Abstract:
Building stock consumes 40% of primary energy consumption in the United States. Among various types of energy loads in buildings, HVAC (heating, ventilation, and air cond...Show MoreMetadata
Abstract:
Building stock consumes 40% of primary energy consumption in the United States. Among various types of energy loads in buildings, HVAC (heating, ventilation, and air conditioning) and EV (electric vehicle) charging are two of the most important ones and have distinct characteristics. HVAC system accounts for 50% of the building energy consumption and typically operates throughout the day, while EV charging is an emerging major energy load that is hard to predict and may cause spikes in energy demand. To maximize building energy efficiency and grid stability, it is important to address both types of energy loads in a holistic framework. Furthermore, on the supply side, the utilization of multiple energy sources such as grid electricity, solar, wind, and battery storage provides more opportunities for energy efficiency, and should be considered together with the scheduling of energy loads. In this paper, we present a novel model predictive control (MPC) based algorithm to co-schedule HVAC control, EV scheduling and battery usage for reducing the total building energy consumption and the peak energy demand, while maintaining the temperature within the comfort zone for building occupants and meeting the deadlines for EV charging. Experiment results demonstrate the effectiveness of our approach under a variety of demand, supply and environment constraints.
Date of Conference: 02-06 November 2014
Date Added to IEEE Xplore: 08 January 2015
Electronic ISBN:978-1-4799-6278-5