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Climate change mitigation in the tourism sector is expanding research areas due to the importance of this sector and its rapid expansion. Aviation's contribution was found to be the most important source of GHG emissions from tourism. Also, the hospitality sector contributes a lot to GHG emissions in tourism destinations. Hospitality, constituting an essential component of the tourism industry, is a sector that has a high potential to reduce GHG and use of energy and water resources. Therefore, it is important to monitor the climate change mitigation performance of tourism destinations to achieve decarbonization of the tourism sector. The main objectives of this paper are to develop indicators of climate change mitigation performance of tourism destinations based on GHG indicators for the transport and hospitality sectors and apply this framework to assessment and ranking based on climate change mitigation performance of 4 main EU geographical regions as tourism destinations: Central, Northern, Southern, and Western Europe. This paper's main methodological approach is comparing and ranking different geographical regions in the European Union by assessing their climate change mitigation performance as tourist destinations. The study's main results showed that Finland, representing North Europe, is the best-performing country in climate change mitigation in tourism destinations. The second-best-performing geographical region is Western Europe. The worst-performing EU region based on climate change mitigation in tourism destinations was Central Europe. The South Europe region was found to be in a slightly better position than Central Europe but worse in comparison with Western Europe and especially in comparison to Northern Europe. The study's main implications provide policy recommendations for Central Europe as a tourism destination to increase energy and water use efficiency and the carbon footprint of the tourism sector. [ABSTRACT FROM AUTHOR]

DC railway electrification was deployed at the beginning of the 20th century in several countries in Europe. Today, this power system is no longer adapted to the demands of increased rail traffic. Due to the relatively low voltage level, the current consumed by the trains reaches several kAs. So, in the worst case, the locomotives cannot operate at their rated power due to the voltage drop along the contact line. Conventional solutions to reduce the voltage drop consist of increasing the cross-section of overhead lines or reducing the length of sectors by installing additional substations. Nevertheless, these solutions are expensive and not always feasible. The implementation of a Modular Battery Energy Storage System (MBESS) can be an alternative solution to reinforce the railway power supply. This paper first presents an MBESS based on elementary blocks associating Full-SiC Isolated DC-DC converter and battery racks. The electrical models of a railway sector and an elementary block are described, and simulations are performed considering real railroad traffic on two sectors of the French National Rail Network, electrified at 1.5 kV. The results show that the installation of an MBESS in the railway sector boosts the locomotive's voltage while also increasing overall system efficiency. [ABSTRACT FROM AUTHOR]

Global energy consumption is growing rapidly, with the frequency and intensity of extreme events constantly increasing, posing a long-term threat to power supply and consumption. Therefore, analyzing the spatiotemporal characteristics of electricity consumption and quantitatively assessing the impact of extreme events on electricity consumption are of great significance. Based on fine-grained electricity consumption data from Europe for the years 2019–2022, this paper employs a data mining perspective and four methods including Z-score, Isolation Forest, Local Outlier Factor, and Autoencoder to detect abnormal electricity consumption during extreme events. Additionally, it combines indicators such as elastic loss, vulnerability, and duration to measure the impact of extreme events on electricity consumption. It is found that low temperatures could lead to abrupt changes in electricity consumption, with Northern Europe being more significantly affected by low temperatures. The COVID-19 pandemic had the most significant impact on electricity consumption in Europe, with the middle part of Europe being the hardest hit during the first wave of the pandemic. Electricity anomalies during the pandemic period were related to national pandemic control policies and exhibited some lag. High temperatures persisted for a longer duration in the middle part of Europe. [ABSTRACT FROM AUTHOR]

Hydropower has played an important role in Europe in recent decades, offering a unique combination of safe, low-cost, and clean power generation. Today, it is still one of the largest renewable energy sources (RES), accounting for about 35 % of RES electricity generation. However, grid stability is threatened by the increasing amount of undispatchable RES. Flexibility and dynamics such as energy storage and rapid response are urgently needed to achieve EU policy goals. In such a context, hydropower can play a key role, not only as a provider of regulated renewable energy but also due to its ability to balance a renewable energy system in the short term and the medium/long term by pumped storage technology. All these aspects underline the new role of hydropower, which aims to strengthen grid stability and power supply resilience and to enable higher penetration of volatile RES. In this context, the aim of this paper is to demonstrate the role of hydropower at the European level as well as the needs and opportunities of modernization to fully exploit its potential. In particular, this study provides, the assessment of the today flexibility offered by hydropower to the power system at the European level by leveraging a database of information collected through the participants to the working groups of the COST Action Pen@Hydropower which includes stakeholders in the hydropower sector of 34 European countries. The study confirms the key role of hydropower in future energy scenarios with 30 % of the flexibility demand at all time scales met by hydropower. Furthermore, the paper presents a review of the digitalization solutions and innovative technologies that support the growth of a new generation of sustainable hydropower together with the modernization opportunities for existing hydropower plants. The results of this work have practical implications for stakeholders in the hydropower sector and policymakers as it provides evidence at the European scale of the role of hydropower technology in balancing the power system and the need to have supportive frameworks and adequate markets to fully exploit the European hydropower potential to achieve the energy transition goals. [ABSTRACT FROM AUTHOR]

The recent geopolitical conflicts in Europe have underscored the vulnerability of the current energy system to the volatility of energy carrier prices. In the prospect of defining robust energy systems ensuring sustainable energy supply in the future, the imperative of leveraging renewable indigenous energy sources becomes evident. However, as such technologies are integrated into the existing system, it is necessary to shift from the current centralized infrastructure to a decentralized production strategy. This paper presents a method to identify a panel of technological solutions at the district level, intended to reduce complexity for the integration of decentralized models into a national-scale model. The framework's novelty lies in combining a global sensitivity analysis for solution generation with clustering to identify typical configurations. The global sensitivity analysis is performed on a mixed integer linear programming model, which optimally sizes and operates district energy systems. The sensitivity analysis determines the most influential parameters of the model using the Morris method and provides a representative sampling of the solution space by leveraging the Sobol sampling strategy. The latter is then clustered using a density-based algorithm to identify typical solutions. The framework is applied to a suburban and residential Swiss neighborhood. The first outcome of the research is the high sensitivity of the model to energy carrier prices. As a result, Sobol's sampling space separates itself into two system types: those based on a natural gas boiler and those relying on a combination of electrical heaters and heat pumps. For both types, the electricity demand is either fulfilled by PV panels or electricity imports. The identified configurations showcase that the framework successfully generates a panel of solutions composed of various system configurations and operations being representative of the overall solution space. • Energy carriers' retail prices are the most influential parameters of a district energy system. • Energy system configurations differ according to the consumed energy carrier and PV penetration. • PV deployment is affected by electricity tariffs but not by technological implementations. [ABSTRACT FROM AUTHOR]

[Display omitted] • Market relevance of aboveground hydrogen storage is investigated. • Aboveground storage will likely play a much smaller role than underground storage. • Storage costs are the most decisive factor for market relevance. • Efficiency and systemic suitability can improve aboveground storage competitiveness. • Based on current projections, aboveground storage will only gain small market shares. Hydrogen storage is expected to play a crucial role in the comprehensive defossilization of energy systems. In this context, the focus is typically on underground hydrogen storage (e.g., in salt caverns). However, aboveground storage, which is independent of geological conditions and might offer other technical advantages, could provide systemic benefits and, thereby, gain shares in the hydrogen storage market. Against this background, this paper examines the market relevance of aboveground compared to underground hydrogen storage. Using the open-source energy system model and optimization framework of Europe, PyPSA-Eur, the influence of geological independence, storage cost relations, and technical storage characteristics (i.e., efficiencies) on mentioned market relevance of aboveground hydrogen storage are investigated. Further, the expectable market relevance based on current cost projections for the future is assessed. The studies show that in terms of hydrogen capacities, aboveground hydrogen storage plays a considerably smaller role compared to underground hydrogen storage. Even when assuming comparatively low aboveground storage cost, it will not exceed 1.7% (1.9 TWh H2,LHV) of total hydrogen storage capacities in a cost-optimal European energy system. Regarding the amounts of annually stored hydrogen, aboveground storage could play a larger role, reaching a maximum share of 32.5% (168 TWh H2,LHV a-1) of total stored hydrogen throughout Europe. However, these shares are only achievable for low cost storage in particularly suited energy system supply configurations. For higher aboveground storage costs or lower efficiencies, shares drop below 10% sharply. The analysis identifies some especially influential factors for achieving higher market relevance. Besides storage costs, the demand-orientation of a particular aboveground storage system (e.g., hydrogen storage at demand pressure levels) plays an essential role in market relevance. Further, overall efficiency can be a beneficial factor. Still, current projections of future techno-economic characteristics show that aboveground hydrogen storage is too expensive or too inefficient compared to underground storage. Therefore, to achieve notable market relevance, rather drastic cost reductions beyond current expectations would be needed for all assessed aboveground hydrogen storage technologies. [ABSTRACT FROM AUTHOR]

Wind energy resource assessment typically requires numerical modeling at fine resolutions, which is computationally expensive for multi‐year timescales. Increasingly, unsupervised machine learning techniques are used to identify representative weather patterns that can help simulate long‐term behavior. Here we develop a novel wind energy workflow that combines the weather patterns from unsupervised clustering with a numerical weather prediction model (WRF) to obtain efficient long‐term predictions of wind farm power and downstream wakes, which provide a good approximation to full WRF simulations at vastly reduced computational cost. We use ERA5 reanalysis data and compare clustering on low altitude pressure and wind velocity, a more relevant variable for wind resource assessment. We also compare varying domain sizes for the clustering. A WRF simulation is run at each cluster center and the results aggregated into a long‐term prediction using a novel post‐processing technique. We consider two case study regions and show that our long‐term predictions achieve good agreement with a year of WRF simulations in 2% of the computational time. Moreover, clustering over a Europe‐wide domain produces good agreement for predicting wind farm power output, but clustering over smaller domains is required for downstream wake predictions which agree with the year of WRF simulations. Our approach facilitates multi‐year predictions of power output and downstream farm wakes, by providing a fast, reliable, and flexible methodology applicable to any global region. Moreover, this constitutes the first tool to help mitigate effects of wind energy loss downstream of wind farms. Plain Language Summary: With the need to transition to renewable energy, wind energy is becoming increasingly important. However, the build‐out of wind farms must be carefully planned, not least because entire wind farms extracting energy from the atmosphere cause large scale wind velocity deficits downstream. Wind energy resource assessment typically relies on numerical models, but these are computationally expensive to run for the multi‐year predictions required for sustainable development. It is therefore common to use a smaller set of weather conditions that are representative of a longer period. Here, we develop such a workflow based on identifying characteristic weather patterns using a clustering technique from the machine learning literature. Based on the characteristic patterns identified, we perform a small number of short numerical model experiments, one for each weather pattern. By applying a novel post‐processing technique to combine the outputs from these experiments, we obtain predictions of wind farm power and downstream wakes over the original multi‐year period, using just 2% of the computation time required by a purely numerical modeling approach. Our workflow thus constitutes a fast, reliable, and flexible methodology that is applicable to any global region, which can be used for the sustainable development of wind energy. Key Points: Fast, reliable and flexible method for long‐term wind energy resource assessment using only six short‐term simulationsEvaluation of suitable variables and domain sizes for deriving weather patternsWeather prediction model is combined with clustering method for energy prediction [ABSTRACT FROM AUTHOR]