Energy Peer-to-Peer Trading Model and Optimization Method for the Prosumers Based on Smart Community

This paper proposes a novel energy peer-to-peer (P2P) trading model to maximize the benefits among prosumers. The energy prices are leaded by the buyers when the total energy of smart community is surplus, the sellers' supply responses to the buyers' buying prices is modeled as a noncooperative game. The proposed model is constructed regarding the buyer as the leader to minimize the cost of the buyer. Contrarily, the energy prices are leaded by the sellers when the total energy of smart community is insufficient, the buyers' demands responses (DR) to the sellers' selling prices is modeled as a noncooperative game. The proposed model is constructed regarding the seller as the leader to maximum the revenue of the seller. In addition, a new distributed iterative algorithm based on interior point method is proposed to solve the objective function to obtain the optimal scheduling scheme for the prosumers. In order to reduce invalid calculation, the modified adjustment parameter and the independence of each prosumer’s convergence are carried out. The results verify the rapid convergence of the algorithm and reflect the benefits of the energy P2P trading model.