Parameter-Free Improved Best-Worst Optimizers and Their Application for Simultaneous Distributed Generation and Shunt Capacitors Allocation in Distribution Networks

• Published in: International transactions on electrical energy systems 2022, Vol.2022, p.1-31
• Main Authors: Leghari, Zohaib Hussain, Hussain, Saddam, Memon, Ahsanullah, Memon, Abdul Hakeem, Baloch, Ashfaque Ahmed
• Format: Article
• Language: English
• Subjects: Algorithms; Contemporary literature; Distributed generation; Genetic algorithms; Loss reduction; Optimization; Optimization algorithms; Optimization techniques; Parameters; Scaling factors; Search algorithms; Shunt capacitors; Systems stability; Taxonomy
• ISSN: 2050-7038; 2050-7038
• DOI: 10.1155/2022/6833488

This paper proposes parameter-free two best-worst optimizers (BWOs) that combine the searching capabilities of Jaya and Rao-1 algorithms to intensify their exploration and exploitation capabilities. For the proposed first optimizer, BWO-1, parallel taxonomy has been adopted for the Jaya and Rao-1 combination to obtain dual sets of updated solutions for a given current solution set. For the proposed second optimizer, BWO-2, along with the parallel taxonomy, a subloop of random scaling factors has been introduced in the solution updating mechanism of both standard Jaya and Rao-1 algorithms to generate multiple sets of updated solutions in a single iteration. The best solutions from obtained multiple solution-sets will survive only and lead to the next iteration. Hence, the proposed solution updating mechanisms for BWO-1 and BWO-2 increase the probability of getting a quality solution under set conditions. The ability and scalability of BWO-1 and BWO-2 were assessed, solving the optimization problem of power loss reduction and voltage deviation minimization in the IEEE 33-bus and 69-bus test systems. The results show that the BWO-2 exhibited a lead of 2.97%, 1.83%, and 0.72% over the Rao-1, Jaya, and BWO-1 techniques, respectively. Besides, the BWO-2 achieves up to 38.76% more reduction in power losses against the existing standard, improved, and hybrid optimization techniques.