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

Zhe Liu, Dan Wang, Hongjie Jia and Ned Djilali

Applied Energy, 2014, vol. 136, issue C, 662-670

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.

Keywords: Operation risk; Optimal power flow; Plug-in hybrid electric vehicle; State of charging; Charging load self-management (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261914010174
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:136:y:2014:i:c:p:662-670

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2014.09.069

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().