Load Factor Improvement of Distribution Feeders by Feeder Reconfiguration Using Binary Particle Swarm Optimization

Abstract

The power industry restructuring has created many opportunities for customers to reduce their electricity bills. In liberal power market, the selling and buying of energy is now becoming like that of other commodities. Introduction of private players and various companies in the energy market has created more competition in the market and hence assuring the reliability and quality of energy supplied to consumers and thus ending the monopolistic behavior of vertically integrated utilities. In such liberal market, there are various options for consumers, the bargaining power of the consumers has now increased. There is continuous flow and exchange of information between the buyers of energy, suppliers of energy and the market. The various participants in the market can make their decisions independently. The consumers of energy take time to time information from the market and make their strategies to get more and more benefit from the market. The group of consumers can adjust their loads based on price signals in the market. To maximize the benefit from the market, load aggregation is a strategy in which different types of consumers make an alliance to secure more competitive price by negotiating with the energy suppliers. In some contracts between group of consumers and the energy suppliers, the load factor is being considered as one critical aspect of load aggregation. If the load factor for a group of consumers is higher, the more flatter is the load profile and in such type of load profile the attraction of energy suppliers is more and so the negotiating power of consumers increases. In order to aggregate the diversified customers in the distribution system and hence to meet the load factor requirement in the power purchasing contract, feeder reconfiguration for load factor improvement is presented in this thesis. Also the losses before and after the reconfiguration are calculated and finally total cost of energy supplied is calculated. Since feeder reconfiguration is a binary type of optimization problem, discrete version of Particle Swarm Optimization is used. A sample three feeder network and a three feeder network from Patan substation of Nepal Electricity Authority is considered and feeder reconfiguration problem is solved. The load factor of the feeders is increased by 1.495%, 15.59%, 7.44% and 17.95%, 5.90%, 6.44% for respective networks. Due to this improvement in load factor, the daily cost of energy supply is decreased from Nrs. 56638.15 to Nrs. 55895.82 and Nrs. 781264.99 to Nrs. 780827.46 respectively although the power loss increased from 1.54 kW to 2.22 kW and 24.29 kW to 42.78 kW.