Relevant research has demonstrated that more potential benefits can be achieved when energy and information are transacted and exchanged locally among different energy consumers. With increasing number of electric vehicles (EVs), various models and solution strategies have been developed for collaboration between building and EV charging station to achieve greater energy efficiency. However, most of the existing research employs centralized decision model which is time consuming for large scale problems and cannot protect private information for each participator. To bridge these research gaps, a guided particle swarm optimizer based distributed decision approach is proposed to study the energy transaction between building and EV charging station. In the proposed decision approach, the marginal price signal of transactive energy is collected to guide iterative direction of particle’s velocity and position which can maximally protect private information of building and EV charging station. A study case based on a commercial building and a nearby charging station in Chicago area is designed for illustration. The experimental results demonstrate that our proposed marginal price guided particle swarm optimizer is more stable and efficient comparing with canonical particle swarm optimizer and two state-of-the-art distributed decision algorithms.

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