Determinants of price fluctuations in the electricity market: a study with PCA and NARDL models
Kun Li,
Joseph D. Cursio,
Yunchuan Sun and
Zizheng Zhu
Economic Research-Ekonomska Istraživanja, 2019, vol. 32, issue 1, 2404-2421
Abstract:
In the modern electricity markets, negative prices and spike prices coexist as a pair of opposite economic phenomena. This study investigates how these extreme prices play as the determinants to drive price fluctuations in the electricity market. We construct a two-stage analysis including a principal component analysis (PCA) and a nonlinear autoregressive distributed lags model (NARDL). We apply this analytical method to the wholesale Pennsylvania, New Jersey and Maryland (PJM) electricity market. We find that according to PCA, in the individual transmission lines, spike prices are determinants with largest explanatory power to the variation of prices, while according to NARDL, from the standpoint of the overall market, negative prices have a larger potential effect on both the real-time market and the forward market. These results are valuable and contributive to managers and operators in the electricity markets for policy decision making.
Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (2)
Downloads: (external link)
http://hdl.handle.net/10.1080/1331677X.2019.1645712 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:taf:reroxx:v:32:y:2019:i:1:p:2404-2421
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/rero20
DOI: 10.1080/1331677X.2019.1645712
Access Statistics for this article
Economic Research-Ekonomska Istraživanja is currently edited by Marinko Skare
More articles in Economic Research-Ekonomska Istraživanja from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().