A Novel Hybrid Optimization-Based Algorithm for the Single and Multi-Objective Achievement With Optimal DG Allocations in Distribution Networks

Abstract

Distribution networks are facing new challenges with the emergence of smart grids, such as capacity limitations, voltage instability, and many others. These challenges can potentially lead to brownouts and blackouts. This paper presents an innovative technique for optimal siting and sizing of distributed generators (DGs) in radial distribution networks (RDNs). The proposed technique uses a novel algorithm that combines improved grey wolf optimization with particle swarm optimization (I-GWOPSO) by incorporating dimension learning-based hunting (DLH). The proposed I-GWOPSO employs a novel aspect of DLH to reduce the gap between local and global searches to maintain a balance. The main optimization objectives aim to optimally site and size the DG with minimization of active power loss, voltage deviation, and improvement of voltage stability in RDNs. Case studies are simulated with IEEE 33-bus and IEEE 69-bus test systems, for the optimal allocation of DG units by considering various power factors. The results validate the efficacy of the proposed algorithm with a significant reduction in real power loss (up to 98.1%), improvement in voltage profile, and optimal reduced cost of DG operation with optimal sizing across all considered cases. A comparative analysis of the proposed approach with existing literature validates the improved performance of the proposed algorithm.

Keywords

• Distributed generation
• dimension learning-based hunting
• grey wolf optimization
• particle swarm optimization
• radial distribution network
• voltage deviation