A new optimization technique for optimal reactive power scheduling using Jaya algorithm

Optimal reactive power scheduling is one of the most complex and challenging problems in modern power system operation. This has recently gained intense attention from the scientist and researchers all over the world due to the introduction of liberalization or deregulation of electricity industry where reactive power is being traded as an ancillary service. This paper proposes an improved algorithm for optimal reactive power scheduling (ORPS) problem and a recently introduced simple yet powerful optimization technique called Jaya Algorithm (JA) is utilized for the same. The main advantage of the Jaya Algorithm is that it does not require any problem dependent control parameters. The optimal reactive power scheduling (ORPS) problem is formulated considering minimization of both losses and system cost satisfying several equality and inequality constraints. The algorithm described in this work is implemented on a standard test system for verifying its effectiveness and randomness. A comparison study of the simulation results is also performed with other modern heuristic techniques. The simulation results prove that it can produce acceptable results.

[1]  Chuangxin Guo,et al.  A multiagent-based particle swarm optimization approach for optimal reactive power dispatch , 2005 .

[2]  P. Subbaraja,et al.  Optimal reactive power dispatch using self-adaptive real coded genetic algorithm , 2016 .

[3]  Kenichi Aoki,et al.  Optimal VAr planning by approximation method for recursive mixed-integer linear programming , 1988 .

[4]  Kwang Y. Lee,et al.  Optimal Real and Reactive Power Control Using Linear Programming , 1993 .

[5]  Q. H. Wu,et al.  Power system optimal reactive power dispatch using evolutionary programming , 1995 .

[6]  Kazem Zare,et al.  Optimal allocation of capacitors in radial/mesh distribution systems using mixed integer nonlinear programming approach , 2014 .

[7]  Lilan Liu,et al.  Optimal reactive power dispatch by improved GSA-based algorithm with the novel strategies to handle constraints , 2017, Appl. Soft Comput..

[8]  Yuancheng Li,et al.  A hybrid artificial bee colony assisted differential evolution algorithm for optimal reactive power flow , 2013 .

[9]  D. Bhagwan Das,et al.  Reactive power dispatch with a hybrid stochastic search technique , 2002 .

[10]  Loi Lei Lai,et al.  Application of evolutionary programming to reactive power planning-comparison with nonlinear programming approach , 1997 .

[11]  Seyed Abbas Taher,et al.  A NEW APPROACH FOR OPTIMAL CAPACITOR PLACEMENT AND SIZING IN UNBALANCED DISTORTED DISTRIBUTION SYSTEMS USING HYBRID HONEY BEE COLONY ALGORITHM , 2013 .

[12]  Jan Taler,et al.  Dimensional optimization of a micro-channel heat sink using Jaya algorithm , 2016 .

[13]  R. Rao.  Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems , 2016 .

[14]  Ulaş Kılıç,et al.  Artificial bee colony algorithm solution for optimal reactive power flow , 2012, Appl. Soft Comput..

[15]  A. J. Urdaneta,et al.  A hybrid genetic algorithm for optimal reactive power planning based upon successive linear programming , 1999 .

[16]  Behrooz Vahidi,et al.  Hybrid shuffled frog leaping algorithm and Nelder-Mead simplex search for optimal reactive power dispatch , 2011 .

[17]  D. Devaraj,et al.  Multi Objective Differential Evolution approach for voltage stability constrained reactive power planning problem , 2014 .

[18]  Weerakorn Ongsakul,et al.  Optimal Reactive Power Dispatch Using Improved Pseudo-gradient Search Particle Swarm Optimization , 2016 .

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