Optimal Energy Scheduling Strategy for Smart Charging of Electric Vehicles from Grid-Connected Photovoltaic System

被引:7
作者
Abdalla, Modawy Adam Ali [1 ]
Min, Wang [1 ]
Haroun, Gomaa A. H. [1 ]
Elhindi, Mohamed [1 ]
机构
[1] Hohai Univ, Coll Energy & Elect Engn, Nanjing 211100, Peoples R China
来源
2021 7TH INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONICS AND INFORMATION ENGINEERING (ICEEIE 2021) | 2021年
关键词
charging stations; electric vehicle (EV); photovoltaic (PV); energy scheduling strategy; optimal scheduling; IMPACT;
D O I
10.1109/ICEEIE52663.2021.9616634
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
With the increasing penetration of electric vehicles (EVs), charging of EVs is one of the main causes of the peak load of a power system. Therefore, the integration of EVs with renewable energy systems is one of the main pillars that reduce peak demand and environmental impact, especially in urban areas. However, the uncoordinated charging of EVs may lead to ineffective use of renewable energy sources. In this context, an optimal energy scheduling strategy has been proposed for charging electric vehicles from a grid-connected photovoltaic system (PV) in the charging station. This proposed strategy is built to schedule the charging of EVs based on the PV generation curve, the arrival and departure time of EVs, the energy required for every EV, and the state of charge of every EV battery. The objective is to reduce the charge of EVs from the grid and increase their charge from the photovoltaic system as much as possible while ensuring the energy needed for the EVs journeys. To evaluate this strategy, the simulation on a sunny day and cloudy day were conducted. The simulation results show the effectiveness of the proposed strategy in reducing the charge of EVs from the grid from 30% to 12% on a cloudy day and from 26% to 8% on a sunny day. In contrast, the EVs charge from PV increases from 70% to 88% on a cloudy day and from 74% to 92% on a sunny day.
引用
收藏
页码:37 / 42
页数:6
相关论文
共 13 条
[1]   Voltage control of solid oxide fuel cell power plant based on intelligent proportional integral-adaptive sliding mode control with anti-windup compensator [J].
Abbaker, A. M. Omer ;
Wang, Haoping ;
Tian, Yang .
TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL, 2020, 42 (01) :116-130
[2]   Two-Stage Energy Management Strategy of EV and PV Integrated Smart Home to Minimize Electricity Cost and Flatten Power Load Profile [J].
Abdalla, Modawy Adam Ali ;
Min, Wang ;
Mohammed, Omer Abbaker Ahmed .
ENERGIES, 2020, 13 (23)
[3]  
Bibra EM, 2021, Technical Report
[4]   Impact of electric vehicle fast charging on power system voltage stability [J].
Dharmakeerthi, C. H. ;
Mithulananthan, N. ;
Saha, T. K. .
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2014, 57 :241-249
[5]  
Ge SY, 2012, 2 INT C ELECT MECH E, V23
[6]   Impact analysis of vehicle-to-grid technology and charging strategies of electric vehicles on distribution networks - A review [J].
Habib, Salman ;
Kamran, Muhammad ;
Rashid, Umar .
JOURNAL OF POWER SOURCES, 2015, 277 :205-214
[7]   Optimal Scheduling of EV Charging at a Solar Power-Based Charging Station [J].
Kabir, Mohammad Ekramul ;
Assi, Chadi ;
Tushar, Mosaddek Hossain Kamal ;
Yan, Jun .
IEEE SYSTEMS JOURNAL, 2020, 14 (03) :4221-4231
[8]   Identification of the one-diode model for photovoltaic modules from datasheet values [J].
Laudani, Antonino ;
Fulginei, Francesco Riganti ;
Salvini, Alessandro .
SOLAR ENERGY, 2014, 108 :432-446
[9]  
National Renewable Energy Laboratory, US
[10]   Photovoltaic Integrated Hybrid Microgrid Structured Electric Vehicle Charging Station and Its Energy Management Approach [J].
Savio, Dominic A. ;
Juliet, Vimala A. ;
Chokkalingam, Bharatiraja ;
Padmanaban, Sanjeevikumar ;
Holm-Nielsen, Jens Bo ;
Blaabjerg, Frede .
ENERGIES, 2019, 12 (01)