Towards a green power sector: Optimal EV integration and CO2 emissions management in Pakistan

This work investigates the impact of electric vehicles (EVs) integration on the power sector of Pakistan, with the aim of minimizing the cost of electricity (COE) generation and reducing CO 2 emissions. A multiperiod mixed integer linear programming model is developed to optimize the energy mix, considering constraints such as electricity demand, CO 2 emissions, and power plant capacity. This study analyses the effect of 30%, 50%, and 70% EV penetration on newly purchased vehicles, determining their impact on cumulative COE and CO 2 emissions. The optimization results indicate that the COE generation is not adversely affected by the adoption of EVs, and a significant reduction in CO 2 emissions can be achieved by increasing non-fossil-based power generation. The study also explores CO 2 mitigation schemes, including fuel balancing (FB) and renewable energy reliance. While FB is found to be ineffective under the given electricity demand levels. The analysis reveals that CO 2 emissions could decrease by up to 11.9 MT with a 70% EV penetration, while maintaining renewable energy sources like solar, wind, and biomass at 20–40% of the energy mix can lead to seasonal emission reductions of up to 37.8% in summer. This work offers preliminary insights into the EV integration in Pakistan’s energy sector and lays a foundation for future policy recommendations aimed at achieving a balanced energy mix and sustained emission reduction.

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Publication Details

Journal
Next Energy
Published
2026-03-24
DOI
https://doi.org/10.1016/j.nxener.2026.100560
Primary Topic
Electric Vehicles and Infrastructure
Type
article
Field-Weighted Citation Impact
0.00
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article

Towards a green power sector: Optimal EV integration and CO2 emissions management in Pakistan

Luqman Hakeem, Muhammad Saeed Uz Zaman, Muhammad Anas Khan, Mohamed A. Bin Shams et al.
Next Energy
Electric Vehicles and Infrastructure
article

Towards a green power sector: Optimal EV integration and CO2 emissions management in Pakistan

Luqman Hakeem, Muhammad Saeed Uz Zaman, Muhammad Anas Khan, Mohamed A. Bin Shams, Babar Hussain, Muhammad Rizwan, Ali Elkamel
article en

Abstract

This work investigates the impact of electric vehicles (EVs) integration on the power sector of Pakistan, with the aim of minimizing the cost of electricity (COE) generation and reducing CO 2 emissions. A multiperiod mixed integer linear programming model is developed to optimize the energy mix, considering constraints such as electricity demand, CO 2 emissions, and power plant capacity. This study analyses the effect of 30%, 50%, and 70% EV penetration on newly purchased vehicles, determining their impact on cumulative COE and CO 2 emissions. The optimization results indicate that the COE generation is not adversely affected by the adoption of EVs, and a significant reduction in CO 2 emissions can be achieved by increasing non-fossil-based power generation. The study also explores CO 2 mitigation schemes, including fuel balancing (FB) and renewable energy reliance. While FB is found to be ineffective under the given electricity demand levels. The analysis reveals that CO 2 emissions could decrease by up to 11.9 MT with a 70% EV penetration, while maintaining renewable energy sources like solar, wind, and biomass at 20–40% of the energy mix can lead to seasonal emission reductions of up to 37.8% in summer. This work offers preliminary insights into the EV integration in Pakistan’s energy sector and lays a foundation for future policy recommendations aimed at achieving a balanced energy mix and sustained emission reduction.

Next EnergyVol. 11
University of Bahrain (BH), Pakistan Institute of Engineering and Applied Sciences (PK), University of Waterloo (CA), Khalifa University of Science and Technology (AE)
Affordable and clean energy
Openalex Percentile: Top 9%
Electric Vehicles and Infrastructure
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Towards a green power sector: Optimal EV integration and CO2 emissions management in Pakistan — Luqman Hakeem, Muhammad Saeed Uz Zaman, et al. · Next Energy (2026) | TGRS Research Map | TGRS