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Power system operation risk analysis considering charging load self-management of plug-in hybrid electric vehicles

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  • Liu, Zhe
  • Wang, Dan
  • Jia, Hongjie
  • Djilali, Ned

Abstract

Many jurisdictions around the world are supporting the adoption of electric vehicles through incentives and the deployment of a charging infrastructure to reduce greenhouse gas emissions. Plug-in hybrid electric vehicles (PHEVs), with offer mature technology and stable performance, are expected to gain an increasingly larger share of the consumer market. The aggregated effect on power grid due to large-scale penetration of PHEVs needs to be analyzed. Nighttime-charging which typically characterizes PHEVs is helpful in filling the nocturnal load valley, but random charging of large PHEV fleets at night may result in new load peaks and valleys. Active response strategy is a potentially effective solution to mitigate the additional risks brought by the integration of PHEVs. This paper proposes a power system operation risk analysis framework in which charging load self-management is used to control system operation risk. We describe an interactive mechanism between the system and PHEVs in conjunction with a smart charging model is to simulate the time series power consumption of PHEVs. The charging load is managed with adjusting the state transition boundaries and without violating the users’ desired charging constraints. The load curtailment caused by voltage or power flow violation after outages is determined by controlling charging power. At the same time, the system risk is maintained under an acceptable level through charging load self-management. The proposed method is implemented using the Roy Billinton Test System (RBTS) and several PHEV penetration levels are examined. The results show that charging load self-management can effectively balance the extra risk introduced by integration of PHEVs during the charging horizon.

Suggested Citation

  • Liu, Zhe & Wang, Dan & Jia, Hongjie & Djilali, Ned, 2014. "Power system operation risk analysis considering charging load self-management of plug-in hybrid electric vehicles," Applied Energy, Elsevier, vol. 136(C), pages 662-670.
    • Handle: RePEc:eee:appene:v:136:y:2014:i:c:p:662-670
    DOI: 10.1016/j.apenergy.2014.09.069
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    References listed on IDEAS

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    1. Williams, Trevor & Wang, Dan & Crawford, Curran & Djilali, Ned, 2013. "Integrating renewable energy using a smart distribution system: Potential of self-regulating demand response," Renewable Energy, Elsevier, vol. 52(C), pages 46-56.
    2. Wang, D. & Parkinson, S. & Miao, W. & Jia, H. & Crawford, C. & Djilali, N., 2012. "Online voltage security assessment considering comfort-constrained demand response control of distributed heat pump systems," Applied Energy, Elsevier, vol. 96(C), pages 104-114.
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    9. Moon, Sang-Keun & Kim, Jin-O, 2017. "Balanced charging strategies for electric vehicles on power systems," Applied Energy, Elsevier, vol. 189(C), pages 44-54.

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