Symbiotic organisms search algorithm for optimal power flow problem based on valve-point effect and prohibited zones

Abstract In this study, symbiotic organisms search (SOS) stochastic method is proposed to solve the optimal power flow (OPF) problem with valve-point effect and prohibited zones, which is one of the most important problems of the modern power system. The SOS approach is defined as the symbiotic relationships observed between two organisms in the ecosystem, which do not need the control parameters unlike other meta-heuristic algorithms in the literature. The effectiveness of the proposed SOS method is tested on modified IEEE 30-bus test system. The OPF problem is considered with four different test cases, such as (1) without valve-point effect and prohibited zones, (2) with valve-point effect, (3) with prohibited zones and (4) with valve-point effect and prohibited zones. The obtained results from the SOS algorithm are compared with the other optimization techniques in the literature. The obtained comparison results indicate that proposed approach is effective to reach optimal solution for the OPF problem.

[1]  Vivekananda Mukherjee,et al.  Solution of optimal power flow using chaotic krill herd algorithm , 2015 .

[2]  Seyed Mohammad Mirjalili,et al.  Multi-Verse Optimizer: a nature-inspired algorithm for global optimization , 2015, Neural Computing and Applications.

[3]  M. A. Abido,et al.  Optimal power flow using Teaching-Learning-Based Optimization technique , 2014 .

[4]  Francisco D. Galiana,et al.  A survey of the optimal power flow literature , 1991 .

[5]  Kit Po Wong,et al.  Evolutionary programming based optimal power flow algorithm , 1999 .

[6]  Shakil Akhtar,et al.  A new hybrid approach for the solution of nonconvex economic dispatch problem with valve-point effects , 2010 .

[7]  G. Emily Manoranjitham,et al.  Retraction notice to “Application of firefly algorithm on optimal power flow control incorporating simplified impedance UPFC model” [Int. J. Electr. Power Energy Syst. 71 (2015) 358–363] , 2016 .

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

[9]  M. Basu,et al.  Multi-objective optimal power flow with FACTS devices , 2011 .

[10]  Majid Nayeripour,et al.  Modified Honey Bee Mating Optimisation to solve dynamic optimal power flow considering generator constraints , 2011 .

[11]  Sakti Prasad Ghoshal,et al.  Particle swarm optimization with an aging leader and challengers algorithm for optimal power flow problem with FACTS devices , 2015 .

[12]  Linda Sliman,et al.  Economic Power Dispatch of Power System with Pollution Control using Multiobjective Ant Colony Optimization , 2007 .

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

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

[15]  M. A. Abido,et al.  Optimal power flow using particle swarm optimization , 2002 .

[16]  Xin-She Yang,et al.  Binary bat algorithm , 2013, Neural Computing and Applications.

[17]  Francisco Herrera,et al.  A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms , 2011, Swarm Evol. Comput..

[18]  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 .

[19]  Ulaş Kılıç,et al.  Backtracking search algorithm-based optimal power flow with valve point effect and prohibited zones , 2014, Electrical Engineering.

[20]  A. Breipohl,et al.  Reserve constrained economic dispatch with prohibited operating zones , 1993 .

[21]  Celal Yaşar,et al.  A new hybrid approach for nonconvex economic dispatch problem with valve-point effect , 2011 .

[22]  Serdar Özyön,et al.  Solution to non-convex economic dispatch problem with valve point effects by incremental artificial bee colony with local search , 2013, Appl. Soft Comput..

[23]  M. R. Irving,et al.  Enhanced Newton-Raphson algorithm for normal, controlled and optimal powerflow solutions using column exchange techniques , 1994 .

[24]  O. Alsac,et al.  Optimal Load Flow with Steady-State Security , 1974 .

[25]  Adam Semlyen,et al.  Hydrothermal optimal power flow based on a combined linear and nonlinear programming methodology , 1989 .

[26]  Andrew Lewis,et al.  S-shaped versus V-shaped transfer functions for binary Particle Swarm Optimization , 2013, Swarm Evol. Comput..

[27]  K. Fahd,et al.  Optimal Power Flow Using Tabu Search Algorithm , 2002 .

[28]  Weerakorn Ongsakul,et al.  Optimal power flow with FACTS devices by hybrid TS/SA approach , 2002 .

[29]  Belkacem Mahdad,et al.  Blackout risk prevention in a smart grid based flexible optimal strategy using Grey Wolf-pattern search algorithms , 2015 .

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

[31]  Bin Zhou,et al.  An improved GSO method for discontinuous non-convex transient stability constrained optimal power flow with complex system model , 2015 .

[32]  Belkacem Mahdad,et al.  Security optimal power flow considering loading margin stability using hybrid FFA-PS assisted with brainstorming rules , 2015, Appl. Soft Comput..

[33]  A. Semlyen,et al.  Hydrothermal Optimal Power Flow Based on a Combined Linear and Nonlinear Programming Methodology , 1989, IEEE Power Engineering Review.

[34]  Canbing Li,et al.  Improved group search optimization method for optimal power flow problem considering valve-point loading effects , 2015, Neurocomputing.

[35]  V. Quintana,et al.  Improving an interior-point-based OPF by dynamic adjustments of step sizes and tolerances , 1999 .

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

[37]  C. Thitithamrongchai,et al.  Self-adaptive Differential Evolution Based Optimal Power Flow for Units with Non-smooth Fuel Cost Functions , 2007 .

[38]  Taher Niknam,et al.  A new hybrid algorithm for optimal power flow considering prohibited zones and valve point effect , 2012 .

[39]  K. Vaisakh,et al.  Multi-objective adaptive Clonal selection algorithm for solving environmental/economic dispatch and OPF problems with load uncertainty , 2013 .

[40]  Tarek Bouktir,et al.  OPTIMAL POWER DISPATCH FOR LARGE SCALE POWER SYSTEM USING STOCHASTIC SEARCH ALGORITHMS , 2008 .

[41]  Seyed Mohammad Mirjalili,et al.  Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm , 2015, Knowl. Based Syst..

[42]  Taher Niknam,et al.  Dynamic optimal power flow using hybrid particle swarm optimization and simulated annealing , 2013 .

[43]  M. Narimani,et al.  A novel approach to multi-objective optimal power flow by a new hybrid optimization algorithm considering generator constraints and multi-fuel type , 2013 .

[44]  Hasan Temurtas,et al.  Particle swarm optimization algorithm for the solution of nonconvex economic dispatch problem with valve point effect , 2011, 2011 7th International Conference on Electrical and Electronics Engineering (ELECO).

[45]  Kerim Çetinkaya,et al.  Comparison of four different heuristic optimization algorithms for the inverse kinematics solution of a real 4-DOF serial robot manipulator , 2015, Neural Computing and Applications.

[46]  Samir Sayah,et al.  Modified differential evolution algorithm for optimal power flow with non-smooth cost functions , 2008 .

[47]  Andrew Lewis,et al.  Biogeography-based optimisation with chaos , 2014, Neural Computing and Applications.

[48]  Andrew Lewis,et al.  Adaptive gbest-guided gravitational search algorithm , 2014, Neural Computing and Applications.

[49]  H. Happ,et al.  Quadratically Convergent Optimal Power Flow , 1984, IEEE Transactions on Power Apparatus and Systems.

[50]  Vassilios Petridis,et al.  Optimal power flow by enhanced genetic algorithm , 2002 .

[51]  M. A. Abido,et al.  Optimal power flow using differential evolution algorithm , 2009 .

[52]  Francisco Herrera,et al.  A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 Special Session on Real Parameter Optimization , 2009, J. Heuristics.

[53]  K. S. Swarup,et al.  Multi Objective Harmony Search Algorithm For Optimal Power Flow , 2010 .

[54]  A. Karami,et al.  Artificial bee colony algorithm for solving multi-objective optimal power flow problem , 2013 .

[55]  Weerakorn Ongsakul,et al.  Optimal Power Flow by Improved Evolutionary Programming , 2006 .

[56]  Min-Yuan Cheng,et al.  Symbiotic Organisms Search: A new metaheuristic optimization algorithm , 2014 .

[57]  Aniruddha Bhattacharya,et al.  Solution of multi-objective optimal power flow using gravitational search algorithm , 2012 .

[58]  N Rajasekar,et al.  An enhanced bacterial foraging algorithm approach for optimal power flow problem including FACTS devices considering system loadability. , 2013, ISA transactions.

[59]  Yog Raj Sood,et al.  Evolutionary programming based optimal power flow and its validation for deregulated power system analysis , 2007 .

[60]  G. Emily Manoranjitham,et al.  RETRACTED: Application of Firefly Algorithm On Optimal Power Flow Control Incorporating Simplified Impedance UPFC Model , 2015 .

[61]  Devendra K. Chaturvedi,et al.  Optimal Power Flow Solution: a GA-Fuzzy System Approach , 2006 .

[62]  P. K. Chattopadhyay,et al.  Application of biogeography-based optimisation to solve different optimal power flow problems , 2011 .