A newly effective method to maximize power loss reduction in distribution networks with highly penetrated distributed generations

Abstract

This paper proposes a newly chaotic maps integrated stochastic fractal search (CMSFS) for solving the optimal distributed generation placement (ODGP) problem in radial distribution networks. The objective of the problem is to minimize the network real power loss satisfying the operational constraints of distributed generations (DGs) and the network. The proposed CMSFS approach is an improvement of the standard SFS approach by integrating chaotic maps into SFS to enhance its solution quality and convergence rate. The experimental results on the IEEE 33, 69, and 118-bus networks and the power loss reduction percentages with the integration of the optimal number of non-unity power factor DG are 99.21%, 99.43% and 92.36%, respectively. Moreover, the simulation results have also shown that the proposed CMSFS can provide better solution quality than many other methods for the considered scenarios. Therefore, the proposed CMSFS can be an effective alternative approach to the ODGP problems in RDNs.

Keywords

• Chaotic maps
• Distributed generators
• Distribution networks
• Power loss reduction maximization
• Radial distribution network
• Stochastic fractal search