Juxtaposing Sub-Sahara Africa's energy poverty and renewable energy potential.


Journal

Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288

Informations de publication

Date de publication:
19 Jul 2023
Historique:
received: 21 02 2023
accepted: 12 07 2023
medline: 20 7 2023
pubmed: 20 7 2023
entrez: 19 7 2023
Statut: epublish

Résumé

Recently, the International Energy Agency (IEA) released a comprehensive roadmap for the global energy sector to achieve net-zero emission by 2050. Considering the sizeable share of (Sub-Sahara) Africa in the global population, the attainment of global energy sector net-zero emission is practically impossible without a commitment from African countries. Therefore, it is important to study and analyze feasible/sustainable ways to solve the energy/electricity poverty in Africa. In this paper, the energy poverty in Africa and the high renewable energy (RE) potential are reviewed. Beyond this, the generation of electricity from the abundant RE potential in this region is analyzed in hourly timestep. This study is novel as it proposes a Sub-Sahara Africa (SSA) central grid as one of the fastest/feasible solutions to the energy poverty problem in this region. The integration of a sizeable share of electric vehicles with the proposed central grid is also analyzed. This study aims to determine the RE electricity generation capacities, economic costs, and supply strategies required to balance the projected future electricity demand in SSA. The analysis presented in this study is done considering 2030 and 2040 as the targeted years of implementation. EnergyPLAN simulation program is used to simulate/analyze the generation of electricity for the central grid. The review of the energy poverty in SSA showed that the electricity access of all the countries in this region is less than 100%. The analysis of the proposed central RE grid system is a viable and sustainable option, however, it requires strategic financial planning for its implementation. The cheapest investment cost from all the case scenarios in this study is $298 billion. Considering the use of a single RE technology, wind power systems implementation by 2030 and 2040 are the most feasible options as they have the least economic costs. Overall, the integration of the existing/fossil-fueled power systems with RE technologies for the proposed central grid will be the cheapest/easiest pathway as it requires the least economic costs. While this does not require the integration of storage systems, it will help the SSA countries reduce their electricity sector carbon emission by 56.6% and 61.8% by 2030 and 2040 respectively.

Identifiants

pubmed: 37468495
doi: 10.1038/s41598-023-38642-4
pii: 10.1038/s41598-023-38642-4
pmc: PMC10356766
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

11643

Informations de copyright

© 2023. The Author(s).

Références

Science. 2018 Jun 29;360(6396):
pubmed: 29954954
Energy Res Soc Sci. 2020 Oct;68:101633
pubmed: 32839691
Nature. 2021 Mar;591(7850):365-368
pubmed: 33727725
iScience. 2022 Feb 14;25(3):103926
pubmed: 35243266

Auteurs

Mustapha Mukhtar (M)

School of Economics and Management, Guangdong University of Petrochemical Technology, Maoming, 525000, People's Republic of China.

Humphrey Adun (H)

Energy Systems Engineering Department, Cyprus International University, TRNC Mersin 10, Mersin, KKTC, Turkey.
Operational Research Centre in Healthcare, Near East University, TRNC Mersin 10, 99138, Nicosia, Turkey.

Dongsheng Cai (D)

College of Nuclear Technology and Automation Engineering, Chengdu University of Technology, 610059, Chengdu, Sichuan, People's Republic of China.

Sandra Obiora (S)

School of Management and Economics, University of Electronic Science and Technology of China, Chengdu, Sichuan, People's Republic of China.

Michael Taiwo (M)

School of Science, Chrisland University, Abeokuta, Ogun State, Nigeria.

Ting Ni (T)

College of Environmental and Civil Engineering, Chengdu University of Technology, 610059, Chengdu, Sichuan, People's Republic of China.

Dilber Uzun Ozsahin (DU)

Operational Research Centre in Healthcare, Near East University, TRNC Mersin 10, 99138, Nicosia, Turkey.
Department of Medical Diagnostic Imaging, College of Health Science, University of Sharjah, 27272, Sharjah, United Arab Emirates.

Olusola Bamisile (O)

College of Nuclear Technology and Automation Engineering, Chengdu University of Technology, 610059, Chengdu, Sichuan, People's Republic of China. Boomfem@hotmail.com.

Classifications MeSH