Multi-Objective Optimization of Distributed Generation Placement and Sizing in Distribution Networks for Active Power Loss Reduction and Voltage Profile Enhancement

Distribution networks always face significant challenges in effectively distributing Distributed Generations (DGs). By selecting suitable locations and sizes for DG sources, the voltage profile and system losses of distribution networks can be greatly improved and reduced. This study's initial emphasis is on the primary goal of lowering active power loss through proper DGs' position and sizing in distribution systems. To tackle this problem, a novel population-based optimization method called Slime Mould Algorithm (SMA) is proposed. Furthermore, this research extends its scope by introducing a multi-objective approach that simultaneously addresses active power loss reduction and voltage profile enhancement. The fuzzy decision-maker methodology and the weighted sum technique are used to tackle this multi-objective issue, in combination with SMA. A 33-bus system is considered for this work. The outcomes achieved from the SMA are contrasted with several evolutionary methods, including Quasi-Oppositional Teaching Learning Based Optimization (QOTLBO), Teaching Learning Based Optimization (TLBO), Sine Cosine Algorithm (SCA), and Grasshopper Optimization Algorithm (GOA), etc. These comparisons demonstrate the computational efficiency, convergence speed, user-friendliness, and other advantages of SMA.