LAPSE:2023.10863
Published Article

LAPSE:2023.10863
Modeling and Analysis of Load Growth Expected for Electric Vehicles in Pakistan (2021−2030)
February 27, 2023
Abstract
The world is facing severe environmental challenges as it heavily relies on a USD 100 trillion fossil-fuel-based economy. Its transition from a fuel-intensive to a material-intensive economy is not well understood. The conventional energy resources are responsible for the excessive generation of Green House Gas (GHG) emissions resulting in increased environmental degradation owing to climate change. The human impact has been cited as highly indisputable in this respect. Pakistan is one of the most climate-vulnerable countries highly suffering from such increased impact of climate change and, thus, has been warned against the excessive use of conventional resources. As such, in the premises of Pakistan, conventional products are being excessively utilized in both power generation and transport sectors. Apart from the electrical power sector, the transport sector is also one of the main contributors to GHG emissions. In this context, the automobile industry has emerged as an environmentally friendly solution, which presents Electric Vehicles (EVs) as an efficient and feasible alternative to mitigate the GHG footprint. The transition from fossil-fuel-based vehicles (FFVs) to EVs is, therefore, considered as a potential way to decarbonize the transport sector, where the socio-economic conditions may be improved to a significant extent. A major prerequisite under planning and implementation in Pakistan is forecasting of load growth of EVs in Pakistan. Therefore, this paper proposes a load growth model (load forecast), used to forecast the load growth expected for electric vehicles in Pakistan from 2021 to 2030. This paper discusses in detail the original and revised models. According to the revised model, total EV energy demand stood at 24.61 GWh in 2020 and increased up to 2862.54 GWh in 2030.
The world is facing severe environmental challenges as it heavily relies on a USD 100 trillion fossil-fuel-based economy. Its transition from a fuel-intensive to a material-intensive economy is not well understood. The conventional energy resources are responsible for the excessive generation of Green House Gas (GHG) emissions resulting in increased environmental degradation owing to climate change. The human impact has been cited as highly indisputable in this respect. Pakistan is one of the most climate-vulnerable countries highly suffering from such increased impact of climate change and, thus, has been warned against the excessive use of conventional resources. As such, in the premises of Pakistan, conventional products are being excessively utilized in both power generation and transport sectors. Apart from the electrical power sector, the transport sector is also one of the main contributors to GHG emissions. In this context, the automobile industry has emerged as an environmentally friendly solution, which presents Electric Vehicles (EVs) as an efficient and feasible alternative to mitigate the GHG footprint. The transition from fossil-fuel-based vehicles (FFVs) to EVs is, therefore, considered as a potential way to decarbonize the transport sector, where the socio-economic conditions may be improved to a significant extent. A major prerequisite under planning and implementation in Pakistan is forecasting of load growth of EVs in Pakistan. Therefore, this paper proposes a load growth model (load forecast), used to forecast the load growth expected for electric vehicles in Pakistan from 2021 to 2030. This paper discusses in detail the original and revised models. According to the revised model, total EV energy demand stood at 24.61 GWh in 2020 and increased up to 2862.54 GWh in 2030.
Record ID
Keywords
energy policy, EVs, GHG emissions, load forecast, load growth, model, MW, Pakistan
Subject
Suggested Citation
Unar NA, Mirjat NH, Aslam B, Qasmi MA, Ansari M, Lohana K. Modeling and Analysis of Load Growth Expected for Electric Vehicles in Pakistan (2021−2030). (2023). LAPSE:2023.10863
Author Affiliations
Unar NA: Energy Systems Management, College of Arts and Sciences, University of San Francisco, San Francisco, CA 94117, USA [ORCID]
Mirjat NH: Department of Electrical Engineering, Mehran University of Engineering and Technology, Jamshoro 76062, Sindh, Pakistan
Aslam B: National Transmission and Dispatch Company (NTDC), Lahore 54000, Punjab, Pakistan
Qasmi MA: National Transmission and Dispatch Company (NTDC), Lahore 54000, Punjab, Pakistan
Ansari M: Department of Electrical Engineering, Mehran University of Engineering and Technology, Jamshoro 76062, Sindh, Pakistan
Lohana K: Department of Electrical Engineering, Mehran University of Engineering and Technology, Jamshoro 76062, Sindh, Pakistan [ORCID]
Mirjat NH: Department of Electrical Engineering, Mehran University of Engineering and Technology, Jamshoro 76062, Sindh, Pakistan
Aslam B: National Transmission and Dispatch Company (NTDC), Lahore 54000, Punjab, Pakistan
Qasmi MA: National Transmission and Dispatch Company (NTDC), Lahore 54000, Punjab, Pakistan
Ansari M: Department of Electrical Engineering, Mehran University of Engineering and Technology, Jamshoro 76062, Sindh, Pakistan
Lohana K: Department of Electrical Engineering, Mehran University of Engineering and Technology, Jamshoro 76062, Sindh, Pakistan [ORCID]
Journal Name
Energies
Volume
15
Issue
15
First Page
5426
Year
2022
Publication Date
2022-07-27
ISSN
1996-1073
Version Comments
Original Submission
Other Meta
PII: en15155426, Publication Type: Journal Article
Record Map
Published Article

LAPSE:2023.10863
This Record
External Link

https://doi.org/10.3390/en15155426
Publisher Version
Download
Meta
Record Statistics
Record Views
211
Version History
[v1] (Original Submission)
Feb 27, 2023
Verified by curator on
Feb 27, 2023
This Version Number
v1
Citations
Most Recent
This Version
URL Here
https://psecommunity.org/LAPSE:2023.10863
Record Owner
Auto Uploader for LAPSE
Links to Related Works
(1.27 seconds) 0.05 + 0.08 + 0.58 + 0.25 + 0 + 0.11 + 0.05 + 0 + 0.05 + 0.09 + 0 + 0.01
