The goal of transmission planning in electric power systems is a robust network which is economical, reliable, and in harmony with its environment taking into account the inherent uncertainties. For reasons of practicality, transmission planners have normally taken an incremental approach and tended to evaluate a relatively small number of expansion alternatives over a relatively short time horizon. In this thesis, two new planning methodologies namely the Deterministic Crowding Genetic Algorithm and the Ant Colony System are applied to solve the long term transmission planning problem. Both optimisation techniques consider a 'green field' approach, and are not constrained by the existing network design. They both identify the optimal transmission network over an extended time horizon based only on the expected pattern of electricity demand and generation sources. Two computer codes have been developed. An initial comparative investigation of the application of Ant Colony Optimisation and a Genetic Algorithm to an artificial test problem has been undertaken. It was found that both approaches were comparable for the artificial test problem.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:311269 |
| Date | January 2000 |
| Creators | Chebbo, Hind Munzer |
| Contributors | Irving, M. R. ; Song, Y. H. |
| Publisher | Brunel University |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://bura.brunel.ac.uk/handle/2438/5781 |
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