At Hamburg University of Technology the combination of an open cycle desiccant-assisted air conditioning system and a geothermal system is investigated in the framework of different research projects for several years. The objective of this study is to investigate the energy efficiency of the overall system and to evaluate the geothermal system during summer and winter mode, based on data measured for a temperate climate region. Monitoring results of the performance for dehumidification and remoistening of supply air are presented. Furthermore, the investigated system is compared to reference air conditioning processes. During summer mode, an average dehumidification efficiency of 1.15 is achieved. The electrical energy savings compared to a conventional reference system sum up to 50% for the investigated cooling period. System operation during winter shows an average moisture recovery efficiency of 0.75. The electrical energy demand for air humidification is reduced by 50% compared to a system with electric isothermal air humidification. The geothermal system is operated efficiently throughout the year for cooling and heating application. Besides the energetic system evaluation, measured data regarding the soil temperature and thermal comfort are presented.

An appropriate design of a chiller plant is crucial to guarantee highly performing solutions. However, several design variables, such as type of systems, total cooling capacity, and hydraulic arrangement, need to be considered. On the one hand, at present, different technical criteria for selecting the most suitable design variables are available. Studies that corroborate the influence of the design variables over the operational variables are missing. In order to fill this knowledge gap, this work proposes a statistical analysis of design variables in chiller plants operating in medium- and large-scale applications and evaluates their influence on energy consumption and life cycle cost (LCC) under the same thermal demand conditions. A case study involving 138 chiller plant combinations featuring different arrangements and a Cuban hotel was selected. The results suggested that the total chiller design and cooling capacity distribution among chillers have a significant influence on the energy consumption of the chiller plant with a Spearman’s Rho and Kendall Tau (τ) correlation index value of −0.625 and 0.559, respectively. However, with LCC, only the cooling capacity distribution among the chillers had a certain influence with a Kendall Tau correlation index value of 0.289. As for the considered total cooling capacity, the applied statistical test showed that this design variable does not have any influence on performing the chiller plant.

The TransiEnt Library is an open-source Modelica Libraryoriginally developed at the Hamburg University ofTechnology. It is a flexible framework for modelling andanalysing the dynamic behaviour of coupled energy systemsunder current and future scenarios. With the addition ofthree new members to the TransiEnt Library consortium,namely Fraunhofer UMSICHT, Gas- und Wärme-InstitutEssen~e.V. and XRG Simulation GmbH, the TransiEnt Libraryhas expanded its portfolio of models and methods forinvestigating the challenges in energy systems. Buildingupon the previous status report, this article presents thelatest developments in the TransiEnt library, highlithingits extended capabilities to model and simulate large,complex energy systems. The recent developments include theautomatic generation of aggregated models at district andregionals levels, as well as the modelling of medium- andlow-voltage electrical distribution networks. In addition,new concepts for the representation of large-scale heatingnetworks have been developed and are presented alongsideillustrative use cases.

In a pilot installation at Hamburg University of Technology, the coupled operation of an open-cycle desiccant-assisted air-conditioning system with an adsorption chiller in combination with cooling ceilings to remove sensible loads is investigated. This article presents experimental results of the year 2013 with a special focus on the performance of the adsorption chiller. Furthermore, the system is compared to two reference systems relying on vapor compression chillers. The average thermal coefficient of performance of the adsorption chiller during the investigated period is satisfying (COP_th = 0.54), while the average electrical coefficient of performance does not meet expectations (COP_el = 6.2). The fan control of the dry cooler can be identified as a main cause for the non-satisfying electrical performance. However, depending on the heat supply, considerable primary energy savings are possible through the investigated system, and the advantages of combined heat and power (CHP) engines can be extended to the summer period.

In this work, we adress challenges associated with multi parameter calibration of complex system models of high computational expense. We propose to replace the Modelica Model for screening of parameter space by a computational effective Machine-Learning Surrogate, followed by a polishing with a gradient-based optimizer coupled to the Modelica Model. Our results show the superiority of this approach compared to common-used optimization strategies. We can resign on determining initial optimization values while using a small number of Modelica model calls, paving the path towards efficient global optimization. The Machine Learning Surrogate, namely a Physics Enhanced Latent Space Variational Autoencoder (PELS-VAE), is able to capture the impact of most influential parameters on small training sets and delivers sufficiently good starting values to the gradient-based optimizer. In order to make this paper self-contained, we give a sound overview to the necessary theory, namely Global Sensitivity Analysis with Sobol Indices and Variational Autoencoders.

Decarbonizing the global energy supply requires more efficient heating and cooling systems. Model predictive control enhances the operation of cooling and heating systems but depends on accurate system models, often based on control volumes. We present an automated framework including time discretization to generate model predictive controllers for such models. To ensure scalability, a primal decomposition exploiting the model structure is applied. The approach is validated on an underground heating system with varying numbers of states, demonstrating the primal decomposition's advantage regarding scalability.