Day-ahead scheduling of electricity generation or unit commitment is an important and challenging operational activity of power system operators. Mixed integer programming (MIP) has been firmly established as an effective technology for this problem for moderate scale integrated systems. In this work, we consider decentralized unit commitment in a large-scale network of generation systems. We develop a decomposition-coordination approach by which independent unit commitment MIP models can be integrated to achieve high quality solutions to the network-wide problem. The approach is based on the alternating direction method of multipliers (ADMM) originally developed for decentralized convex optimization. We adapt ADMM to the highly nonconvex unit commitment problem and demonstrate its computational effectiveness.
This talk is based on joint works with Javad Feizollahi, Mitch Costley, Andy Sun and Santiago Grijalva.
Shabbir Ahmed is a College of Engineering Dean’s Professor in the School of Industrial & Systems Engineering at the Georgia Institute of Technology. His research interests are in large-scale stochastic and discrete optimization methodology, and their applications in energy and networked systems. He has over 70 publications in these areas. Dr. Ahmed was a Chair of the Stochastic Programming Society, a Vice-chair of the INFORMS Optimization Society, and is council member of the Mathematical Optimization Society. He serves on the editorial board of various journals including Mathematical Programming and Operations Research. Dr. Ahmed's honors include the Stewart Fellowship from Georgia Tech, the National Science Foundation CAREER award, two IBM Faculty Awards, the Coca-Cola Junior Professorship from ISyE, and the INFORMS Dantzig Dissertation award.