Use of Optimization Tools for Managing Large-Scale Hydropower Systems

Date:  Wednesday, September 6, 2006
Time:  3 p.m. 
Place: 255 Aerospace Engineering Building 

William Yeh, UCLAWilliam W-G. Yeh
Distinguished Professor and Chair
Department of Civil and Environmental Engineering
University of California, Los Angeles

Biography
Yeh is a distinguished professor and the current Chair of the Department of Civil Engineering, UCLA.  Professor Yeh joined UCLA after completing receiving PhD from Stanford in 1967.  He is the elected fellow of American Society of Civil Engineers (ASCE) and American Geophysical Union (AGU).  He received the prestigious Robert E. Horton Award from AGU in 1989 and the Julian Hinds Award from ASCE in 1994.  He has published over 100 journal articles and a textbook in the water resource management area.  Over the period three decades, Professor Yeh has supervised 47 PhD dissertations at UCLA.  The details of his research accomplishments are available at: http://cee.ucla.edu/cgi-bin/peop_faculty.php?uid=4&fpg=0.

ABSTRACT

Use of Optimization Tools for Managing Large-Scale Hydropower Systems
A practical monthly optimization model is developed for the management and operation of the Brazilian hydropower system. The system, one of the largest in the world, consists of 75 hydropower plants with an installed capacity of 69,375 MW. The basic model is formulated in nonlinear programming (NLP). The NLP model is the most general formulation and provides a foundation for analysis by other methods. The formulated NLP model was first linearized and solved by linear programming (LP). A comparative analysis was made of the results obtained from the linearized model and the NLP model. The results show that the simplest linearized model is suitable for long-term planning. For example, the LP model could be used for system capacity expansion studies or to explore various design parameters in connection with feasibility studies. The NLP model, the most accurate model in the suite, is particularly suited for setting up guidelines for real-time operations using inflow forecast with frequent updating. The performance of the NLP model was checked against the historical operational records, and the comparison yields indications of superior performance. A small improvement in operation of such large-scale system translates into enormous benefits.