Risk-Averse Energy Trading in Multienergy Microgrids: A Two-Stage Stochastic Game Approach

Abstract

Multienergy microgrids are a promising solution to improve overall energy (electricity, cooling, heating, etc.) efficiency. In this paper, a new optimal energy trading strategy is developed considering the risk from uncertain energy supply and demand in a set of individual multienergy microgrids. According to the historical data about energy supply of each microgrid, an aggregator aims to maximize each microgrid's profit while minimizing the risk of overbidding for renewable energy resources trading based microgrids. A novel two-stage stochastic game model with Cournot Nash pricing mechanism and the conditional value-at-risk criterion is proposed to characterize the payoff function of each microgrid. The sample average approximation (SAA) technique is employed to approximate the stochastic Nash equilibrium of the game model. The existence of the SAA Nash equilibrium is investigated and the corresponding Nash equilibrium seeking algorithm is also realized in a distributed manner. The proposed method is validated by numerical simulations on real-world data collected in Australia, and the results show that the SAA Nash equilibrium based strategy can effectively reduce the risk of not meeting the demand and improve the economic benefits for each microgrid.