Quantifying Weather and Climate Impacts on Electricity Demand and Agricultural Productivity in the US: Implications for Climate Change Adaptation
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2024
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This dissertation consists of three Chapters. The first chapter discusses the weather impact on electricity consumption and the policy implications from the temperature sensitive shiftable electricity demand. Growth of intermittent renewable energy and climate change make it increasingly difficult to manage electricity demand variability. Transmission and centralized storage technologies can help, but are costly. An alternative to centralized storage is to make better use of shiftable demand, but it is unclear how much shiftable demand exists. A significant share of electricity demand is used for cooling and heating, and low-cost technologies exists to shift these loads. With sufficient insulation, energy used for air conditioning and space heating can be stored in ice or hot water from hours to days. In this study, we combine regional hourly demand with fine-grained weather data across the United States to estimate temperature-sensitive demand, and how much demand variability can be reduced by shifting temperature-sensitive loads within each day, with and without improved transmission. We find that approximately three quarters of within-day demand variability can be eliminated by shifting only half of temperature-sensitive demand. The variability-reducing benefits of employing available shiftable demand complement those gained from improved interregional transmission, and greatly mitigate the challenge of serving higher peaks under climate change.
The second chapter compares the predictions of a process-based crop model (SIMPLE) and statistical model, each of which links high-resolution weather to crop yields, across hundreds of thousands of representative sampled corn and fields in the United States. Grid-level weather is aggregated to the county-level and linked to actual county-level outcomes over 20 years. The statistical model has a better fit overall, and it captures much of the extreme-heat impact on crop yield in 2012, while the SIMPLE model does not. Under climate change scenarios, the statistical model predicts a decline in average yield of 20 bu/ac while the SIMPLE model predicts an increase of 5.2 bu/ac. Similarly, we compare the models for soybeans yield forecast, the outcome is inline with the outcome for corn, which is statistical model has a better fit and captures the extreme heat effect.
The third chapter compares air temperature in three different reanalysis dataset by comparing the goodness-of-fit from hourly electricity consumption regression discussed in chapter one. Weather variables in different reanalysis datasets have different values, and they indicate different results in applied studies, such as those predicting the impacts of climate change. Different reanalysis datasets may perform better in different locations and for different weather metrics. To help reconcile differences and evaluate the performance I compare air temperature in three reanalysis datasets, ERA5-land, NARR and MERRA2 for the continental US, by comparing how well each data set predicts a physical outcome, hourly electricity demand. I find that air temperature from ERA5-land results in the most accurate predictions on average. Results are more ambiguous at the regional level. ERA5-land has the best fit in the east interconnect, while prediction performance is inconsistent in the west interconnect, possibly due to more subtle and extreme geographical differences within the balancing authorities examined.
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Agriculture economics, Energy, Climate change, Agriculture, Climate Change, Electricity, Policy, Weather
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94 pages
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