Improving the Techno-Economic Pattern for Distributed Generation-Based Distribution Networks via Nature-Inspired Optimization Algorithms

The massive increase in the utilization of Distributed Generation (DG) units traditional Electric Distribution Networks (EDNs) enforces distribution companies’ operators to enhance technical performance EDNs while considering economic perspectives. This challenge paves way for developing a multi-objective optimization platform tackle techno-economic problems respecting system uncertainties as well operational policy companies. As motivating solution this problem, paper introduces application three nature-inspired algorithms techniques enhancing through integration multiple Renewable Energy Resources (RERs). Grasshopper Optimization Algorithm (GOA), Salp Swarm (SSA) and Moth Flame (MFO), have been employed comparative study minimize active power losses, Fast Voltage Stability Index (FVSI) reduce total costs, penetration level specified margin framework DG units’ operating factor constraints. proposed implemented MATLAB environment applied on various benchmark IEEE test systems (33-bus, 57-bus 300-bus) mimic, small large EDNs. A realistic part Egyptian network (171-bus) is also introduced practical, applicable case study. attained results show that suggested especially using MFO, more effective successful determining finding optimal locations capacities different types getting value objective function minimum time within number iterations.