University of California, Riverside

Department of Electrical and Computer Engineering



Distinguished Colloquium Speaker: Professor Peter Luh presents a talk on:"A Novel Decomposition and Coordination Approach for Large Day-Ahead Unit Commitment with Combined Cycle Units"


Distinguished Colloquium Speaker: Professor Peter Luh presents a talk on:"A....
 
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Distinguished Colloquium Speaker: Professor Peter Luh presents a talk on:"A Novel Decomposition and Coordination Approach for Large Day-Ahead Unit Commitment with Combined Cycle Units"

November 6, 2017 - 11:10 am
Winston Chung Hall, 205/206

ABSTRACT

Day-Ahead Unit Commitment is an important problem faced by an Independent System Operators (ISO) ora vertically integrated electric utility company, and involves millions of dollars each day. The widely usedbranch-and-cut method, however, suffers from poor performance when there are a large number ofcombined cycle units, as for the case of MISO, the largest ISO in the States. The purpose of this talk is todemonstrate how such problems can be solved for near-optimal solutions within a specified amount of timefor this class of mixed-integer linear programming (MILP) problems. To this end, our recently developedSurrogate Lagrangian relaxation (SLR), which overcomes all major difficulties of traditional Lagrangianrelaxation, is used. System-wide coupling constraints are first relaxed, and the relaxed problem isdecomposed into unit-wise subproblems solved by branch-and-cut. Subproblem solutions are thencoordinated based on surrogate subgradients after one or multiple subproblems are solved without the needto estimate the optimal dual cost. To accelerate convergence when considering a large number of combinedcycle units, SLR is significantly enhanced by adding quadratic penalties on constraint violations. Thesepenalty terms are then innovatively linearized through a novel use of absolute value functions, a much more accurate way to linearize than existing methods. A few other considerations are also incorporated to finetune the algorithm. As demonstrated using MISO cases, our method provides near-optimal solutions within specified amounts of time by exploiting both separability and linearity. It significantly outperforms branch and-cut, and represents a new generation of powerful methods for complicated MILP problems.

BIOGRAPHY

Peter B. Luh received his B.S. from National Taiwan University, M.S. from M.I.T., and Ph.D. from HarvardUniversity. He has been with the University of Connecticut since 1980, and currently is the SNET Professor of Communications & Information Technologies. He is also a member of the Chair Professors Group, Center for Intelligent and Networked Systems (CFINS) in the Department of Automation, Tsinghua University, Beijing, China. Professor Luh is a Life Fellow of IEEE, and the elected Chair of IEEE TAB Periodicals Committee 2018-19. He was the VP of Publications of IEEE Robotics and Automation Society (RAS, 2008-2011), the founding Editor-in-Chief of the IEEE Transactions on Automation Science and Engineering (2003-2007), and the Editor-in-Chief of IEEE Transactions on Robotics and Automation (1999-2003). He was also a co-founder of the IEEE Conference on Automation Science and Engineering. His research interests include smart power systems; intelligent manufacturing systems; and smart green and safe buildings. He received RAS 2013 Pioneer Award for his pioneering contributions to the development of near-optimal and efficient planning, scheduling, and coordination methodologies for manufacturing and power systems; and RAS 2017 George Saridis Leadership Award for his exceptional vision and leadership in strengthening and advancing Automation.

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University of California, Riverside
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Electrical and Computer Engineering
Suite 343 Winston Chung Hall
University of California, Riverside
Riverside, CA 92521-0429

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