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基于多种群进化鲸鱼优化算法的可再生分布式发电机和电动汽车的优化布局

Optimal placement of renewable distributed generators and electric vehicles using multi-population evolution whale optimization algorithm.

作者信息

Zangmo Rinchen, Sudabattula Suresh Kumar, Mishra Sachin, Dharavat Nagaraju, Golla Naresh Kumar, Sharma Naveen Kumar, Jadoun Vinay Kumar

机构信息

School of Electronics and Electrical Engineering, Lovely Professional University, Phagwara, 144411, Punjab, India.

School of Computer Science and Artificial Intelligence, SR University, Warangal, 506371, Telangana, India.

出版信息

Sci Rep. 2024 Nov 18;14(1):28447. doi: 10.1038/s41598-024-80076-z.

DOI:10.1038/s41598-024-80076-z
PMID:39558060
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11574299/
Abstract

This research takes on a crucial task- exploring the optimal placement of Renewable Distributed Generators such as Solar Photovoltaic, wind turbines and Electric Vehicles into the Radial Distribution System (RDS). This is a strategic move aimed at minimising power loss (P) and improving the voltage profile and stability index. The RDGs are integrated into RDS with and without considering the uncertainty of the different load demands for 24 h. The probability function of Beta and Weibull distribution functions are employed to attain the solar irradiance and wind speed in a particular region. In addition, EVs are also integrated into RDS, employing meta-heuristic algorithms intended to reduce power loss (PLoss) and improve the voltage profile. The study uses an Indian 28-bus test system mimicking a balanced radial distribution network to integrate distributed generators (DGs) and EV charging stations. The simulated results demonstrate that integrating DGs into power systems has offered considerable benefits, including reduced PLoss, heightened efficiency, decreased dependency on centralised generation, and improved environmental sustainability. It is discovered that the Multi-population Evolution Whale Optimization Algorithm (MEWOA) produces better results than other methods in the literature and is valuable and practical for handling these nonlinear optimisation situations.

摘要

本研究承担了一项关键任务——探索将太阳能光伏、风力涡轮机和电动汽车等可再生分布式发电机优化配置到径向配电系统（RDS）中。这是一项战略举措，旨在将功率损耗（P）降至最低，并改善电压分布和稳定性指标。在考虑和不考虑24小时不同负荷需求不确定性的情况下，将分布式发电机（RDG）集成到径向配电系统中。采用贝塔分布函数和威布尔分布函数的概率函数来获取特定区域的太阳辐照度和风速。此外，电动汽车也被集成到径向配电系统中，采用元启发式算法以降低功率损耗（PLoss）并改善电压分布。该研究使用一个模拟平衡径向配电网的印度28节点测试系统来集成分布式发电机（DG）和电动汽车充电站。模拟结果表明，将分布式发电机集成到电力系统中带来了诸多益处，包括降低PLoss、提高效率、减少对集中发电的依赖以及改善环境可持续性。研究发现，多群体进化鲸鱼优化算法（MEWOA）比文献中的其他方法产生了更好的结果，对于处理这些非线性优化情况具有重要价值和实用性。

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本文引用的文献

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