Hydrogen is an energy carrier which can be used to store variable renewable energy and face varying electricity demand and generation-driven fluctuations reducing the negative effects of the ...increasing share of renewables in the European energy mixes.
The aim of this paper is to pinpoint and assess non-economic barriers (NEBs) that can slow down the deployment of hydrogen technologies and infrastructures in Europe.
The method includes an expert opinion survey involving selected European experts to identify main NEBs, estimate their impacts, describe their effects, highlight suitable measures to overcome them and pinpoint hitches that make difficult to overtake them.
Collecting the information gathered by written questionnaires and in-depth interviews of experts of almost all European countries, the obtained results provide an overall framework of main NEBs identifying the role of forthcoming policies and governmental actions to overcome each barrier.
Lastly, preliminary exploitation strategies have been outlined for improving the utilization of hydrogen as an effective energy carrier for storing and to more efficiently use renewable energy produced in Europe.
•Non-economic barriers for the deployment of hydrogen technologies were analyzed.•An expert opinion survey has been conducted involving European experts.•Five main non-economic barriers were identified and analyzed in each country.
H2 production from solar electricity in the region of the Atacama Desert – Chile – has been identified as strategical for global hydrogen exportation. In this study the full supply chain of solar ...hydrogen has been investigated for 2018 and projected to scenarios for 2025-2030. Multi-year hourly electrical profiles data have been used from real operating PV plants and simulated Concentrated Solar Power “CSP” plants with Thermal Energy Storage “TES” as well as commercial electricity Power Purchase Agreement “PPA” prices reported in the Chilean electricity market were considered. The Levelized Cost of Hydrogen “LCOH” of each production pathway is calculated by a case-sensitive techno-economic MATLAB/Simulink model for utility scale (multi-MW) alkaline and PEM electrolyser technologies. Successively, different distribution, storage and transportation configurations are evaluated based on the 2025 Japanese case study according to the declared H2 demand. Transport in the form of liquefied hydrogen (LH2) and via ammonia (NH3) carrier is compared from the port of Antofagasta, CL to the port of Osaka, JP.
•Cost-competitive solar hydrogen can be produced in Chile.•Oversized PV-supplied systems lead to lower LCOH than smaller CSP-supplied systems.•The LCOH of PV-PPA+ALK can reach 2.20 US$/kg in 2018 and 1.67 US$/kg in 2025.•Storage and transport phases are CAPEX intensive, favoured by flat supply schemes.•Competitive H2 can be delivered in Japan in 2018 and 2025 respect to target prices.
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•HOMER optimisation analysis to plan smart Island energy systems.•Comparison between different storage solutions and power-to transport strategies.•Hydrogen potential use in insular ...public transport sector.•Comparison between fuel cell electric buses and enriched natural gas fuelled buses.
Energy costs, carbon dioxide emissions, security of supply and system stability are common challenges in small islands. Many European islands have become pilot sites of energy innovation, but this green transition goes slowly in other ones usually not connected to the national grid. This study investigates the economic and environmental sustainability related to the integration of hydrogen and batteries storage in small islands, considering at the same time the use of the stored hydrogen for fuelling Fuel Cell Electric Vehicles and Hydrogen Compressed Natural Gas vehicles to meet electricity and public transportation demand of islands, so as to increase the Renewable Energy Sources penetration level. Selecting the island of Favignana (Italy) as case study, HOMER software has been used to carry out the energy analysis of different scenarios, in order to identify the most effective energy solution from both technological and economical point of views. Using economic and environmental indicators, the outcomes show that the implementation of a hybrid storage system with batteries and electrolyser can be an adequate and reliable option for increasing energy independency of small island and decarbonizing transport sector optimizing economic and environmental sustainability.
•A novel deep learning-based evolutionary model proposed for wind speed forecasting.•A new evolutionary hierarchy-based decomposition method introduced.•A developed evolutionary algorithm proposed in ...order to hyper-parameter tuning.•Generalised normal distribution algorithm is improved by an adaptive local search.•The proposed hybrid model improves the accuracy of short-term wind speed.
Due to expanding global environmental issues and growing energy demand, wind power technologies have been studied extensively. Accurate and robust short-term wind speed forecasting is crucial for large-scale integration of wind power generation into the power grid. However, the seasonal and stochastic characteristics of wind speed make forecasting a challenging task. This study adopts a novel hybrid deep learning-based evolutionary approach in an attempt to improve the accuracy of wind speed prediction. This hybrid model consists of a bidirectional long short-term memory neural network, an effective hierarchical evolutionary decomposition technique and an improved generalised normal distribution optimisation algorithm for hyper-parameter tuning. The proposed hybrid approach was trained and tested on data gathered from an offshore wind turbine installed in a Swedish wind farm located in the Baltic Sea with two forecasting horizons: ten-minutes ahead and one-hour ahead. The experimental results indicated that the new approach is superior to six other applied machine learning models and a further seven hybrid models, as measured by seven performance criteria.
Offshore Wind Energy (OWE) can be considered the Renewable Energy Sources (RESs) with a higher potential of newly installed power in marine areas more the following decades. As a primary phase of the ...Offshore Wind Farms (OWFs) development, focusing on the long-term Offshore Wind (OW) potential assessment and mapping is necessary to highlight the best areas for turbine generators installations. In this case, accurate assessment and mapping of long-term OWs can help pinpoint previously not considered marine areas. In this regard, the Iranian islands located in the Persian Gulf can be called one of these forgotten areas in dire need of energy supply due to their remoteness from the mainland. To these aims, the long-term Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) re-analysis dataset has been used to identify possible locations of the Offshore Wind Turbine Generators (OWTGs) installations. In particular, an OW classification based on the 40 years of monthly data and the last 10-years of hourly data highlighted the best areas for OWTGs potential installations in the 12 Iranian islands of the Persian Sea. The time-series method has been designed, tested, and developed to understand better and manage the OW potential and mapping of the Iranian islands decision-making process. Furthermore, the time-series method has been applied to the generated energy source based on the OW speed used in the Iranian islands. Finally, exploring results show Iranian islands, such as Kharg, Siri and Abu Musa islands, have attractive OWE potentials for OWTGs installations.
•Offshore wind speed assessment and mapping using reanalysis data of the Persian Gulf.•Offshore wind analysis for wind turbines potential installations in the Iranian islands.•Time-series method designed, tested, and developed to the decision-making sites prioritisation.•Offshore wind farms site prioritisation using time-series data analysis.
Energy transitions are reshaping global and national energy systems and appropriate decision-making strategies are needed to drive an effective change in response to pressing global issues. ...Governmental institutions, industry, academia, and civil society are all participating to this global change, playing different roles. Open energy models and associated data are essential to promote open science practices, and create an effective science-policy interaction. For example, they can foster multi-disciplinary research addressing the co-evolution of energy technologies and human behaviour more transparently and, more in general, they can improve the interaction of multiple linked models and data, by improving them with respect to the current state of the art. In this paper, we present an analysis of features of open energy models and data, highlighting essential information that can be shared among communities of researchers in the energy field to foster multidisciplinary research. This information inherently embodies different key concepts and perspectives in modelling that affect both simulation and optimization processes employed for energy systems planning, design and operation. Indeed, this shared knowledge is crucial to overcome critical technical issues (e.g. end-use energy efficiency improvements, energy conversion processes, energy infrastructures operation, etc.) that may inhibit successful energy transitions. Finally, ecosystems of interacting open data and models are key assets for the development of next generation energy services and technologies, based on innovative business models in which the problem of monitoring, verifying and tracking performance transparently (at multiple levels) will be fundamental.
•Energy transitions are reshaping global, national and local energy systems.•A more effective interaction among science, policy and society is needed in transitions.•Open energy modelling principles are contributing to the evolution of research.•Ecosystems of open energy models can be used for multi-disciplinary research.•Ecosystem of models could enable the development of next generation energy services and technologies.
•New optimization algorithm and neural network for renewables prediction intervals.•A new hybrid feature selection method to select the most suitable inputs.•A new combined point forecast model to ...energy consumption prediction in microgrids.•Energy consumption analysis of Favignana Island microgrid using Pareto analysis.•Application in Favignana microgrid for energy consumption wind and solar prediction.
Nowadays, wind and solar power generation have a major impact in many microgrid hybrid energy systems based on their cost and pollution. On the other hand, accurate forecasting of wind and solar power generation is very important for energy management in microgrids. Therefore, a novel prediction interval model, consisting of several sections (wavelet transform, hybrid feature selection, Group Method of Data Handling neural network, and modified multi-objective fruit fly optimization algorithm), has been developed to short-term predict wind speed and solar irradiation and to investigate the energy consumption of microgrids. The renewables prediction and the energy consumption analysis have been applied to the Favignana island microgrid, in the south of Italy, using the new proposed point forecast model (Group Method of Data Handling neural network and modified fruit fly optimization algorithm – GMDHMFOA) and a Pareto analysis. The results show that the proposed interval prediction model has a good performance in different confidence levels (95%, 90%, and 85%) to predict wind speed and solar irradiation than other already existing methods. In addition, the proposed point forecast model (GMDHMFOA) has an acceptable error and better performance than the other ones commonly used in predicting energy consumption. Lastly, the monthly energy consumption in different stations of the microgrid can be predicted by using the proposed model and provides suitable solutions for energy management of the microgrid.
•The Smart Energy Hub system is presented.•A reversible Solid Oxide Cell is coupled with battery and hydrogen tank.•The system operation is investigated in real buildings integration.•An increase of ...self-sufficiency is achieved to balance the weak connection to the Grid.•CAPEX and OPEX learning curves are considered for the economic analysis.
Hydrogen is assuming a crescent role in the decarbonising initiatives. Moreover, hydrogen can supply the 3 most energy intense sectors, i.e. transport, heat and electricity, allowing the sector coupling. To do so, a production unit, the electrolyser, and a consumer one, the fuel cell, are needed. Actually, reversible Solid Oxide Cell technology presents the possibility to install only one device acting bi-directionally. It offers different advantages thank to its (i) compact design, thus ensuring space and cost savings; (ii) ability to meet thermal and electric demand, thus reducing emissions in both those sectors; (iii) possibility to store, the electricity excess coming from renewable sources in form of hydrogen, ensuring an unlimited and seasonal storage possibility.
In this study, the deployment of a real reversible Solid Oxide Cell was simulated in different scenarios considering the data recorded in one year in the island of Procida, Italy. Up to date, the use of this technology was mostly relegated to the industrial sector or to prototype tests. While, this research aimed to analyse the functioning of near commercialization technology in civil environments such as hotels, offices and hospitals to understand its feasibility in this new context. It wants to be proved that advantages of this emerging technology can be exploited as well in the civil environment. Three economic indicators, i.e. Payback Period, Internal Return Rate and Net Present Value were selected to evaluate the simulated scenarios, while, the primary energy saving, the emission reduction and its storage efficacy were studied to evaluate the environmental achievements. To perform the simulations, the MATLAB model ConfigDym built by Sylfen was used. Finally, a sensitivity analysis in terms of economics was carried out. The results show an important decrease in emissions and an energy self-sufficiency increase of at least 29% and 58% respectively, differently the economic analysis returns a payback period currently near to its lifetime, while for the future a three years period is reachable.
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