Radial Network Reconfiguration Using Genetic Algorithm Based on the Matroid Theory

This paper deals with distribution network (DN) reconfiguration for loss minimization. To solve this combinatorial problem, a genetic algorithm (GA) is considered. In order to enhance its ability to explore the solution space, efficient genetic operators are developed. After a survey of the existing DN topology description methods, a theoretical approach based on the graph and matroid theories (graphic matroid in particular) is considered. These concepts are used in order to propose new intelligent and effective GA operators for efficient mutation and crossover well dedicated to the DN reconfiguration problem. All resulting individuals after GA operators are claimed to be feasible (radial) configurations. Moreover, the presented approach is valid for planar or nonplanar DN graph topologies and avoids tedious mesh checks for the topology constraint validation. The proposed method is finally compared to some previous topology coding techniques used by other authors. The results show smaller or at least equal power losses with considerably less computation effort.

[1]  Bertrand Raison,et al.  Flexible Electric Infrastructures for Advanced Distribution Automation , 2006 .

[2]  E. Lopez,et al.  Minimal loss reconfiguration using genetic algorithms with restricted population and addressed operators: real application , 2006, IEEE Transactions on Power Systems.

[3]  S. Sivanagaraju,et al.  An Efficient Genetic Algorithm for Loss Minimum Distribution System Reconfiguration , 2006 .

[4]  D. Das.  A fuzzy multiobjective approach for network reconfiguration of distribution systems , 2006, IEEE Transactions on Power Delivery.

[5]  Johann Dréo,et al.  Metaheuristics for Hard Optimization: Methods and Case Studies , 2005 .

[6]  A. G. Expósito,et al.  Path-based distribution network modeling: application to reconfiguration for loss reduction , 2005, IEEE Transactions on Power Systems.

[7]  Ying-Yi Hong,et al.  Determination of network configuration considering multiobjective in distribution systems using genetic algorithms , 2005, IEEE Transactions on Power Systems.

[8]  M.G. Da Silva,et al.  Distribution network reconfiguration with reliability constraints , 2004, 2004 International Conference on Power System Technology, 2004. PowerCon 2004..

[9]  Ji Wang,et al.  The improved clonal genetic algorithm & its application in reconfiguration of distribution networks , 2004, IEEE PES Power Systems Conference and Exposition, 2004..

[10]  Jin-O Kim,et al.  Optimal service restoration and reconfiguration of network using Genetic-Tabu algorithm , 2004 .

[11]  P. Bastard,et al.  Online reconfiguration considering variability demand: applications to real networks , 2004, IEEE Transactions on Power Systems.

[12]  H.C.S. Rughooputh,et al.  A real-parameter genetic algorithm for optimal network reconfiguration , 2003, IEEE International Conference on Industrial Technology, 2003.

[13]  Alexander Schrijver,et al.  Combinatorial optimization. Polyhedra and efficiency. , 2003 .

[14]  K. Tomsovic,et al.  Adaptive Power Flow Method for Distribution Systems with Dispersed Generation , 2002, IEEE Power Engineering Review.

[15]  Y. H. Song,et al.  Modern heuristics application to distribution system optimization , 2002, 2002 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.02CH37309).

[16]  J. Z. Zhu,et al.  Optimal reconfiguration of electrical distribution network using the refined genetic algorithm , 2002 .

[17]  T. Genji,et al.  Comparative Study of Modern Heuristic Algorithms to Service Restoration in Distribution Systems , 2001, IEEE Power Engineering Review.

[18]  D. Rajicic,et al.  A new method for handling PV nodes in backward/forward power flow for radial and weakly meshed networks , 2001, 2001 IEEE Porto Power Tech Proceedings (Cat. No.01EX502).

[19]  Thomas H. Cormen,et al.  Introduction to algorithms [2nd ed.] , 2001 .

[20]  Ming-Tong Tsay,et al.  Distribution feeder reconfiguration with refined genetic algorithm , 2000 .

[21]  Y.-T. Hsiao,et al.  Enhancement of restoration service in distribution systems using a combination fuzzy-GA method , 2000 .

[22]  I. Mareels,et al.  An efficient brute-force solution to the network reconfiguration problem , 2000 .

[23]  Fernando Alvarado,et al.  Solving power flow problems with a Matlab implementation of the power system applications data dictionary , 1999, Proceedings of the 32nd Annual Hawaii International Conference on Systems Sciences. 1999. HICSS-32. Abstracts and CD-ROM of Full Papers.

[24]  Fuzzy decision model for the reconfiguration of distribution networks using genetic algorithms , 1999 .

[25]  M. Moghavvemi,et al.  Maximizing radial voltage stability and load balancing via loss minimization in distribution networks , 1998, Proceedings of EMPD '98. 1998 International Conference on Energy Management and Power Delivery (Cat. No.98EX137).

[26]  M. Kitagawa,et al.  Implementation of genetic algorithm for distribution systems loss minimum re-configuration , 1992 .

[27]  V. Glamocanin.  Optimal loss reduction of distributed networks , 1990 .

[28]  Felix F. Wu,et al.  Network Reconfiguration in Distribution Systems for Loss Reduction and Load Balancing , 1989, IEEE Power Engineering Review.

[29]  D. Shirmohammadi,et al.  Reconfiguration of electric distribution networks for resistive line losses reduction , 1989 .

[30]  Dariush Shirmohammadi,et al.  Reconfiguration of Electric Distribution Networks for Resistive Line Loses Reduction , 1989, IEEE Power Engineering Review.

[31]  J. J. Grainger,et al.  Distribution feeder reconfiguration for loss reduction , 1988 .

[32]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[33]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .

[34]  J. A. Bondy,et al.  Graph Theory with Applications , 1978 .

[35]  J. Kruskal.  On the shortest spanning subtree of a graph and the traveling salesman problem , 1956 .

[36]  H. Whitney.  On the Abstract Properties of Linear Dependence , 1935 .