TY - GEN
T1 - Model predictive control based on thermal dynamic building model in the demand-side management
AU - Mai, Weijie
AU - Chung, C. Y.
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/11/10
Y1 - 2016/11/10
N2 - Flexible load response allows to manage local power consumption in response to supply conditions, such as high market price, peak demand or regulation signal, thus, lower grid operating costs, increased system reliability and improved energy efficiency can be achieved. Besides, demand response becomes large contributor to overall building expense reduction, especially in large grid-congested cities. HVAC system in commercial building accounts for a huge amount of power consumption and greatly contributes to the peak load. In this work, we propose an economic model predictive controller based on a thermal dynamic building model that discussed in our previous work. Simulation results show that the proposed controller can effectively and optimally control the HVAC system to utilize the time-varying electricity prices and the future disturbances to minimize the electricity costs, significantly reduce the peak demand and increase energy saving and efficiency, while respecting the comfort level.
AB - Flexible load response allows to manage local power consumption in response to supply conditions, such as high market price, peak demand or regulation signal, thus, lower grid operating costs, increased system reliability and improved energy efficiency can be achieved. Besides, demand response becomes large contributor to overall building expense reduction, especially in large grid-congested cities. HVAC system in commercial building accounts for a huge amount of power consumption and greatly contributes to the peak load. In this work, we propose an economic model predictive controller based on a thermal dynamic building model that discussed in our previous work. Simulation results show that the proposed controller can effectively and optimally control the HVAC system to utilize the time-varying electricity prices and the future disturbances to minimize the electricity costs, significantly reduce the peak demand and increase energy saving and efficiency, while respecting the comfort level.
KW - Demand side management
KW - Model predictive control
KW - Physics-based load modeling
UR - http://www.scopus.com/inward/record.url?scp=85001555419&partnerID=8YFLogxK
U2 - 10.1109/PESGM.2016.7741437
DO - 10.1109/PESGM.2016.7741437
M3 - Conference article published in proceeding or book
AN - SCOPUS:85001555419
T3 - IEEE Power and Energy Society General Meeting
BT - 2016 IEEE Power and Energy Society General Meeting, PESGM 2016
PB - IEEE Computer Society
T2 - 2016 IEEE Power and Energy Society General Meeting, PESGM 2016
Y2 - 17 July 2016 through 21 July 2016
ER -