Denmark

The NEWGASMET project has the overall objective to increase knowledge about the accuracy and durability of commercially available gas meters after exposure to renewable gases. This should lead to the improvement of existing meter designs and flow calibration standards. One of the recently released results is a proposal for a set of test gases to represent the range of non-conventional gases in the scope of the revision of the gas meter standards. In details these were proposed to be used in the CEN/TC237 standards and the OIML-R137:2014. During the project meetings concerns have been raised regarding the applicability of such test gases to EMC tests for static meters. Today such tests are performed in air but there is a clear agreement that the behaviour of the meter during EMC tests can be influenced by the renewable gas type. At least this agreement exists for the ultrasonic measurement technology while further discussion might be needed for the mass flow. However it is not simply possible to redesign the current EMC tests by replacing air with the defined gas mixtures as this would be quite impractical especially considering the explosive nature of the test gases.

With the constant increase in variable renewable energy production in electricity and district heating systems integration with the gas system is a way to provide flexibility to the overall energy system. In the sustainable transition towards a zero-emission energy system traditional natural gas can be substituted by renewable gasses derived from anaerobic digestion or thermal gasification and hydrogen. In this paper we present a methodology for modelling renewable gas options and limits on biomass resources across sectors in the energy optimisation model Balmorel. Different scenarios for socio-economic pathways to emission neutral electricity and district heating systems in Denmark Sweden Norway and Germany show that a renewable based energy system benefits from a certain percentage of gas as a supplement to other flexibility options like interconnectors. Especially upgraded biogas from anaerobic digestion serves as a substitute for natural gas in all scenarios. Allocating only 10% of available biomass to the electricity and district heating sector leads to full exploitation of the scarce biomass resource by boosting biogas and syngas with hydrogen. The need for renewable gasses is highest in Germany and least in Norway where hydro-power provides flexibility in terms of storable and dispatchable electricity production. The scenarios show that a required ‘‘late sprint" from fossils to achieve a zero-emission energy system in 2050 causes (1) significant higher accumulated emissions and (2) a system which strongly relies on fuels also in an emission free system instead of stronger integration of the electricity and district heating systems through electrification as well as stronger integration of the power systems across countries through interconnectors.

Nowadays nearly one quarter of global carbon dioxide emissions are attributable to energy use in industry making this an important target for emission reductions. The scope of this study is hence that to define a cost-optimized decarbonization strategy for an energy and carbon intensive industry using an Italian refinery as a case study. The methodology involves the coupling of EnergyPLAN with a Multi-Objective Evolutionary Algorithm (MOEA) considering the minimization of annual cost and CO2 emissions as two potentially conflicting objectives and the energy technologies’ capacities as decision variables. For the target year 2025 EnergyPLAN+MOEA has allowed to model a range of 0-100 % decarbonization solutions characterized by optimal penetration mix of 22 technologies in the electrical thermal hydrogen feedstock and transport demand. A set of nine scenarios with different land use availabilities and implementable technologies each consisting of 100 optimal systems out of 10000 simulated ones has been evaluated. The results show on the one hand the possibility of achieving medium-high decarbonization solutions at costs close to current ones on the other how the decarbonization pathways strongly depend on the available land for solar thermal photovoltaic and wind as well as the presence of a biomass supply chain in the region.

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.

To enhance renewable energy (RE) generation and maintain power balance energy storage systems are of utmost importance. This research introduces a cutting-edge Power-to-Hydrogen (PtH) framework that harnesses hydrogen as a clean and versatile energy storage medium. The primary focus of this study lies in optimizing power flow within a microgrid (G) equipped with RE and energy storage systems considering various factors such as RE generation power demand battery charge cycles and operational costs. To achieve the optimal balance between power generation and consumption a sophisticated mathematical solution is devised. This solution governs the charging and discharging patterns for both battery and electrolyzer ensuring a harmonious power equilibrium. The use of short-term forecasting further refines the optimization process adapting the parameters based on anticipated RE sources and load requirements. To fine-tune the power management solution for day-to-day operations an artificial neural fuzzy inference system (ANFIS)-based shortterm prediction model is employed. The predictive analysis provides confidence intervals for crucial aspects including power generation demand battery charging cycles and hydrogen generation. This facilitates precise cost estimation across various hydrogen and heat price ranges. the proposed PtH optimization framework offers an efficient approach to balance power generation and consumption in Gs driven by RE sources and energy storage. To validate the proposed approach numerical simulations are performed based on data from wind and solar farms load requirements and cost of energy. The results show that the proposed energy management strategy significantly reduces operational costs and optimizes PtH generation while maintaining power balance within the microgrid (G). The predictive approach helps fine-tune the optimization process improving efficiency and cost-effectiveness. The research convincingly demonstrate the economic advantages of adopting hydrogen as an energy storage medium paving the way for a cleaner and more sustainable energy future.

Hard-to-electrify sectors will require renewable fuels to facilitate the green transition in the future. Therefore it is crucial to identify promising production locations while taking into account the local biomass resources variable renewable energy sources and the synergies between sectors. In this study investments and dispatch operations are optimised of a large catalogue of renewable fuel production technologies in the open-source software SpineOpt and this is soft-linked to the comprehensive energy system model Balmorel. We analyse future production pathways by comparing various levels of hydrogen infrastructure including large-scale hydrogen storage and assess system impacts. The results indicate that methanol may provide synergies in its multipurpose use as an early (2030–2040) shipping fuel and later as an aviation fuel through further refining if ammonia becomes more competitive (2050). We furthermore show that a hydrogen infrastructure increases the competitiveness of non-flexible hydrogen-based fuel production technologies. Offshore electrolysis hubs decrease energy system impacts in scenarios with 105 TWh of Nordic hydrogen export. However hydrogen export scenarios are much costlier compared to scenarios with no export unless a high hydrogen price is received. Finally we find that emission taxes in the range of 250–265 e/tCO2 will be necessary for renewable fuels to become competitive.

Environmental concerns and the imperative to achieve net-zero carbon emissions have driven the exploration of efficient and sustainable advancements in automobile technologies. The automotive sector is undergoing a significant transformation primarily propelled by the adoption of green fuel technologies. Among the most promising innovations are green vehicle technologies and the integration of non-conventional power sources including advanced batteries (featuring high energy density) fuel cells (capable of long-range energy generation with water as the sole byproduct) and super-capacitors (characterized by rapid charge–discharge capabilities). This article examines the performance efficiency and adaptability of these power sources for electric vehicles (EVs) providing a comprehensive comparison of their functional capabilities. Additionally it analyzes the integration of super-capacitors with batteries and fuel cells emphasizing the potential of hybrid systems to enhance vehicle performance optimize energy management and extend operational range. The role of power converters in such systems is also discussed underscoring their critical importance in ensuring efficient energy transfer and effective energy management.

Green hydrogen and ammonia are forecast to play key roles in the deep decarbonization of the global economy. Here we explore the potential of using green hydrogen and ammonia to couple the energy agriculture and industrial sectors with India’s nationalscale electricity grid. India is an ideal test case as it currently has one of the most ambitious hydrogen programs in the world with projected electricity demands for hydrogen and ammonia production accounting for over 1500 TWh/yr or nearly 25% of India’s total electricity demand by 2050. We model the ambitious deep decarbonization of India’s electricity grid and half of its steel and fertilizer industries by 2050. We uncover modest risks for India from such a strategy with many benefits and opportunities. Our analysis suggests that a renewables-based energy system coupled with ammonia off-take sectors has the potential to dramatically reduce India’s greenhouse emissions reduce requirements for expensive long-duration energy storage or firm generating capacity reduce the curtailment of renewable energy provide valuable short-duration and long-duration load-shifting and system resilience to inter-annual weather variations and replace tens of billions of USD in ammonia and fuel imports each year. All this while potentially powering new multi-billion USD green steel and maritime fuel export industries. The key risk for India in relation to such a strategy lies in the potential for higher costs and reduced benefits if the rest of the world does not match their ambitious investment in renewables electrolyzers and clean storage technologies. We show that such a pessimistic outcome could result in the costs of green hydrogen and ammonia staying high for India through 2050 although still within the range of their gray counterparts. If on the other hand renewable and storage costs continue to decline further with continued global deployment all the above benefits could be achieved with a reduced levelized cost of hydrogen and ammonia (10–25%) potentially with a modest reduction in total energy system costs (5%). Such an outcome would have profound global implications given India’s central role in the future global energy economy establishing India’s global leadership in green shipping fuel agriculture and steel while creating an affordable sustainable and secure domestic energy supply.

We develop a bidding strategy for a hybrid power plant combining co-located wind turbines and an electrolyzer constructing a price-quantity bidding curve for the day-ahead electricity market while optimally scheduling hydrogen production. Without risk management single imbalance pricing leads to an all-or-nothing trading strategy which we term “betting”. To address this we propose a data-driven pragmatic approach that leverages contextual information to train linear decision policies for both power bidding and hydrogen scheduling. By introducing explicit risk constraints to limit imbalances we move from the all-or-nothing approach to a “trading” strategy where the plant diversifies its power trading decisions. We evaluate the model under three scenarios: when the plant is either conditionally allowed always allowed or not allowed to buy power from the grid which impacts the green certification of the hydrogen produced. Comparing our data-driven strategy with an oracle model that has perfect foresight we show that the risk-constrained data-driven approach delivers satisfactory performance.

Alkaline water electrolysis (AWE) is the most mature electrochemical technology for hydrogen production from renewable electricity. Thus its mathematical modeling is an important tool to provide new perspectives for the design and optimization of energy storage and decarbonization systems. However current models rely on numerous empirical parameters and neglect variations of temperature and concentration alongside the electrolysis cell which can impact the application and reliability of the simulation results. Thus this study proposes a simple four-parameter semi-empirical model for AWE system analysis which relies on minimal fitting data while providing reliable extrapolation results. In addition the effect of model dimensionality (i.e. 0D 1/2D and 1D) are carefully assessed in the optimization of an AWE system. The results indicate that the proposed model can accurately reproduce literature data from four previous works (R 2 ≥ 0.98) as well as new experimental data. In the system optimization the trade-offs existing in the lye cooling sizing highlight that maintaining a low temperature difference in AWE stacks (76-80°C) leads to higher efficiencies and lower hydrogen costs.