Bargaining strategies in bilateral electricity trading based on fuzzy Bayesian learning. (July 2021)

• Record Type: Journal Article
• Authors: Yi, Zuo; Xin-gang, Zhao; Yu-zhuo, Zhang
• Is Part Of: International journal of electrical power & energy systems. Volume 129(2021)
• Publication Date: 2021-07
• Subjects: Bilateral electricity trading -- Bargaining strategy -- Nash bargaining solution -- Fuzzy Bayesian learning; 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.2021.106856
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: The bargaining strategy based on fuzzy Bayesian learning is proposed. The bidding strategies meet the Nash bargaining solution and improve negotiation efficiency. Alternative bidding strategy and simultaneous bidding strategy are compared. The bidding strategies of bargaining consider information interaction. Abstract: Bilateral electricity trading is a general transaction mode in electricity market, market subjects' trading strategies will influence social welfare of the market. This study aims to explore effective bargaining strategies promoting the realization of Nash bargaining solution in bilateral electricity trading. The non cooperative bargaining models with preferences in incomplete information and the fuzzy Bayesian learning are combined to optimize the trading strategies. The results show that: (1) the bargaining strategy considering the preferences of both parties to balance the utility maximization and the acceptance of offers is equal allocation of benefits, and it promotes the formation of equilibrium close to social welfare maximization; (2) fuzzy Bayesian learning can accelerate the bargaining process; (3) compared with simultaneous offers bargaining, in alternating offers bargaining, the convergence to equilibrium is faster and parties have first-mover advantage.