M.S. Thesis Defense: Adam Bernstein

Thursday, November 21, 2013, 1:00-3:00pm

MacLean 132

“Optimization Modeling of U.S. Renewable Electricity Deployment Using Local Input Variables”

Thesis Committee:
Mark Borsuk, Ph.D. (Chair)
Stephen Powell, Ph.D.
Michael Gerst, Ph.D.

Abstract: For the past 5 years, state Renewable Portfolio Standard (RPS) laws have been a primary driver of renewable electricity (RE) deployments in the United States. However, four decreasing variables: (i) natural gas prices, (ii) growth in electricity demand, (iii) ease of transmission grid balancing of intermittent RE, and (iv) suitable sites for RE development, may limit the efficacy of RPS laws over the remainder of the current RPS statutes’ lifetime. An outsized proportion of U.S. RE build occurs in a small number of favorable locations, increasing the effects of these variables. A state-by-state analysis is necessary to study the U.S. electric sector and generate technology specific generation forecasts. We used LP optimization modeling similar to the National Renewable Energy Laboratory (NREL) Renewable Energy Development System (ReEDS) to forecast RE deployment across the 8 U.S. states with the largest electricity load, and found state-level RE projections to Year 2030 significantly lower than those implied in the Energy Information Administration (EIA) 2013 Annual Energy Outlook forecast. Combined with the tendency of prior research and RE forecasts to focus on larger national and global scale models, we posit that further bottom-up state and local analysis is needed for more accurate policy assessment. Current optimization software eliminates much of the need for algorithm coding and programming, allowing for rapid model construction and updating across many customized state and local RE forecasts.

For more information, contact Daryl Laware at daryl.a.laware@dartmouth.edu.