Dynamic Economic Load Dispatch oe Thermal Power System Using Genetic Algorithm
ESTIJ • 2013
Publication Information
Authors
W.M.Mansour, M.M.Salama, S.M. Abdelmaksoud, H.A. Henry
Keywords
Economic load dispatch; Ramp rate limits; Particle
swarm optimization; Genetic algorithm
Journal
ESTIJ
Publisher
IRACST
Volume
3
Issue
2 April 2013
Pages
345-352
publication.type
International
Paper Link
Open Link
Supplementary Materials
Not Available
Abstract
Abstract—Economic Load Dispatch (ELD) problem is one of the
most important problems to be solved in the operation and
planning of a power system. The main objective of the economic
load dispatch problem is to determine the optimal schedule of
output powers of all generating units so as to meet the required
load demand at minimum operating cost while satisfying system
equality and inequality constraints. This paper presents an
application of Genetic Algorithm (GA) for solving the ELD
problem to find the global or near global optimum dispatch
solution. The proposed approach has been evaluated on 26-bus,
6-unit system with considering the generator constraints, ramp
rate limits and transmission line losses. The obtained results of
the proposed method are compared with those obtained from the
conventional lambda iteration method and Particle Swarm
Optimization (PSO) Technique. The results show that the
proposed approach is feasible and efficient
most important problems to be solved in the operation and
planning of a power system. The main objective of the economic
load dispatch problem is to determine the optimal schedule of
output powers of all generating units so as to meet the required
load demand at minimum operating cost while satisfying system
equality and inequality constraints. This paper presents an
application of Genetic Algorithm (GA) for solving the ELD
problem to find the global or near global optimum dispatch
solution. The proposed approach has been evaluated on 26-bus,
6-unit system with considering the generator constraints, ramp
rate limits and transmission line losses. The obtained results of
the proposed method are compared with those obtained from the
conventional lambda iteration method and Particle Swarm
Optimization (PSO) Technique. The results show that the
proposed approach is feasible and efficient
Staff Members - Benha University