• Fit-for-purpose simulation model developed for PVT-heat pump systems in buildings • Early-stage integration of control strategies leads to different design decisions • Including control strategies shifted radiator, PV, and insulation design decisions • Integrated building-energy system approaches are needed over isolated studies The adoption of renewable energy technologies in buildings is rising. While the performance of such systems heavily depends on the use of suitable control strategies, most design processes tend to treat controls as an afterthought. Especially in parametric studies that compare different system configurations in early design stages, simplified control approaches are used. The separation between design and operation risks discarding promising solutions, as simplified controls used during design often misrepresent how these systems can potentially perform in operation. This study investigates the impact of integrating tailored control strategies in the early design stage of residential energy systems, focusing on a Dutch terraced house and considering the inclusion of photovoltaic-thermal panels, a heat pump, and thermal storage during renovation. Through parametric simulations using TRNSYS and Python, five control strategies are evaluated, that vary in their approach to improving self-consumption of on-site PV electricity. The results demonstrate that control strategies have the ability to shift demand to more favorable periods, thereby influencing operational performance and hence design decisions. Systems with superior controls showed up to 35% reduction in annual operational emissions for heating compared to baseline controls. In some cases, solutions dismissed under simplified control assumptions performed competitively when controls that aligned with the systems’ operational principles were applied. The study shows that a control-aware design process that treats control strategies as integral design parameters, rather than post-design operational measures, reduces the risk of misrepresenting a system’s actual potential, and thereby supports better-informed decisions.
Karanth et al. (Sun,) studied this question.