A New Optimal Solution to Environmentally Constrained Economic Dispatch Using Modified Real Coded Genetic Algorithm

This paper presents a novel optimization algorithm for environmentally constrained economic dispatch (ECED) problem using modified real coded genetic algorithm (MRCGA). The ECED problem is formulated as a non-linear constrained multi-objective optimization dilemma satisfying both equality and inequality constraints. The regenerating population procedure is added to the conventional RCGA in order to improve escaping the local minimum solution by a new combination of crossover and mutation technique. To solve ECED problem the predictable RCGA is customized specially by the concept of self adaptation of mutation distribution followed by polynomial mutation approach with arithmetic crossover. To test performance compatibility between them, a six units system is being considered and the better simulation results produce improved solution compare to different methods.

[1]  A. T. Johns,et al.  Environmental/economic dispatch using fuzzy logic controlled genetic algorithms , 1997 .

[2]  Xin Yao,et al.  Parallel Problem Solving from Nature PPSN VI , 2000, Lecture Notes in Computer Science.

[3]  Kalyanmoy Deb,et al.  A combined genetic adaptive search (GeneAS) for engineering design , 1996 .

[4]  D. P. Kothari,et al.  Stochastic economic emission load dispatch , 1993 .

[5]  Sushil Kumar,et al.  Nonconvex economic load dispatch using an efficient real-coded genetic algorithm , 2009, Appl. Soft Comput..

[6]  Hans-Paul Schwefel,et al.  Evolution strategies – A comprehensive introduction , 2002, Natural Computing.

[7]  H.C.S. Rughooputh,et al.  Environmental/economic dispatch of thermal units using an elitist multiobjective evolutionary algorithm , 2003, IEEE International Conference on Industrial Technology, 2003.

[8]  M. A. Abido,et al.  A niched Pareto genetic algorithm for multiobjective environmental/economic dispatch , 2003 .

[9]  Yaonan Wang,et al.  Environmental/economic power dispatch problem using multi-objective differential evolution algorithm , 2010 .

[10]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[11]  D. P. Kothari,et al.  Combined Economic and Emission Dispatch Using Improved Backpropagation Neural Network , 2000 .

[12]  P. K. Chattopadhyay,et al.  Solving economic emission load dispatch problems using hybrid differential evolution , 2011, Appl. Soft Comput..

[13]  P. K. Chattopadhyay,et al.  Evolutionary programming techniques for economic load dispatch , 2003, IEEE Trans. Evol. Comput..

[14]  Kalyanmoy Deb,et al.  A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II , 2000, PPSN.

[15]  J. Nanda,et al.  ECONOMIC-EMISSION LOAD DISPHTCH THROUGH GOAL PROGRAMMING TECHNIIJUES , 1988 .