Dynamic Electricity Generation for Addressing Daily Air Quality Exceedances in the USEPA Grant Number: R835219
Title: Dynamic Electricity Generation for Addressing Daily Air Quality Exceedances in the US
Investigators: West, J. Jason , Blumsack, Seth , Vizuete, William
Institution: University of North Carolina at Chapel Hill , Pennsylvania State University
EPA Project Officer: Chung, Serena
Project Period: June 1, 2012 through May 31, 2014 (Extended to May 31, 2015)
Project Amount: $250,000
RFA: Dynamic Air Quality Management (2011) RFA Text | Recipients Lists
Research Category: Air Quality and Air Toxics , Air
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.
We will investigate the design of a dynamic management system for ozone exceedances, using a three-dimensional air quality model (CAMx) in combination with a model of the daily management of the electrical grid. For a selected historical episode with an exceedance of the 8-hour ozone standard, we will run CAMx with an online source apportionment technique (APCA) to indicate the contributions of different electric power sources to peak ozone. We will then test different decision rules for a regional electric dispatch model, affecting power generation and transmission, which will differ in both the form of new objectives or constraints and their stringency. The decision rules will decrease generation at power plants most strongly influencing the ozone peak, increasing elsewhere, and we will evaluate choices in when to change generation decisions. Rerunning CAMx with new power plant emissions, we will evaluate the efficacy of the action using several criteria above. In Task 2, we will incorporate uncertainty in the air quality model in expressing the probabilities of meeting the ozone standard. Finally in Task 3, we will automate this dynamic management system to an entire summer, for selected management decision rules, and including model uncertainty. In doing so, we will consider system robustness over a sequence of daily episodes in different cities, and evaluate implications for meeting annual PM2.5 standards.
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.