Negotiation strategy for discharging price of EVs based on fuzzy Bayesian learning

Qian Zhang; Weiyu Tan; Jiajia Cai; Zhong Wang; Chunyan Li

Negotiation strategy for discharging price of EVs based on fuzzy Bayesian learning

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Author(s): Qian Zhang¹ ; Weiyu Tan¹ ; Jiajia Cai² ; Zhong Wang¹ ; Chunyan Li¹
- Affiliations: 1: State Key Laboratory of Power Transmission Equipment & System Security and New Technology , Chongqing University , Chongqing , People's Republic of China ;
  2: State Grid Suining Power Supply Company , Suining , People's Republic of China
Source: Volume 12, Issue 20, 13 November 2018, p. 4396 – 4406
DOI: 10.1049/iet-gtd.2017.1724 , Print ISSN 1751-8687, Online ISSN 1751-8695

Received 15/11/2017, Accepted 23/02/2018, Revised 31/01/2018, Published 01/03/2018

To stimulate the participation of electric vehicles (EVs) in vehicle-to-grid (V2G) activities, some economic incentives should be offered to the EV owners and the discharging price is negotiated by EV aggregator and electricity grid. Here, this study proposes a negotiation strategy between EV aggregator and electricity grid which focuses on how to develop a reasonable mechanism for discharging price, and then the bilateral negotiation function models of discharging price based on fuzzy Bayesian learning are established. In the models, the certain parameters are calculated according to the profits and cost of the EV aggregator and electricity grid; and the fuzzy probability calculation method is formulated to estimate and calculate the uncertain parameters of the functions of both sides, respectively. Additionally, the negotiation function models based on fuzzy Bayesian learning is utilised for updating and correcting the deviation of estimates and the discharging price is finally found out by the parameters above. Through numerical cases, the negotiation strategy proposed in this study is verified to be effective in the early promotion of V2G.

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Negotiation strategy for discharging price of EVs based on fuzzy Bayesian learning

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