Each year the U.S. government makes significant investments in improving weather forecast models. In this paper, we use a multidisciplinary approach to examine how utilities can benefit from improved wind-speed forecasts to more efficiently use wind-generated electricity and subsequently increase economic activity. Specifically, we examine how improvements to the National Oceanic and Atmospheric Administration's high-resolution rapid refresh model (HRRR) wind forecasts can provide (1) cost savings for utilities and (2) increase in real household income. To do so, we compare 12-h-ahead wind forecasts with real-time observations for two HRRR model transitions (i.e., when one model is operational, the other is being tested). We compare estimates of actual and predicted wind power under the publicly available and developmental models, with reduced forecast errors allowing for better utility decision-making and lower production costs. We then translate potential cost savings into electricity price changes, which are entered as exogenous shocks to eight regional computable general equilibrium models constructed for the U.S. Overall, we find that households would have seen a potential $60 million increase in real income for our sample (13% of all contiguous U.S. land-based turbine capacity), which had the updated HRRR models been in place during the two transition periods; applying our estimated savings for the sample of turbines to the entire array of turbines shows a potential real household income increase in approximately $384 million during these time frames.
Estimating the economic impacts of improved wind speed forecasts in the United States electricity sector
Hwayoung Jeon, Brad Hartman, Harvey Cutler, Rebecca Hill, Yuchen Hu, Tao Lu, Martin Shields, David D. Turner; Estimating the economic impacts of improved wind speed forecasts in the United States electricity sector. J. Renewable Sustainable Energy 1 May 2022; 14 (3): 036101. https://doi.org/10.1063/5.0081905
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