A data-driven approach to investigate the impact of air temperature on the efficiencies of coal and natural gas generators. (1st November 2019) - Meng, Measrainsey; Sanders, Kelly T.
Highlights: Dry-cooled generators are most negatively impacted by rises in air temperature. Cooling water conditions are better predictors for wet-cooled generator efficiency. Natural gas combustion units in hot and dry areas likely utilize inlet air cooling. Abstract: The efficiency of a thermoelectric generator is dependent on a number of operational and climatic variables, including ambient air temperature. To date, there has not been a data-driven analysis of the impacts of climate variability on electricity generator performance that includes a statistically representative set of generators. This study develops regression models to estimate changes in the efficiencies of over one thousand coal and natural gas generators as a function of ambient air temperature and operational variables, across different fuel types, prime movers, cooling systems, and climate zones during the years ranging from 2008 to 2017. The efficiencies of generators with dry cooling, particularly those in hot and dry climates, demonstrated the greatest sensitivity to increases in ambient temperature. Results for generators utilizing wet cooling systems were largely inconclusive, most likely because other factors, such as cooling water temperature, are better predictors of efficiency. Natural gas combustion generator efficiencies exhibit large sensitivities to rises in air temperature in theoretical models but had a counterintuitive trend in our findings, where losses were relatively small in the hottest and driest climates. This result is likely due to the fact that natural gas combustion generators in hot and arid regions often utilize inlet air cooling technologies to reduce the temperature of ambient air before it enters the compressor, thereby mitigating efficiency losses. The analytical framework developed offers generalized methods for cleaning, processing, and merging federally available electricity generation and climate datasets to increase their value in future studies.