Alkaline water electrolysis is a key technology for large-scale hydrogen production powered by renewable energy. As conventional electrolyzers are designed for operation at fixed process conditions, the implementation of fluctuating and highly intermittent renewable energy is challenging. This contribution shows the recent state of system descriptions for alkaline water electrolysis and renewable energies, such as solar and wind power. Each component of a hydrogen energy system needs to be optimized to increase the operation time and system efficiency. Only in this way can hydrogen produced by electrolysis processes be competitive with the conventional path based on fossil energy sources. Conventional alkaline water electrolyzers show a limited part-load range due to an increased gas impurity at low power availability. As explosive mixtures of hydrogen and oxygen must be prevented, a safety shutdown is performed when reaching specific gas contamination. Furthermore, the cell voltage should be optimized to maintain a high efficiency. While photovoltaic panels can be directly coupled to alkaline water electrolyzers, wind turbines require suitable converters with additional losses. By combining alkaline water electrolysis with hydrogen storage tanks and fuel cells, power grid stabilization can be performed. As a consequence, the conventional spinning reserve can be reduced, which additionally lowers the carbon dioxide emissions.
Abstract The recast of the European Union Renewable Energy Directive (RED II) entered into force in December 2018, followed by the Internal Electricity Market Directive (IEMD) and Regulation (IEMR) as part of the Clean Energy for all Europeans Package. The RED II, that the 28 Member States have until June 2021 to transpose into national law, defines “Renewable Energy Communities” (RECs), introduces a governance model for them and the possibility of energy sharing within the REC. It also provides an “enabling framework” to put RECs on equal footing with other market players and to promote and facilitate their development. This article defines "renewable energy clusters" that are comprised of complementarity of different energy sources, flexibility, interconnectivity of different actors and bi-directionality of energy flows. We argue that RECs and RE clusters are socio-technical mirrors of the same concept, necessary in a renewable energy transition. To test how these new rules will fare in practice, drawing on a secondary dataset of 67 best-practice cases of consumer (co-)ownership from 18 countries, each project is assessed using the criteria of cluster potential, and for the extent that they meet the RED II governance requirements of heterogeneity of members and of ownership structure. Nine cases were identified as having cluster potential all of which were in rural areas. Of these, five projects were found to be both RECs and RE clusters. The absence of the governance and heterogeneity criteria is observed in projects that fall short of the cluster elements of flexibility, bi-directionality and interconnectivity, while cluster elements occur where the governance and heterogeneity criteria are met. When transposing the new rules into national law we recommend careful attention to encourage complementarity of renewables, RECs in urban contexts and “regulatory sandboxes” for experimentation to find the range of optimal preferential conditions of the “enabling framework”.
Campos Inês, Pontes Luz Guilherme, Marín González Esther
et al.
Abstract The transition to a low-carbon future based on renewable energy sources is leading to a new role for citizens, from passive energy consumers to active energy citizens - the so-called renewable energy (RE) prosumers. Recent EU energy policy seeks to mainstream RE prosumers in each Member State. This study carries out a cross-country comparison between the regulatory frameworks of nine countries and regions - Belgium (Flanders region only), Croatia, France, Germany, Italy, Portugal, Spain, Netherlands and the United Kingdom - to reveal the main challenges and opportunities that these have posed to collective RE prosumers (i.e. renewable energy communities, citizen energy communities and jointly-acting renewable self-consumers). Four countries have had more favourable frameworks for collective prosumers: France, Germany, Netherlands and United Kingdom. The results indicate that the current legal framework at the EU level represents a clear opportunity for collective prosumers. Spain and Portugal have both already shifted from a restrictive regulation to implementing in 2019 a legal framework for collectives. The study provides a starting point to distil policy implications for improving legal frameworks relevant for collective RES prosumers across Europe.
Kashif Gohar Deshmukh, Mohd Sameeroddin, Daud Abdul
et al.
Abstract Renewable energy was the only source available for the generation of energy since the ancient time. However, after the discovery of fossil fuels (initially as coal, after that crude oil and lately gas) it has lost ground in the 19th and 20th centuries in most industrialized countries majorly for heating and transportation purposes. Renewable sources such as biomass (In wood form) for heating, cooking, and lighting; then wind energy for navigation and for driving mills; lastly hydropower, also for driving mills; were only sources of energy available prior to the introduction of fossil fuels. From the late 20th century, renewable energy has become much popular because fossil fuels are depleting and have a serious negative impact on the environment; globally new policies and measures are widely implemented now to encourage its use but still it is a difficult to find the right support mechanism for the development of renewable energy, since technologies and costs are evolving exponentially. The present investigation is aimed to study the present situation and to forecast the future of renewable energy in the various sectors such as industrial, automobile, electricity generation and also focuses on how the consumption of fossil fuels can be brought down.
ABSTRACT This paper brings a new understanding to the relative importance of different uncertainty sources across forecast horizons up to 7 days ahead. It presents a method for probabilistic wind power forecasting that quantifies uncertainty from weather forecasts and weather‐to‐power conversion separately. The study reveals that weather‐to‐power uncertainty is more significant for short‐term forecasts, while weather forecast uncertainty dominates mid‐term forecasts, with the transition point varying between wind farms. Offshore farms typically see this shift at shorter lead times than onshore. By addressing both uncertainty sources, the proposed forecast method achieves state‐of‐the‐art results for lead times of 6 to 162 h, eliminating the need for separate models for short‐ and mid‐term forecasting. Importantly, it also significantly improves short‐term forecasts during high weather uncertainty periods, where methods based on deterministic weather forecasts dramatically underestimate total uncertainty. The findings are supported by an extensive, reproducible case study comprising 73 wind farms in Great Britain over 5 years.
Seongmin Kim, In-Saeng Suh, Travis S. Humble
et al.
Developing high-performance materials is critical for diverse energy applications to increase efficiency, improve sustainability and reduce costs. Classical computational methods have enabled important breakthroughs in energy materials development, but they face scaling and time-complexity limitations, particularly for high-dimensional or strongly correlated material systems. Quantum computing (QC) promises to offer a paradigm shift by exploiting quantum bits with their superposition and entanglement to address challenging problems intractable for classical approaches. This perspective discusses the opportunities in leveraging QC to advance energy materials research and the challenges QC faces in solving complex and high-dimensional problems. We present cases on how QC, when combined with classical computing methods, can be used for the design and simulation of practical energy materials. We also outline the outlook for error-corrected, fault-tolerant QC capable of achieving predictive accuracy and quantum advantage for complex material systems.
Abderaouf Bahi, Amel Ourici, Ibtissem Gasmi
et al.
Accurate forecasting of renewable energy generation is essential for efficient grid management and sustainable power planning. However, traditional supervised models often require access to labeled data from the target site, which may be unavailable due to privacy, cost, or logistical constraints. In this work, we propose FreeGNN, a Continual Source-Free Graph Domain Adaptation framework that enables adaptive forecasting on unseen renewable energy sites without requiring source data or target labels. Our approach integrates a spatio-temporal Graph Neural Network (GNN) backbone with a teacher--student strategy, a memory replay mechanism to mitigate catastrophic forgetting, graph-based regularization to preserve spatial correlations, and a drift-aware weighting scheme to dynamically adjust adaptation strength during streaming updates. This combination allows the model to continuously adapt to non-stationary environmental conditions while maintaining robustness and stability. We conduct extensive experiments on three real-world datasets: GEFCom2012, Solar PV, and Wind SCADA, encompassing multiple sites, temporal resolutions, and meteorological features. The ablation study confirms that each component memory, graph regularization, drift-aware adaptation, and teacher--student strategy contributes significantly to overall performance. The experiments show that FreeGNN achieves an MAE of 5.237 and an RMSE of 7.123 on the GEFCom dataset, an MAE of 1.107 and an RMSE of 1.512 on the Solar PV dataset, and an MAE of 0.382 and an RMSE of 0.523 on the Wind SCADA dataset. These results demonstrate its ability to achieve accurate and robust forecasts in a source-free, continual learning setting, highlighting its potential for real-world deployment in adaptive renewable energy systems. For reproducibility, implementation details are available at: https://github.com/AraoufBh/FreeGNN.
David Franzmann, Nils Ludwig, Jochen Linßen
et al.
The transformation of the energy system has raised concerns about the reliability of fully renewable energy systems. We address this question for a 2050 European energy system using an economically optimal adequacy assessment. Our results show that a cost-optimal, fully renewable European system can be as reliable as a fossil-based one, with an average loss of load of only 0.03% due to variability in renewable generation. Outages primarily affect industrial and service sectors, while household supply remains largely uninterrupted. Regional differences in supply security emerge, with outages concentrated in countries with a low Value of Lost Load (VoLL). We demonstrate that system reliability can be fully ensured at negligible additional cost (+0.17%) by modestly increasing hydrogen turbine (+10%) and battery capacities (+15%) beyond the cost-optimal levels. We conclude that well-designed renewable energy systems are stable, with hydrogen-based backup being a key enabler of reliability.
Abstract Renewable energy appears to be the most optimal alternative to fossil fuel and the widely accepted pathway towards the mitigation of climate change. However, the costs of adopting renewable energy are high, and it appears the wealth of nations, the stages of economic development and growth and institutional willingness and quality are important in winning this global challenge. However, there is limited information on the interplay of all the factors that are perceived as critical in moving the world towards the use of renewable energy sources to meet most of the domestic and industrial energy needs. This study investigates the inter-temporal causal relationship between institutions, renewable energy, carbon emissions and economic growth for 45 sub-Saharan Africa countries using annual data for the period 1960–2017. We used the generalised method of moment panel vector autoregression (GMM-PVAR) technique to explore the linkages. From a general perspective, the results reveal that no causal relationship exists between institutions and economic growth, but a bidirectional causality exists between economic growth and renewable energy. Our results indicate that economic growth causes carbon emissions, and institutions are more likely to respond to carbon emissions and renewable energy but prompts no causality exists between carbon emissions and renewable energy. Interestingly, these results differ between countries with different institutional origin. The policy implications are discussed.
This paper presents a comprehensive review of machine learning (ML) based approaches, especially artificial neural networks (ANNs) in time series data prediction problems. According to literature, around 80% of the world’s total energy demand is supplied either through fuel-based sources such as oil, gas, and coal or through nuclear-based sources. Literature also shows that a shortage of fossil fuels is inevitable and the world will face this problem sooner or later. Moreover, the remote and rural areas that suffer from not being able to reach traditional grid power electricity need alternative sources of energy. A “hybrid-renewable-energy system” (HRES) involving different renewable resources can be used to supply sustainable power in these areas. The uncertain nature of renewable energy resources and the intelligent ability of the neural network approach to process complex time series inputs have inspired the use of ANN methods in renewable energy forecasting. Thus, this study aims to study the different data driven models of ANN approaches that can provide accurate predictions of renewable energy, like solar, wind, or hydro-power generation. Various refinement architectures of neural networks, such as “multi-layer perception” (MLP), “recurrent-neural network” (RNN), and “convolutional-neural network” (CNN), as well as “long-short-term memory” (LSTM) models, have been offered in the applications of renewable energy forecasting. These models are able to perform short-term time-series prediction in renewable energy sources and to use prior information that influences its value in future prediction.
Bangladesh is strongly seeking renewable energy sources to meet its increasing electricity demand as part of the worldwide shift to sustainable energy. Even in areas where the use of solar energy would be quite practical, many educational institutions nationwide still rely on the traditional power grid despite having significant solar potential. This paper fills a crucial research gap by providing a thorough technoeconomic analysis of a grid-tied solar photovoltaic (PV) system designed for an educational institution in Narail, Bangladesh, an application that is little documented in national literature. Essential design criteria, including the best system orientation, comprehensive loss analysis, precise component specifications, and energy yield projections, were all included in the system model, which was created using local meteorological data. According to the operational study, the suggested system can generate about 133 MWh of energy per year, of which 100 MWh is exported to the national grid and 33 MWh is set aside for self-consumption. A remarkably competitive levelized cost of energy (LCOE) of $0.0577/kWh is attained by the design. Additionally, the system is expected to lower carbon dioxide emissions by around 77.5 tonnes annually or more than 1900 tonnes throughout its 25-year operating lifespan, assuming a conservative 1% annual degradation rate. These findings highlight the economic feasibility and environmental sustainability of grid-connected solar PV systems for educational institutions in Bangladesh, which have the potential to improve energy security and significantly contribute to national carbon reduction efforts.