V2V Energy Trading in Residential Microgrids Considering Multiple Constraints via Bayesian Game

The V2V (vehicle-to-vehicle) energy swapping strategy provides an alternative charging for electric vehicles (EVs) to alleviate the problem of overload caused by the un-coordinately charges in residential microgrids (MGs) during the peak. However, stochastic factors of EVs and neglected network losses cause inflexibility and impracticality of the existing methods. Focused on the stochastic factors of energy trading caused by EVs and network constraints in residential MGs scenario, a V2V Energy Trading in Residential Microgrids (VETRM) considering multiple constraints via Bayesian game model is proposed. Frist, the Bayesian game modeled types of players by information including the stochastic characteristics of EVs, which results in uncertainties that the game participants determine the roles of seller or buyer that depends on the states of power surplus or lack. Then, utility functions based on the sensitivity analysis to the impact of V2V transactions on the network and to guarantee the exchange of energies to inviolate network constraints are established. The solution of model based on game equilibrium has been rigorously derived. Two scenarios fulfill the comparisons of existing models and proposed model for simulation including IEEE 33 and IEEE 123 bus system that the proposed model improve the revenues of EV in reducing energy loss and smoothing the peak of the power system to demonstrate the effectiveness of the VETRM model.