Intelligent negotiation agent with learning capability for energy trading between building and utility grid

In this paper, a particle swarm optimization (PSO) based negotiation agent with learning capability is proposed to facilitate the bi-directional energy trading between the building and the utility grid. A comprehensive set of factors in the integrated smart building and utility grid system is taken into account in developing the negotiation model. In addition, the learning capability of the negotiation agent is developed to adaptively adjust the trader's decisions according to the opponent's behaviors. The feasibility of the proposed negotiation agent is evaluated by the simulation results. It turns out that the proposed intelligent agent is capable of making rational deals in bi-directional energy trading by maximizing the trader's payoffs with reduced negotiation time.