Service life of EV batteries used in power grid frequency regulation

Guo, Yuhan; Jiang, Yi; Yu, Jie; Tang, Yaohua

doi:10.1109/control.2016.7737597

Cited by 4 publications

(1 citation statement)

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“…Reference [21] proposed a grouping-converting strategy to avoid frequent charging and discharging. Based on ambient temperature, cycle index, current, and charging/discharging depth, [22] proposed a cycle life model of EV batteries. It was concluded that the frequency stability of the power grid could be improved when EVs and generator sets cooperated.…”

Section: Introductionmentioning

confidence: 99%

Decision Aid Model for Private-owned Electric Vehicles Participating in Frequency Regulation Ancillary Service Market

Wang,

Sun,

Wang

et al. 2024

Journal of Modern Power Systems and Clean Energy

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To reduce the difficulty and enhance the enthusiasm of private-owned electric vehicles (EVs) to participate in frequency regulation ancillary service market (FRASM), a decision aid model (DAM) is proposed. This paper presents three options for EV participating in FRASM, i. e., the base mode (BM), unidirectional charging mode (UCM), and bidirectional charging/discharging mode (BCDM), based on a reasonable simplification of users' participating willingness. In BM, individual EVs will not be involved in FRASM, and DAM will assist users to set the optimal charging schemes based on travel plans under the time-of-use (TOU) price. UCM and BCDM are two modes in which EVs can take part in FRASM. DAM can assist EV users to create their quotation plan, which includes hourly upper and lower reserve capabilities and regulation market mileage prices. In UCM and BCDM, the difference is that only the charging rate can be adjusted in the UCM, and the EVs in BCDM can not only charge but also discharge if necessary. DAM can estimate the expected revenue of all three modes, and EV users can make the final decision based on their preferences. Simulation results indicate that all the three modes of DAM can reduce the cost, while BCDM can get the maximum expected revenue.

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Section: Introductionmentioning

confidence: 99%