Dynamic optimization for minimal HVAC demand with latent heat storage, heat recovery, natural ventilation, and solar shadings

Abstract

Satisfying thermal comfort in indoor spaces is still a challenge in terms of energy saving, and several HVAC (Heating, Ventilation, and Air-Conditioning) systems have been proposed for this purpose. This paper conducts an analysis to evaluate and optimize the long-term operation of a novel HVAC system installed at The Green Village, a living lab in Delft, the Netherlands. This system comprises all-glass facades with steerable solar shades, sky windows, a climate tower equipped with Phase-Change Material (PCM), a heat recovery unit, and a heat pump. The current analysis draws on transient modeling to predict the system's behavior while relying on constrained nonlinear optimization to select the optimal design parameters (e.g. floor heat capacity and solar absorptance) and optimal operational conditions (e.g. use of PCM and heat recovery unit, aperture of sky windows and solar shadings). The goal is to schedule the control inputs to operate the system as much as possible as a passive energy system, with minimal active power all year round. The results show that the optimization can reduce the yearly heat demand by around 10.6%, with the solar shadings being the most significant component to be optimized. Furthermore, the optimized system is capable to supply 58% of the annual thermal demand passively – In this case, an auxiliary thermal demand of only 27 kWh/m²/year is required, which may qualify the system as a low-energy building.