IoT-based monitoring and control of substations and smart grids with renewables and electric vehicles integration

被引:32
作者
Ullah, Zia [1 ]
Rehman, Anis Ur [1 ]
Wang, Shaorong [1 ]
Hasanien, Hany M. [2 ,3 ]
Luo, Peng [4 ]
Elkadeem, Mohamed R. [5 ]
Abido, Mohammad A. [5 ,6 ]
机构
[1] Huazhong Univ Sci & Technol, Sch Elect & Elect Engn, Wuhan, Peoples R China
[2] Ain Shams Univ, Fac Engn, Elect Power & Machines Dept, Cairo 11517, Egypt
[3] Future Univ Egypt, Fac Engn & Technol, Cairo 11835, Egypt
[4] Guangdong Ocean Univ, Sch Elect & Informat Engn, Zhanjiang 524088, Peoples R China
[5] King Fahd Univ Petr & Minerals KFUPM, Interdisciplinary Res Ctr Renewable Energy & Power, Dhahran 31261, Saudi Arabia
[6] King Fahd Univ Petr & Minerals, Elect Engn Dept, Dhahran 31261, Saudi Arabia
关键词
Energy Management; IoT Monitoring; Renewable Energy Integration; Smart Grid; Electric Vehicle Integration; Electric Load Pattern; ENERGY MANAGEMENT; SYSTEM; HOME;
D O I
10.1016/j.energy.2023.128924
中图分类号
O414.1 [热力学];
学科分类号
摘要
The integrated renewable energy resources (RERs) based smart grid in the power distribution network (PDN) has financial and ecological benefits. However, the emergence of RER-based microgrids and substations without realtime monitoring of their power parameters leads to various challenges in the PDN, such as suboptimal resource allocation, poor load management, grid instability, and lack of real-time decision-making capabilities. To mitigate these challenges, increase the system's visibility, and make efficient decisions, intelligent monitoring and control system is required for both smart grid and power substation. This proposed study develops IoT-based monitoring and control of power substations and associated distributed smart grids to make effective decisions of integration/segregation into the PDN. The proposed IoT-based integration/segregation of smart grids and load management can mitigate the stated challenges effectively. Using the HOMER Grid & REG;, the research also investigates the annualized power production pattern of smart grids and the power consumption pattern of integrated loads to enable proactive decisions about energy management. The proposed study implements IoT technology for power parameters monitoring of substations and smart grids for their effective use, as it considers four types of load management, including industrial, domestic, commercial, and electric vehicles, with the aid of IoT technology to avoid power fluctuations and contingencies. Effective load management has been adopted based on annualized energy consumption, load patterns and real-time monitoring, and active decisions making. The results highlight that the proposed IoT-based approach helps advance smart grid integration into the PDN and enhance energy load management, consequently reducing energy costs and suppressing carbon emissions. The validation of the proposed model is verified by the constructed prototype, where the achieved real-time monitoring and control of power substations and smart grids into PDN is performed to make effective decisions related to energy and load management. Moreover, it effectively allows power distribution companies to manage loads during high demand or crises and enhances grid stability and energy efficiency.
引用
收藏
页数:13
相关论文
共 50 条
[21]   Adaptive Power System for IoT-Based Smart Agriculture Applications [J].
Abou Emira, Shahenaz S. ;
Youssef, Khaled Y. ;
Abouelatta, Mohamed .
2019 15TH INTERNATIONAL COMPUTER ENGINEERING CONFERENCE (ICENCO 2019), 2019, :126-131
[22]   Plug-in hybrid electric vehicles and smart grids: Investigations based on a microsimulation [J].
Waraich, Rashid A. ;
Galus, Matthias D. ;
Dobler, Christoph ;
Balmer, Michael ;
Andersson, Goeran ;
Axhausen, Kay W. .
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2013, 28 :74-86
[23]   Power System Integration of Electric Vehicles: A Review on Impacts and Contributions to the Smart Grid [J].
Inci, Mustafa ;
Celik, Ozgur ;
Lashab, Abderezak ;
Bayindir, Kamil Cagatay ;
Vasquez, Juan C. ;
Guerrero, Josep M. .
APPLIED SCIENCES-BASEL, 2024, 14 (06)
[24]   IoT-Based Smart Energy Management in Hybrid Electric Vehicle Using Driving Pattern [J].
Anbazhagan, Geetha ;
Kim, Daegeon ;
Maragatharajan, M. .
IEEE INTERNET OF THINGS JOURNAL, 2023, 10 (21) :18633-18640
[25]   A New Design of IoT-Based Network Architecture for Monitoring and Controlling Power Consumption in Distribution Grids [J].
Ngo Minh Khoa ;
Le Van Dai ;
Nguyen An Toan ;
Doan Duc Tung .
INTERNATIONAL JOURNAL OF RENEWABLE ENERGY RESEARCH, 2021, 11 (03) :1460-1468
[26]   IoT-Based Traffic Prediction for Smart Cities [J].
Miao, Zhinong ;
Liao, Qilong .
IEEE ACCESS, 2025, 13 :52369-52384
[27]   Toward an Applied Cyber Security Solution in IoT-Based Smart Grids: An Intrusion Detection System Approach [J].
Yin, Xiao Chun ;
Liu, Zeng Guang ;
Nkenyereye, Lewis ;
Ndibanje, Bruce .
SENSORS, 2019, 19 (22)
[28]   Chance-Constrained Energy Management System for Power Grids With High Proliferation of Renewables and Electric Vehicles [J].
Wang, Bo ;
Dehghanian, Payman ;
Zhao, Dongbo .
IEEE TRANSACTIONS ON SMART GRID, 2020, 11 (03) :2324-2336
[29]   IoT-based Smart Grid System Design for Smart Home [J].
Swastika, Adi Candra ;
Pramudita, Resa ;
Hakimi, Rifqy .
2017 3RD INTERNATIONAL CONFERENCE ON WIRELESS AND TELEMATICS (ICWT), 2017, :49-53
[30]   An IoT-based intelligent smart energy monitoring system for solar PV power generation [J].
Rao C.K. ;
Sahoo S.K. ;
Yanine F.F. .
Energy Harvesting and Systems, 2024, 11 (01)