relation: https://khub.utp.edu.my/scholars/20572/
title: Optimizing Photovoltaic Systems for Power Losses Reduction under Time-Varying Loads: A Coyote Optimization Approach
creator: Isa, S.S.M.
creator: Ibrahim, M.N.
creator: Ahmad, A.M.
creator: Nordin, S.
creator: Dahlan, N.Y.
creator: Jamahori, H.F.B.
description: Integrating photovoltaic (PV) systems into power grids is essential for mitigating power losses and improving grid stability, especially under conditions of significant time-varying loads. This research examines the optimization of the placement and sizing of PV systems to minimize power losses under these dynamic conditions, contrasting with previous studies that utilized constant load models. The research employs the Coyote Optimization Algorithm (COA) using real-world load and PV generation data, focusing on industrial, residential, and commercial loads. The results indicate that COA reduces power losses by 13.45, 20.03 and 31.27 under industrial, residential and commercial loads respectively compared to the base case PV system. The optimal PV location was consistently found to be at bus 6, while the optimal PV size varied across load types. Additionally, there is a notable improvement in voltage profiles across all load types. This research offers valuable insights into developing efficient and reliable grid-connected PV systems, highlighting the potential of COA in enhancing system performance through load-specific optimization strategies. © 2024 IEEE.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2024
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Isa, S.S.M. and Ibrahim, M.N. and Ahmad, A.M. and Nordin, S. and Dahlan, N.Y. and Jamahori, H.F.B.  (2024) Optimizing Photovoltaic Systems for Power Losses Reduction under Time-Varying Loads: A Coyote Optimization Approach.  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85207094229&doi=10.1109%2fICCSCE61582.2024.10696590&partnerID=40&md5=b4d7d6a6c5a10754ba1da323571bc24d
relation: 10.1109/ICCSCE61582.2024.10696590
identifier: 10.1109/ICCSCE61582.2024.10696590