Stochastically optimized, carbon-reducing dispatch of storage, generation, and loads

Alberto J. Lamadrid, Daniel L. Shawhan, Carlos Edmundo Murillo-Sanchez, Ray Daniel Zimmerman, Yujia Zhu, Daniel J. Tylavsky, Andrew G. Kindle, Zamiyad Dar

Research output: Contribution to journalArticle

23 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number7029704
Pages (from-to)1064-1075
Number of pages12
JournalIEEE Transactions on Power Systems
Volume30
Issue number2
DOIs
StatePublished - Mar 1 2015

Keywords

  • Energy storage
  • environmental economics
  • optimization
  • power generation dispatch
  • power system economics
  • power system planning
  • power system simulation
  • renewable energy sources
  • smart grids
  • uncertainty
  • wind energy

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

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    Lamadrid, A. J., Shawhan, D. L., Murillo-Sanchez, C. E., Zimmerman, R. D., Zhu, Y., Tylavsky, D. J., Kindle, A. G., & Dar, Z. (2015). Stochastically optimized, carbon-reducing dispatch of storage, generation, and loads. IEEE Transactions on Power Systems, 30(2), 1064-1075. [7029704]. https://doi.org/10.1109/TPWRS.2014.2388214