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An IoT- Based Decision Support Tool for Improving the Performance of Smart Grids Connected With Distributed Energy Sources and Electric Vehicles

Journal Article


Download full-text (Open Access)

Abstract


  • vThe growing penetration of distributed energy sources (DES), such as photovoltaic (PV) solar power, battery energy systems and electric vehicles (EVs) into low voltage distribution networks is creating serious challenges for distribution network operators. Uncertain nature of these DES and EV charging is a key factor to cause unbalance, which degrade network performance in terms of energy loss, voltage unbalance, and voltage profile of the distribution network, etc. Some methods were proposed to mitigate such negative impact of these uncertain DES and EV charging from both centralized and decentralized approaches by controlling charging or discharging power of EVs. However, these methods involve all active EVs to participate in coordination and this causes significant inconvenience to EV owners along with requirements of complex communication infrastructure and huge data processing overhead. This article proposes an Internet of Things -based centralized control strategy to coordinate EV and DES distribution by using the differential evolution (DE) optimization algorithm. The obtained results show that the proposed control strategy can improve network performance (voltage imbalance, neutral current, energy loss, and node voltage) significantly. In addition, the control strategy is less demanding on communication infrastructure and convenient for EV owners as well as having a lighter data processing overhead.

UOW Authors


  •   Islam, Md Rabiul
  •   Lu, Haiyan (external author)
  •   Hossain, M J. (external author)
  •   Li, Li (external author)

Publication Date


  • 2020

Citation


  • M. Islam, H. Lu, M. J. Hossain & L. Li, "An IoT- Based Decision Support Tool for Improving the Performance of Smart Grids Connected With Distributed Energy Sources and Electric Vehicles," IEEE Transactions on Industry Applications, vol. 56, (4) pp. 4552-4562, 2020.

Scopus Eid


  • 2-s2.0-85089566061

Ro Full-text Url


  • https://ro.uow.edu.au/cgi/viewcontent.cgi?article=5308&context=eispapers1

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/4280

Number Of Pages


  • 10

Start Page


  • 4552

End Page


  • 4562

Volume


  • 56

Issue


  • 4

Place Of Publication


  • United States

Abstract


  • vThe growing penetration of distributed energy sources (DES), such as photovoltaic (PV) solar power, battery energy systems and electric vehicles (EVs) into low voltage distribution networks is creating serious challenges for distribution network operators. Uncertain nature of these DES and EV charging is a key factor to cause unbalance, which degrade network performance in terms of energy loss, voltage unbalance, and voltage profile of the distribution network, etc. Some methods were proposed to mitigate such negative impact of these uncertain DES and EV charging from both centralized and decentralized approaches by controlling charging or discharging power of EVs. However, these methods involve all active EVs to participate in coordination and this causes significant inconvenience to EV owners along with requirements of complex communication infrastructure and huge data processing overhead. This article proposes an Internet of Things -based centralized control strategy to coordinate EV and DES distribution by using the differential evolution (DE) optimization algorithm. The obtained results show that the proposed control strategy can improve network performance (voltage imbalance, neutral current, energy loss, and node voltage) significantly. In addition, the control strategy is less demanding on communication infrastructure and convenient for EV owners as well as having a lighter data processing overhead.

UOW Authors


  •   Islam, Md Rabiul
  •   Lu, Haiyan (external author)
  •   Hossain, M J. (external author)
  •   Li, Li (external author)

Publication Date


  • 2020

Citation


  • M. Islam, H. Lu, M. J. Hossain & L. Li, "An IoT- Based Decision Support Tool for Improving the Performance of Smart Grids Connected With Distributed Energy Sources and Electric Vehicles," IEEE Transactions on Industry Applications, vol. 56, (4) pp. 4552-4562, 2020.

Scopus Eid


  • 2-s2.0-85089566061

Ro Full-text Url


  • https://ro.uow.edu.au/cgi/viewcontent.cgi?article=5308&context=eispapers1

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/4280

Number Of Pages


  • 10

Start Page


  • 4552

End Page


  • 4562

Volume


  • 56

Issue


  • 4

Place Of Publication


  • United States