AI SearchAdaptive for economic dispatch considering non-smooth cost functions
Call Number: AIT Thesis no.ET-08-13 Material type:
SeriesSeries: Asian Institute of Technology. Thesis ; no. ET-08-13Publication details: Pathum Thani, Thailand : Asian Institute of Technology, 2008 Description: 52 p. : illSubject(s): Online resources: Dissertation note: Thesis (M.Eng.) - Asian Institute of Technology, 2008 Summary: This thesis proposes Adaptive Particle Swarm Optimization (APSO) for Economic Dispatch (ED) with non-smooth cost functions considering valve-point effects. The practical ED problem has non-smooth cost functions subject to equality and inequality constraints which cannot be solved by conventional mathematical approaches. The APSO includes a self-adaptive weight scale to improve the convergence rate. The APSO is tested on 3 different systems ranging from 3 to 40 generators and compared to Evolutionary Programming (EP), Genetic Algorithm (GA), Simulated Annealing (SA), Tabu Search (TS), and Standard Particle Swarm Optimization (PSO). Test results indicate that the APSO generator fuel costs are less than the others, leading to generator fuel cost savings
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Submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Energy
Thesis (M.Eng.) - Asian Institute of Technology, 2008
A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Energy
This thesis proposes Adaptive Particle Swarm Optimization (APSO) for Economic Dispatch (ED) with non-smooth cost functions considering valve-point effects. The practical ED problem has non-smooth cost functions subject to equality and inequality constraints which cannot be solved by conventional mathematical approaches. The APSO includes a self-adaptive weight scale to improve the convergence rate. The APSO is tested on 3 different systems ranging from 3 to 40 generators and compared to Evolutionary Programming (EP), Genetic Algorithm (GA), Simulated Annealing (SA), Tabu Search (TS), and Standard Particle Swarm Optimization (PSO). Test results indicate that the APSO generator fuel costs are less than the others, leading to generator fuel cost savings
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