Reference
L. A. de Araujo Passos, P. van den Engel,
S. Baldi, and B. De Schutter, "Dynamic optimization for minimal HVAC demand
with latent heat storage, heat recovery, natural ventilation, and solar
shadings,"
Energy Conversion and Management, vol. 276,
p. 116573, 2023.
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/m
2/year is required, which may qualify the system as a
low-energy building.
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BibTeX
@article{deAvan:23-019,
author = {de Araujo Passos, Luigi Antonio and van den Engel, Peter and
Baldi, Simone and De Schutter, Bart},
title = {Dynamic Optimization for Minimal {HVAC} Demand with Latent Heat
Storage, Heat Recovery, Natural Ventilation, and Solar Shadings},
journal = {Energy Conversion and Management},
volume = {276},
pages = {116573},
year = {2023}
}