Optimal power flow using a hybridization algorithm of arithmetic optimization and aquila optimizer

• Published in: Expert Systems with Applications
• Main Author: Ahmadipour M.; Murtadha Othman M.; Bo R.; Sadegh Javadi M.; Mohammed Ridha H.; Alrifaey M.
• Format: Article
• Language: English
• Published: Elsevier Ltd 2024
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175606133&doi=10.1016%2fj.eswa.2023.121212&partnerID=40&md5=b3583c7237f6f3950b727f31f01d3281

In this paper, a hybridization method based on Arithmetic optimization algorithm (AOA) and Aquila optimizer (AO) solver namely, the AO-AOA is applied to solve the Optimal Power Flow (OPF) problem to independently optimize generation fuel cost, power loss, emission, voltage deviation, and L index. The proposed AO-AOA algorithm follows two strategies to find a better optimal solution. The first strategy is to introduce an energy parameter (E) to balance the transition between the individuals’ procedure of exploration and exploitation in AO-AOA swarms. Next, a piecewise linear map is employed to reduce the energy parameter's (E) randomness. To evaluate the performance of the proposed AO-AOA algorithm, it is tested on two well-known power systems i.e., IEEE 30-bus test network, and IEEE 118-bus test system. Moreover, to validate the effectiveness of the proposed (AO-AOA), it is compared with a famous optimization technique as a competitor i.e., Teaching-learning-based optimization (TLBO), and recently published works on solving OPF problems. Furthermore, a robustness analysis was executed to determine the reliability of the AO-AOA solver. The obtained result confirms that not only the AO-AOA is efficient in optimization with significant convergence speed, but also denotes the dominance and potential of the AO-AOA in comparison with other works. © 2023 Elsevier Ltd