Distributed generator and capacitor-embedded reconfiguration in three-phase unbalanced distribution systems using teaching learning-based optimization

In the transmission and distribution networks, both active and reactive power play significant roles. While active power does the beneficial work, reactive power maintains the voltage that necessitates management from a system reliability perspective. The voltage variation from the nominal range may result in unintentional operation and early component failure. To maximize the amount of real power that can be transported over the power-transmitting media, the system must also control reactive power flow. In a three-phase unbalanced distribution system reinforced with distributed generator units (DGUs) and shunt capacitor units (SCUs), this paper suggests a teaching learning-based optimization (TLBO) approach to be applied to combinatorial problems for simultaneous reduction of real power loss and net reactive power flow, enhance voltage stability index (VSI) and minimize aggregated voltage deviation index. A multiobjective formulation based on TLBO has been implemented to choose the ideal sizes and placements of DGUs and SCUs in large distribution networks. The case studies have been carried out on an IEEE 33-bus standard system and a large 123-bus unbalanced system. The analysis of the IEEE 33- and 123-bus test systems reveals the economic efficiency of the suggested TLBO algorithm. Notably, by applying DG and capacitor simultaneously in the 33-bus system, a significant reduction of 59.35%, 55.05%, and 50.76% (phases a, b, and c), and for the 123-bus system, a significant reduction of 43.65%, 19%, and 48.65% (phases a, b, and c) in total active power losses have been achieved. The results suggest that the proposed method renders significant improvement in voltage stability and reduces power losses concerning the base case for both test systems.