Hosting Capacity Evaluation Method for Power Distribution Networks Integrated with Electric Vehicles

软件部署 功率(物理) 计算机科学 操作员(生物学) 功率流 可靠性工程 边界(拓扑) 航程(航空) 数学优化 电力系统 汽车工程 工程类 数学 物理 数学分析 航空航天工程 抑制因子 操作系统 基因 化学 转录因子 量子力学 生物化学
作者
Wei Dai,Junping Wang,Hui Hwang Goh,Jianmin Zhao,Jiangyi Jian
出处
期刊:Journal of modern power systems and clean energy [Springer Nature]
卷期号:11 (4): 1564-1575 被引量:1
标识
DOI:10.35833/mpce.2022.000515
摘要

The large-scale deployment of electric vehicles (EVs) poses critical challenges to the secure and economic operation of power distribution networks (PDNs). Therefore, a method for evaluating the hosting capacity that enables a PDN to determine the EV chargeable area (EVCA) to satisfy the charging demand and ensure the secure operation is proposed in this paper. Specifically, the distribution system operator (DSO) serves as a public entity to manage the integration of EVs by determining the presence of the charging load in the EVCA. Hence, an EVCA optimization model is formulated on the basis of the coupling effect of the charging nodes to determine the range of the available charging power. In this model, nonlinear power flow equations and operational constraints are considered to maintain the solvability of the power flow of the PDN. Subsequently, a novel multipoint approximation technique is proposed to quickly search for the boundary points of the EVCA. In addition, the impact of the demand response (DR) mechanism on the hosting capacity is explored. The results show that the presence of the DR significantly enlarged the EVCA during peak hours, thus revealing the suitability of the DR mechanism as an important supplement to accommodate the EV charging load. The examined case studies demonstrate the effectiveness of the proposed model and show that the unmanaged allocation of the charging load impedes secure operation. Finally, the proposed method provides a reference for the allocation of the EV charging load and a reduction in the risk of line overloading.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
祁淑娴发布了新的文献求助10
1秒前
1秒前
要减肥笑阳完成签到 ,获得积分10
2秒前
Dr.L发布了新的文献求助10
2秒前
情怀应助hehehe采纳,获得10
2秒前
2秒前
2秒前
3秒前
内向语梦发布了新的文献求助10
3秒前
MXX完成签到 ,获得积分10
3秒前
今后应助黎敏采纳,获得10
4秒前
女侠发布了新的文献求助10
4秒前
4秒前
4秒前
婧婧婧发布了新的文献求助30
5秒前
韦老虎发布了新的文献求助200
5秒前
彭于晏应助iwhisper采纳,获得10
5秒前
5秒前
Singularity应助醉熏的梦易采纳,获得20
6秒前
6秒前
孔蓓蓓完成签到 ,获得积分10
6秒前
6秒前
Orange应助内向语梦采纳,获得10
6秒前
万能图书馆应助布公采纳,获得10
6秒前
6秒前
科研通AI6.1应助茶弥采纳,获得10
7秒前
悲凉的紊发布了新的文献求助10
8秒前
9秒前
阿邦完成签到,获得积分10
9秒前
9秒前
9秒前
QQQ发布了新的文献求助10
9秒前
Deannn778发布了新的文献求助10
10秒前
婧婧婧完成签到,获得积分10
10秒前
10秒前
10秒前
大力发布了新的文献求助10
11秒前
xc发布了新的文献求助10
11秒前
芝岸完成签到 ,获得积分10
11秒前
sg123_发布了新的文献求助10
11秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Kinesiophobia : a new view of chronic pain behavior 3000
Les Mantodea de guyane 2500
Molecular Biology of Cancer: Mechanisms, Targets, and Therapeutics 2000
Standard: In-Space Storable Fluid Transfer for Prepared Spacecraft (AIAA S-157-2024) 1000
Signals, Systems, and Signal Processing 510
Discrete-Time Signals and Systems 510
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 计算机科学 有机化学 物理 生物化学 纳米技术 复合材料 内科学 化学工程 人工智能 催化作用 遗传学 数学 基因 量子力学 物理化学
热门帖子
关注 科研通微信公众号,转发送积分 5948601
求助须知:如何正确求助?哪些是违规求助? 7116224
关于积分的说明 15912008
捐赠科研通 5081384
什么是DOI,文献DOI怎么找? 2732049
邀请新用户注册赠送积分活动 1692411
关于科研通互助平台的介绍 1615376