• DocumentCode
    1787601
  • Title

    Co-scheduling of HVAC control, EV charging and battery usage for building energy efficiency

  • Author

    Tianshu Wei ; Qi Zhu ; Maasoumy, Mehdi

  • Author_Institution
    Electr. & Comput. Eng., Univ. of California, Riverside, Riverside, CA, USA
  • fYear
    2014
  • fDate
    2-6 Nov. 2014
  • Firstpage
    191
  • Lastpage
    196
  • 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.
  • Keywords
    HVAC; building management systems; electric vehicles; energy conservation; power consumption; power grids; power system stability; predictive control; scheduling; secondary cells; EV charging schedulling; HVAC control coscheduling; MPC based algorithm; battery usage; building energy consumption reduction; building energy efficiency maximisation; electric vehicle charging; energy demand spike; energy load; grid stability; heating, ventilation, and air conditioning; multiple energy source utilization; predictive control based algorithm; Batteries; Buildings; Energy consumption; Equations; Mathematical model; Power grids; Prediction algorithms;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer-Aided Design (ICCAD), 2014 IEEE/ACM International Conference on
  • Conference_Location
    San Jose, CA
  • Type

    conf

  • DOI
    10.1109/ICCAD.2014.7001351
  • Filename
    7001351