AI SearchMulti-area economic dispatch by particle swarm optimization with time-varying acceleration coefficients
Call Number: AIT Thesis no.ET-11-14 Material type:
SeriesSeries: Asian Institute of Technology. Thesis ; no. ET-11-14Publication details: Pathum Thani, Thailand : Asian Institute of Technology, 2011Description: 36 leaves : illSubject(s): Online resources: Dissertation note: Thesis (M. Eng.) - Asian Institute of Technology, 2011 Summary: This thesis proposes a particle swarm optimization with time-varying acceleration coefficients (PSO-TVAC) technique for solving multi-area economic dispatch (MAED) problem. The objective of MAED is to determine the best generation schedule in multiarea power system for a given load with minimum cost, while satisfying power balance generator constraints, power generation limits and tie line constraints. The multi-area tieline limit constraints are used to ensure the system security and reliability. To evaluate the performance, the PSO-TV AC is implemented on 2, 4 and 14 area systems. The simulation results show that the proposed method is superior to evolutionary programming (EP), classical economic dispatch (CED), incremental network flow programming (INFP), spatial dynamic programming (SDP), and basic particle swarm optimization (BPSO) in terms of lower cost and faster computing time.
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Thesis (M. Eng.) - Asian Institute of Technology, 2011
A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Energy.
This thesis proposes a particle swarm optimization with time-varying acceleration coefficients (PSO-TVAC) technique for solving multi-area economic dispatch (MAED) problem. The objective of MAED is to determine the best generation schedule in multiarea power system for a given load with minimum cost, while satisfying power balance generator constraints, power generation limits and tie line constraints. The multi-area tieline limit constraints are used to ensure the system security and reliability. To evaluate the performance, the PSO-TV AC is implemented on 2, 4 and 14 area systems. The simulation results show that the proposed method is superior to evolutionary programming (EP), classical economic dispatch (CED), incremental network flow programming (INFP), spatial dynamic programming (SDP), and basic particle swarm optimization (BPSO) in terms of lower cost and faster computing time.
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