Peer-to-Peer Energy Trading Using Prediction Intervals of Renewable Energy Generation

The rapid development of renewable energy generation and demand side flexible resource makes the operation of distribution network and the organisation of power market facing greater uncertainty challenges. This paper proposes a novel receding horizon peer-to-peer energy transaction model based on the prediction intervals of renewable energy generation to manage the volatility in the range of a distribution network. A peer-to-peer energy interval matching algorithm is proposed to fully explore the flexibility in demand side for mitigating the output fluctuation of renewable energy generation locally. Then the responsibilities of undertaking the uncertainty risk from renewable generations are assigned to the counter-part consumers who have been matched with the renewable energy generations in a peer-to-peer market. The autonomy energy management problem under distribution network of each consumer is formulated as a cooperative gaming problem using the Nash bargaining theory. The uncertainty risk is considered into the Nash bargaining problem by utilizing voltage chance constraints and conditional value at risk based return-risk utility, of which the quantile connotations are consistent with the quantile results of the probability prediction of renewable energy generations. Moreover, an alternating direction method of multipliers algorithm based distributed methodology is developed to solve the Nash bargaining problem in a distributed manner. Numerical results demonstrate the effectiveness of the presented peer-to-peer energy trading model.