• Energy Research
• 11. Sustainability close
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Abstract Microgrids integrating local renewable energy sources at low-voltage level show promising potentials in realizing a reliable, efficient, and clean supply of electricity. Further improvements are expected when such a microgrid is operated based on direct current (dc) instead of alternating current (ac) infrastructure for power distribution commonly in use today. Our study aims to systemically quantify the gap between environmental impacts of microgrids at building level using the case study of power distribution within office buildings. For this purpose, a scalable comparative life cycle assessment (LCA) is conducted based on a technical bottom-up analysis of differences between ac and dc microgrids. Particularly, our approach combines the micro-level assessment of required power electronic components with the macro-level requirements for daily operation derived from a generic grid model. The results indicate that the environmental impacts of employed power electronics are substantially reduced by operating a microgrid based on dc power distribution infrastructure. Our sensitivity analyses show that efficient dc microgrids particularly lead to savings in climate change impact emissions. In addition, our study shows that the state-of-the-art scaling rules of power electronics currently used in LCAs leads to inaccurate results. In contrast, the proposed methodology applies a more technical approach, which enables a detailed analysis of the environmental impacts of power electronic components at system level. Thus, it provides the foundation for an evaluation criterion for a comprehensive assessment of technological changes within the framework of energy policy objectives.

Higher shares of fluctuating generation from renewable energy sources in the power system lead to an increase in grid balancing demand. One approach for avoiding curtailment of renewable energies is to use excess electricity feed-in for heating applications. To assess in which regions power-to-heat technologies can contribute to renewable energy integration, detailed data on the spatial distribution of the heat demand are needed. We determine the overall heat load in the residential building sector and the share covered by electric heating technologies for each administrative district in Germany, with a temporal resolution of 15 minutes. Using a special evaluation of German census data, we defined 729 building categories and assigned individual heat demand values. Furthermore, heating types and different classes of installed heating capacity were defined. Our analysis showed that the share of small-scale single-storey heating and large-scale central heating is higher in cities, whereas there is more medium-scale central heating in rural areas. This results from the different shares of single and multi-family houses in the respective regions. To determine the electrically-covered heat demand, we took into account heat pumps and resistive heating technologies. All results, as well as the developed code, are published under open source licenses and can thus also be used by other researchers for the assessment of power-to-heat for renewable energy integration. 18 pages, 23 figures

Abstract A variety of novel, recyclable and reusable, construction materials has already been studied within literature during the past years, aiming at improving the overall energy efficiency ranking of the building envelope. However, several studies show that a delicate control of indoor climating elements can lead to a significant performance improvement by exploiting the building’s savings potential via smart adaptive HVAC regulation to exogenous uncertain disturbances (e.g. weather, occupancy). Building Optimization and Control (BOC) systems can be categorized into two different groups: centralized (requiring high data transmission rates at a central node from every corner of the overall system) and decentralized 1 (assuming an intercommunication among neighboring constituent systems). Moreover, both approaches can be further divided into two subcategories, respectively: model-assisted (usually introducing modeling oversimplifications) and model-free (typically presenting poor stability and very slow convergence rates). This paper presents the application of a novel, decentralized, agent-based , model-free BOC methodology (abbreviated as L4GPCAO) to a modern non-residential building (E.ON. Energy Research Center’s main building), equipped with controllable HVAC systems and renewable energy sources by utilizing the existing Building Management System (BES). The building testbed is located inside the RWTH Aachen University campus in Aachen, Germany. A combined rule criterion composed of the non-renewable energy consumption (NREC) and the thermal comfort index – aligned to international comfort standards – was adopted in all cases presented herein. Besides the limited availability of the specified building testbed, real-life experiments demonstrated operational effectiveness of the proposed approach in BOC applications with complex, emerging dynamics arising from the building’s occupancy and thermal characteristics. L4GPCAO outperformed the control strategy that was designed by the planers and system provider, in a conventional manner, requiring no more than five test days.

Comprehensive global decarbonization will require that transportation services cease to rely on fossil fuels. Here we develop a generic life-cycle cost model to address two closely related questions central to the emergence of sustainable transportation: (i) the utilization rates (hours of operation) that rank-order alternative drivetrains in terms of their cost, and (ii) the cost-efficient share of clean energy drivetrains in a vehicle fleet of competing drivetrains. Calibrating our model framework in the context of urban transit buses, we examine how the comparison between diesel and battery-electric buses varies with the specifics of the duty cycle (route). We find that even for less favorable duty cycles, battery-electric buses will entail lower life-cycle costs once utilization rates exceed 20% of the annual hours. Yet, the current economics of that particular application still calls for a one-third share of diesel drivetrains in a cost-efficient fleet.

Smart charging systems can reduce the stress on the power grid from electric vehicles by coordinating the charging process. To meet user requirements, such systems need input on charging demand, i.e., departure time and desired state of charge. Deriving these parameters through predictions based on past mobility patterns allows the inference of realistic values that offer flexibility by charging vehicles until they are actually needed for departure. While previous studies have addressed the task of charging demand predictions, there is a lack of work investigating the heterogeneity of user behavior, which affects prediction performance. In this work we predict the duration and energy of residential charging sessions using a dataset with 59,520 real-world measurements from 267 electric vehicles. While replicating the results put forth in related work, we additionally find substantial differences in prediction performance between individual vehicles. An in-depth analysis shows that vehicles that on average start charging later in the day can be predicted better than others. Furthermore, we demonstrate how knowledge that a vehicles charges over night significantly increases prediction performance, reducing the mean absolute percentage error of plugged-in duration predictions from over 200 % to 15 %. Based on these insights, we propose that residential smart charging systems should focus on predictions of overnight charging to determine charging demand. These sessions are most relevant for smart charging as they offer most flexibility and need for coordinated charging and, as we show, they are also more predictable, increasing user acceptance. Applied Energy, 370 ISSN:0306-2619 ISSN:1872-9118

Abstract The energetic use of residues from agriculture can foster the transition towards a more renewable energy supply. However, sustainability issues have to be considered along the entire provision chain as they affect the resource and energy potential as well as the achievable contribution to climate mitigation. Straw is one of the most important agricultural residues in Germany. It is not yet used for energy purposes extensively and compared to other agricultural feedstock it shows low competition with food, feed or fiber. This paper analyses on the one hand the sustainable potential of cereal straw for energy application in Germany considering the actual agricultural conditions, and on the other hand the global warming potential from different energy provision chains based on straw. Different humus-balance tools that are able to assess the organic matter (OM) demand to presume soil fertility. The analysis of straw potentials was applied at NUTS 3 level for Germany, based on statistical data. The results of this analysis were used as input data for the modeling of concepts for straw provision and use. Greenhouse gas (GHG) emissions were calculated for each concept in order to compare the global warming potential of various energy applications, to investigate the relative contribution of different production steps and to compare them with fossil energy applications. In total, 29.8 Tg of straw (fresh matter) are produced annually in Germany (1999–2007). Approximately 4.8 Tg of the total straw occurrence are annually required by animal husbandry. Between 7.97 and 13.25 Tg straw can be classified as sustainable straw. Highest straw potential (3.99 Mg ha−1) can be found in parts of Schleswig-Holstein, Mecklenburg–West Pomerania, North Rhine-Westphalia and Lower Saxony. But there are also regions that show a net deficit. The cumulated GHG emissions for the resulting concepts are between 8 and 35 g CO2-eq. MJ−1. In comparison to fossil energy applications, the highest reduction potential occurs for concepts for combined heat and power (CHP) provision, i.e. 223 g CO 2 -eq . MJ el - 1 . This study highlights the possible contribution of straw as renewable energy carrier, but also demonstrates that there are regional restrictions for straw use.

Abstract In this paper, an open source tool is introduced to represent urban energy infrastructure in the City of Philadelphia, and different renewable energy scenarios are compared with respect to minimization of the standard deviation of the residual load. Renewable energy sources play a critical role in the world’s ongoing energy transition in response to climate change. Urban Energy Systems may be particularly sensitive to this transition due to the high energy demand density associated with urban environments. Open energy analysis and modeling tools can provide important information that can be used by urban energy planners, policy makers, and other stakeholders during this transition. In the present study, we apply FlexiGIS, an open energy modeling tool developed in a European context, to a case study in the City of Philadelphia. Due to the importance of open access to energy data, we pay particular attention to open energy data sources. Notably, OpenStreetMap was incomplete in its spatial coverage, but alternate open data resources were identified. This work conducts an optimization of the renewable energy mix to minimize the amount of balancing energy required for the residual load. We observe that Philadelphia has an optimal mix of renewables that favors a roughly even share of wind and solar, but that, compared to a previous case study in Oldenburg, Germany, requires more balancing energy at comparable levels of renewable penetration.