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
Link To Document