Multiobjective Ant Lion Optimization for Performance Improvement of Modern Distribution Network

Abstract

Multiobjective ant lion optimization (MALO) is a technique developed to imitate ant foraging behavior. This method has many advantages, including straightforward, scalable, flexible, balanced, and fast response. The MALO technique consists of five stages: ants perform optimization by random walking by updating their position, building traps, inserting ants into traps, capturing prey, and rebuilding traps. MALO has been successfully used to find optimal solutions to power system problems. Computer-assisted operations characterize modern distribution networks to solve complex problems. The complexity of the distribution network problem is owing to the integration of distributed energy resources (DERs). A DER is a renewable energy power plant with a capacity of up to 10 MW that has gained popularity in recent years. In its application, the integration of DERs into the distribution network can cause new problems, namely load imbalances or excessive voltage increases on the buses where the DER is injected. Therefore, good planning is required to place the DER. This study proposes a multiobjective optimization technique based on MALO to determine the optimal DER location and capacity. MALO is a relatively new optimization method that has the potential to improve distribution network performance. Test cases were conducted for an IEEE 33-bus radial power-distribution network. Four scenarios were considered, integrating DER types I, II, III, and IV. In each design, the placement of one DER, two DERs, and three DERs was modeled to optimize the location and capacity. The results of the multiobjective optimization show that the MALO technique can improve the distribution network performance, which is characterized by a significant power loss reduction, an increase in the bus voltage profile, and a balanced load on each feeder.