Paper Conference

Proceedings of ASim Conference 2016: 3rd Asia conference of IBPSA-China, Japan, Korea


Optimization of Energy Efficient Operation of HVAC System as Demand Response with Distributed Energy Resource

Hansol Hansol

Abstract: In this paper, we describe a model predictive control (MPC) framework that optimally determines control profiles of the HVAC (Heating Ventilation and Air Conditioning) system as demand response in presence of on-site distributed energy resources such as energy storage system and energy generation system. The approach determines not only the optimal operations of HVAC system but also how to optimally source the energy needed to power the HVAC system from multiple sources of energy such as grid electricity and on-site stored electricity and on-site generated electricity. 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. The MINLP is approximated as Mixed Integer Linear Programming (MILP), which is easier to solve by linearizing the transfer function of the neural network model.
Keywords: HVAC, MPC, Demand Response, Optimization, Neural Network, Data-Driven Model