Smart algorithms for power prediction in smart EV charging stations

IF 0.9 4区 工程技术 Q3 ENGINEERING, MULTIDISCIPLINARY
M. Subashini , V. Sumathi
{"title":"Smart algorithms for power prediction in smart EV charging stations","authors":"M. Subashini ,&nbsp;V. Sumathi","doi":"10.1016/j.jer.2023.11.028","DOIUrl":null,"url":null,"abstract":"<div><p>Power prediction in solar powered electric vehicle (EV) charging stations is very essential for smooth and uninterrupted operations due to the high oscillatory output of renewables and their dependence on various atmospheric factors. The need for early prediction helps EV stations improve their power performance and utilize available power by designing intelligent charge scheduling algorithms. This study introduces a novel design approach for an off-grid photovoltaic (PV)-powered EV charging station, which involves three main stages: evaluating and analyzing different solar irradiance prediction models (theoretical, empirical, and artificial neural network (ANN) models), forecasting day-ahead solar power profiles, and optimizing charge scheduling for pre-booked vehicles using energy storage systems (ESS). The effectiveness of various solar irradiance prediction models is assessed to identify the best-performing model. The proposed approach employs a novel algorithmic procedure to fine-tune the selected model using a basic dataset. Power prediction simulations are conducted using MATLAB, while Python is utilized for model development. The feed forward neural network (FFNN) model for irradiance prediction has a 0.88 R<sup>2</sup> score; the anisotropic general regression neural network (AGRNN), isotropic GRNN both have 0.94 and 0.95 R<sup>2</sup> values for direct PV current prediction, providing a strong base for reliable forecasting models. The significance of ESS backup for effective charging stations is clearly demonstrated by a remarkable 20 kW peak shaving.</p></div>","PeriodicalId":48803,"journal":{"name":"Journal of Engineering Research","volume":null,"pages":null},"PeriodicalIF":0.9000,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2307187723003334/pdfft?md5=410c3c5c80e9f64e5c5f033f34f03811&pid=1-s2.0-S2307187723003334-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Engineering Research","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2307187723003334","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

Abstract

Power prediction in solar powered electric vehicle (EV) charging stations is very essential for smooth and uninterrupted operations due to the high oscillatory output of renewables and their dependence on various atmospheric factors. The need for early prediction helps EV stations improve their power performance and utilize available power by designing intelligent charge scheduling algorithms. This study introduces a novel design approach for an off-grid photovoltaic (PV)-powered EV charging station, which involves three main stages: evaluating and analyzing different solar irradiance prediction models (theoretical, empirical, and artificial neural network (ANN) models), forecasting day-ahead solar power profiles, and optimizing charge scheduling for pre-booked vehicles using energy storage systems (ESS). The effectiveness of various solar irradiance prediction models is assessed to identify the best-performing model. The proposed approach employs a novel algorithmic procedure to fine-tune the selected model using a basic dataset. Power prediction simulations are conducted using MATLAB, while Python is utilized for model development. The feed forward neural network (FFNN) model for irradiance prediction has a 0.88 R2 score; the anisotropic general regression neural network (AGRNN), isotropic GRNN both have 0.94 and 0.95 R2 values for direct PV current prediction, providing a strong base for reliable forecasting models. The significance of ESS backup for effective charging stations is clearly demonstrated by a remarkable 20 kW peak shaving.

智能电动汽车充电站功率预测的智能算法
由于可再生能源的高振荡输出及其对各种大气因素的依赖性,太阳能电动汽车(EV)充电站的功率预测对于平稳、不间断地运行非常重要。对早期预测的需求有助于电动汽车充电站通过设计智能充电调度算法来改善其供电性能和利用可用电力。本研究为离网光伏(PV)供电的电动汽车充电站介绍了一种新颖的设计方法,包括三个主要阶段:评估和分析不同的太阳辐照度预测模型(理论模型、经验模型和人工神经网络(ANN)模型),预测前一天的太阳能功率曲线,以及利用储能系统(ESS)优化预订车辆的充电调度。对各种太阳辐照度预测模型的有效性进行了评估,以确定性能最佳的模型。所提出的方法采用了一种新颖的算法程序,利用基本数据集对所选模型进行微调。功率预测模拟使用 MATLAB 进行,模型开发使用 Python。用于辐照度预测的前馈神经网络(FFNN)模型的 R2 值为 0.88;用于光伏直流电预测的各向异性一般回归神经网络(AGRNN)和各向同性 GRNN 的 R2 值分别为 0.94 和 0.95,为可靠的预测模型奠定了坚实的基础。20 千瓦的显著削峰效果清楚地表明,ESS 后备电源对于充电站的有效运行具有重要意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Journal of Engineering Research
Journal of Engineering Research ENGINEERING, MULTIDISCIPLINARY-
CiteScore
1.60
自引率
10.00%
发文量
181
审稿时长
20 weeks
期刊介绍: Journal of Engineering Research (JER) is a international, peer reviewed journal which publishes full length original research papers, reviews, case studies related to all areas of Engineering such as: Civil, Mechanical, Industrial, Electrical, Computer, Chemical, Petroleum, Aerospace, Architectural, Biomedical, Coastal, Environmental, Marine & Ocean, Metallurgical & Materials, software, Surveying, Systems and Manufacturing Engineering. In particular, JER focuses on innovative approaches and methods that contribute to solving the environmental and manufacturing problems, which exist primarily in the Arabian Gulf region and the Middle East countries. Kuwait University used to publish the Journal "Kuwait Journal of Science and Engineering" (ISSN: 1024-8684), which included Science and Engineering articles since 1974. In 2011 the decision was taken to split KJSE into two independent Journals - "Journal of Engineering Research "(JER) and "Kuwait Journal of Science" (KJS).
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信