Open AccessNSGA-II Technique for Multi-objective Generation Dispatch of Thermal Generators with Nonsmooth Fuel Cost Functions
Author(s) -
M. Rajkumar,
K. Mahadevan,
S. Kannan,
S. Baskar
Publication year - 2014
Publication title -
journal of electrical engineering and technology/journal of electrical engineering and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.226
H-Index - 27
eISSN - 2093-7423
pISSN - 1975-0102
DOI - 10.5370/jeet.2014.9.2.423
Subject(s) - topsis , mathematical optimization , sorting , benchmark (surveying) , multi objective optimization , convergence (economics) , genetic algorithm , pareto principle , economic dispatch , computer science , point (geometry) , optimization problem , electric power system , power (physics) , mathematics , algorithm , operations research , physics , geometry , geodesy , quantum mechanics , economic growth , economics , geography
Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is applied for solving Combined Economic Emission Dispatch (CEED) problem with valve-point loading of thermal generators. This CEED problem with valve-point loading is a nonlinear, constrained multi-objective optimization problem, with power balance and generator capacity constraints. The valve-point loading introduce ripples in the input-output characteristics of generating units and make the CEED problem as a nonsmooth optimization problem. To validate its effectiveness of NSGA-II, two benchmark test systems, IEEE 30-bus and IEEE 118-bus systems are considered. To compare the Pareto-front obtained using NSGA-II, reference Pareto-front is generated using multiple runs of Real Coded Genetic Algorithm (RCGA) with weighted sum of objectives. Comparison with other optimization techniques showed the superiority of the NSGA-II approach and confirmed its potential for solving the CEED problem. Numerical results show that NSGA-II algorithm can provide Pareto-front in a single run with good diversity and convergence. An approach based on Technique for Ordering Preferences by Similarity to Ideal Solution (TOPSIS) is applied on non-dominated solutions obtained to determine Best Compromise Solution (BCS).