Application of modified NSGA-II algorithm to Combined Economic and Emission Dispatch problem

This paper presents a modified NSGA-II algorithm for economic and emission dispatch problem. The available NSGA-II has the drawbacks such as, lack of uniform diversity in obtained non-dominated solutions and absence of a lateral diversity-preserving operator among the currently-best non-dominated solutions. These two drawbacks have been overcome by introducing dynamic crowding distance (DCD) and controlled elitism (CE) into the NSGA-II. The developed algorithm MNSGA-II has been applied to a standard IEEE 14-, 30-, 57- and 118-bus systems to check its applicability. The results authenticate the potential and effectiveness of MNSGA-II algorithm for CEED problem. Moreover in order to certify the results arrived, four different performance metrics gamma, delta, minimum spacing and Inverted Generational Distance (IGD) were used. These metrics will be supportive for evaluating the closeness to the reference pareto-optimal front. An approach based on Technique for Ordering Preferences by Similarity to Ideal Solution (TOPSIS) is used to decide the choice of a solution from all pareto-optimal solutions, as the best compromise solution.

[1]  Li Xuebin.  RETRACTED: Study of multi-objective optimization and multi-attribute decision-making for economic and environmental power dispatch , 2009 .

[2]  Kwang Y. Lee,et al.  Application of Multi Objective Evolutionary Programming to Combined Economic Emission Dispatch Problem , 2007, 2007 International Joint Conference on Neural Networks.

[3]  H. Sasaki,et al.  Multiobjective optimal generation dispatch based on probability security criteria , 1988 .

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

[5]  Vahid Vahidinasab,et al.  Multiobjective environmental/techno-economic approach for strategic bidding in energy markets , 2009 .

[6]  Mohammad Ali Abido,et al.  Multiobjective evolutionary algorithms for electric power dispatch problem , 2006, IEEE Transactions on Evolutionary Computation.

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

[8]  Narayana Prasad Padhy,et al.  Comparison and application of evolutionary programming techniques to combined economic emission dispatch with line flow constraints , 2003 .

[9]  R. Bharathi,et al.  Optimization of combined economic and emission dispatch problem — A comparative study , 2007, 2007 International Power Engineering Conference (IPEC 2007).

[10]  Malabika Basu,et al.  Dynamic economic emission dispatch using nondominated sorting genetic algorithm-II , 2008 .

[11]  Allen J. Wood,et al.  Power Generation, Operation, and Control , 1984 .

[12]  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.

[13]  S. A. Al-Baiyat,et al.  Economic load dispatch multiobjective optimization procedures using linear programming techniques , 1995 .

[14]  G. Sheblé,et al.  Power generation operation and control — 2nd edition , 1996 .

[15]  D. P. Kothari,et al.  Security Constrained Unit Commitment Problem with Operational, Power Flow and Environmental Constraints , 2009 .

[16]  N. S. Babu,et al.  Analytical solution for combined economic and emissions dispatch , 2008 .

[17]  A. C. Liew,et al.  Multiobjective generation scheduling using fuzzy optimal search technique , 1994 .

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

[19]  Jun Wu,et al.  Dynamic Crowding Distance?A New Diversity Maintenance Strategy for MOEAs , 2008, 2008 Fourth International Conference on Natural Computation.