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This paper delves into an econometric analysis to uncover the differences between Nordic European Countries - NEC (Norway, Denmark, Finland, Sweden) and South-East European Countries - SEEC (Greece, Bulgaria, Romania, Hungary). The focus is on exploring the interplay among electricity generation from Renewable Energy Sources (RES), CO 2 emissions, urban population and GDP per capita in these two European regions. The study employs cross-sectional dependence, first and second-generation panel unit root tests, Pedroni panel and Westerlund panel cointegration tests, ARDL, Dumitrescu-Hurlin causality test to establish the relation among variables. The findings reveal that in both regions, CO 2 emissions are significantly influenced by GDP per capita, urbanization and RES generation. Furthermore, urbanization plays an important role. When examining causality in SEEC, three bidirectional relationships are observed: urbanization and CO 2 , urbanization and GDP, urbanization and generation from RES, and two unidirectional causalities: CO 2 and GDP, generation from RES and CO 2. In contrast, for the NEC, there are two bidirectional causalities: urbanization and CO 2 , urbanization and generation from RES, and two unidirectional causalities: GDP and CO 2 , generation from RES and CO 2. These results highlight the nuanced and varied nature of environmental and economic dynamics across various European regions. • Relationship between CO 2 emissions, GDP per capita and electricity production from RES. • Investigating two groups of EU countries located in the North and SE Europe. • Estimations with Fully Modified and Dynamic Ordinary Least Squares. • Causality and cointegration between variables. [ABSTRACT FROM AUTHOR]

Hydrogen is among the energy carriers, which are most often considered for bringing about a sustainable energy system. Yet, so far, hydrogen has not delivered as an energy carrier. The core objective of this paper is to provide a comprehensive analysis of the state-of-the-art and the future prospects of green hydrogen in the European energy system from economic, energetic and CO 2 emissions point-of-view. The analysis shows that there are some increasing opportunities for hydrogen use in industry and in the transport sector, when electrification is not possible or is too expensive, as well as a storage in the European electricity system. However, a hydrogen-based energy system will remain a vision at least over the next decades. The major reason for this is the unfavorable economics mostly due to high investment costs in the whole supply chain. In addition, the overall efficiencies in the hydrogen chains are moderate in general. The full environmental benignity of hydrogen as an energy carrier is only provided when renewable energy sources are used for hydrogen production. However, in Europe, the potentials for green hydrogen are very limited due to the insufficient expansion of renewable electricity generation. For this reason, many European countries are considering options for green hydrogen import. The future of hydrogen is highly dependent on the supporting policy framework. To reduce the risk in the investment in hydrogen infrastructure, as well as to justify the promotion of green hydrogen, it is very important that Europe works out a very clear and realistic long-term implementation strategy. • Hydrogen might have potential in the European energy system, yet a hydrogen-based energy system stays visionary. • Green hydrogen from renewable electricity via electrolyzers is key for environmental benefits and decarbonization. • Hydrogen should be prioritized for uses where electrification is not feasible. [ABSTRACT FROM AUTHOR]

• Agent-Based Model to emulate real-world performance of microgrid with P2P trading. • Holistic simulation of economic, technical, and ecological metrics. • Validation of Model with urban area of medium-sized German city. • Comparison of microgrid metrics before and during the European energy crisis. • Conclusions for research, practice, and energy policy. The shift towards renewable energy sources (RES) in energy systems is becoming increasingly important. Residential energy generation and storage assets, smart home energy management systems, and peer-to-peer (P2P) electricity trading in microgrids can help integrate and balance decentralized renewable electricity supply with an increasingly electrified power, heat, and transport demand, reducing costs and CO 2 emissions. However, these microgrids are difficult to model because they consist of autonomous and interacting entities, leading to emergent phenomena and a high degree of complexity. Agent-based modeling is an established technique to simulate the complexity of microgrids. However, the existing literature still lacks real-world implementation studies and, as a first step, models capable of validating the existing results with real-world data. To this end, we present an agent-based model and analyze the corresponding microgrid performance with real-world data. The model quantifies economic, technical, and environmental metrics to simulate microgrid performance holistically and, in line with state-of-the-art research, consists of self-interested, autonomous agents with specific load profiles, RES generation, and demand-response potential. The model can simulate a P2P marketplace where electricity is traded between agents. In the second part of the paper, we validate the model with data from a medium-sized German city. In this case study, we also compare microgrid performance in 2022, during the energy market crisis in Europe, with historical data from 2019 to assess the effects of energy market shocks. Our results show how microgrids with P2P trading can reduce electricity costs and CO 2 emissions. However, our trading mechanism illustrates that the benefits of energy-community trading are almost exclusively shared among prosumers, highlighting the need to consider distributional issues when implementing P2P trading. [ABSTRACT FROM AUTHOR]

The increasing share of intermittent Renewable Energy Sources (iRES) in the energy mix leads to a growing need for their accurate representation in energy system models. In this paper, we analyze how the results of generation planning models are impacted by the spatial representation used to model intermittent renewable technologies. It is shown that the spatial resolution, and to a lesser extent the spatial aggregation method used in a planning model, have a significant influence on the obtained system cost. The effect is quantified in a case study of European geographical scale, by varying the spatial resolution from highly resolved (hundreds of sites) to highly aggregated (1 site). A resolution-dependency of the system cost is observed under multiple renewable generation targets, and different renewable capacity restrictions. In doing so, we highlight the importance of implementing an appropriate capacity limit, especially when a high spatial resolution is used. Additionally, basic spatial aggregation methods -to process high resolution input data- are compared. • Isolation of renewable representation effects in a stylized GEP model. • iRES resolution has a strong impact on GEP model results for Europe. • Capacity limits play an important role in this effect, especially in high resolution. • Spatial aggregation method only impacts models with low resolutions. [ABSTRACT FROM AUTHOR]

In light of offshore wind expansions in the North and Baltic Seas in Europe, further ideas on using offshore space for renewable-based energy generation have evolved. One of the concepts is that of energy islands, which entails the placement of energy conversion and storage equipment near offshore wind farms. Offshore placement of electrolysers will cause interdependence between the availability of electricity for hydrogen production and for power transmission to shore. This paper investigates the trade-offs between integrating energy islands via electricity versus hydrogen infrastructure. We set up a combined capacity expansion and electricity dispatch model to assess the role of electrolysers and electricity cables given the availability of renewable energy from the islands. We find that the electricity system benefits more from connecting close-to-shore wind farms via power cables. In turn, electrolysis is more valuable for far-away energy islands as it avoids expensive long-distance cable infrastructure. We also find that capacity investment in electrolysers is sensitive to hydrogen prices but less to carbon prices. The onshore network and congestion caused by increased activity close to shore influence the sizing and siting of electrolysers. • Distance to shore and investment in offshore electrolysis are mutually dependent. • Offshore electrolysis is more valuable for far-away energy islands. • Offshore grids are beneficial for large-scale offshore wind integration. • Electrolyser capacity is sensitive to hydrogen prices but less to carbon prices. • Onshore grid developments influence offshore development significantly. [ABSTRACT FROM AUTHOR]

This paper investigates how the European electricity and heating system is impacted when medium-scale energy communities (ECs) are developed widely across Europe. We study the response on the capacity expansion of the cross-border transmission and national generation and storage within the European electricity and heating system with and without ECs in selected European countries. The representation of ECs has a special focus on flexibility, and we analyze the difference between flexibility responses by ECs towards local versus global cost minimization. Results show that EC development decreases total electricity and heating system costs on the transition towards a decarbonized European system in line with the 1.5 °C target, and less generation and storage capacity expansion is needed on a national scale to achieve climate targets. We also identify a conflict of interest between optimizing EC flexibility towards local cost minimization versus European cost minimization. • We study investments in the European energy market with energy community roll-out. • We compare flexibility responses with local versus European perspective. • Energy communities partly replace centralized generation and storage capacity. • Energy community roll-out shifts investments from onshore to offshore wind. • There is conflicting interest between local and European flexibility utilization. [ABSTRACT FROM AUTHOR]

• Early smart grid R&D advancements can indicate future technological developments. • A method for analyzing the evolution of smart grid technologies is proposed. • The Network theory is used to justify the existence of a main path of technologies. • Four innovation trajectories exist around smart energy applications. • The proposed approach complements existing methods of main path analysis. This paper investigates how the strategic decisions of innovative firms to collaborate with other firms in research and development projects shape the early evolution of smart grid technologies as an emerging technological system across Europe. The early innovations and developments in an emerging technological system are crucial for understanding further developments of the system. This early stage is highly influenced by the strategic decisions and lobbying power of the firms experimenting with different technological innovations, and the policies trying to maintain competition and steer the direction of innovative activities. Therefore, it is important to analyze how innovative firms behave in terms of collaborating with other firms and doing research on specific energy solutions, which reflect their strategic priorities. By taking a complex network approach, this paper investigates a network of smart grid R&D projects to analyze the convergence of smart grid research activities towards a stream of innovative solutions, and the variety of innovative activities branching out from these solutions. The results verify the convergence of smart grid activities towards one specific stream of innovations, and reveal the existence of four variations centered around different technological applications. The results can guide strategy development in innovative firms to take part in research collaborations or focus on specific smart energy technologies, and have implications for policy makers aiming to influence the direction of smart grid research programs, as well as for researchers in the field of smart grid innovation and technological innovation systems. [ABSTRACT FROM AUTHOR]

• Stakeholders see great potential in PEDs for energy poverty reduction. • Energy poverty mitigation needs to be included in PEDs from the onset. • PED replication can synergistically address both decarbonization and energy poverty mitigation. • Increasing levels of energy poverty makes PEDs more financially viable as mitigation tools. • More consideration needs to be given to the social dimension in decisions on new PED creation. 100 Positive Energy Districts (PEDs) are to be created in Europe by 2025, with a stated goal of urban decarbonization. These are highly energy efficient residential urban areas, powered entirely through renewables. PED creation is to be guided by principles of quality of life, sustainability, and inclusiveness (specifically focusing on affordability and energy poverty prevention). Although there is research into the decarbonization aspects of PEDs, there has been little focus on the guiding principles, and their potential to reduce energy vulnerability. Using energy vulnerability factors and an energy justice framework, this article examines how the topic of energy vulnerability mitigation is perceived by professional PED stakeholders. Stakeholders from multiple countries were interviewed in order to determine how and to what extent they approached the topic of inclusivity and energy vulnerability. The contribution of this paper to academic research is in helping to frame energy vulnerability in European smart city urban areas, focusing on the perceptions of key stakeholders. This contributes to research on the identification and evaluation of innovations such as PEDs which offer a potential model for an inclusive transition. Furthermore, this article offers a contribution for policymakers, informing PED replication policies with a focus on the synergistic aims of decarbonization and energy vulnerability mitigation. [ABSTRACT FROM AUTHOR]

[Display omitted] • Methodology to optimize commercial EV pool composition for V2G services. • Simulation of EV pooling to provide balancing power (FCR) and arbitrage trading. • Optimized pool composition enables an increase in revenue per EV of up to 7-fold. • Showcase analysis of battery-specific costs arising from degradation in V2G. The market ramp-up of electromobility is shifting vehicle-to-grid (V2G) issues into the focus of research and industry. Electric vehicles (EVs) have the potential to support the trend towards renewable energies in their role as storage units during idle times. To participate in balancing power and energy markets, EVs are pooled via aggregators. Instead of a random composition, aggregators can smartly compose their pools and add only those vehicles that actually contribute to the pool's performance, gaining advantages over competitors. The optimization methods presented in this paper form optimized pool combinations based on the power and energy capability profiles of commercial EVs. Genetic algorithms are used to determine the revenues of the possible pools per participating EV. The use cases analyzed are the provision of balancing power on the frequency containment reserve (FCR) market of Central Europe and energy arbitrage trading on the European power exchange intraday continuous and day-ahead auction spot markets. The results show that through smart pool composition, an aggregator can increase revenue per vehicle by up to seven-fold across the markets compared to randomly assembled pools. In the Central European market, for example, the potential V2G revenues on the FCR market (380 €) exceeded those of arbitrage trading (28 € − 203 €) in 2020. In a simulation, we show the increased degradation of the vehicle battery in V2G operation compared to sole use for mobility with a smart charging strategy. However, the additional revenue can make V2G financially worthwhile, depending on costs for measuring equipment, bidirectional charging stations, and aggregator costs. [ABSTRACT FROM AUTHOR]

• Road-freight TCO compared for 5 drive-technologies in 3 applications, 10 countries. • A database of costs for road-freight TCO parameters is newly compiled. • Low-carbon vehicles largely competitive in light- and medium-duty segments. • Low-carbon vehicles competitive in heavy-duty segments in selected countries. • Findings indicate three important TCO parameters to drive this competitiveness. In light of the Paris Agreement, road-freight represents a critically difficult-to-abate sector. In order to meet the ambitious European transport sector emissions reduction targets, a rapid transition to zero-carbon road-freight is necessary. However, limited policy assessments indicate where and how to appropriately intervene in this sector. To support policy-makers in accelerating the zero-carbon road-freight transition, this paper examines the relative cost competitiveness between commercial vehicles of varying alternative drive-technologies through a total cost of ownership (TCO) assessment. We identify key parameters that, when targeted, enable the uptake of these more sustainable niche technologies. The assessment is based on a newly compiled database of cost parameters which were triangulated through expert interviews. The results show that cost competitiveness for low- or zero-emission niche technologies in certain application segments and European countries is exhibited already today. In particular, we find battery electric vehicles to show great promise in the light- and medium-duty segments, but also in the heavy-duty long-haul segments in countries that have enacted targeted policy measures. Three TCO parameters drive this competitiveness: tolls, fuel costs, and CAPEX subsidies. Based on our analysis, we propose that policy-makers target OPEX before CAPEX parameters as well utilize a mix of policy interventions to ensure greater reach, increased efficiency, and increased policy flexibility. [ABSTRACT FROM AUTHOR]

This paper presents a multistage adaptive robust generation expansion planning model, which accounts for short-term unit commitment and ramping constraints, considers multi-period and multi-regional planning, and maintains the integer representation of generation units. The uncertainty of electricity demand and renewable power generation is taken into account through bounded intervals, with parameters that permit control over the level of conservatism of the solution. The multistage robust optimization model allows the sequential representation of uncertainty realization as they are revealed over time. It also guarantees the non-anticipativity of future uncertainty realizations at the time of decision-making, which is the case in practical real-world applications, as opposed to two-stage robust and stochastic models. To render the resulting multistage robust problem tractable, decision rules are employed to cast the uncertainty-based model into an equivalent mixed integer linear (MILP) problem. The re-formulated MILP problem, while tractable, is computationally prohibitive even for moderately sized systems. We, thus, propose a solution method relying on the reduction of the information basis of the decision rules employed in the model, and validate its adequacy to efficiently solve the problem. The importance of considering multistage robust frameworks for accounting for net-load uncertainties in generation expansion planning is illustrated, particularly under a high share of renewable energy penetration. A number of renewable penetration scenarios and uncertainty levels are considered for a case study covering future generation expansion planning in Europe. The results confirm the effectiveness of the proposed approach in coping with multifold operational uncertainties and for deriving adequate generation investment decisions. Moreover, the quality of the solutions obtained and the computational performance of the proposed solution method is shown to be suitable for practical policy-making generation expansion planning problems, seeking to evaluate the impact of uncertainty on future system-wide performance. • A multistage adaptive robust optimization model for generation expansion planning. • Key operational uncertainties due to large shares of renewable energy are considered. • Novel solution method based on the reduction of information basis for decision rules. • Significant reduction in calculation time for solving the multistage robust model. • Practical relevance of the proposed approach for system planning under uncertainties. [ABSTRACT FROM AUTHOR]

Involving residential actors in the energy transition is crucial for its success. Local energy generation, consumption and trading are identified as desirable forms of involvement, especially in energy communities. The potentials for energy communities in the residential building stock are high but are largely untapped in multi-family buildings. In many countries, rapidly evolving legal frameworks aim at overcoming related barriers, e.g. ownership structures, principal–agent problems and system complexity. But academic literature is scarce regarding the techno-economic and environmental implications of such complex frameworks. This paper develops a mixed-integer linear program (MILP) optimisation model for assessing the implementation of multi-energy systems in an energy community in multi-family buildings with a special distinction between investor and user; the model is applied to the German Tenant Electricity Law. Based on hourly demands from appliances, heating and electric vehicles, the optimal energy system layout and dispatch are determined. The results contain a rich set of performance indicators that demonstrate how the legal framework affects the technologies' interdependencies and economic viability of energy communities with multi-energy systems. Certain economic technology combinations may fail to support national emissions mitigation goals and lead to lock-ins in Europe's largest residential building stock. The subsidies do not lead to the utilisation of a battery storage. Despite this, self-sufficiency ratios of more than 90% are observable for systems with combined heat and power plants and heat pumps. Social C O 2 mitigation costs range between 147.5–272.8 € / t CO 2 . Finally, the results show the strong influence of the heat demand on the system layout. • System optimisation of distinct operators and consumers in multi-family buildings. • Inclusion of Tenant Electricity Law leads to complex investment and dispatch decisions. • Highest profitability combining photovoltaic, combined heat and power and heat pump. • Combined heat and power is favoured, but profits depend on building's heating demand. • Legislation incentivises energy communities but may offset national CO 2 mitigation. [ABSTRACT FROM AUTHOR]

This paper provides a detailed account of the impact of different offshore wind siting strategies on the design of the European power system. To this end, a two-stage method is proposed. In the first stage, a highly-granular siting problem identifies a suitable set of sites where offshore wind plants could be deployed according to a pre-specified criterion. Two siting schemes are analysed and compared within a realistic case study. These schemes essentially select a pre-specified number of sites so as to maximize their aggregate power output and their spatiotemporal complementarity, respectively. In addition, two variants of these siting schemes are provided, wherein the number of sites to be selected is specified on a country-by-country basis rather than Europe-wide. In the second stage, the subset of previously-identified sites is passed to a capacity expansion planning framework that sizes the power generation, transmission and storage assets that should be deployed and operated in order to satisfy pre-specified electricity demand levels at minimum cost. Results show that the complementarity-based siting criterion leads to system designs which are up to 5% cheaper than the ones relying on the power output-based scheme when offshore wind plants are deployed with no consideration for country-based deployment targets. On the contrary, the power output-based scheme leads to system designs which are consistently 2% cheaper than the ones leveraging the complementarity-based siting strategy when such constraints are enforced. The robustness of the reported results is supported by a sensitivity analysis on offshore wind capital expenditure and inter-annual weather variability, respectively. • Impact of offshore wind power plant siting strategies is analysed. • Method combining siting and capacity expansion planning models is proposed. • Siting schemes maximizing production or spatiotemporal complementarity are studied. • First scheme consistently cheaper across different problem instances. • Results robust to offshore cost assumptions and inter-annual weather variability. [ABSTRACT FROM AUTHOR]

The number of large-scale solar thermal installations has increased rapidly in Europe in recent years, with 70 % of these systems operating with flat-plate solar collectors. Since these systems cannot be easily switched on and off but directly depend on the solar radiation, they have to be combined with other technologies or integrated in large energy systems. In order to most efficiently integrate and operate solar systems, it is of great importance to consider their expected energy yield to better schedule heat production, storage and distribution. To do so the availability of accurate forecasting methods for the future solar energy yield are essential. Currently available forecasting methods do not meet three important practical requirements: simple implementation, automatic adaption to seasonal changes and wide applicability. For these reasons, a simple and adaptive forecasting method is presented in this paper, which allows to accurately forecast the solar heat production of flat-plate collector systems considering weather forecasts. The method is based on a modified collector efficiency model where the parameters are continuously redetermined to specifically consider the influence of the time of the day. In order to show the wide applicability the method is extensively tested with measurement data of various flat-plate collector systems covering different applications (below 200 ° Celsius), sizes and orientations. The results show that the method can forecast the solar yield very accurately with a Mean Absolute Range Normalized Error (MARNE) of about 5 % using real weather forecasts as inputs and outperforms common forecasting methods by being nearly twice as accurate. • Self-learning approach with only very few parameters to be determined. • Automatically considers seasonal changes and pollution of collector fields. • Simple regarding its mathematical structure and straightforward to implement. • Experimental long-term validation with data from various collector systems. • Superior to state-of-the-art forecasting methods regarding accuracy. [ABSTRACT FROM AUTHOR]