Optimal Reactive Power Dispatch Using Differential Evolution Algorithm with Voltage Profile Control

This paper proposes an efficient differential evolution (DE) algorithm for the solution of the optimal reactive power dispatch (ORPD) problem. The main objective of ORPD is to minimize the total active power loss with optimal setting of control variables. The continuous control variables are generator bus voltage magnitudes. The discrete control variables are transformer tap settings and reactive power of shunt compensators. In DE algorithm the other form of differential mutation operator is used. It consists to add the global best individual in the differential mutation operator to improve the solution. The DE algorithm solution has been tested on the standard IEEE 30-Bus test system to minimize the total active power loss without and with voltage profile improvement. The results have been compared to the other heuristic methods such as standard genetic algorithm and particle swarm optimization method. Finally, simulation results show that this method converges to better solutions.

[1]  James A. Momoh,et al.  Improved interior point method for OPF problems , 1999 .

[2]  K. S. Swarup,et al.  Network loss minimization with voltage security using differential evolution , 2008 .

[3]  Q. H. Wu,et al.  Optimal reactive power dispatch using an adaptive genetic algorithm , 1997 .

[4]  R. E. Marsten,et al.  A direct nonlinear predictor-corrector primal-dual interior point algorithm for optimal power flows , 1993 .

[5]  R. Yokoyama,et al.  Improved genetic algorithms for optimal power flow under both normal and contingent operation states , 1997 .

[6]  K. S. Swarup,et al.  Differential evolution approach for optimal reactive power dispatch , 2008, Appl. Soft Comput..

[7]  S. Granville Optimal reactive dispatch through interior point methods , 1994 .

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

[9]  J.G. Vlachogiannis,et al.  A Comparative Study on Particle Swarm Optimization for Optimal Steady-State Performance of Power Systems , 2006, IEEE Transactions on Power Systems.

[10]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[11]  K. Lee,et al.  A United Approach to Optimal Real and Reactive Power Dispatch , 1985, IEEE Transactions on Power Apparatus and Systems.

[12]  William F. Tinney,et al.  Optimal Power Flow Solutions , 1968 .

[13]  R. Adapa,et al.  The quadratic interior point method solving power system optimization problems , 1994 .

[14]  David Sun,et al.  Multi-year multi-case optimal VAR planning , 1990 .

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

[16]  M. Abedi,et al.  Differential Evolution Algorithm for Security-Constrained Energy and Reserve Optimization Considering Credible Contingencies , 2011, IEEE Transactions on Power Systems.