The effects of renewable energy scaling on sub-annual air pollution dynamics from the power sector: a Western U.S. case study

A co-benefit of decarbonization of the power grid is reduced emissions of other air pollutants known to harm human health. However, this co-benefit is typically quantified on an average annual basis, without considering sub-annual dynamics. Here, we investigate how increased penetration of renewable energy could affect the spatial and temporal dynamics of emissions from fossil fuel power plants, as well as associated human health damages. Focusing on the Western United States as a case study, we combine operational grid modeling and air pollution assessment to model changes in precursor emissions and health damages under several renewable energy scaling scenarios. A co-benefit of decarbonization of the power grid is reduced emissions of other air pollutants known to harm human health. However, this co-benefit is typically quantified on an average annual basis, without considering sub-annual dynamics. Here, we investigate how increased penetration of renewable energy could affect the spatial and temporal dynamics of emissions from fossil fuel power plants, as well as associated human health damages. Focusing on the Western United States as a case study, we combine operational grid modeling and air pollution assessment to model changes in precursor emissions and health damages under several renewable energy scaling scenarios. Our findings indicate that as renewable energy penetration increases, average emissions and estimated health damages are reduced. However, emissions and damages on the worst days are significantly slower to improve, largely due to the grid relying on fossil fuels during periods of extreme scarcity. We also observe that at significant (>6x) scaling of renewable energy generation, the timing of the worst days shifts from summer to winter.