Alaas Zuhair, Moustafa Ghareeb, Mansour Hany
Department of Electrical and Electronics Engineering, Faculty of Engineering and Computer Science, Jazan University, Jizan 45142, Saudi Arabia.
Department of Electrical Engineering, Faculty of Engineering, Suez Canal University, Ismailia, Egypt.
PLoS One. 2025 Mar 12;20(3):e0319298. doi: 10.1371/journal.pone.0319298. eCollection 2025.
In distribution grids, excessive energy losses not only increase operational costs but also contribute to a larger environmental footprint due to inefficient resource utilization. Ensuring optimal placement of photovoltaic (PV) energy systems is crucial for achieving maximum efficiency and reliability in power distribution networks. This research introduces the Pelican Optimizer (PO) algorithm to optimally integrate solar PV systems to radial electrical distribution grids. The PO is a novel bio-inspired optimization algorithm that draws inspiration from pelicans' intelligence and behavior which incorporates unique methods for exploration and exploitation, improving its effectiveness in various optimization challenges. It introduces a hyper-heuristic for phase change, allowing the algorithm to dynamically adjust its strategy based on the problem's characteristics. The suggested PO aims to reduce the energy losses to the possible minimum value. The developed PO version is tested on the Ajinde 62-bus network, a practical Nigerian distribution system, and a typical IEEE grid with 69 nodes. The simulation findings demonstrate the enhanced PO version's efficacy, showing a significant decrease in losses of energy. With the Ajinde 62-node grid, the suggested PO version obtains a substantial 30.81% decrease in the total energy loss expenses in contrast to the initial scenario. Similarly, the IEEE 69-node grid achieves a significant decrease of 34.96%. Additionally, the model's findings indicate that the proposed PO version performs comparably to the Differential Evolution (DE), Particle Swarm Optimization (PSO), and Satin bowerbird optimizer (SBO) algorithms.
在配电网中，过多的能量损耗不仅会增加运营成本，还会因资源利用效率低下而导致更大的环境足迹。确保光伏（PV）能源系统的最佳布局对于实现配电网络的最大效率和可靠性至关重要。本研究引入了鹈鹕优化器（PO）算法，以将太阳能光伏系统最佳地集成到径向配电网中。PO是一种新颖的受生物启发的优化算法，它从鹈鹕的智能和行为中汲取灵感，其中包含独特的探索和利用方法，提高了其在各种优化挑战中的有效性。它引入了一种用于相变的超启发式方法，使算法能够根据问题的特征动态调整其策略。建议的PO旨在将能量损耗降低到可能的最小值。所开发的PO版本在阿金德62节点网络（一个实际的尼日利亚配电系统）和一个具有69个节点的典型IEEE电网中进行了测试。仿真结果证明了增强版PO的有效性，显示出能量损耗显著降低。对于阿金德62节点电网，建议的PO版本与初始情况相比，总能量损失费用大幅降低了30.81%。同样，IEEE 69节点电网实现了34.96%的显著降低。此外，该模型的结果表明，建议的PO版本与差分进化（DE）、粒子群优化（PSO）和缎蓝亭鸟优化器（SBO）算法的性能相当。
