| Author: | Tuen, Kin-wa |
| Title: | Genetic algorithm approach to scheduling of generators |
| Degree: | M.Sc. |
| Year: | 1998 |
| Subject: | Electric power systems Genetic algorithms Hong Kong Polytechnic University -- Dissertations |
| Department: | Multi-disciplinary Studies |
| Pages: | 56 p. : ill. ; 30 cm |
| Language: | English |
| Abstract: | This dissertation presents a genetic algorithm (GA) approach for solving generating units scheduling problem and modifications are adopted to improve the performance of GA for this problem. Solving the scheduling problem is able to determine a start-up and shut-down schedule for all available generating units in a power system over a period of time to meet the forecasted load demand at minimum cost. From the schedule, near optimum total production cost is achieved while satisfying a large set of operating constraints. The operating constraints including fuel cost, start-up costs, minimum start-up time and shut-down time etc. must be met for the generators schedule. Owing to nonconvex and combinatorial nature of the generating units scheduling problem, conventional programming methods are difficult to solve this problem. However, the application of GA is suitable for this problem. GAs are adaptive search techniques to determine the global optimal solution of a combinatorial optimization problem which are based on the mechanics of natural genetics and natural selection. Because of the ability of GA to obtain the global optimal solution, application of GAs on this problem has been implemented. A simple GA that yields good results in many optimization problems consists of three main processes. They are initialization, reproduction and evaluation. The procedures for each process are described in this dissertation in which modifications are adopted to improve the performance of GA for the generating units scheduling problem. |
| Rights: | All rights reserved |
| Access: | restricted access |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| b14211439.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 1.86 MB | Adobe PDF | View/Open |
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