Bargaining strategies in bilateral electricity trading based on fuzzy Bayesian learning

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.