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.
Highlights
Artificial intelligence (AI)-based techniques have been widely applied in various fields
The Ant lion (AL) parameters consist of a population of 100, a distributed energy resources (DER) capacity limit of at least 10% and a maximum of 30% of the total network load, DER locations that can be located on bus 2 to bus 33, and a maximum number of iterations of 100
The maximum power capacity of the DER is 30% of the total load power, where the full load power is 3715 kW, while the minimum ability is 10% of the entire load
Summary
Artificial intelligence (AI)-based techniques have been widely applied in various fields. AI-based techniques, in particular using a metaheuristic approach, have become very popular in the last decade. One of the essential advantages of AI-based techniques with a metaheuristic approach is that they are simple but capable of solving complex problems. Some of these techniques are developed based on intelligent computing and are used for optimization purposes. One technique worth considering for solving complex optimizations is multi-objective ant lion optimization (MALO). MALO is a technique developed by imitating the behavior of the ant king foraging
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