APPLICATION OF BAT ALGORITHM-BASED METHOD FOR MULTI-OBJECTIVE OPTIMAL NETWORK RECONFIGURATION AND DISTRIBUTED GENERATION PLACEMENT IN RADIAL DISTRIBUTION NETWORK
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- NGN 3000
ABSTRACT
This study presents an Application of Bat Algorithm-Based Method (BA) forMultiObjective Optimal Network Reconfiguration andDistributed Generation placement in Radial Distribution Network. The BA approach presented in this work enables the reconfiguration of the network as well as the optimal distributed generation(DG) placement in one seamless algorithm. This would minimize errors encountered in using analytical approaches and also improve on the accuracy of the results obtained. In the developed method, the base case active and reactive power losses for the standard IEEE 33-bus were first determined using forward-backward algorithm as 208.46kW and 111.67kW respectively. Then, the developed BA method was applied on the IEEE-33 to determine the optimal DG sizes and the location as well as the optimal reconfiguration of the network.The DGssizes and locations were determined as 957kW, 870kW, 822kW as well as 593kVar, 539kVar, 509kVar at buses 29, 31 and 12 respectively. The total active and reactive power loss obtained after the DG placement as well as network reconfiguration were 15.2353kW and 12.0593kVAr respectively.Thus, the developed method recorded a loss reduction of 92.6915% and 53.56% for active and reactive power loss respectively over the base case, while the voltage profile of 0.91075pu for base casewas improved to 0.9918pu.Furthermore, the results were compared with the work of Syahputraet al., wherethe developed method recorded an improvement of 6.32% on active power loss reduction and 1.04% on voltage profile improvement over the results of Syahputra. The developed method was also implemented on a simulated feeder of Sabon-Gari at Zaria distribution network with the view to optimize the synchronous DG placement as well as network reconfiguration. The results indicated that active and reactive power losses were reduced by 88.77% and 88.18% respectively, while the voltage profile has beenimproved to 4.1% over the base case.All simulations were implemented on MAT LAB 2013b environment.
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