EAGER: Connecting Smart Communities with Intelligent Transportation Systems - Energy Management of Predictive Occupancy Information

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Building and transportation are two essential and interactive elements for the society. Various means of transportation convey occupants among different buildings/communities via modern transportation networks. Occupants are central to operation of buildings and communities, especially in terms of energy use. Predictive information on building occupancy can be dramatically beneficial for improving the efficiency of building energy management. As occupants are transported to buildings/communities, the occupant arrival information can be predicted to through Intelligent Transportation Systems (ITS), enabled by vehicle-to-infrastructure communication and data analytics. This is expected to significantly benefit the optimization of energy management for buildings and communities. For occupant-carrying plug-in electric vehicles (PEVs), such predictive arrival information can also help optimize the charging management and vehicle-to-grid operation. This EArly-concept Grant for Exploratory Research (EAGER) award supports an exploratory research on integrating the ITS to energy management of Smart Communities, i.e. enhancing the building/community energy management with ITS predicted occupancy information. The results of this research will address major fundamental issues relevant to the integration of ITS and Smart Community, which will benefit the technology development. The findings of the project will be disseminated to a broader community via a dedicated website showing animations and video clips based on simulation results. In addition, seminars and workshops for government, K-16 faculty and students, and general public are planned to illustrate and disseminate the results of the research.Two scenarios of community level energy management are used to demonstrate the potential benefits brought by such community-ITS integration: 1) predictive energy management of community/district cooling system under demand response, enhanced with aggregated stochastic estimation of arrival time of upcoming building occupants; 2) decentralized charging management of parking-lot PEV enhanced with stochastic estimation of arrival time and arrival battery state-of-charge (SOC) of upcoming PEVs. With the moving-horizon prediction of in-coming occupant/vehicle arrival information enabled by ITS integration, stochastic prediction of arrival time will be performed, as well as the PEV arrival SOCs. Through aggregation of the stochastic arrival time estimation, dynamic data-driven modeling of occupant-to-load relations, and weather information, a stochastic model predictive control (MPC) strategy is applied for district cooling of community buildings with a central chilled-water plant, under demand response operation. An important issue of uncertainty propagation will be addressed in order to gain the understanding on how the stochastic estimate of occupant arrival information would affect the ultimate performance of the stochastic MPC and stochastic optimization for the energy management problems.