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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Mandala, M. Gupta, C.P. |
| Copyright Year | 2012 |
| Description | Author affiliation: Department of Electrical Engineering, Indian Institute of Technology, Roorkee, India (Mandala, M.) || Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee, India (Gupta, C.P.) |
| Abstract | One of the major operating challenges in the deregulated electricity market is to manage transmission system congestion to ensure its secure operation. This paper proposes an optimal congestion management approach in a deregulated electricity market using optimal location of TCSC. Sensitivity factors are used to find the optimal location TCSC. After placing TCSC the investment cost of TCSC and generator rescheduling cost is minimized using Particle Swarm Optimization and Bacterial Foraging Particle swarm Optimization. Numerical results on test systems namely IEEE 30 Bus and IEEE 118 bus systems are presented for illustration purpose and the results are compared with Particle swarm optimization (PSO) in terms of solution quality. The comprehensive experimental results prove that the BFPSO is one among the challenging optimization methods which is indeed capable of obtaining higher quality solutions for the proposed problem. |
| Starting Page | 1 |
| Ending Page | 6 |
| File Size | 818550 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781467345064 |
| e-ISBN | 9781467345088 |
| DOI | 10.1109/PEDES.2012.6484273 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-12-16 |
| Publisher Place | India |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Power capacitors Thyristors Microorganisms Load flow Contracts Sensitivity Particle swarm optimization voltage stability congestion management optimal location of TCSC Bacterial Foraging Particle Swarm Optimization cost of TCSC |
| Content Type | Text |
| Resource Type | Article |
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