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Impact of EV penetration on Volt–VAR Optimization of distribution networks using real-time co-simulation monitoring platform

Author

Listed:
  • Manbachi, M.
  • Sadu, A.
  • Farhangi, H.
  • Monti, A.
  • Palizban, A.
  • Ponci, F.
  • Arzanpour, S.

Abstract

This paper aims to investigate the impact of different Electric Vehicle (EV) penetration on quasi real-time Volt–VAR Optimization (VVO) of smart distribution networks. Recent VVO solutions enable capturing data from Advanced Metering Infrastructure (AMI) in quasi real-time to minimize distribution networks loss costs and perform Conservation Voltage Reduction (CVR) to save energy. The emergence of EVs throughout distribution feeder increases grid complexity and uncertainty levels that could affect AMI-based VVO objectives. Hence, this paper primarily introduces an AMI-based VVO engine, able to minimize grid loss and Volt–VAR control assets operating costs while maximizing CVR benefit. It then presents a real-time co-simulation platform comprised of the VVO engine, grid model in a real-time simulator and monitoring platform, communicating with each other through DNP.3 protocol, to test the precision and performance of AMI-based VVO in presence of different EV penetration levels. Accordingly, 33-node distribution feeder is studied through different EV penetration scenarios. The results show significant changes in AMI-based VVO performance especially in CVR sub-part of VVO according to EV model and type. Thus, this study could lead near future VVO solutions to gain higher levels of accuracy and efficiency considering smart microgrid components such as EV in their models.

Suggested Citation

  • Manbachi, M. & Sadu, A. & Farhangi, H. & Monti, A. & Palizban, A. & Ponci, F. & Arzanpour, S., 2016. "Impact of EV penetration on Volt–VAR Optimization of distribution networks using real-time co-simulation monitoring platform," Applied Energy, Elsevier, vol. 169(C), pages 28-39.
    • Handle: RePEc:eee:appene:v:169:y:2016:i:c:p:28-39
    DOI: 10.1016/j.apenergy.2016.01.084
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    References listed on IDEAS

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    Cited by:

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    3. Manbachi, Moein & Farhangi, Hassan & Palizban, Ali & Arzanpour, Siamak, 2016. "Smart grid adaptive energy conservation and optimization engine utilizing Particle Swarm Optimization and Fuzzification," Applied Energy, Elsevier, vol. 174(C), pages 69-79.
    4. Dan Wang & Qing’e Hu & Jia Tang & Hongjie Jia & Yun Li & Shuang Gao & Menghua Fan, 2017. "A Kriging Model Based Optimization of Active Distribution Networks Considering Loss Reduction and Voltage Profile Improvement," Energies, MDPI, vol. 10(12), pages 1-19, December.
    5. Bhandari, Vivek & Sun, Kaiyang & Homans, Frances, 2018. "The profitability of vehicle to grid for system participants - A case study from the Electricity Reliability Council of Texas," Energy, Elsevier, vol. 153(C), pages 278-286.
    6. Tang, Jia & Wang, Dan & Wang, Xuyang & Jia, Hongjie & Wang, Chengshan & Huang, Renle & Yang, Zhanyong & Fan, Menghua, 2017. "Study on day-ahead optimal economic operation of active distribution networks based on Kriging model assisted particle swarm optimization with constraint handling techniques," Applied Energy, Elsevier, vol. 204(C), pages 143-162.
    7. Su, Hongzhi & Wang, Chengshan & Li, Peng & Li, Peng & Liu, Zhelin & Wu, Jianzhong, 2019. "Novel voltage-to-power sensitivity estimation for phasor measurement unit-unobservable distribution networks based on network equivalent," Applied Energy, Elsevier, vol. 250(C), pages 302-312.

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