, 2023. "Techno-economic appraisal of electric vehicle charging stations integrated with on-grid photovoltaics on existing fuel stations: A multicity study framework," Renewable Energy, Elsevier, vol. 209(C), pages 133-144.
    • Handle: RePEc:eee:renene:v:209:y:2023:i:c:p:133-144
    DOI: 10.1016/j.renene.2023.03.128
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    2. Zhou, Yuekuan & Liu, Xiaohua & Zhao, Qianchuan, 2024. "A stochastic vehicle schedule model for demand response and grid flexibility in a renewable-building-e-transportation-microgrid," Renewable Energy, Elsevier, vol. 221(C).

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