Climate change threatens all parts of the US electric power system, from electricity generation to distribution. An important dimension of this issue is the impact on electricity demand. While many studies have looked at these impacts, few have tried to represent this effect at higher temporal resolutions (such as daily or sub-daily) or to analyze its seasonal aspects. Our study expands on previous work to improve our understanding of how climate change can affect patterns of hourly electricity demand, the differences in these effects over different seasons, and how this in turn could affect the operations of the power system. For this analysis, we combine a linear regression model, a simplified economic dispatch model, and projections from twenty different climate models to analyze how climate change may affect seasonal demand patterns and, consequently, power plants dispatch. We use this method to analyze a case study of the Tennessee Valley Authority (TVA). The results suggest that climate change can result in an average increase in annual electricity consumption in the TVA region of 6% by the end of the century and an increase in the frequency of peak demand values (the maximum quantity of electricity demanded during an hour). However, this increase is not uniformly distributed throughout the year. During summer, total electricity consumption can increase on average by 20% while during winter, it may decrease on average by 6% by the end of the century. Such changes in demand could result in changes in the typical dispatch patterns of TVA’s power plants. Estimated summer time capacity factors would increase (8 to 37% for natural gas and 71 to 84% for coal) and winter time capacity factor decrease (3% to virtually zero for natural gas and 67 to 60% for coal). Such results could affect the decision-making process of planning agents in the power sector.
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The inclusion of additional time-varying explanatory variables may absorb residual variation, hence producing more precise estimates. However, adding more controls will not necessarily produce an estimate of the coefficient of interest that is closer to the true parameter. If the additional controls are themselves outcomes of changes in climatic variables, which may well be the case for controls such as GDP, institutional measures, population, and socioeconomic variables, including them will induce an “over-controlling problem.” (see (Dell et al. 2014)). For example, suppose that poorer counties in the USA tend to be both hot and have low-quality institutions. If hot climates were to cause low-quality institutions, which in turn cause low income, then controlling for institutions can have the effect of partially eliminating the explanatory power of climatic variables, even if climate is the underlying fundamental cause.
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The authors would like to thank Prof. Alex Davis in the Engineering and Public Policy Department at Carnegie Mellon University for his suggestions on the model formulation. We also thank Bart Nijssen and Yifan Cheng at the Department of Civil and Environmental Engineering in the University of Washington for sharing their climate model data with us and their help processing the data using MTCLIM. The authors would like to thank the anonymous reviewers for their valuable comments and suggestions to improve the quality of the paper.
This work was supported by the National Science Foundation (NSF) as part of the Resilient Interdependent Infrastructure Processes and Systems (RIPS) program via Grant Number EFRI-1441131.
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Ralston Fonseca, F., Jaramillo, P., Bergés, M. et al. Seasonal effects of climate change on intra-day electricity demand patterns. Climatic Change 154, 435–451 (2019) doi:10.1007/s10584-019-02413-w