Mexico and U.S. power systems under variations in natural gas prices. (September 2021) - Sarmiento, Luis; Molar-Cruz, Anahi; Avraam, Charalampos; Brown, Maxwell; Rosellón, Juan; Siddiqui, Sauleh; Rodríguez, Baltazar Solano
Abstract: This study examines the impact of natural gas prices on the power systems of Mexico and the United States. For this, we develop an integrated modeling framework by soft linking three different techno-economic bottom-up models of the power and energy systems, one partial equilibrium model of the natural gas sector, and a partial equilibrium model of the Mexican energy sector. Our results show several interesting results: high natural gas prices raise the use of carbon-intensive technologies in the short-term and boost renewable investments at longer time intervals, increasing emissions in earlier periods and reducing them thereafter. Regarding system costs, because of more capital-intensive green power and lower expenditures in raw energy carriers, capital costs rise and operating costs decrease in the long haul. Furthermore, we see an increase in natural gas demand when its price is low, reducing long-term capital and operating costs through cheaper energy inputs in natural gas facilities and a lower share of capital-intensive renewable facilities in the power system. Concerning emissions, low natural-gas prices decrease coal use in the United States, reducing anthropogenic emissions until the last stages of the optimization period. For Mexico, they show heterogeneous results across models. Policymakers can use this study's results to understand the influence of natural gas prices in the Mexican and United States energy sectors. Highlights: High gas prices increase the use of coal and oil in the short term. This increase in substitute fossil fuels raises-short term emissions of CO2. In the long haul, high prices raise the construction of renewable facilities. These new green facilities lead to a long-term reduction in anthropogenic CO2. Low gas prices have heterogeneous and interesting effects across models and scenarios.