Optimal placement and sizing of distributed generation in distribution power system based on multi-objective harmony search algorithm

This study deals with the optimal placement and sizing issue in distribution power system with distributed generation (DG). This issue is a sophisticated multi-objective optimization problem with constraints of investment and power system operation demands. In this paper, minimum power loss, minimum voltage deviation and maximal voltage stability margin are taken into account for the optimization of DG planning. To solve the comprehensive multi-objective optimization problem, a meta-heuristic searching algorithm harmony search (HS) is improved with the fast non-dominated sorting approach, which forms the novel intelligent optimization algorithm called multi-objective harmony search (MOHS). Simulation on IEEE 33-bus test system and comparisons with some other multi-objective evolutionary algorithms yield promising results for optimal placement and sizing of DG based on MOHS.

[1]  Hossein Nezamabadi-pour,et al.  An Improved Multi-Objective Harmony Search for Optimal Placement of DGs in Distribution Systems , 2013, IEEE Transactions on Smart Grid.

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

[3]  Zong Woo Geem,et al.  A New Heuristic Optimization Algorithm: Harmony Search , 2001, Simul..

[4]  Weidong Xiao,et al.  Determining Optimal Location and Size of Distributed Generation Resources Considering Harmonic and Protection Coordination Limits , 2013, IEEE Transactions on Power Systems.

[5]  A. Padilha-Feltrin,et al.  Distributed Generation Impact Evaluation Using a Multi-Objective Tabu Search , 2009, 2009 15th International Conference on Intelligent System Applications to Power Systems.

[6]  Jian S. Dai,et al.  2010 IEEE Conference on Robotics, Automation and Mechatronics , 2010 .

[7]  Mohammad Ali Abido,et al.  Multiobjective evolutionary algorithms for electric power dispatch problem , 2006, IEEE Transactions on Evolutionary Computation.

[8]  P. K. Chattopadhyay,et al.  Hybrid Differential Evolution With Biogeography-Based Optimization for Solution of Economic Load Dispatch , 2010, IEEE Transactions on Power Systems.

[9]  Ehab F. El-Saadany,et al.  DG allocation for benefit maximization in distribution networks , 2013, IEEE Transactions on Power Systems.

[10]  Hans B. Puttgen,et al.  Distributed generation: Semantic hype or the dawn of a new era? , 2003 .

[11]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[12]  Mahmoud-Reza Haghifam,et al.  DG allocation with application of dynamic programming for loss reduction and reliability improvement , 2011 .

[13]  Taher Niknam,et al.  Multiobjective economic/emission dispatch by multiobjective θ-particle swarm optimisation , 2012 .

[14]  Lothar Thiele,et al.  Comparison of Multiobjective Evolutionary Algorithms: Empirical Results , 2000, Evolutionary Computation.