The thesis explores the potential of Genetic Algorithms (GAs) for optimising the operation of electric power systems. It discusses methods which have resulted in significant direct cost saving in operating an electric power system. In particular, the thesis demonstrates the simple search procedure and the powerful search ability of Gas in multi-modal, multi-objective problems, which are resisted by the most well known conventional techniques. Special emphasis has been given to the effectiveness of the enhanced genetic based algorithms and the importance of sophisticated problem structures. Finally, the feasibility and suitability of genetic based algorithms for power system optimisations are verified on a real power supply system. The basic requirement in operating a power system is to ensure that the whole system is run at the minimum possible cost, and the lowest possible pollution level, while reliability and security are maintained. These requirements have resulted in a wide range of power system optimisation problems. In this work, a selection of problems concerning operation economy, security and environmental impact have been dealt with by Genetic Algorithms. These problems are in order of increasing complexity as the project progresses: they range from static problems to dynamic problems, single objective to multi-objectives, softly constrained problems to harshly constrained problems, simple problem structure to more rigorous problem structure. Despite the diversity, GAs consistently produce solutions comparable to conventional techniques over the wide range of problem spectrum. It has been clearly demonstrated that a sophisticated problem structure can bring significant financial benefits in system operation, it has however added further complexity to the problem, where the best result may only be sought from the genetic based algorithms. The enhancements of Genetic Algorithms have been investigated with the aim of further improving the quality and speed of the solution. They have been enhanced in two levels: the first is to develop advanced genetic strategies, and this is subsequently refined by choosing optimal parameter values to further improve the strategies. The outcome of the study clearly indicates that genetic based algorithms are very attractive techniques for solving the ever more complicated optimisations of electric power systems. The basic requirement in operating a power system is to ensure that the whole system is run at the minimum possible cost, and the lowest possible pollution level, while reliability and security are maintained. These requirements have resulted in a wide range of power system optimisation problems. In this work, a selection of problems concerning operation economy, security and environmental impact have been dealt with by Genetic Algorithms. These problems are in order of increasing complexity as the project progresses: they range from static problems to dynamic problems, single objective to multi-objectives, softly constrained problems to harshly constrained problems, simple problem structure to more rigorous problem structure. Despite the diversity, GAs consistently produce solutions comparable to conventional techniques over the wide range of problem spectrum. It has been clearly demonstrated that a sophisticated problem structure can bring significant financial benefits in system operation, it has however added further complexity to the problem, where the best result may only be sought from the genetic based algorithms. The enhancements of Genetic Algorithms have been investigated with the aim of further improving the quality and speed of the solution. They have been enhanced in two levels: the first is to develop advanced genetic strategies, and this is subsequently refined by choosing optimal parameter values to further improve the strategies. The outcome of the study clearly indicates that genetic based algorithms are very attractive techniques for solving the ever more complicated optimisations of electric power systems.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:361511 |
| Date | January 1996 |
| Creators | Li, Furong |
| Publisher | Liverpool John Moores University |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://researchonline.ljmu.ac.uk/5567/ |
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