The deregulation of the electric power market introduced a strong element of competition. Power plant operators strive to develop advanced operational strategies to maximize the profitability in the dynamic electric power market. New methodologies for gas turbine power plant operational modeling and optimization are needed for power plant operation to enhance operational decision making, and therefore to maximize power plant profitability by reducing operations and maintenance cost and increasing revenue.
In this study, a profit based, lifecycle oriented, and unit specific methodology for gas turbine based power plant operational modeling was developed, with the power plant performance, reliability, maintenance, and market dynamics considered simultaneously. The generic methodology is applicable for a variety of optimization problems, and several applications for operational optimization were implemented using this method.
A multiple time-scale method was developed for gas turbine power plants long term generation scheduling. This multiple time-scale approach allows combining the detailed granularity of the day-to-day operations with global (seasonal) trends, while keeping the resulting optimization model relatively compact. Using the multiple timescale optimization method, a profit based outage departure planning method was developed, and the key factors for this profit based approach include power plant aging, performance degradation, reliability deterioration, and the energy market dynamics. A novel approach for gas turbine based power plant sequential preventive maintenance scheduling was also introduced. Finally, methods to evaluate the impact of upgrade packages on gas turbine power plant performance, reliability, and economics were developed, and TIES methodology was applied for effective evaluation and selection of gas turbine power plant upgrade packages.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/10580 |
| Date | 21 April 2005 |
| Creators | Zhao, Yongjun |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
| Format | 1500259 bytes, application/pdf |
Page generated in 0.0017 seconds