Multi-objective optimal power flow based on improved strength Pareto evolutionary algorithm

An improved strength Pareto evolutionary algorithm is proposed to solve the multi-objective optimal power flow problem. The fuel cost and emission are considered as two objective functions for the optimal flow problem. In the proposed algorithm, there are three aspects of improvements in the original strength Pareto evolutionary algorithm. First, the external archive population is only composed of the variable size of non-dominated individuals in environmental selection operator. Secondly, the Euclidean distance between the elite individuals and its k-th neighboring individuals is adopted to update the external archive population. Thirdly, the local search strategy is embedded into strength Pareto evolutionary algorithm. The performance of the proposed method has been tested on the IEEE 30-bus and IEEE 57-bus systems. The simulation results show that the proposed method is able to produce well distributed Pareto optimal solutions for the multi-objective optimal power flow problem. Compared with the results obtained by other methods, the superiority of the proposed method is verified.

[1]  Taher Niknam,et al.  A modified shuffle frog leaping algorithm for multi-objective optimal power flow , 2011 .

[2]  C. Fuerte-Esquivel,et al.  Advanced SVC models for Newton-Raphson load flow and Newton optimal power flow studies , 2000 .

[3]  R. Tavakkoli-Moghaddam,et al.  A new hybrid multi-objective Pareto archive PSO algorithm for a bi-objective job shop scheduling problem , 2011, Expert Syst. Appl..

[4]  K. S. Swarup,et al.  Multiagent based differential evolution approach to optimal power flow , 2012, Appl. Soft Comput..

[5]  Yuehua Huang,et al.  A new quantum inspired chaotic artificial bee colony algorithm for optimal power flow problem , 2015 .

[6]  Mojtaba Ghasemi,et al.  Multi-objective optimal power flow considering the cost, emission, voltage deviation and power losses using multi-objective modified imperialist competitive algorithm , 2014 .

[7]  To Fu Ma,et al.  Reduced gradient method combined with augmented Lagrangian and barrier for the optimal power flow problem , 2008, Appl. Math. Comput..

[8]  Manoj Kumar Tiwari,et al.  Interactive Particle Swarm: A Pareto-Adaptive Metaheuristic to Multiobjective Optimization , 2008, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[9]  R. Salgado,et al.  Optimal power flow solutions through multi-objective programming , 2012 .

[10]  Marco Laumanns,et al.  SPEA2: Improving the strength pareto evolutionary algorithm , 2001 .

[11]  H. R. E. H. Bouchekara,et al.  Optimal power flow using black-hole-based optimization approach , 2014, Appl. Soft Comput..

[12]  M. A. Abido,et al.  Optimal power flow using the league championship algorithm: A case study of the Algerian power system , 2014 .

[13]  Uthen Leeton,et al.  Optimal power flow solution using improved harmony search method , 2013, Appl. Soft Comput..

[14]  M. Tripathy,et al.  Security constrained optimal power flow solution of wind-thermal generation system using modified bacteria foraging algorithm , 2015 .

[15]  T. Niknam,et al.  A modified teaching–learning based optimization for multi-objective optimal power flow problem , 2014 .

[16]  Kala Meah,et al.  Genetic evolving ant direction particle swarm optimization algorithm for optimal power flow with non-smooth cost functions and statistical analysis , 2013, Appl. Soft Comput..

[17]  Mahmoud A. Abo-Sinna,et al.  A solution to the optimal power flow using genetic algorithm , 2004, Appl. Math. Comput..

[18]  Samir Sayah,et al.  Optimal power flow with environmental constraint using a fast successive linear programming algorithm: Application to the algerian power system , 2008 .

[19]  Serhat Duman,et al.  Optimal power flow using gravitational search algorithm , 2012 .

[20]  L. Wehenkel,et al.  Experiments with the interior-point method for solving large scale optimal power flow problems , 2013 .