• DocumentCode
    3665313
  • Title

    Dynamic programming for optimal load-shedding of office scale battery storage and plug-loads

  • Author

    Michael Sankur;Daniel Arnold;David Auslander

  • Author_Institution
    Department of Mechanical Engineering, University of California, Berkeley, 94720, USA
  • fYear
    2015
  • fDate
    7/1/2015 12:00:00 AM
  • Firstpage
    1
  • Lastpage
    5
  • Abstract
    Plug-loads are often neglected in commercial demand response (DR) despite being a major contributor to building energy consumption. Improvements in technology like smart power strips are prompting the incorporation of plug-loads as a DR resource alongside building HVAC and lighting. Office scale battery storage (OSBS) systems are also candidates as a DR resource due to their ability to run on battery power. In this work, we present a model predictive control (MPC) framework for optimal load-shedding of plug-loads and OSBS.We begin with discussion of the context of this work, and present two models of OSBS systems. A model predictive controller for OSBS and plug-load load-shed scheduling is presented. We discuss casting the MPC as a dynamic program, and an algorithm to solve the dynamic program. Simulation results show the efficacy and utility of dynamic programming, and quantify the performance of OSBS systems.
  • Keywords
    "Batteries","System-on-chip","Uninterruptible power systems","Buildings","Load modeling","Mathematical model","Dynamic programming"
  • Publisher
    ieee
  • Conference_Titel
    Power & Energy Society General Meeting, 2015 IEEE
  • ISSN
    1932-5517
  • Type

    conf

  • DOI
    10.1109/PESGM.2015.7285755
  • Filename
    7285755