Optimal Distributed Generation Placement and Network Reconfiguration Using Hybrid Algorithm

Abstract

Recent advances in electric utility sector show that it is beneficial to inject the power and store energy at the distribution levels. But the non-optimal placement of distributed generations may adversely affect the distribution system performance. Therefore, distributed generations should be installed optimally. In this paper, a hybrid method combining genetic algorithm (GA) and particle swarm optimization (PSO) is developed and applied to determine the optimal size and location of distributed generations along with best network topology to reduce power losses and improve voltage profiles. The simulation results show that simultaneous reconfiguration and distributed generation placement is superior to improve the network performance. The power loss reduction after installation of active power distributed generations together with network reconfiguration is 73.91% in the IEEE 33-bus distribution network compared to base case power loss. Moreover, voltage profiles are improved significantly in all scenarios as compared to the base case profile. In nutshell, a proposed hybrid algorithm is efficient to solve the optimal distributed generation placement problem along with network reconfiguration.