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Market-based planning of transmission network using genetic algorithm

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

Abstract

The reconstruction and deregulation have brought fundamental changes to many aspects of power system operation and management. As a result, the planning activity is no longer dominated by the system operator. The deregulation has also brought many uncertainties and constraints to the system planning area. Therefore, the traditional centralized expansion planning approach will face difficulties in a market environment and new planning approaches are needed. In this paper, a market-oriented planning objective for transmission network expansion has been developed, which aims at maximizing the overall benefit from network expansion while considering critical constraints, such as power flow balance and transmission capacities. The planning objective has been solved by a Genetic Algorithm (GA) to achieve the best available expansion plan. The IEEE 14-bus system is used to test the developed method.
Original languageEnglish
Title of host publication2004 International Conference on Probabilistic Methods Applied to Power Systems
Pages826-831
Publication statusPublished - 2004
Externally publishedYes
Event2004 International Conference on Probabilistic Methods Applied to Power Systems - Ames, IA, United States
Duration: 12 Sept 200416 Sept 2004

Publication series

Name2004 International Conference on Probabilistic Methods Applied to Power Systems

Conference

Conference2004 International Conference on Probabilistic Methods Applied to Power Systems
PlaceUnited States
CityAmes, IA
Period12/09/0416/09/04

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

Research Keywords

  • Electricity Market
  • Genetic Algorithms
  • Transmission Planning

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