Explore the vast range of titles available.

Due to the high degree of intermittency of renewable energy sources (RES) and the growing interdependences amongst formerly separated energy pathways, the modeling of adequate energy systems is crucial to evaluate existing energy systems and to forecast viable future ones. However, this corresponds to the rising complexity of energy system models (ESMs) and often results in computationally intractable programs. To overcome this problem, time series aggregation (TSA) is frequently used to reduce ESM complexity. As these methods aim at the reduction of input data and preserving the main information about the time series, but are not based on mathematically equivalent transformations, the performance of each method depends on the justifiability of its assumptions. This review systematically categorizes the TSA methods applied in 130 different publications to highlight the underlying assumptions and to evaluate the impact of these on the respective case studies. Moreover, the review analyzes current trends in TSA and formulates subjects for future research. This analysis reveals that the future of TSA is clearly feature-based including clustering and other machine learning techniques which are capable of dealing with the growing amount of input data for ESMs. Further, a growing number of publications focus on bounding the TSA induced error of the ESM optimization result. Thus, this study can be used as both an introduction to the topic and for revealing remaining research gaps.

Hydrogen fuel cell vehicles can complement other electric vehicle technologies as a zero-emission technology and contribute to global efforts to achieve the emission reduction targets. This article spotlights the current deployment status of fuel cells in road transport. For this purpose, data collection was performed by the Advanced Fuel Cells Technology Collaboration Programme. Moreover, the available incentives for purchasing a fuel cell vehicle in different countries were reviewed and future perspectives summarized. Based on the collected information, the development trends in the last five years were analyzed and possible further trends that could see the realization of the defined goals derived. The number of registered vehicles was estimated to be 51,437 units, with South Korea leading the market, with 90% of the vehicles being concentrated in four countries. A total of 729 hydrogen refueling stations were in operation, with Japan having the highest number of these. The analysis results clearly indicate a very positive development trend for fuel cell vehicles and hydrogen refueling stations in 2021, with the highest number of new vehicles and stations in a single year, paralleling the year’s overall economic recovery. Yet, a more ambitious ramp-up in the coming years is required to achieve the set targets.

Recent studies focusing on greenhouse gas emission reduction strategies indicate that material recycling has a significant impact on energy consumption and greenhouse gas emissions. The question arises how these effects can be quantified. Material recycling is not at all or insufficiently considered in energy system models, which are used today to derive climate gas mitigation strategies. To better assess and quantify the effects one option would be to couple energy system models and material flow models. The barriers and challenges of a successful coupling are addressed in this article. The greatest obstacles are diverging temporal horizons, the mismatching of system boundaries, data quality and availability, and the underrepresentation of industrial processes. A coupled model would enable access to more robust and significant results, a response to a greater variety of research questions and useful analyses. Further to this, collaborative models developed jointly by the energy system and material analysis communities are required for more cohesive and interdisciplinary assessments.

Carbon-free transportation is envisaged by means of fuel cell electric vehicles (FCEV) propelled by hydrogen that originates from renewably electricity. However, there is a spatial and temporal gap in the production and demand of hydrogen. Therefore, hydrogen storage and transport remain key challenges for sustainable transportation with FCEVs. In this study, we propose a method for calculating a spatially resolved highway routing model for Germany to transport hydrogen by truck from the 15 production locations (source) to the 9683 fueling stations (sink) required by 2050. We consider herein three different storage modes, namely compressed gaseous hydrogen (CGH2), liquid hydrogen (LH2) and liquid organic hydrogen carriers (LOHC). The model applies Dijkstra’s shortest path algorithm for all available source-sink connections prior to optimizing the supply. By creating a detailed routing result for each source-sink connection, a detour factor is introduced for “first and last mile” transportation. The average detour factor of 1.32 is shown to be necessary for the German highway grid. Thereafter, the related costs, transportation time and travelled distances are calculated and compared for the examined storage modes. The overall transportation cost result for compressed gaseous hydrogen is 2.69 €/kgH2, 0.73 €/kgH2 for liquid hydrogen, and 0.99 €/kgH2 for LOHCs. While liquid hydrogen appears to be the most cost-efficient mode, with the integration of the supply chain costs, compressed gaseous hydrogen is more convenient for minimal source-sink distances, while liquid hydrogen would be suitable for distances greater than 130 km.

Potential analyses identify possible locations for renewable energy installations, such as wind turbines and photovoltaic arrays. The results of previous potential studies for Germany, however, are not consistent due to different assumptions, methods, and datasets being used. For example, different land-use datasets are applied in the literature to identify suitable areas for technologies requiring open land. For the first time, commonly used datasets are compared regarding the area and position of identified features to analyze their impact on potential analyses. It is shown that the use of Corine Land Cover is not recommended as it leads to potential area overestimation in a typical wind potential analyses by a factor of 4.7 and 5.2 in comparison to Basis-DLM and Open Street Map, respectively. Furthermore, we develop scenarios for onshore wind, offshore wind, and open-field photovoltaic potential estimations based on land-eligibility analyses using the land-use datasets that were proven to be best by our pre-analysis. Moreover, we calculate the rooftop photovoltaic potential using 3D building data nationwide for the first time. The potentials have a high sensitivity towards exclusion conditions, which are also currently discussed in public. For example, if restrictive exclusions are chosen for the onshore wind analysis the necessary potential for climate neutrality cannot be met. The potential capacities and possible locations are published for all administrative levels in Germany in the freely accessible database (Tool for Renewable Energy Potentials—Database), for example, to be incorporated into energy system models.

The future European power system is projected to rely heavily on variable renewable energy sources (VRES), primarily wind and solar generation. However, the difficulties inherent to storing the primary energy of these sources is expected to pose significant challenges in terms of their integration into the system. To account for the high variability of renewable energy sources VRES, a novel pan-European dispatch model with high spatio-temporal resolution including load shifting is introduced here, providing highly detailed information regarding renewable energy curtailments for all Europe, typically underestimated in studies of future systems. which also includes modeling of load shifting. The model consists of four separate levels with different approaches for modeling thermal generation flexibility, storage units and demand as well as with spatial resolutions and generation dispatch formulations. Applying the developed model for the future European power system follows the results of corresponding transmission expansion planning studies, which are translated into the desired high spatial resolution. The analysis of the “large scale-RES” scenario for 2050 shows considerable congestion between northern and central Europe, which constitutes the primary cause of VRES curtailments of renewables. In addition, load shifting is shown to mostly improve the integration of solar energy into the system and not wind, which constitutes the dominant energy source for this scenario. Finally, the analysis of the curtailments time series using ideal converters shows that the best locations for their exploitation can be found in western Ireland and western Denmark.

The required acceleration of onshore wind deployment requires the consideration of both economic and social criteria. With a spatially explicit analysis of the validated European turbine stock, we show that historical siting focused on cost-effectiveness of turbines and minimization of local disamenities, resulting in substantial regional inequalities. A multi-criteria turbine allocation approach demonstrates in 180 different scenarios that strong trade-offs have to be made in the future expansion by 2050. The sites of additional onshore wind turbines can be associated with up to 43% lower costs on average, up to 42% higher regional equality, or up to 93% less affected population than at existing turbine locations. Depending on the capacity generation target, repowering decisions and spatial scale for siting, the mean costs increase by at least 18% if the affected population is minimized — even more so if regional equality is maximized. Meaningful regulations that compensate the affected regions for neglecting one of the criteria are urgently needed.

Energy system models that consider variable renewable energy sources (VRESs) are computationally complex. The greater spatial scope and level of detail entailed in the models exacerbates complexity. As a complexity-reduction approach, this paper considers the simultaneous spatial and technological aggregation of energy system models. To that end, a novel two-step aggregation scheme is introduced. First, model regions are spatially aggregated to obtain a reduced region set. The aggregation is based on model parameters such as VRES time series, capacities, etc. In addition, spatial contiguity of regions is considered. Next, technological aggregation is performed on each VRES, in each region, based on their time series. The aggregations’ impact on accuracy and complexity of a cost-optimal, European energy system model is analyzed. The model is aggregated to obtain different combinations of numbers of regions and VRES types. Results are benchmarked against an initial resolution of 96 regions, with 68 VRES types in each. System cost deviates significantly when lower numbers of regions and/or VRES types are considered. As spatial and technological resolutions increase, the cost fluctuates initially and stabilizes eventually, approaching the benchmark. Optimal combination is determined based on an acceptable cost deviation of <5% and the point of stabilization. A total of 33 regions with 38 VRES types in each is deemed optimal. Here, the cost is underestimated by 4.42%, but the run time is reduced by 92.95%.

Visual landscape impacts on scenic and populated places are among significant factors affecting local acceptance of large-scale renewable energy projects. Through the combination of large-scale reverse viewshed and techno-economic energy system analyses, we assess their potential impacts for nationwide energy systems. In our case study of Germany, moderate consideration of visual impact by placing renewables out of sight of the most scenic and densely populated areas does not have a significant impact on future energy system costs and design. In contrast, in scenarios assuming high sensitivity to visual impacts, annual energy system costs would increase by up to 38% in 2045. The energy system’s resilience would also be compromised due to the increasing reliance on green hydrogen imports and the uncertain mass adoption of rooftop photovoltaics. Our analytical framework facilitates careful planning that considers the visual impact of renewable energy infrastructure, thus enabling socially acceptable deployment while understanding the implications for system costs and transformation pathways. Moderately reducing the visibility of renewable energy infrastructure in Germany is found to have a negligible impact on energy system costs and design. Strict visibility restrictions, however, may reduce the system’s resilience and increase costs by 38% in 2045.

The usage of renewable energy sources (RESs) to achieve greenhouse gas (GHG) emission reduction goals requires a holistic transformation across all sectors. Due to the fluctuating nature of RESs, it is necessary to install more wind and photovoltaics (PVs) generation in terms of nominal power than would otherwise be required in order to ensure that the power demand can always be met. In a near fully RES-based energy system, there will be times when there is an inadequate conventional load to meet the overcapacity of RESs, which will lead to demand regularly being exceeded and thereby a surplus. One approach to making productive use of this surplus, which would lead to a holistic transformation of all sectors, is “sector coupling” (SC). This paper describes the general principles behind this concept and develops a working definition intended to be of utility to the international scientific community. Furthermore, a literature review provides an overview of relevant scientific papers on the topic. Due to the challenge of distinguishing between papers with or without SC, the approach adopted here takes the German context as a case study that can be applied to future reviews with an international focus. Finally, to evaluate the potential of SC, an analysis of the linking of the power and transport sectors on a worldwide, EU and German level has been conducted and is outlined here.