A reactive power planning procedure considering iterative identification of VAR candidate buses

This article proposes two-step procedure for solving the reactive power planning (RPP) problem. An iterative method is introduced in the first step to place the additional sources of reactive power and their associated maximum sizes. In the second step, several integrated strategies of differential evolution (DE) are suggested to optimize the RPP variables. Three types of objective function are investigated which aims at minimizing system power losses, minimizing the costs of operation and VAR investment and improving the voltage profile distribution at load buses. The strategies performance is examined on IEEE 30-bus test system and on the West Delta network as a real Egyptian section. The evolution of the system considering the annual growth rate of peak load in the Egyptian system has been taken into consideration at different loading levels. Application of the proposed method is carried out on large-scale power system of 354-bus test system. The strategies robustness and consistency are compared to DE, genetic algorithm and particle swarm optimizer. The proposed two-step procedure using the proposed DE strategy is assessed compared to single-step RPP procedure. Furthermore, its mutation and crossover scales are optimally specified. Simulation outcomes denote that the proposed DE strategy is excessively superior, more powerful and consistent than the other compared optimizers which indicate that the proposed strategy of DE algorithm can be very efficient to solve the RPP. The proposed strategies are proven as alternative solution strategies, especially for large-scale power systems.

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

[2]  Leandro dos Santos Coelho,et al.  Earthworm optimisation algorithm: a bio-inspired metaheuristic algorithm for global optimisation problems , 2018, Int. J. Bio Inspired Comput..

[3]  Shin-Ju Chen,et al.  Comparative study of evolutionary computation methods for active-reactive power dispatch , 2012 .

[4]  Ragab A. El-Sehiemy,et al.  A novel adequate bi-level reactive power planning strategy , 2016 .

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

[6]  Francois Vallee,et al.  A new approach based on the experimental design method for the improvement of the operational efficiency in Medium Voltage distribution networks , 2015 .

[7]  P. N. Suganthan,et al.  Differential Evolution: A Survey of the State-of-the-Art , 2011, IEEE Transactions on Evolutionary Computation.

[8]  K. R. Vadivelu,et al.  Soft Computing Technique Based Reactive Power Planning using Combining Multi-objective Optimization with Improved Differential Evolution , 2014 .

[9]  Shih-Cheng Horng,et al.  Iterative simulation optimization approach for optimal volt-ampere reactive sources planning , 2012 .

[10]  Carlos Henggeler Antunes,et al.  Robustness Analysis in Evolutionary Multi-Objective Optimization Applied to VAR Planning in Electrical Distribution Networks , 2009, EvoCOP.

[11]  Chaohua Dai,et al.  Reactive power dispatch considering voltage stability with seeker optimization algorithm , 2009 .

[12]  Ragab A. El-Sehiemy,et al.  Integrated Strategies of Backtracking Search Optimizer for Solving Reactive Power Dispatch Problem , 2018, IEEE Systems Journal.

[13]  Gai-Ge Wang,et al.  A New Improved Firefly Algorithm for Global Numerical Optimization , 2014 .

[14]  M. Saravanan,et al.  Application of particle swarm optimization technique for optimal location of FACTS devices considering cost of installation and system loadability , 2007 .

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

[16]  Ragab A. El Sehiemy,et al.  Multi-objective fuzzy-based procedure for enhancing reactive power management , 2013 .

[17]  Naoto Yorino,et al.  Multi-load level reactive power planning considering slow and fast VAR devices by means of particle swarm optimisation , 2008 .

[18]  Sakthivel Padaiyatchi,et al.  OPF-based reactive power planning and voltage stability limit improvement under single line outage contingency condition through evolutionary algorithms , 2013 .

[19]  R D Zimmerman,et al.  MATPOWER: Steady-State Operations, Planning, and Analysis Tools for Power Systems Research and Education , 2011, IEEE Transactions on Power Systems.

[20]  Amir Hossein Gandomi,et al.  A hybrid method based on krill herd and quantum-behaved particle swarm optimization , 2015, Neural Computing and Applications.

[21]  L.M. Tolbert,et al.  Voltage stability constrained optimal power flow (VSCOPF) with two sets of variables (TSV) for reactive power planning , 2008, 2008 IEEE/PES Transmission and Distribution Conference and Exposition.

[22]  R. A. El-Sehiemy,et al.  Optimal reactive power dispatch using ant colony optimization algorithm , 2011 .

[23]  Xianzhong Duan,et al.  Study of differential evolution for optimal reactive power flow , 2007 .

[24]  Ragab A. El-Sehiemy,et al.  Solving multi-objective optimal power flow problem via forced initialised differential evolution algorithm , 2016 .

[25]  Omar H. Abdalla,et al.  A systematic sensitivity approach for optimal reactive power planning , 1990, Proceedings of the Twenty-Second Annual North American Power Symposium.

[26]  K. Thanushkodi,et al.  Reactive Power Planning considering the highest load buses using Evolutionary Programming , 2009 .

[27]  S. Ramesh,et al.  Application of modified NSGA-II algorithm to multi-objective reactive power planning , 2012, Appl. Soft Comput..

[28]  N. Kumarappan,et al.  Genetic algorithm based reactive power optimization under deregulation , 2007 .

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

[30]  P. Venkatesh,et al.  Application of PSO technique for optimal location of FACTS devices considering system loadability and cost of installation , 2005, 2005 International Power Engineering Conference.

[31]  L.M. Tolbert,et al.  Survey of reactive power planning methods , 2005, IEEE Power Engineering Society General Meeting, 2005.

[32]  Amir Hossein Gandomi,et al.  Chaotic cuckoo search , 2015, Soft Computing.

[33]  Abdullah M. Shaheen,et al.  A review of meta-heuristic algorithms for reactive power planning problem , 2015, Ain Shams Engineering Journal.

[34]  Igor Kuzle,et al.  Two-stage optimization algorithm for short-term reactive power planning based on zonal approach , 2011 .

[35]  M. Willjuice Iruthayarajan,et al.  Reactive power planning with voltage stability enhancement using covariance matrix adapted evolution strategy , 2011 .

[36]  James D. McCalley,et al.  Optimal planning of static and dynamic reactive power resources , 2014 .

[37]  Javier Contreras,et al.  A Multi-Stage Stochastic Non-Linear Model for Reactive Power Planning Under Contingencies , 2013, IEEE Transactions on Power Systems.

[38]  Bala Venkatesh,et al.  An efficient multi-objective fuzzy logic based successive LP method for optimal reactive power planning , 2001 .

[39]  Youcef Amrane,et al.  A new Optimal reactive power planning based on Differential Search Algorithm , 2015 .

[40]  Masoud Rashidinejad,et al.  An application of hybrid heuristic method to solve concurrent transmission network expansion and reactive power planning , 2013 .

[41]  Zhihua Cui,et al.  Monarch butterfly optimization , 2015, Neural Computing and Applications.

[42]  Ragab A. El-Sehiemy,et al.  A novel fruit fly framework for multi-objective shape design of tubular linear synchronous motor , 2017, The Journal of Supercomputing.

[43]  Mohammad Ali Abido,et al.  Differential evolution algorithm for optimal reactive power dispatch , 2011 .

[44]  P. Renuga,et al.  Reactive Power Planning using Differential Evolution: Comparison with Real GA and Evolutionary Programming , 2009 .

[45]  Mohammad Shahidehpour,et al.  A decentralized approach for optimal reactive power dispatch using a Lagrangian decomposition method , 2012 .

[46]  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..

[47]  D. Devaraj,et al.  Adaptive Particle Swarm Optimization Approach for Optimal Reactive Power Planning , 2008, 2008 Joint International Conference on Power System Technology and IEEE Power India Conference.

[48]  D. P. Kothari,et al.  Improved particle swarm optimization applied to reactive power reserve maximization , 2010 .

[49]  Adel A. Abou El Ela,et al.  A fuzzy-based maximal reactive power benefits procedure , 2014, IEEE PES Innovative Smart Grid Technologies, Europe.

[50]  Leon M. Tolbert,et al.  A Framework to Quantify the Economic Benefit from Local VAR Compensation , 2013 .