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In view of the current geopolitical situation, the EU and its Member States are accelerating efforts to reduce their dependency on fossil fuel imports, while simultaneously tackling the climate crisis. With heating in buildings accounting for a large share of the energy consumption in the EU, policies to phase-out fossil fuels for heating and to switch to renewables are key elements. In the context of the proposed actions to phase-out fossil fuels at EU level, this article provides a systematic analysis of current and planned phase-out regulations for fossil fuel boilers in the EU Member States. The paper quantifies the share of energy consumption for heating that is addressed by such regulations and finds that the current regulations only address about 10% of the total fossil energy consumption for heating in the EU. The share increases to almost 30% when considering the planned regulations, reflecting the fact that large energy consumers such as Germany and the Netherlands have announced such regulations. The analysis shows that several Member States with high energy consumption for heating have neither implemented nor announced plans to introduce phase-out regulations for fossil fuel heating. The study concludes that immediate policy action is needed both at EU and at Member State levels. At EU level, the proposed introduction of an end-date for stand-alone fossil fuel boilers needs to be substantiated and implemented into the legislative framework. At the national level, phase-out regulations for fossil fuel boilers need to be expanded both in quantity and scope. [Display omitted] • Systematic analysis of phase-out regulations for fossil fuel boilers in the EU. • Only 10% of the EU fossil energy used for heating is covered by phase-out regulations. • Including planned regulations, the share of heating energy covered by phase-out regulations increases to almost 30%. • Comparison of different approaches for implementing regulations for phasing out fossil fuel boilers. [ABSTRACT FROM AUTHOR]

In recent years, wind power has emerged as an important source of renewable energy. When onshore and offshore wind farm regions are connected to the grid for power generation, consistent multi-location short-term wind power predictions are extremely valuable in terms of assuring the power system's safety, sustainability, and economic operation. An abrupt variation in wind power generation influences the efficiency of the regional power grid. This makes accurate short-term forecasting essential for high-level planning and scheduling of power grids. To address the issue, this paper presents two variants of recurrent neural networks (RNN): gated recurrent unit (GRU) and long short-term memory (LSTM) models considering substantially better prediction accuracy to forecast a country-wide (Germany) wind power data for daily (t + 1), and multi-step (t + 3, t + 5 , and t + 12) hours ahead. In addition, wind velocities [m/s] measured at heights of 2, 10, and 50-m (above ground level) are exploited as an essential characteristic among the available input variables and evaluated each feature subset based on four training divisions (80-20%, 70-30%, 60-40%, and 50-50%) and compared the results with ARIMA and SVR approaches in the literature. The findings reveal that the RNN-GRU model not only can achieve higher predicting accuracy but also has a faster learning speed over long sequences. • Multivariate analysis of hourly wind power predictions is carried out. • The prediction model integrated RNN-GRU and RNN-LSTM. • A countrywide dataset for Germany in 2016 is employed for the forecast. • Accuracy and computational efficiency were used to evaluate the performance of proposed time series models. • Comparison with reported models and ablation strategies supports the design of individual modules in RNN-GRU. • RNN-GRU with multi-step/multi-location demonstrated potential for large-scale wind farm applications. [ABSTRACT FROM AUTHOR]

The future market diffusion of alternative fuels in the passenger car sector is of great interest to both carmakers and policymakers in order to decrease CO 2 emissions. The decision to buy a car is not totally objective and only partly based on cost. For this reason, those modeling the future market evolution of cars powered by alternative fuels try to include behavioral and non-cost related aspects. This paper analyzes the integration of user behavior into market diffusion models and compares three models that include this aspect. The comparison comprises three parts: first, it compares the modeling approaches, then uses a harmonized data set to model the future market diffusion of alternative fuel vehicles, with and without behavioral aspects. The most important aspects of user behavior included in the models are the use of charging infrastructure, the limited model availability, the consideration of range anxiety as a hampering factor or the willingness-to-pay-more for alternative drivetrains as a supporting factor, as well as a distinction of users' driving distances. User behavior is considered in various ways, but always has a limiting effect on electric vehicle market diffusion. While a model that distinguishes individual users and driving distances stresses the high relevance of this aspect, it is considered less important in models with a more aggregated inclusion of user behavior based on logit functions. • Integrating user behavior into models reduces sales of alternative fuel vehicles. • High market shares of electric vehicles in 2030 (up to 50%) nonetheless. • User behavior important, but policies, energy prices and purchase prices also matter. • Individual effects of user behavior can be better analyzed in disaggregated models. • Aggregated models are less sensitive to changes in user behavior. [ABSTRACT FROM AUTHOR]

The future market diffusion of alternative fuels in the passenger car sector is of great interest to both carmakers and policymakers in order to decrease CO 2 emissions. The decision to buy a car is not totally objective and only partly based on cost. For this reason, those modeling the future market evolution of cars powered by alternative fuels try to include behavioral and non-cost related aspects. This paper analyzes the integration of user behavior into market diffusion models and compares three models that include this aspect. The comparison comprises three parts: first, it compares the modeling approaches, then uses a harmonized data set to model the future market diffusion of alternative fuel vehicles, with and without behavioral aspects. The most important aspects of user behavior included in the models are the use of charging infrastructure, the limited model availability, the consideration of range anxiety as a hampering factor or the willingness-to-pay-more for alternative drivetrains as a supporting factor, as well as a distinction of users' driving distances. User behavior is considered in various ways, but always has a limiting effect on electric vehicle market diffusion. While a model that distinguishes individual users and driving distances stresses the high relevance of this aspect, it is considered less important in models with a more aggregated inclusion of user behavior based on logit functions. • Integrating user behavior into models reduces sales of alternative fuel vehicles. • High market shares of electric vehicles in 2030 (up to 50%) nonetheless. • User behavior important, but policies, energy prices and purchase prices also matter. • Individual effects of user behavior can be better analyzed in disaggregated models. • Aggregated models are less sensitive to changes in user behavior. [ABSTRACT FROM AUTHOR]

The interest in low-carbon hydrogen technologies is growing fast in politics and the economy. The ramp-up of a hydrogen market is a critical phase, which requires the engagement and coordination of many heterogeneous stakeholders. A better understanding of who these stakeholders are and what relationships, chances, and risks they perceive is crucial to guide a hydrogen market ramp-up. This paper conducts a stakeholder analysis for Germany with a focus on the market ramp-up period. Interviews with 36 hydrogen experts, literature, and stakeholders from 78 real-world hydrogen research and demonstration projects are analysed with qualitative content analysis and social network analysis. In total, 49 stakeholder groups are identified and defined accordingly. The results indicate that established stakeholders' roles will significantly change in a future hydrogen market. Risks range from economic and supply chain risks to impacts on international policy. Chances are found along economic, ecological, and political dimensions. Political intervention during the market ramp-up should mostly focus on the economic gap between low-carbon hydrogen and fossil alternatives and on prioritising the allocation of scarce hydrogen supply on heterogeneous demand. Simultaneously, a long-term strategy should be envisaged to guarantee a competitive and non-discriminatory hydrogen market in the future. • Review of real-world hydrogen projects, literature, and expert interviews. • Identification of 49 stakeholder categories. • Analysis of chances, risks, and potential conflicts. • Electricity utilities are found to be a central player on the supply side. • Discussion of implications for the market ramp-up. [ABSTRACT FROM AUTHOR]

The transparency and open availability of energy system models and their input data are of particular importance due to their increasing complexity and policy relevance. In recent years, a large number of model-based scenario analyses have been carried out. These analyses are based on diverse model approaches and lead to a rather broad range of results, which due to different data structures and mathematical approaches are hardly directly comparable. In this paper, detailed and harmonized scenario input parameters are the basis of a systematic model experiment including four electricity system models. In the following, the different model approaches are classified and their respective results are discussed transparently. Consequently, differences in results can be interlinked directly with model properties. The results are compared focusing on a selection of output parameters, such as investment and dispatch decisions in flexible power plants, storage dispatch, wholesale electricity prices, CO 2 emissions and generation adequacy in hours with critical supply situations in Germany until 2030. Differences in the results are traced back to conceptual differences as the models can be distinguished not only with regard to their mathematical approaches, but also to their level of detail. Results indicate that next to the differences of the mathematical approaches (i.e., linear optimization vs. agent-based simulation), the myopic foresight perspective (e.g., rolling planning algorithm with 24- and 36-hours loops vs. perfect foresight in a closed loop for one year) are decisive for the range of obtained results. • Systematic comparison of four electricity system models. • Analysis of differences in model results for a future German power system. • Foresight perspective and mathematical approach influence model outcomes. • Investments of at least 2.6 GW seem profitable in Germany in 2030. • Need for 12 GW of additional flexibility to ensure generation adequacy in 2030. [ABSTRACT FROM AUTHOR]

Meeting the energy goals of the European Union requires new ways of managing energy. Decentralized energy management, cross-commodity energy production and usage optimization are promising means. Future neighbourhoods will include multiple forms of energy such as electricity, heat, and cooling. According to our vision, the smart neighbourhoods, can optimize energy across different vectors by sharing resources in a controlled way. The first contribution of this paper is a comprehensive review on market, regulatory and technological status to support the transition towards distributed cross-commodity energy management with focus on Germany and Finland. Markets disruption could lead to more decentralized structures. Mechanisms therefore have been proposed, but mostly without sector integration. Clean Energy Package includes legislation that is supportive towards cross-commodity energy sharing solutions. Corresponding implementation in Germany and Finland may be successful in both countries even though it differs. Regarding technology, the article identifies required technical components (IoT, AI and blockchain) focusing on their support decentralized and cross-sector energy systems. Key components of IoT are wireless communication and interoperability middleware. AI provides key technologies for modelling and optimization of sector-integrated and distributed energy systems. Even if there has been lots of research, practical implementations are still lacking. Furthermore, many technical challenges still exist for blockchain based solutions in decentralized energy systems. Moreover, organizational, and legal responsibilities need to be clarified to support the adoption of blockchains in decentralized energy systems. Finally, the article gives recommendations regarding an increasing implementation of cross-commodity sharing and how it can contribute to the energy transition. • Giving an overview of cross-commodity energy sharing in Finland and Germany. • It is shown how recent energy market development support energy transition. • Comparing the regulatory framework for smart components in energy markets. • Combination of IoT, AI and Blockchain to enable decentralized energy markets. [ABSTRACT FROM AUTHOR]

The European power system will have to change significantly in next decades to meet the ambitious targets set by the upcoming European Climate Law. In particular, intermittent renewable energy will be increasingly injected in the system creating potential stability problems. Already in the last five years it has become evident that if the grid expansion does not keep pace with increasing demand for transmission capacity, congestion will happen more and more often in the European power network. To handle congestion, Transmission System Operators extensively use redispatching, requiring individual plants to alter their market-based functioning pattern in order to cope with technical limitations of the network. While the cost of redispatching actions is well known, the change in emission patterns of both greenhouse gases and air pollutants caused by these actions is not, mostly because of a lack of detailed publicly available data. This paper covers such a gap taking Germany as a case study, thanks to the quality of the available data. Following the standard methodology for estimating the direct emissions from fossil fuelled and renewable fed power plants, the study estimates the changes in the emissions of four regulated air pollutants (namely NO x , SO 2 , Particulate Matter and CO) together with those of CO 2 related to redispatching, for the years 2015–2019. The case study of Germany shows how the management of congestions through redispatching does not necessarily imply an increase in the emissions associated with power production in a country, although it has not been possible to complete the study with a full picture of the consequences for other countries emissions because of lack of consistent data. • Power redispatching in Germany in 2015–2019 did not increase GHG and air pollutants emissions. • The impact of redispatching on emissions from neighbouring countries has not been assessed because of lack of data. • Curtailment of wind and solar power is increasing: Opportunities for further potential emissions reductions are lost. • Congestion in the power network remains an issue in Europe. [ABSTRACT FROM AUTHOR]

While it is generally accepted that our fossil fuel-dominated energy systems must undergo a sustainable transition, researchers have often neglected the potential impacts of this on water and land systems. However, if unintended environmental impacts from this process are to be avoided, understanding its implications for land use and water demand is of crucial importance. Moreover, developed countries may induce environmental stress beyond their own borders, for instance through extensive imports of bioenergy. In this paper, Germany serves as an example of a developed country with ambitious energy transformation targets. Results show that in particular, the politically-driven aspiration for more organic farming in Germany results in a higher import quota of biomass, especially biofuels. These imports translate into land demand, which will exceed the area available in Germany for bioenergy by a factor of 3–6.5 by 2050. As this will likely bring about land stress in the respective exporting countries, this effect of the German energy transformation ought to be limited as much as possible. In contrast, domestic water demand for the German energy system is expected to decrease by over 80% through 2050 due to declining numbers of fossil-fuelled power plants. However, possible future irrigation needs for bioenergy may reduce or even counterbalance this decreasing effect. In addition, energy policy targets specific to the transport sector show a high sensitivity to biomass imports. In particular, the sector-specific target for greenhouse gas reductions will seemingly promote biomass imports, leading to the above-described challenges in the pursuit of sustainability. • Implication of the German energy transition for land use and water demand analysed. • Potential stress in land use beyond Germany due to biomass imports analysed. • More organic farming in Germany can result in higher biomass imports. • Land for biomass imports exceeds Germany's available land by 3–6.5x by 2050. • Declining fossil power generation leads to lower water stress in Germany by 2050. [ABSTRACT FROM AUTHOR]