• Energy Research

Abstract: Various Collective Heating and Cooling Systems (CHCS) have emerged as promising low-carbon energy solutions for buildings. However, the absence of tailored decision guidelines often hinders decision-makers from identifying the optimal system for any given case. This research introduces a novel methodology for comprehensive evaluation of different CHCS under diverse case-specific boundary conditions, leading to informed recommendations. The proposed methodology integrates occupants' preferences for thermal comfort and costs into a holistic Key Performance Indicator (KPI) score, i.e. a weighted sum of normalised indicators including indoor thermal comfort, domestic hot water comfort, and levelised cost of energy. By applying this methodology to evaluate three advanced central change-over temperature CHCS across various building sizes and family types, our study demonstrates the effectiveness of this approach. The results suggest that 4-pipe systems are preferable when prioritising thermal comfort, whereas decentralised booster heat pumps are recommended for cost reduction. Notably, for small apartment buildings inhabited by working families, a 2-pipe system with decentralised storage might be preferred. These insights underscore the importance of incorporating occupants' preferences into multi-objective decision-making. Furthermore, the holistic KPI score methodology can assess different control strategies and provide valuable insights for policymakers when extended with additional indicators.

Collective heating systems have multiple end-users with time-varying, often different temperature demands. There are several concepts catering to this, e.g., multi-pipe networks and 2-pipe networks with or without decentralised booster systems. In this study, we focus on 2-pipe networks with a changing supply temperature by smart use of decentralised storage. By grouping high-temperature demands, the average supply temperature can be lowered during large parts of the day, which is beneficial for system efficiency. The actual energy-saving potential, however, can be case-specific and is expected to depend on design choices and implemented control strategies. In this paper, these dependencies are assessed and identified by implementing two optimised rule-based control strategies, providing in such a way a bench-mark for other control strategies. The results show that grouping yields energy savings of up to 36% at similar peak demand as with conventional control strategies. The energy-saving potential is greatest for large storage volumes and small networks, but large networks with large storage and proper control choices can also achieve around 30% energy savings. Moreover, high-temperature time can easily be reduced to less than 40% of the day, which could make space cooling without decentralised booster heat pumps possible, but this requires further research.

Abstract: To maximize the sustainable and economic benefits of collective heating systems, proper sizing is fundamental. This paper presents the validation of a novel sizing approach for collective systems producing and distributing heat for both space heating and domestic hot water, utilizing residential heat meter data. A validation methodology is developed to overcome the limitations of this type of data to identify the peak heat demand and estimate the peak heat demand under design outdoor conditions. The latter is estimated utilizing multiple linear regression coupled with an analysis of the maximum deviations. The power-storage characteristic, which shows all combinations of thermal power and thermal storage to meet the peak heat demand is determined and used to validate the novel sizing approach for six case studies. Although the results are promising, undersizing problems may arise in cases with decentralized heat storage