Simultaneously Distributed Generation Allocation and Network Reconfiguration in Distribution Network Considering Different Loading Levels

Abstract

This paper introduces a novel application of the recently developed meta-heuristic algorithm called Geometric Mean Optimization (GMO). The algorithm combines the unique properties of the geometric mean operator in mathematics with the power loss sensitivity index (PLSI) to address various optimization problems in distribution networks. Specifically, the paper focuses on the problems of optimal network reconfiguration (NR), optimal distributed generation (DG) unit allocation with optimal power factor (OPF) and unity power factor (UPF), as well as simultaneous optimal NR and DG unit allocation while considering UPF and OPF. The proposed technique considers operational constraints and three loading levels (0.5 p.u loading - light load level, 1.0 p.u loading - nominal load level, and 1.6 p.u loading - heavy load level) to solve single and multi-objective functions such as maximizing voltage stability index (VSI) and minimizing total active power loss (TAPL) and voltage deviation (VD) in the distribution network (DN). To evaluate the effectiveness of the proposed technique, experiments were conducted on IEEE 33 bus and 69-bus networks. The results of simultaneous optimal NR and DG unit allocation with OPF showed significant improvements in terms of VSI, TAPL, and VD compared to other scenarios, including optimal simultaneous NR and DG unit allocation with UPF, only DG unit allocation with UPF and OPF, and only NR and base case. Moreover, when considering multiple objectives, the simultaneous allocation of NR and DG units with OPF consistently yielded better results for all load conditions. Furthermore, the proposed technique was compared to existing algorithms in the literature, specifically for the objective of TAPL at the nominal load level. The comparison demonstrated that the combined technique outperformed other methods in terms of TAPL for all cases, highlighting its efficacy. The proposed technique exhibited high accuracy and convergence speed, making it a favorable choice for simultaneous optimal NR and DG unit allocation with UPF and OPF across different load conditions.

Keywords

• Distributed generation
• distribution network
• geometric mean optimization
• network reconfiguration
• power loss sensitivity index