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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]

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]