Linear Model-Predictive Controller (LMPC) for Building’s Heating Ventilation and Air Conditioning (HVAC) System

Abstract

Model predictive control (MPC) is a widely used technique for temperature set-point tracking and energy optimization of Heating Ventilation and Air Conditioning (HVAC) systems in buildings. Unfortunately, a nonlinear thermal building model leads to a computationally expensive nonlinear MPC problem that is not suitable for real-time control and optimization. This paper presents a novel approximate linearized model for building's thermal dynamics and the HVAC system power consumption that leads to a computationally efficient linear model predictive controller (LMPC) for the buildings' HVAC systems. We employ feedback linearization technique to obtain an equivalent linearized model for the nonlinear thermal building dynamics and use constraint mapping approach to obtain a linearized formulation for new control variables. Next, using piecewise linearization, we obtain a linearized analytical model for the HVAC system power consumption. The proposed LMPC technique is validated using multiple simulation case studies. We demonstrate that the proposed LMPC technique is not only computationally efficient but also accurately approximates the nonlinear optimal control decisions.

Proactive demand-side participation: Centralized versus transactive demand-Supply coordination

“…Local BESS Model: The dynamics for the BESS can be formulated based on the model presented in [18,38] as follows: Discussion on generalization to other heating/cooling loads and timeshiftable loads: It should be noted that since the HVAC system is solely responsible for 40% of the buildings energy consumption [39], we consider it as a representative of the buildings heating and cooling loads in this work. However, the demand flexibility of the buildings can be provided by other heating and cooling systems such as combined heat and power (CHP), auxiliary boilers (AB), absorption chiller, and heat pumps (HPs), and so on.…”

“…Building thermal model dynamic is nonlinear and usually is linearized based on Jacobian approach around an equilibrium point, i.e., the set-point temperature [37]. However, this approach is not valid when the room temperature varies significantly, which is when the building is overheated or overcooled to gain economic benefits [39]. Therefore, [44] uses main nonlinear building thermal load model to minimize the cost of transacted energy while meeting the HVAC system's requirements and satisfying the comfort level of the occupants.…”

Regret Analysis of Learning-Based MPC with Partially-Unknown Cost Function

“…where x t ∈ [20,24] in degrees Celsius, k r > 0 is the time constant of the room, k c > 0 is the temperature change over a 15 minutes system delay caused by cooling for a duty cycle of u t ∈ [0, 0.5] (i.e., the AC is on for ∆ * u t minutes over a system delay), k v > 0 is the time constant for heat transfer from the room to the outside, v t is the outside temperature in degrees Celsius, and q t is the heating load of the occupants and equipment within the room over a system delay. We note that the time constants k r and k v are dimensionless.…”

Coalition Formation of Microgrids with Distributed Energy Resources and Energy Storage in Energy Market

“…The profit made by the participation of EB in the DSM program is calculated by (14). Equation (18) describes the relation between the consumed amount of electric and thermal power generated by the thermal pump.…”

“…When the latter is controlled, it is defined as energy management [10], [11]. The DR considering the energy optimization of heating ventilation and air conditioning (HVAC) systems is considered in [12] - [14]. In [12], the authors develop a price-responsive DR for HVAC systems of buildings.…”