Minimization of Voltage Sag Costs by Optimal Reconfiguration of Distribution Network Using Genetic Algorithms

The paper describes genetic-algorithm (GA)-based optimization software for reconfiguration of a distribution network in order to minimize financial losses due to voltage sags. The developed methodology starts with a selected number of switches which generate various topologies. Load flows are then performed to evaluate the feasibility of topologies. For each feasible topology, fault analysis is performed to first calculate voltage sags at different buses in the network and then to calculate financial losses incurred by voltage sags at buses with sensitive industrial processes. The result of the optimization is the topology which yields the lowest voltage sag cost to customers. The main features of the GA include double-point crossover and adaptive mutation. The developed software tool is applied to the 295-bus generic distribution system.

[1]  J.V. Milanovic,et al.  Probabilistic assessment of financial losses due to interruptions and voltage sags - part II: practical implementation , 2006, IEEE Transactions on Power Delivery.

[2]  Luís Ferreira,et al.  Optimal distribution planning by evolutionary computation-how to make it work , 2001, 2001 IEEE/PES Transmission and Distribution Conference and Exposition. Developing New Perspectives (Cat. No.01CH37294).

[3]  J.V. Milanovic,et al.  Probabilistic assessment of equipment trips due to voltage sags , 2006, IEEE Transactions on Power Delivery.

[4]  Pedro M. S. Carvalho,et al.  On spanning-tree recombination in evolutionary large-scale network problems - application to electrical distribution planning , 2001, IEEE Trans. Evol. Comput..

[5]  Yonghua Song,et al.  Modern optimisation techniques in power systems , 1999 .

[6]  Y. Huang.  Enhanced genetic algorithm-based fuzzy multi-objective approach to distribution network reconfiguration , 2002 .

[7]  J.V. Milanovic,et al.  Stochastic prediction of voltage sags by considering the probability of the failure of the protection system , 2006, IEEE Transactions on Power Delivery.

[8]  H.C.S. Rughooputh,et al.  A fuzzy logic controlled genetic algorithm for optimal electrical distribution network reconfiguration , 2004, IEEE International Conference on Networking, Sensing and Control, 2004.

[9]  R. Taleski,et al.  Distribution network reconfiguration for energy loss reduction , 1997 .

[10]  Vladimiro Miranda,et al.  Genetic algorithms in optimal multistage distribution network planning , 1994 .

[11]  M.R. Irving,et al.  An efficient genetic algorithm for optimal large-scale power distribution network planning , 2003, 2003 IEEE Bologna Power Tech Conference Proceedings,.

[12]  J.V. Milanovic,et al.  Probabilistic assessment of financial losses due to interruptions and voltage sags-part I: the methodology , 2006, IEEE Transactions on Power Delivery.