A probability load modeling method for the charging demand of large-scale PEVs accounting users' charging willingness. (November 2016)

• Record Type: Journal Article
• Authors: Xu, Hao; Ouyang, Fan; Liu, Haifeng; Miao, Shihong; Tang, Haiguo; Liang, Wenwu; Li, Hui; Shi, Dongyuan
• Is Part Of: International journal of electrical power & energy systems. Volume 82(2016:Nov.)
• Page Start: 331
• Page End: 338
• Publication Date: 2016-11
• Subjects: Plug-in electric vehicle -- Charging demand -- Residential distribution system -- Charging willingness -- User set -- Probabilistic load model; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2016.03.013
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: The strategy takes the charging willingness of PEV users into consideration. The strategy accounts for the difference in charging frequencies among users. A detailed classification on PEV users is conducted. The total charging demand on each weekday is calculated. The aggregated charging demand resulted is more rational and credible. Abstract: This paper presents a new strategy in order to model the charging power demand due to large-scale plug-in electric vehicles (PEVs) as realistic a fashion as possible and analyze their impact on the residential power distribution system. The strategy takes the charging willingness of PEV users into consideration, and accounts for the difference in charging frequencies among users. A detailed classification, derived from the historical data on users' driving patterns, on PEV users is conducted in order to ensure that users in the same user set have the same charging properties. Seven probability load models for PEV charging are established for these user sets, and each model accounts the inherent randomness in the usages and recharges of PEVs. After the consideration of charging willingness, the charging demand differs among weekdays. The aggregated charging demand from a user set on each weekday is calculated based on the Law of Large Numbers, and the total charging demand from all PEVs on each weekday can be obtained by accumulating the aggregated charging demand of the user sets with charging willingness. The strategy can ensure a high utilization of the battery capacity, and the aggregated charging demand resulted is more rational and credible. The proposed charging load modeling strategy is finally applied on the electric load profile on a winter day in Manitoba.