Environmental impacts of high penetration renewable energy scenarios for Europe

The prospect of irreversible environmental alterations and an increasingly volatile climate pressurises societies to reduce greenhouse gas emissions, thereby mitigating climate change impacts. As global electricity demand continues to grow, particularly if considering a future with increased electrification of heat and transport sectors, the imperative to decarbonise our electricity supply becomes more urgent. This letter implements outputs of a detailed power system optimisation model into a prospective life cycle analysis framework in order to present a life cycle analysis of 44 electricity scenarios for Europe in 2050, including analyses of systems based largely on low-carbon fossil energy options (natural gas, and coal with carbon capture and storage (CCS)) as well as systems with high shares of variable renewable energy (VRE) (wind and solar). VRE curtailments and impacts caused by extra energy storage and transmission capabilities necessary in systems based on VRE are taken into account. The results show that systems based largely on VRE perform much better regarding climate change and other impact categories than the investigated systems based on fossil fuels. The climate change impacts from Europe for the year 2050 in a scenario using primarily natural gas are 1400 Tg CO2-eq while in a scenario using mostly coal with CCS the impacts are 480 TgCO2-eq. Systems based on renewables with an even mix of wind and solar capacity generate impacts of 120–140 TgCO2-eq. Impacts arising as a result of wind and solar variability do not significantly compromise the climate benefits of utilising these energy resources. VRE systems require more infrastructure leading to much larger mineral resource depletion impacts than fossil fuel systems, and greater land occupation impacts than systems based on natural gas. Emissions and resource requirements from wind power are smaller than from solar power

Can resource classes substitute spatial resolution in energy system models? A spatial scaling analysis.

While regional aggregation of areas can increase the computation speed of energy system models (ESM), this can also lead to an underestimation of the localised high quality of variable renewable energy (VRE) sources, which vary strongly depending on the site. Partly, the power transmission grid is able to level out the locality of production and consumption with energy systems spanning across regions as far as the European continent with large amount of power traded, which leads to a spatial averaging of the feed in profiles. Resource classes can subdivide VRE potentials into different quality classes of distinct feed-in profiles and potentials to compensate for the coarser resolution of the aggregated regions. This will lead to higher full load hours for the better located VRE power plants and thus lower levelised costs of electricity for those plants. This talk will examine the trade-offs between a high spatial resolution on the one hand and the accuracy of the feed in of VRE sources on the other hand. Therein the focus lies on the dispatch and expansion of the different technologies. The investigation focusses on wind resource classes, since wind speeds vary much more spatially than solar irradiance. Germany has been chosen for this investigation as its wind resource is diverse: the flatt northern parts offer high wind potentials whereas the resource quality in the south is much more dependent on the local topology

Influence of Future Time Perspective on Involvement: an Approach With Two Studies

The aim of this research is to extend current knowledge of older consumers' behaviour, focusing on involvement and future time perspective. Furthermore, we propose recommendations for customer approaches in the context of colon cancer prevention, as older consumers increasingly face new challenges in the realm of medical decision-making

Downscaling ERA5 wind speed data: a machine learning approach considering topographic influences

Energy system modeling and analysis can provide comprehensive guidelines to integrate renewable energy sources into the energy system. Modeling renewable energy potential, such as wind energy, typically involves the use of wind speed time series in the modeling process. One of the most widely utilized datasets in this regard is ERA5, which provides global meteorological information. Despite its broad coverage, the coarse spatial resolution of ERA5 data presents challenges in examining local-scale effects on energy systems, such as battery storage for small-scale wind farms or community energy systems. In this study, we introduce a robust statistical downscaling approach that utilizes a machine learning approach to improve the resolution of ERA5 wind speed data from around 31 km × 31 km to 1 km × 1 km. To ensure optimal results, a comprehensive preprocessing step is performed to classify regions into three classes based on the quality of ERA5 wind speed estimates. Subsequently, a regression method is applied to each class to downscale the ERA5 wind speed time series by considering the relationship between ERA5 data, observations from weather stations, and topographic metrics. Our results indicate that this approach significantly improves the performance of ERA5 wind speed data in complex terrain. To ensure the effectiveness and robustness of our approach, we also perform thorough evaluations by comparing our results with the reference dataset COSMO-REA6 and validating with independent datasets

Dunkelflaute and long-term electric energy shortage events in Europe

In central Europe, slowly moving low-pressure systems in winter can cause prolonged periods of low wind and solar power generation with simultaneously increased demand for electricity for heating. Information about such electric energy shortage events is important for long term planning of storage capacities and other flexibility options in energy supply systems with high shares of variable renewable energy (VRE) sources. Furthermore, multi-annual remaining residual loads may cause additional needs of VRE generators. We use the TYNDP Distributed Energy1 scenario and 30 years of ERA5 reanalysis data2 to investigate shortage events of different duration that, given the installed capacities from the scenario, would have happened in Europe between 1990 and 2020. The information helps assessing the amount of energy required for balancing or generator extension. We also identify the calendar dates when the events would have occurred. The identified most critical calendar periods can be used as input to specify the set-up of further energy systems analysis studies

Leitstudie 2010

Strategien zu erarbeiten, die aufzeigen, wie das langfristige Klimaschutzziel 2050 in Deutschland erreicht werden kann, ist das oberste Ziel von Studien, die seit gut einem Jahrzehnt vom DLR-ITT, Abteilung Systemanalyse und Technikbewertung mit wechselnden Projektpartnern für das BMU und das UBA durchgeführt werden. In der Leitstudie 2010 entstanden auf der Basis differenzierter und aktualisierter Potenzialabschätzungen, die technische, strukturelle und ökologische Kriterien berücksichtigen, und detaillierten Technik- und Kostenanalysen zu den Einzeltechnologien der Erneuerbaren verschiedene Szenarien ihres möglichen langfristigen Ausbaus in Wechselwirkung mit den übrigen Teilen der Energieversorgung in Deutschland. Für die Leitstudie 2010 haben die Projektpartner DLR, Stuttgart und Fraunhofer-IWES, Kassel erstmals mittels geeigneter Modelle eine vollständige dynamische und teilweise räumlich aufgegliederte Simulation der Stromversorgung durchgeführt. Außerdem wird der Untersuchungsraum für diese Simulation auf ganz Europa (einschließlich einiger nordafrikanischer Länder) ausgedehnt, um die Wechselwirkungen eines nationalen Umbaus der Energieversorgung mit der Entwicklung in Nachbarregionen erfassen zu können

Teaching Power-Sector Models Social and Political Awareness

Energy-system scenarios are widely used to relate the developments of the energy supply and the resulting carbon-emission pathways to political measures. To enable scenario analyses that adequately capture the variability of renewable-energy resources, a specialised type of power-sector model (PSM) has been developed since the beginning of this century, which uses input data with hourly resolution at the national or subnational levels. These models focus on techno-economic-system optimisation, which needs to be complemented with expert socioeconomic knowledge in order to prevent solutions that may be socially inacceptable or that oppose political goals. A way to integrate such knowledge into energy-system analysis is to use information from framework scenarios with a suitable geographical and technological focus. We propose a novel methodology to link framework scenarios to a PSM by applying complexity-management methods that enable a flexible choice of base scenarios that are tailored to suit different research questions. We explain the methodology, and we illustrate it in a case study that analyses the influence of the socioeconomic development on the European power-system transition until 2050 by linking the power-sector model, REMix (renewable-energy mix), to regional framework scenarios. The suggested approach proves suitable for this purpose, and it enables a clearer link between the impact of political measures and the power-system development

Spatial correlation structures of wind speed and irradiance in Europe as modelled in regional climate models and the ERA5 reanalysis

Objective & Background For comprehensive energy systems analysis considering high shares of weather-dependent renewables, the spatiotemporal characteristics of meteorological input data (in particular, wind speed and irradiance) are of considerable interest. Studies aiming to evaluate the potential effects of future climate change rely on model-based projections of these variables. In order to reach as high a spatiotemporal resolution as possible, global climate models (GCM) can be used as drivers for regional climate models (RCM). In doing so, global climate effects are considered on large scales, while the higher resolution computations are limited to the domain of interest. Here, we analyse such regional climate model outputs as well as reanalysis data with regards to their suitability in energy systems analysis, using spatial correlation structures of the wind and solar resource anomalies. Method Based on 10 years of historical experiments of EURO-CORDEX regional climate model output and ERA5 reanalyses, we compute climate anomalies of both wind speed and irradiance for each point in space and time by subtracting the multi-year mean of each point from the instantaneous values at that point. In doing so, we largely remove effects of seasonal and diurnal cycles, especially in the case of irradiance. From each of these wind speed and irradiance anomaly fields, we sample distinct two-dimensional correlation structures (i.e., maps of the correlation coefficient between each pixel’s time series and a reference pixel’s series). Increasing the number of reference pixels hence increases the number of spatial correlation structures available in the analysis. We finally estimate the degree of similarity between the spatial correlation structures of each EURO-CORDEX member and its corresponding ERA5 counterpart using the coefficient of pattern correlation (i.e., the correlation coefficient of the mapped spatial correlation structures in vector form). Principal Findings Kernel density estimates and box-plot statistics of all available pattern correlations show that the wind speed anomaly spatial correlation structures tend to be better represented than their irradiance counterparts. Between different driving global models, the performance is relatively similar for wind speed anomaly correlation structures, while solar irradiance anomaly structures can be associated with a few unusually low pattern correlations for some global models. In terms of regional climate models, these cases of low pattern correlation values are largely associated with the SMHI-RCA4 regional model during the summer. Discussion This initial comparison of CORDEX model output and ERA5 reanalysis based on correlation structures in wind speed and solar irradiance anomalies allows to help in the selection of global and regional models when compiling an ensemble for use in energy systems analysis. In this context, the spatial correlation structures of wind speed need less attention when selecting model outputs, while the irradiance anomaly projections of the regional climate model SMHI-RCA4 should be handled with caution. Increasing the number of reference points used in the spatial correlation structure calculations may lead to further insights regarding potential regional differences in CORDEX model performances

Speeding up energy system optimization models - lessons learned from heuristic approaches, parallel solvers and large scale models

Most state-of-the art optimizing energy system models are characterised by a high temporal and spatial resolution to include detailed information of local weather conditions. This became necessary through the integration of renewable energy sources such as photovoltaics and wind energy. Similarly the integration of cross-sectoral technologies for the decarbonisation of the energy, heating and transportation sector makes energy system models more complex and as a consequence the time required for solving the problem. To address this increasing computational demand the BEAM-ME project brought together experts from the fields of energy systems analysis, mathematics, operations research, and informatics to establish interdisciplinary solutions. The talk provides an overview of the final project results and more in-depth highlights from two stage heuristic approaches and the parallel interior point solver PIPS-IPM++. Depending on the problem at hand and available computation resources a speed-up factor of up to 26 was achieved. Taking up the results from the BEAM-ME project an outlook on the follow-up project UNSEEN shows how the significant reduction in time required for solving the problems can be used to generate a more holistic view on the near-optimal solution space. This allows providing decision makers with a wide range of alternatives showing the trade-offs between several decision criteria