Stochastically Optimized, Carbon-Reducing Dispatch of Storage, Generation, and Loads

TitleStochastically Optimized, Carbon-Reducing Dispatch of Storage, Generation, and Loads
Publication TypeJournal Article
Year of Publication2015
AuthorsAlberto J Lamadrid, Daniel L Shawhan, Carlos E Murillo-Sanchez, Ray D Zimmerman, Yujia Zhu, Daniel J Tylavsky, Andrew G Kindle, Zamiyad Dar
JournalIEEE Transactions on Power Systems
Pagination1064 - 1075
Date Published03/2015
KeywordsCERTS, reliability and markets, RM07-002

We present a new formulation of a hybrid stochastic-robust optimization and use it to calculate a look-ahead, security-constrained optimal power flow. It is designed to reduce carbon dioxide (CO2) emissions by efficiently accommodating renewable energy sources and by realistically evaluating system changes that could reduce emissions. It takes into account ramping costs, CO2 damages, demand functions, reserve needs, contingencies, and the temporally linked probability distributions of stochastic variables such as wind generation. The inter-temporal trade-offs and transversality of energy storage systems are a focus of our formulation. We use it as part of a new method to comprehensively estimate the operational net benefits of system changes. Aside from the optimization formulation, our method has four other innovations. First, it statistically estimates the cost and CO2 impacts of each generator's electricity output and ramping decisions. Second, it produces a comprehensive measure of net operating benefit, and disaggregates that into the effects on consumers, producers, system operators, government, and CO2 damage. Third and fourth, our method includes creating a novel, modified Ward reduction of the grid and a thorough generator dataset from publicly available information sources. We then apply this method to estimating the impacts of wind power, energy storage, and operational policies.

Short TitleIEEE Trans. Power Syst.