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Publication
WSC 2015
Conference paper
Simulation and optimization of energy efficient operation of HVAC system as demand response with distributed energy resources
Abstract
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.