• Energy Research

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 Background Smart home technologies (SHT) make it easier than ever to track energy demands and are expected to contribute to the implementation of sustainability strategies. In particular, they are supposed to enable promising demand side management strategies by altering user behaviour towards sustainability while ensuring the balance of energy supply and demand. For determining environmental impacts of products and technologies, the methodology of life cycle assessment (LCA) is an established tool. While large parts of LCAs are standardised, the consideration of user behaviour related effects has not been specified. By adopting an interdisciplinary perspective, this literature study contributes to the future development of a standardized methodology for the operationalisation of behaviour in LCAs. Results Three main strategies for operationalising behaviour in LCA studies were identified: (1) behaviour theory-based approaches, (2) model-based behaviour predictions and (literature-based) deductions, and (3) averages and assumptions. The results of this literature study show that the selection of the strategy is crucial as the user behaviour and methods used for LCAs have a significant impact on the environmental and economic payback periods and calculated overall impact of SHTs. Findings from the social sciences on practices and household activities that can be influenced by SHTs, are not systematically applied. Conclusions Our literature analysis makes it clear that LCA results depend on various factors. Selected operationalisation and methodological approaches, respectively, can play a key role. Depending on the method chosen the results can vary by several orders of magnitude and are not always comparable. Simplified approaches for integrating user behaviour into LCAs like assumptions and average values can be a first step in accounting for the relevance of behaviour. However, it is important to bear in mind that these approaches may not reflect actual user behaviour, as this can be subjected to a limited changeability of certain household practices and habits. On the basis of the results, the authors recommend greater interdisciplinary co-operation in the conduction of LCAs on SHTs, ranging from a common definition of the scope, to the implementation of socio-scientific research and survey methods, to the derivation of policies.

Abstract Climate change mitigation requires a fundamental transformation in the power supply system particularly in cities. Urban energy models integrated in Geographic Information Systems (GIS) have been playing a central role in shaping this transformation. In this regards, openness and transparency have been recently gaining a prominent importance and attracting increasing political interest. As renewables share has grown to high levels in cities, flexibilisation options including storage become vital to ensure a reliable, affordable and sustainable Urban Electricity System (UES). Energy modelling provides policymakers with qualitative and quantitative insights required for the planning and operation of future UES. Hence, the representation of UES requires an appropriate characterisation of different urban energy requirements that should be adequately incorporated in a spatial-temporal framework including both static and dynamic datasets. This paper introduces an open GIS-based platform for the optimisation of flexibility options costs and operation in urban areas. The platform reproduces the urban energy infrastructure (spatial dimension), simulates demand and supply (spatial and temporal dimension) and performs a linear programming optimisation to explore scenarios for the economic deployment of micro-generation and decentralised storage. The total UES costs and required storage capacities for different urban energy scenarios are investigated here. A key finding of this contribution is that investing in local electricity storage and on-site renewable power generation can significantly reduce the total system costs and increase urban self-sufficiency. The developed platform is showcased for the city of Oldenburg.

Abstract Purpose The concept of criticality concerns the probability and the possible impacts of shortages in raw-material supply and is usually applied to regional economies or specific industries. With more and more products being highly dependent on potentially critical raw materials, efforts are being made to also incorporate criticality into the framework of life cycle sustainability assessment (LCSA). However, there is still some need for methodological development of indicators to measure raw-material criticality in LCSA. Methods We therefore introduce ‘economic product importance’ (EPI) as a novel parameter for the product-specific evaluation of the relevance and significance of a certain raw material for a particular product system. We thereby consider both the actual raw-material flows (life cycle inventories) and the life cycle cost. The EPI thus represents a measure for the material-specific product-system vulnerability (another component being the substitutability). Combining the product-system vulnerability of a specific product system towards a certain raw material with the supply disruption probability of that same raw material then yields the product-system specific overall criticality with regard to that raw material. In order to demonstrate our novel approach, we apply it to a case study on a battery-electric vehicle. Results Since our approach accounts for the actual amounts of raw materials used in a product and relates their total share of costs to the overall costs of the product, no under- or over-estimation of the mere presence of the raw materials with respect to their relevance for the product system occurs. Consequently, raw materials, e.g. rare earth elements, which are regularly rated highly critical, do not necessarily reach higher criticality ranks within our approach, if they are either needed in very small amounts only or if their share in total costs of the respective product system is very low. Accordingly, in our case study on a battery-electric vehicle product system, most rare earth elements are ranked less critical than bulk materials such as copper or aluminium. Conclusion Our EPI approach constitutes a step forward towards a methodology for the raw-material criticality assessment within the LCSA framework, mainly because it allows a product-specific evaluation of product-system vulnerability. Furthermore, it is compatible with common methods for the supply disruption probability calculation — such as GeoPolRisk, ESP or ESSENZ — as well as with available substitutability evaluations. The practicability and usefulness of our approach has been shown by applying it to a battery-electric vehicle.

Abstract As the world is already highly urbanised, energy systems in cities are already responsible for significant amount of the global Greenhouse Gas (GHG) emissions. Therefore, climate change mitigation demands a fundamental transformation in the Urban Energy Systems (UES), energy markets and energy policies. In this context, the large shift to micro-generation from renewable energy sources and their integration in the current energy system are a technical challenge for future energy systems design and operation. This will be further exacerbated if flexibilisation technologies such as storage are not efficiently integrated. For this purpose, an accurate modelling and representation of UES requires the characterisation of different urban energy requirements. These requirements, along with the urban fabric of cities, should be adequately incorporated in a spatial-temporal framework including both static and dynamic datasets. In this context, urban energy models provide policymakers with qualitative and quantitative insights for the planning of future UES. Within this framework, urban energy models integrated in Geographic Information Systems (GIS) will play an important role due to their multi-layer approach. This study introduces an open source GIS-based platform called FlexiGIS for the optimisation of energy systems in cities. FlexiGIS is used in this contribution to optimally allocate distributed battery storage in urban areas. The FlexiGIS platform provides the urban energy infrastructure (spatial dimension), simulates electricity consumption and generation (spatial and temporal dimension) and performs a linear optimisation for the economic deployment of micro-generation and decentralised storage under different energy scenarios. The first case study considers the city as a single system or ‘energy cell’, while the second one assumes that the city is divided into connected subsystems or districts. The total UES costs and required storage capacities for the investigated scenarios are obtained using optimisation. A key finding is that, for the investigated scenarios, investing in local electricity storage and renewable power generation can significantly reduce the total system costs and increase urban self-sufficiency. This study also highlights that the off-grid scenario (isolated city) is not an optimal choice.