Oppositional cuckoo optimization algorithm to solve DG allocation problem of radial distribution system

Optimal sizing and placement of distributed generator (DG) units in radial distribution system are becoming very attractive to researchers these days. In this paper, cuckoo optimization algorithm (COA) is applied in order to find the optimal location of DG to optimize the power loss in radial distribution system. Furthermore, an oppositional based learning (OBL) is introduced with COA for improving the convergence speed of COA. The proposed oppositional COA (OCOA) methodology is successfully applied to the 33 bus and 69 bus radial distribution systems in order to show the effectiveness and efficiency of this optimization. The simulation result of proposed methods are compared with the other population based optimization like genetic algorithm (GA), particle swarm optimization (PSO), GA/PSO, bacteria foraging optimization algorithm (BFOA) in order to show the usefulness of this proposed approach.

[1]  Shahryar Rahnamayan,et al.  Opposition-Based Differential Evolution Algorithms , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[2]  Ramesh C. Bansal,et al.  Analytical strategies for renewable distributed generation integration considering energy loss minimization , 2013 .

[3]  Ranjit Roy,et al.  Enhancement of loading capacity of distribution system through distributed generator placement considering techno-economic benefits with load growth , 2014 .

[4]  M. P. Selvan,et al.  Hierarchical Agglomerative Clustering Algorithm method for distributed generation planning , 2014 .

[5]  Ramin Rajabioun,et al.  Cuckoo Optimization Algorithm , 2011, Appl. Soft Comput..

[6]  J. Teng A direct approach for distribution system load flow solutions , 2003 .

[7]  Nadarajah Mithulananthan,et al.  AN ANALYTICAL APPROACH FOR DG ALLOCATION IN PRIMARY DISTRIBUTION NETWORK , 2006 .

[8]  Lin Han,et al.  A Novel Opposition-Based Particle Swarm Optimization for Noisy Problems , 2007, Third International Conference on Natural Computation (ICNC 2007).

[9]  Ahmed N. Abdalla,et al.  Optimal placement and sizing of distributed generators based on a novel MPSI index , 2014 .

[10]  A. C. Rueda-Medina,et al.  A mixed-integer linear programming approach for optimal type, size and allocation of distributed generation in radial distribution systems , 2013 .

[11]  Chandan Kumar Chanda,et al.  Placement of wind and solar based DGs in distribution system for power loss minimization and voltage stability improvement , 2013 .

[12]  M.M.A. Salama,et al.  Opposition-Based Differential Evolution , 2008, IEEE Transactions on Evolutionary Computation.

[13]  Tuba Gozel,et al.  An analytical method for the sizing and siting of distributed generators in radial systems , 2009 .

[14]  A MohamedImran,et al.  Optimal size and siting of multiple distributed generators in distribution system using bacterial foraging optimization , 2014, Swarm Evol. Comput..

[15]  Yunhua Li,et al.  Optimal Placement and Sizing of Distributed Generation via an Improved Nondominated Sorting Genetic Algorithm II , 2015, IEEE Transactions on Power Delivery.

[16]  Hamid R. Tizhoosh,et al.  Applying Opposition-Based Ideas to the Ant Colony System , 2007, 2007 IEEE Swarm Intelligence Symposium.

[17]  Kyu-Ho Kim,et al.  Dispersed generator placement using fuzzy-GA in distribution systems , 2002, IEEE Power Engineering Society Summer Meeting,.

[18]  Hui Wang,et al.  Opposition-based particle swarm algorithm with cauchy mutation , 2007, 2007 IEEE Congress on Evolutionary Computation.

[19]  Hamid R. Tizhoosh,et al.  Opposition-Based Learning: A New Scheme for Machine Intelligence , 2005, International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06).

[20]  Krischonme Bhumkittipich,et al.  Optimal Placement and Sizing of Distributed Generation for Power Loss Reduction Using Particle Swarm Optimization , 2013 .

[21]  Nadarajah Mithulananthan,et al.  Multiple Distributed Generator Placement in Primary Distribution Networks for Loss Reduction , 2013, IEEE Transactions on Industrial Electronics.

[22]  Nadarajah Mithulananthan,et al.  Analytical Expressions for DG Allocation in Primary Distribution Networks , 2010, IEEE Transactions on Energy Conversion.

[23]  Carmen L. T. Borges,et al.  Optimal distributed generation allocation for reliability, losses, and voltage improvement , 2006 .

[24]  A. R. Wallace,et al.  Optimal power flow evaluation of distribution network capacity for the connection of distributed generation , 2005 .

[25]  N. C. Sahoo,et al.  A fuzzy genetic approach for network reconfiguration to enhance voltage stability in radial distribution systems , 2006 .

[26]  Liu Yang,et al.  Size and Location of Distributed Generation in Distribution System Based on Immune Algorithm , 2012 .

[27]  Mohammad Reza Mohammadi,et al.  Optimal placement of multitypes DG as independent private sector under pool/hybrid power market using GA-based Tabu Search method , 2013 .

[28]  Marcus Randall,et al.  Anti-pheromone as a Tool for Better Exploration of Search Space , 2002, Ant Algorithms.

[29]  Mohammad Hassan Moradi,et al.  A Combination of Genetic Algorithm and Particle Swarm Optimization for Optimal Distributed Generation Location and Sizing in Distribution Systems with Fuzzy Optimal Theory , 2012 .

[30]  Tsai-Hsiang Chen,et al.  Dual Genetic Algorithm-Based Approach to Fast Screening Process for Distributed-Generation Interconnections , 2011, IEEE Transactions on Power Delivery.

[31]  Antonio José Gil Mena,et al.  Optimal distributed generation location and size using a modified teaching–learning based optimization algorithm , 2013 .

[32]  Dan Simon,et al.  Oppositional biogeography-based optimization , 2009, 2009 IEEE International Conference on Systems, Man and Cybernetics.

[33]  M. M. Aman,et al.  Optimal placement and sizing of a DG based on a new power stability index and line losses , 2012 .