This research evaluates the performance of a Phase Change Material (PCM) battery integrated into the climate system of a new transparent meeting center. The main research questions are: a. “Can the performance of the battery be calculated?” and b. “Can the battery reduce the heating and cooling energy demand in a significant way?” The first question is answered in this document. In order to be able to answer the second question, especially the way the heat loading in winter should be improved, then more research is necessary. In addition to the thermal battery, which consists of Phase Change Material plates, the climate system has a cross-flow heat exchanger and a heat pump. The battery should play a central role in closing the thermal balance of the lightweight building, which can be loaded with hot return or cold outdoor air. The temperature of the battery plates is monitored by multi-sensors and simulated by the use of PHOENICS (Computational Fluid Dynamics) and MATLAB. This paper reports reasonable agreement between the numerical predictions and the measurements, with a maximum variance of 10%. The current coefficient of performance for heating and cooling is already high, more than 27. There is scope for increasing this much further by making use of the very low-pressure difference of the battery (below 25 Pascal), low pressure fans and the ventilation system as a whole.

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.

Controlling the operation of HVAC (Heating, Ventilation, and Air-Conditioning) systems is arguably the most effective way to reach desired indoor conditions in buildings. Nevertheless, such control may involve complex dynamics when dealing with passive energy technologies. In this paper, we focus on maximizing the passive operation of HVAC in a novel low-energy building design by means of Model Predictive Control (MPC). The low-energy building design, located in The Green Village, consists of a thermal chimney and solar shades over all-glass facades to provide the required indoor air conditioning as passively as possible. The MPC controller is based on a transient grey box model and a hierarchical control architecture to satisfy thermal comfort while minimizing the active energy requirements. Using sensor data collected from the actual building in April and May 2021, the grey box model shows a good agreement with the measurements, since the variance accounted for is 90% in most cases. Moreover, via a comparative study among different MPC architectures we show that managing the distinct transient response of each component (shades and chimney) is the best for successful overall performance – e.g. considering linear agents for shading and nonlinear agents for ventilation. The hierarchical MPC architecture established outperforms the standard ones by 22.7% in terms of control performance. We also compare the proposed MPC approach against the rule-based control method currently implemented in the actual building, which indicates that MPC demands about 78% less active energy, highlighting the proposed optimization-based control approach.

Investigating supercritical natural fluids for efficient and clean energy production has become a trending research topic due to their technical and environmental advantages. However, on account of the supercritical operational conditions, using specially-developed components increases manufacturing prices, especially when dealing with solar-powered plants assisted by thermal energy storage (TES) systems. This paper assesses the economic and environmental trends of an integrated supercritical carbon dioxide (s-CO₂) solar-powered plant. The system is composed of a packed-bed TES system, a solar field, and a power block while considering conventional backup heating. Transient year-around numerical simulations explore several operational conditions relying on detailed cost and typical meteorological year (TMY) data. Also, the modeling accounts for the system's environmental sustainability through a penalization cost regarding CO₂ emissions due to auxiliary heating. With parametric analyses, the study assesses the compromise solutions minimizing the levelized cost of energy (LCOE). The results revealed the possible feasibility of the integrated system using such a TES technology for s-CO₂ and evidenced several venues for further examination. In the end, a sensitivity analysis investigates the influence of the specific costs and TMY data on the LCOE.

Energy-saving devices are extensively sought in several fields, including heating, ventilation, and air-conditioning (HVAC) tasks in buildings. This paper investigates six model predictive control (MPC) strategies as a way to optimize the operation of a solar shaded, natural ventilated building located at TU Delft campus. Such building is based on an innovative combination of a thermal chimney and glazing walls for harvesting passive energy. The main challenge is dealing with the system dynamics. The predictive controllers we consider include both linear and nonlinear MPC, and four hierarchical MPC strategies. All controllers aim to minimize auxiliary power consumption and, consequently, increasing the energy savings. The six control strategies performance are evaluated using reference values for thermal comfort, while relying on simulations performed in MATLAB for calculations. The hierarchical MPC architecture which considers a hybrid structure with nonlinear tracker for ventilation and linear agents for heating purposes appears the most promising one.