Electricity portfolio planning model incorporating renewable energy characteristics
Jung-Hua Wu and
Applied Energy, 2014, vol. 119, issue C, 278-287
Traditional electricity planning models pursue minimal costs, yet their design often results in an underestimation of the true benefits of renewable energy. This paper attempts to introduce different complementary approaches to traditional electricity planning model to incorporate various renewable energy characteristics and uses Taiwan’s electricity sector as a case study. The portfolio theory, learning curve theory and the capacity credit are applied in the proposed model to reflect characteristics of renewable energy, such as a hedge against fossil fuel price volatility, significant technological progress, and intermittent generation. Simulation results demonstrate that using renewable energies has the advantage of hedging against the volatile fossil fuel price risk as well as reducing carbon dioxide emissions. Considering the intermittency of renewable energies requires LNG-fired plants to serve as the backup generators. However, wind power can only account for limited share of total installed capacity due to the limited land resources in Taiwan. Therefore, taking intermittency into account only demonstrates a small influence of the reserve margins of the entire power system and the total generation costs.
Keywords: Electricity planning model; Portfolio theory; Learning curve; Capacity credit; Intermittency (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations View citations in EconPapers (14) Track citations by RSS feed
Downloads: (external link)
Full text for ScienceDirect subscribers only
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:119:y:2014:i:c:p:278-287
Ordering information: This journal article can be ordered from
http://www.elsevier. ... 405891/bibliographic
Access Statistics for this article
Applied Energy is currently edited by J. Yan
More articles in Applied Energy from Elsevier
Series data maintained by Dana Niculescu ().