TY - GEN
T1 - Simulation and optimization of energy efficient operation of HVAC system as demand response with distributed energy resources
AU - Lee, Young M.
AU - Horesh, Raya
AU - Liberti, Leo
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2016/2/16
Y1 - 2016/2/16
N2 - Optimal control of building's HVAC (Heating Ventilation and Air Conditioning) system as a demand response may not only reduce energy cost in buildings, but also reduce energy production in grid, stabilize energy grid and promote smart grid. In this paper, we describe a model predictive control (MPC) framework that optimally determines control profiles of the HVAC system as demand response. A Nonlinear Autoregressive Neural Network (NARNET) is used to model the thermal behavior of the building zone and to simulate various HVAC control strategies. The optimal control problem is formulated as a Mixed-Integer Non-Linear Programming (MINLP) problem and it is used to compute the optimal control profile that minimizes the total energy costs of powering HVAC system considering dynamic demand response signal, on-site energy storage system and energy generation system while satisfying thermal comfort of building occupants within the physical limitation of HVAC equipment, on-site energy storage and generation systems.
AB - Optimal control of building's HVAC (Heating Ventilation and Air Conditioning) system as a demand response may not only reduce energy cost in buildings, but also reduce energy production in grid, stabilize energy grid and promote smart grid. In this paper, we describe a model predictive control (MPC) framework that optimally determines control profiles of the HVAC system as demand response. A Nonlinear Autoregressive Neural Network (NARNET) is used to model the thermal behavior of the building zone and to simulate various HVAC control strategies. The optimal control problem is formulated as a Mixed-Integer Non-Linear Programming (MINLP) problem and it is used to compute the optimal control profile that minimizes the total energy costs of powering HVAC system considering dynamic demand response signal, on-site energy storage system and energy generation system while satisfying thermal comfort of building occupants within the physical limitation of HVAC equipment, on-site energy storage and generation systems.
U2 - 10.1109/WSC.2015.7408227
DO - 10.1109/WSC.2015.7408227
M3 - Conference contribution
AN - SCOPUS:84962799519
T3 - Proceedings - Winter Simulation Conference
SP - 991
EP - 999
BT - 2015 Winter Simulation Conference, WSC 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - Winter Simulation Conference, WSC 2015
Y2 - 6 December 2015 through 9 December 2015
ER -
