Optimal mix of climate-related energy in global electricity systems
File(s)
Accepted version
Author(s)
Chu, Cheng-Ta
Hawkes, Adam D
Type
Journal Article
Abstract
Existing studies on high renewable share electricity systems are usually based on least cost optimization. Running the related models can be time-consuming when space-time resolution is high. This study investigates the optimal mix of climate-related energies for most countries in the world with optimization models based on three criteria: cost, residual load variability, and portfolio output variability. The objectives of minimizing residual load variability and portfolio output variability are to ensure the overall complementarity of the generation portfolio, which may result in less conventional dispatchable units needed in a system. Compromise solutions based on the three objectives are proposed as the optimal mix. This method can produce solutions in acceptable modelling time, and considers the portfolio output characteristics which can make higher climate-related energy penetration more practical. The results show that the compromise solutions can effectively minimize the three objective values in most countries. The results also suggest that wind power is crucial in higher renewable share systems while solar power does not reach over 50% capacity share.
Date Issued
2020-11
Date Acceptance
2020-06-21
Citation
Renewable Energy, 2020, 160, pp.955-963
ISSN
0960-1481
Publisher
Elsevier BV
Start Page
955
End Page
963
Journal / Book Title
Renewable Energy
Volume
160
Copyright Statement
© 2020 Elsevier Ltd. All rights reserved. This manuscript is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence http://creativecommons.org/licenses/by-nc-nd/4.0/
Identifier
https://www.sciencedirect.com/science/article/pii/S0960148120310211?via%3Dihub
Subjects
Energy
0906 Electrical and Electronic Engineering
0913 Mechanical Engineering
0915 Interdisciplinary Engineering
Publication Status
Published online
Date Publish Online
2020-07-22