Multiobjective Ant Lion Optimization for Performance Improvement of Modern Distribution Network

Abstract

Multi-objective ant lion optimization (MALO) is a technique developed by imitating the behavior of the ant king foraging. This method has many advantages: straightforward, scalable, flexible, good balance, 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 attracted the attention of many researchers and has been used successfully to find optimal solutions for power system problems. Computer-assisted operations characterize modern distribution networks to solve complex problems. The complexity of the distribution network problem is due to the integration of distributed energy resources (DER). DER is a renewable energy power plant with up to 10 MW, gaining popularity in recent times. In its application, the integration of DER into the distribution network can cause new problems, namely, load imbalances or excessive voltage increases on the buses where the DER is injected. Therefore, sound planning is needed to place DER. This research proposes a multi-objective optimization technique based on MALO to determine the optimal DER location and capacity. The MALO is a relatively new optimization method that has the potential to improve distribution network performance. Test cases have been carried out for the IEEE 33-bus distribution network. Four scenarios have been carried out, namely integrating DER type-I, type-II, type-III, and type-IV. In each design, the placement of 1 DER, 2 DERs, and 3 DERs is modelled to optimize the location and capacity. The results of multi-objective optimization show that the MALO technique can improve the distribution network performance, characterized by a significant reduction in power losses, an increase in the bus voltage profile, and a balanced load on each feeder.

Pareto Front-Based Multiobjective Optimization of Distributed Generation Considering the Effect of Voltage-Dependent Nonlinear Load Models

“…Multiobjective ALO (MALO) [41], modified shuffled frog leaping (MSLF) [42], hybrid GA-DE and strength Pareto evolutionary algorithm (SPEA) (GA-DE-SPEA2) [43] and hybrid MINLP and multi-criteria evaluation method [44]. In these papers, various conflicting objective functions are considered to find the DG or SC allocation problems.…”