Dynamic Economic Load Dispatch oe Thermal Power System Using Genetic Algorithm
ESTIJ • 2013
معلومات البحث
المؤلفون
W.M.Mansour, M.M.Salama, S.M. Abdelmaksoud, H.A. Henry
الكلمات المفتاحية
Economic load dispatch; Ramp rate limits; Particle
swarm optimization; Genetic algorithm
المجلة العلمية
ESTIJ
الناشر
IRACST
المجلد
3
العدد
2 April 2013
الصفحات
345-352
publication.type
International
رابط البحث
Open Link
المواد المرفقة
Not Available
الملخص
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
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