Overview

This dissertation addresses the challenges of traditional centralized power generation, fossil fuel depletion, high emissions, growing demand, and transmission losses by focusing on optimal planning of Distributed Generation (DG) in distribution networks. Advanced metaheuristic algorithms are created to make systems work better, keep voltage stable, handle more load, and make more money. A Butterfly Optimization Algorithm (BOA) with an ℰ-constraint approach is proposed to minimize losses and improve loadability. Further, a Pareto-based Multi-Objective Chaotic Velocity Butterfly Optimization Algorithm (MOCVBOA) is introduced for planning non-dispatchable (PV, WT) and dispatchable (PV-BESS, WT-Biomass) DGs under renewable and load uncertainties. The dissertation also examines DG planning under Plug-In Electric Vehicle (PHEV) charging scenarios using TOPSIS-based optimal solution selection. Finally, the planning of PV and PV-BESS units, considering both conventional and PHEV loads under private and public charging scenarios, is analyzed. Results show that optimal DG integration significantly reduces energy losses, improves voltage profiles, and mitigates PHEV-induced stress.

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