A stochastic game for energy resource trading in the context of Energy Internet

In this paper, the problem of energy resource trading in an Energy Internet context is studied. By collecting information about energy bidding of multiple microgrids, an aggregator aims to maximize each microgrids' profit while minimizing the risk of overbidding for renewable energy resources trading-based microgrids. A novel stochastic game-theoretic model and the conditional value-at-risk (CVaR) measurement are introduced to characterize the payoff function of each microgrid. The sample average approximation technique is employed to approximate the stochastic Nash equilibrium. The existence of a sample average approximated (SAA) Nash equilibrium is investigated and the corresponding seeking algorithm is also proposed. The results are illustrated by numerical simulations, which show the SAA Nash equilibrium could reduce the risk of not meeting the demand and improve the economic benefits.