Human activities in buildings are connected by various transportation measures. For the emerging Smart and Connected Communities (S&CC), 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 electrified vehicles. This paper presents a predictive energy management strategy for smart community with a distributed model predictive control framework, in which the upcoming building occupancy and charging load are assumed to be predictable to certain extent based on the ITS information. An illustrative example of smart community is used for simulation study based on a Modelica simulation model, in which a chilled-water plant sustains the ventilation and air conditioning of three buildings, and each building is assumed to host a number of charging stations. Simulation study is performed to validate the proposed strategy.
Data-Centric Hierarchical Distributed Model Predictive Control for Smart Grid Energy Management
