Correlation analysis on wind and hydro resources with electricity demand and prices in New Zealand. (1st January 2015)

• Record Type: Journal Article
• Authors: Suomalainen, Kiti; Pritchard, Geoffrey; Sharp, Basil; Yuan, Ziqi; Zakeri, Golbon
• Is Part Of: Applied energy. Volume 137(2015:Jan. 01)
• Page Start: 445
• Page End: 462
• Publication Date: 2015-01-01
• Subjects: Correlation analysis -- Fourier regression -- Energy systems planning -- Wind-hydro resource dynamics; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2014.10.015
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: We study correlation of wind and hydro with electricity demand and prices. We discuss the trade-offs of developing wind power in different geographic areas. Increased wind power may balance price spikes in a hydro-based electricity market. We separate daily deviations from the expected seasonal value. For some locations deviations significantly correlate with electricity prices. Abstract: In this paper we study the spatial and temporal distribution of the wind and hydro resources, together with electricity demand and prices, with the objective to gain insights on the trade-offs of developing wind power in different parts of the country. It is particularly important to understand the impacts of a high penetration level of wind power on an electricity market that is already highly influenced by seasonal hydro power availability. The correlation between the seasonal patterns of wind, hydro, demand and prices has received little attention in the literature. In our approach, we separate daily deviations from the expected seasonal value, and show that for some locations it is the anomalies that have a more significant correlation with electricity prices than the seasonal patterns themselves. This indicates that the correlation results for anomalies from different wind sites can give valuable information on wind resource availability in critical periods of the year, such as dry seasons, and thereby enables identifying sites that can most optimally balance price volatility during these periods while potentially also maximising profits to investors. Even though the numerical results pertain to New Zealand, our methodology is applicable to any electricity system that utilises wind and hydro resources such as Chile, Colombia and several North American and Northern European jurisdictions.