Distributed optimization-based power trade strategy for we-energy in energy internet

A novel distributed power trade strategy is proposed to address the pricing problem for we-energy-based energy internet, considering the psychological fluctuation caused by the self-decision ability of the we-energy during the process of buying and selling power. A pricing model is presented based on the distributed optimization theory, by which the minimum generation cost of the energy internet can be achieved. Furthermore, to accurately analyze the process of bidding pricing with the disturbance resulted from the psychological fluctuation of each we-energy, the multi-agent system with an uncertainty function is utilized. One example is used to verify the effectiveness of the proposed framework.