Integrated energy management of a smart community with electric vehicle charging using scenario based stochastic model predictive control

Abstract

Occupant activities in buildings are connected by different transportation measures. For smart communities, it is possible to synergize the energy management of smart buildings with the vehicle operation/travel information available from transportation infrastructure, e.g. the intelligent transportation systems (ITS). Such information enables the prediction of upcoming building occupancy and upcoming charging load of electric vehicles (EVs). This paper presents a predictive energy management strategy for smart community, which features water-based district cooling for a cluster of buildings driven by a multi-chiller central plant, and each building hosts a number of EV charging stations. A scenario-based stochastic model predictive control (SCMPC) framework, in which the upcoming building occupancy and charging load, ambient temperature, humidity and solar irradiance are assumed to be stochastically predictable. The SCMPC targets demand response operation at community level, involving both time-of-use and demand charges, and the combined power consumption by the central plant and EV charging observing to the transformer limit as a coupled constraint. To evaluate the propagation of the above uncertain factors through the receding-horizon SCMPC process, moving-horizon probabilistic models are established for EV arrival information. By developing a functional mockup interface (FMI) based co-simulation platform developed with Modelica and Python, the proposed method is validated via simulation study, and the performance indices are evaluated.