An examination of electricity generation by utility organizations in the Southeast United States. (1st December 2016)

• Record Type: Journal Article
• Authors: Craig, Christopher A.; Feng, Song
• Is Part Of: Energy. Volume 116:Part 1(2016)
• Page Start: 601
• Page End: 608
• Publication Date: 2016-12-01
• Subjects: Electricity -- Pro-conservationism -- Clean energy -- Fossil fuels -- Climate change -- ARIMA; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2016.10.013
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: This study examined the impact of climatic variability on electricity generation in the Southeast United States. The relationship cooling degree days (CDD) and heating degree days (HDD) shared with electricity generation by fuel source was explored. Using seasonal autoregressive integrated weighted average (ARIMA) and seasonal simple exponentially smoothed models, retrospective time series analysis was run. The hypothesized relationship between climatic variability and total electricity generation was supported, where an ARIMA model including CDDs as a predictor explained 57.6% of the variability. The hypothesis that climatic variability would be more predictive of fossil fuel electricity generation than electricity produced by clean energy sources was partially supported. The ARIMA model for natural gas indicated that CDDS were the only predictor for the fossil fuel source, and that 79.4% of the variability was explained. Climatic variability was not predictive of electricity generation from coal or petroleum, where simple seasonal exponentially smoothed models emerged. However, HDDs were a positive predictor of hydroelectric electricity production, where 48.9% of the variability in the clean energy source was explained by an ARIMA model. Implications related to base load electricity from fossil fuels, and future electricity generation projections relative to extremes and climate change are discussed. Highlights: Models run to examine impact of climatic variability on electricity generation. Cooling degree days explained 57.6% of variability in total electricity generation. Climatic variability was not predictive of coal or petroleum generation. Cooling degree days explained 79.4% of natural gas generation. Heating degree days were predictive of nuclear and hydroelectric generation.