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DYNAMIC ELECTRICITY GENERATION FOR ADDRESSING DAILY AIR QUALITY EXCEEDANCES IN THE US
Impact/Purpose:
Air quality is forecast daily in the US. This information can inform daily management decisions in the electric power sector, attempting to avoid daily air quality standard exceedances by redispatching generation from regions strongly influencing the exceedance to elsewhere. Here we propose to design and evaluate a dynamic management system for the electric power sector with the goal of avoiding daily exceedances of air quality standards for ozone in the eastern US. We will first demonstrate this dynamic system for a selected episode, evaluating choices in how this system might operate, and incorporating model uncertainty into the design. We will then demonstrate its application over a full summer season, and evaluate this system based on costs, fuel consumption, greenhouse gas emissions, electrical system reliability, success in avoiding local ozone exceedances, and changes in ozone and PM2.5 over the eastern US, considering environmental justice.
Description:
We will design, demonstrate, and evaluate a dynamic management system for managing daily air quality, exploring different elements of the design of this system such as how air quality forecasts can best be used, and decision rules for the electrical dispatch model. We will evaluate these different designs based on their cost, effectiveness, and ancillary effects (reliability, greenhouse gases, PM2.5). The costs will be compared with comparable reductions in ozone exceedance metrics from traditional smokestack controls. If dynamic management looks promising, it could be deployed widely in the US and elsewhere in the future, improving air quality at a reduced cost, and future investments in generation and transmission could aim to make dynamic air management more effective.