In order to enhance the operational efficacy of distribution power networks (DPNs) across techno economic and environmental aspects within a real-time operational framework, meticulous regulation of active as well as reactive power is imperative. In the present study, a Comprehensive Teaching-Learning based Optimization (CTLBO) algorithm is employed for network reconfiguration (NR) and optimal allocation of Distributed generations (DGs) along with Distribution Static Synchronous Compensators (DSTATCOMs) for single-objective in the IEEE 33-bus radial distribution systems (RDSs). Several case studies demonstrate that simultaneous NR and DGs along with DSTATCOM allocation is the most effective solution for reduction of network active power losses ultimately reduces operational costs and emission. The results further demonstrates the superiority in terms of convergence characteristics, solution robustness and global optimality of the CTLBO algorithm under complex , multi-criteria constraints for NR and DGs along with DSTATCOM allocation in RDS against established bio-inspired metaheuristics such as the Gravitational Search Algorithm (GSA), Fireworks algorithm (FWA), Harmony Search Algorithm (HSA), Genetic Algorithm (GA) and Refined genetic algorithm (RGA).
Imran Ahmad Quadri (Thu,) studied this question.