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Abstract The integration of renewable energy sources into building energy systems and the progressive coupling between the thermal and electrical domains makes the analysis of these systems increasingly complex. At the same time, however, more and more building monitoring data is being collected. The manual evaluation of this data is time-consuming and requires expert knowledge. Hence, there is a strong need for tools that enable the automatic knowledge extraction from these huge data sets to support system integrators and favor the development of smart energy services, e.g., predictive maintenance. One crucial step in knowledge extraction is the detection of change points and hidden states in measurements. In this work, we present a tool for automated detection of hidden operation modes based on multivariate time series data deploying motif-aware state assignment (MASA). The tool is evaluated utilizing measurements of a heat pump and compared to two baseline algorithms, namely k-Means and k-Medoids. MASA performs particularly well on noisy data, where it shows only a small deviation in the number of detected change points compared to the ground truth with up to 77% accuracy. Furthermore, it almost always outperforms the baseline algorithms, which in turn require extensive preprocessing.

This paper presents experiences of laboratory testing and field demonstration of a distribution network congestion management algorithm. Development process for new smart grid functionalities and automation architectures is discussed both in general level and through the example case of congestion management. The practical implementation of the developed algorithm is explained and test setups in all development process steps (offline simulations, laboratory testing and field demonstration) are presented. The objectives in different stages of the algorithm development and testing process are discussed and lessons learned from all the steps are represented.

AbstractWorld-wide coal reserves can supply the global demand for primary energy for several centuries. However, low thickness and structural complexity may constrain the economic exploitation of many coal deposits. Taking into account these circumstances, underground coal gasification (UCG) can offer an economical and sustainable approach for coal exploitation and subsequent feedstock generation from the syngas. The UCG process produces a high-calorific synthesis gas mainly consisting of methane, hydrogen and carbon dioxide, which can be used for electricity generation or feedstock production at the surface. Considering the latter, the Urea process can be applied to establish the nitrogen based fertilizer carbamide (CH4N2O). The required feedstock for carbamide production in the Urea process can be supplied by UCG syngas. The aim of the present study was the development of an integrated carbon utilisation concept based on the coupled UCG-Urea process. A significant amount of carbon dioxide from the UCG synthesis gas is required for carbamide production in the Urea process, while the excessive carbon dioxide can be re-injected into the cavities resulting in the coal seams and surrounding strata after the gasification process. Thus, a new approach for utilisation of carbon dioxide resulting from coal combustion was developed to provide a coupled technology also comprising geological storage of excessive carbon dioxide. A theoretical feasibility study considering UCG-Urea process economics and potentials of UCG and carbon dioxide storage in the gasified strata was conducted for a selected study area in northern Bangladesh revealing the high competitiveness of the combined technology on the international feedstock markets.

Based on the characteristics of turbulent combustion in lean-premix combustion chambers, this paper presents a combustion model which solves transport equations for six chemical species. The source terms are calculated by probability weighted integration of 35 elementary reaction rates. The model presented here does not include any adjustable parameters. Therefore, it is universal in its character for conditions of highly turbulent premixed lean to stoichiometric combustion. The model is applicable to fuel compositions including methane, carbon monoxide, and hydrogen. The application is shown for a test case burning methane in lean-premixed mode.

Renewable & sustainable energy reviews 159, 112163 (2022). doi:10.1016/j.rser.2022.112163 special issue: "MODEX: energy system model comparisons through harmonized applications / Edited by Hans-Christian Gils, Christoph Weber, Dominik Möst, Jochen Linßen" Published by Elsevier Science, Amsterdam [u.a.]

The purpose of this study was to investigate the calendar aging of lithium-ion batteries by using both open circuit and floating current measurements. Existing degradation studies usually focus on commercial cells. The initial electrolyte composition and formation protocol for these cells is often unknown. This study investigates the role of electrolyte additives, specifically, vinylene carbonate (VC) and fluoroethylene carbonate (FEC), in the aging process of lithium-ion batteries. The results showed that self-discharge plays a significant role in determining the severity of aging for cells without additives. Interestingly, the aging was less severe for the cells without additives as they deviated more from their original storage state of charge. It was also observed that the addition of VC and FEC had an effect on the formation and stability of the solid electrolyte interphase (SEI) layer on the surface of the carbonaceous anode. By gaining a better understanding of the aging processes and the effects of different electrolyte additives, we can improve the safety and durability of lithium-ion batteries, which is critical for their widespread adoption in various applications.

In this study, we explore synergetic effects between biomass and CO2 utilization to reduce both GHG emissions and renewable resource use.

Abstract The design of adsorptive heat transformers (adsorption heat pumps and chillers and adsorption thermal energy storage) requires knowledge on the heat and mass transfer resistances in adsorbents. However, heat and mass transfer cannot be distinguished in conventional experimental setups, since only pressure data is available. In this work, we present an approach to distinguish and quantify heat and mass transfer resistances in adsorbents. For this purpose, we extended the Large-Temperature-Jump method (LTJ) with an infrared camera (IR) and combined the new IR-LTJ method with dynamic modeling. The IR camera determines the surface temperature of the adsorbent as an additional information. Subsequently, the data from the IR-LTJ setup is used in dynamic models to quantify time-resolved heat and mass transfer coefficients. We conducted experiments for one layer of granulated Fuji Siogel for use in an adsorption chiller with the temperature set 10/30/70. We show that the suggested method is able to determine heat and mass transfer coefficients.

9th International Renewable Energy Storage Conference, IRES 2015 / Edited by Peter Droege 9th International Renewable Energy Storage Conference, IRES 2015, Düsseldorf, Germany, 9 Mar 2015 - 11 Mar 2015; Engergy procedia, 73, 145-153 (2015). doi:10.1016/j.egypro.2015.07.662

Sustainable energy, grids and networks 39, 101455 - (2024). doi:10.1016/j.segan.2024.101455 Published by Elsevier, Amsterdam [u.a.]