Directorate-General for Energy

Abstract Among the existing energy storage technologies, lithium‐ion batteries (LIBs) have unmatched energy density and versatility. From the time of their first commercialization in 1991, the growth in LIBs has been driven by portable devices. In recent years, however, large‐scale electric vehicle and stationary applications have emerged. Because LIB raw material deposits are unevenly distributed and prone to price fluctuations, these large‐scale applications have put unprecedented pressure on the LIB value chain, resulting in the need for alternative energy storage chemistries. The sodium‐ion battery (SIB) chemistry is one of the most promising “beyond‐lithium” energy storage technologies. Herein, the prospects and key challenges for the commercialization of SIBs are discussed. By comparing the technological evolutions of both LIBs and SIBs, key differences between the two battery chemistries are unraveled. Based on outstanding results in power, cyclability, and safety, the path toward SIB commercialization is seen imminent.

The maximum energy that lithium-ion batteries can store decreases as they are used because of various irreversible degradation mechanisms. Many models of degradation have been proposed in the literature, sometimes with a small experimental data set for validation. However, a comprehensive comparison between different model predictions is lacking, making it difficult to select modelling approaches which can explain the degradation trends actually observed from data. Here, various degradation models from literature are implemented within a single particle model framework and their behavior is compared. It is shown that many different models can be fitted to a small experimental data set. The interactions between different models are simulated, showing how some of the models accelerate degradation in other models, altering the overall degradation trend. The effects of operating conditions on the various degradation models is simulated. This identifies which models are enhanced by which operating conditions and might therefore explain specific degradation trends observed in data. Finally, it is shown how a combination of different models is needed to capture different degradation trends observed in a large experimental data set. Vice versa, only a large data set enables to properly select the models which best explain the observed degradation.

Given the foreseeable pervasiveness of artificial intelligence (AI) in modern societies, it is legitimate and necessary to ask the question how this new technology must be shaped to support the maintenance and strengthening of constitutional democracy. This paper first describes the four core elements of today's digital power concentration, which need to be seen in cumulation and which, seen together, are both a threat to democracy and to functioning markets. It then recalls the experience with the lawless Internet and the relationship between technology and the law as it has developed in the Internet economy and the experience with GDPR before it moves on to the key question for AI in democracy, namely which of the challenges of AI can be safely and with good conscience left to ethics, and which challenges of AI need to be addressed by rules which are enforceable and encompass the legitimacy of democratic process, thus laws. The paper closes with a call for a new culture of incorporating the principles of democracy, rule of law and human rights by design in AI and a three-level technological impact assessment for new technologies like AI as a practical way forward for this purpose.This article is part of a theme issue 'Governing artificial intelligence: ethical, legal, and technical opportunities and challenges'.

In 2011 the European Commission introduced a limit for non-volatile particle number (PN) emissions >23 nm from light-duty vehicles and the stated intent is to implement similar legislation for on-road heavy-duty engines at the next legislative stage. This paper reviews the recent literature regarding the operation-dependent emission of PN from light-duty vehicles and heavy-duty engines, and the measurement procedure used for regulatory purposes. \nThe repeatability of the PN method is of the order 5% and higher scatter of the results could easily be explained by the effect of the vehicles or the aftertreatment devices on the PN emissions (e.g. the fill state of the diesel particulate filters). Reproducibility remains an issue since it may exceed 30%. These high variability levels are mainly associated with calibration uncertainties of the PN instruments and drifts of the PN systems (especially those of the Particle Number Counters - PNCs) over time. \nCorrelation measurements between the full dilution tunnels (Constant Volume Samplers - CVS) and the proportional Partial Flow Dilution Systems (PFDS) showed agreement within 15% for the PN method down to 1x1011 p/kWh. At lower concentrations, the PN background of the CVS and/or the PFDS can result in larger inconsistencies.\nThe filter-based Particulate (matter) Mass (PM) and the PN emissions correlate well down to 1-2 mg/km for light-duty vehicles and also 2-3 mg/kWh for heavy-duty applications. The correlation improves when only elemental carbon mass is considered: It is relatively good down to 0.1-0.3 mg/km or mg/kWh; one order of magnitude below the PM levels.

Deposition of metallic Li is a severe aging mechanism in Lithium-ion cells. This study evaluates the influence of the main operating parameters leading to deposition of Li: temperature, charging C-rate, and end-of-charge voltage. Therefore both, graphite anodes and NMC cathodes from commercial 16Ah pouch cells are reconstructed into 3-electrode full cells. The position of the reference electrode between anode and cathode allows acquiring anode potentials vs. (Li/Li+). Extensive evaluations of data reveal critical combinations of operating parameters to avoid Li deposition. The results from the reconstructed 3-electrode cells are compared with independently performed aging tests of the original 16Ah cells.

© 2015 International Building Performance Simulation Association (IBPSA). Occupant behaviour has long been of main interest in the domain of building energy-savings and indoor air quality and its importance is recognized by its wide coverage in the literature. In the recent developments of detailed transient building energy simulations, including the occupant behaviour as a boundary condition for the thermal comfort, system efficiency calculations have been a major research topic, given its significant impact. A simultaneous growing interest in district energy simulations raises similar questions at the aggregate level, where upscaling from the building to an aggregate neighbourhood level at the spatial scale of a low-voltage feeder results in a natural regression to the mean lowering uncertainty, compared to the level of the household. The presented work starts with the description of StROBe, a stochastic residential occupant behaviour for district energy simulations integrating the modelling of receptacle loads, internal heat gains, thermostat settings and hot water tappings based on occupancy and activity prerequisites. Given this model, the uncertainty for district energy simulations is addressed. The epistemic uncertainties are elaborated first by comparing the model results with the reference values and then denoting local disaggregation of demographic statistics as a possible main hiatus of general modelling methods for building energy occupant behaviour used at the neighbourhood level. Finally, the aleatory uncertainty caused by the StROBe in integrated district energy simulations is quantified. Here, the expected value of the objective functions has, to a large extent, the same minimizers as the measures of the proposed robustness. As such, optimizing an objective value for its expected value generally seems to result in a optimum near the optimum of robustness. However, 95% of the observed objectives lay between 0.81 and 1.6 times the expected value for a feeder larger than 10 houses, and between 0.88 and 1.3 times the expected value for more than 20 houses, denoting an overall ‘rather small’ uncertainty on the possible objective functions caused by the user behaviour. Furthermore, we show that the design of the building energy system has an impact on the robustness of the objective criteria and it could thus be minimized as part of an optimisation exercise.

This chapter contains section titled: Introduction Tar Removal Applications A Road Map for Gasification Conclusions References

A method is presented which estimates the spectrum of a uniform sampled signal, which is sinusoidal, periodic, or composed of sinusoids of arbitrary frequencies. The proposed algorithm uses the Fast Fourier Transform algorithm. If frequency resolution is sufficient to distinguish different tones, the algorithm eliminates leakage and gives unbiased and highly accurate estimates for the amplitudes, phases, and frequencies.

Using the cytotoxic activity of specific isoantisera it was found that free peritoneal macrophages of mouse radiation chimeras are completely replaced by donor-type macrophages about 6 weeks after irradiation and administration of homologous bone marrow. Possible implications regarding the genotype of the fixed reticuloendothelial cells of chimeras are discussed.

Deep energy retrofit (DER) of the existing building stock is a meaningful strategy to reduce fossil fuel consumption and CO2 emissions. However, the investment volumes required to undertake DER are enormous. In Europe, cumulative demand for DER is estimated at close to 1000 billion EUR until 2050. Public expenditures and political measures can help to stimulate and guide DER, but substantial private investments are required to achieve significant results. In this paper, we analyze the economic and financial implications for renovating an office building to the “Passive House” standard. This is achieved by applying a dynamic Life Cycle Cost & Benefit Analysis (LCCBA) to model the cash flows (CF). The model also includes an appraisal of debt and equity financing implications, and a multi-parameter sensitivity analysis to analyze impacts of input parameter deviations. In the second part of the paper, we use the “multiple project benefits” (MPB) concept to identify project-based co-benefits of DER (with a focus on productivity), to make the business case more attractive. Results show that the DER project cash flow over a 25-year period achieves a 21-year dynamic payback with an IRR of below 2%. Levelized Cost of Heat Savings is 100 EUR/MWh with a 70% capital expenditure and 15% interest cost share. The Loan Life Cover Ratio comes out to 1.2. To make the business case more attractive, pecuniary MPBs identified are increased rents, real estate values, (employee) productivity, maintenance costs, and CO2 savings, in addition to societal benefits. Compared to simpler economic modeling, the dynamic LCCBA cash flow model provides solid grounds not only for DER business case analysis, project structuring, and financial engineering, but also for policy design. CFs from future energy cost savings alone are often insufficient in convincing investors. However, they can co-finance DER investments substantially. Consideration of project level MPBs can offer meaningful monetary contributions, and also help to identify strategic allies for project implementation; however, the “split incentive” dilemma requires differentiation between tenants and different types of investors. Furthermore, the approach supports policy-makers to develop policy measures needed to achieve 2050 goals.

Abstract. The Paris Agreement has underlined the role of cities in combating climate change. The Global Covenant of Mayors for Climate & Energy (GCoM) is the largest international initiative dedicated to promoting climate action at a city level, covering globally over 10 000 cities and almost half the population of the European Union (EU) by end of March 2020. The fifth Intergovernmental Panel on Climate Change (IPCC) report notes that there is a lack of comprehensive, consistent datasets of cities' greenhouse gas (GHG) emission inventories. In order to partly address this gap, we present a harmonised, complete and verified dataset of GHG inventories for 6200 cities in European and Southern Mediterranean countries, signatories of the GCoM initiative. To complement the reported emission data, a set of ancillary data that have a direct or indirect potential impact on cities' climate action plans were collected from other datasets, supporting further research on local climate action and monitoring the EU 27 (the 27 member states of the EU) progress on Sustainable Development Goal (SDG) 13 on climate action. The dataset (Kona et al., 2020) is archived and publicly available with the DOI https://doi.org/10.2905/57A615EB-CFBC-435A-A8C5-553BD40F76C9.

Lithium-sulfur batteries are emerging as strong contenders in energy storage; however, a cohesive design framework, systematic performance analysis and benchmarks remain absent. This study bridges this gap by examining recent advancements, with a focus on functional sulfur host materials, using a data-driven approach. Through a meticulous literature review, we digitize 866 galvanostatic cycling and rate capability plots, along with the collection of key host material properties—such as specific surface area and polysulfide binding/adsorption energy—as well as essential cell design parameters including sulfur loading, electrode formulation, and electrolyte-to-sulfur ratios, to standardize performance using specific energy and power metrics. This approach enables us mapping field advancements and identify impactful research contributions. Additionally, irrespective of materials chemistry, a comprehensive analysis of this database helps us to disclose general patterns that apply universally across all cells, highlight the most constructive and detrimental regions of the design-parameter space, and perceive potential synergies. These insights outline key areas for optimization, guiding future development of practical lithium-sulfur battery technology. Despite promises of Li-S batteries as high energy storage systems, a cohesive design framework, systematic performance analysis, and benchmarks remain absent. Here, authors map recent advancements in the literature to identify general patterns that apply universally across all cell formats and materials chemistry, outlining key areas for optimization and future development towards practical Li-S batteries.

Liberalization of electricity markets has brought focus on the optimal use of generation and transmission infrastructure. In such a scenario, where the power transmission systems are being operated closer to their critical limits, Ancillary Services (AS) play an important role in ensuring secure and cost-effective operation of power systems. Emerging converter-based HVDC technologies and integration of renewable energy sources (RES) have changed the power system dynamics which are based on classical power plant operation and synchronous generator dynamics. Transmission system interconnections between different countries and integrated energy markets in Europe have led to a reduction in the use of energy from non-renewable fossil-based sources. This review paper gives an insight into ancillary services definitions and market practices for procurement and activation of these ancillary services in different control areas within the European Network of Transmission System Operators for Electricity (ENTSO-E). The focus lies particularly on ancillary services from HVDC systems. It is foreseen that DC elements will play an important role in the control and management of the future power system and in particular through ancillary services provision. Keeping this in view, the capability of HVDC systems to provide ancillary services is presented.

Abstract A possible strategy to give a simultaneous boost to the energy and power attributes of the current generation of lithium‐ion batteries is developing thick porous electrodes with a high loading of active material alongside optimal percolation networks for the ions and electrons. However high the insertion capacity and kinetics of the single particle lithium‐insertion materials, the energy and power density of the cell can be capped by the ionic and electronic transport limitations in the porous electrode. In this work, a physical picture grounded in experiment and theory is proposed to spotlight and quantify the pivotal role of the micro‐scale porosity and active‐material loading in determining the tortuosity, effective transport properties, and performance limitations of porous electrodes. The outcome is a phenomenological picture coupled with a theoretical framework for the deconvolution of the relative shares of the electronic and ionic transport limitations over short and long ranges regarding the performance limitation of lithium‐ion batteries.

Abstract In this work, the ATOM (intrinsic a‐Si:H/TiO x /low work function metal) structure is investigated to realize high‐performance passivating electron‐selective contacts for crystalline silicon solar cells. The absence of a highly doped Si region in this contact structure is meant to reduce the optoelectrical losses. We show that a low contact resistivity ( ρ c ) can be obtained by the combined effect of a low work function metal, such as calcium (Φ 2.9 eV), and Fermi‐level depinning in the metal‐insulator‐semiconductor contact structure (where in our case TiO x acts as the insulator on the intrinsic a‐Si:H passivating layer). TiO x grown by ALD is effective to achieve not only a low ρ c but also good passivation properties. As an electron contact in silicon heterojunction solar cells, inserting interfacial TiO x at the i‐a‐Si:H/Ca interface significantly enhances the solar cell conversion efficiency. Consequently, the champion solar cell with the ATOM contact achieves a V OC of 711 mV, FF of 72.9%, J SC of 35.1 mA/cm 2 , and an efficiency of 18.2%. The achievement of a high V OC and reasonable FF without the need for a highly doped Si layer serves as a valuable proof of concept for future developments on passivating electron‐selective contacts using this structure.

The paper discusses the general wind power forecast error curve and how several power systems have exploited the shape of this curve to successfully incorporate significant amounts of wind energy at very low cost. The paper examined some of these systems in more detail to better understand how wind variability and wind forecast uncertainty are efficiently handled through these approaches. The paper also show that these elegant approaches for integrating wind into dispatch, although quite simple from a weather forecasting point of view, actually clarify the requirements and increase the value of sophisticated wind power forecasts in other time frames and for additional users. Taken together, these approaches can efficiently and reliably incorporate wind energy in power system operations and power markets.

In this work, a computationally efficient multi-scale and multi-dimensional model is set up to describe the electrochemical, electrical and thermal behavior for a generic pouch cell format. As solving the model in multiple spatial dimensions would require an extensive amount of computational resources, we apply effective spatial discretization techniques, namely the orthogonal collocation and Lobatto IIIA method. In order to reduce the number of electrochemical submodels, a coupling method based on node point interpolation is introduced. The proposed model shows an improvement in solution time by a factor of up to 60 while maintaining its accuracy compared to the finite element method solution. To investigate the spatial accuracy, simulation quantities such as potential distribution and temperature distribution for constant current discharge profiles are examined. With the aid of experimental data gained from Swagelok T-Cells, the model parameters are tuned in for discharge current rates of up to 10C and projected to a 40 Ah cell design. Due to the greatly reduced computational time, the proposed reformulated model can be used for complex physics-based simulations that are typically too computationally expensive with standard modeling approaches such as online estimation and parameter optimization.

The smart grid paradigm and the development of smart meters have led to the availability of large volumes of data. This data is expected to assist in power system planning/operation and the transition from passive to active electricity users. With recent advances in machine learning, this data can be used to learn system dynamics. This study explores two model‐free reinforcement learning (RL) techniques – policy iteration (PI) and fitted Q‐iteration (FQI) for scheduling the operation of flexibility providers – battery and heat pump in a residential microgrid. The proposed algorithms are data‐driven and can be easily generalised to fit the control of any flexibility provider without requiring expert knowledge to build a detailed model of the flexibility provider and/or microgrid. The algorithms are tested in multi‐agent collaborative and single‐agent stochastic microgrid settings – with the uncertainty due to lack of knowledge on future electricity consumption patterns and photovoltaic production. Simulation results show that PI outperforms FQI with a 7.2% increase in photovoltaic self‐consumption in the multi‐agent setting and a 3.7% increase in the single‐agent setting. Both RL algorithms perform better than a rule‐based controller, and compete with a model‐based optimal controller, and are thus, a valuable alternative to model‐ and rule‐based controllers.

The transition of the European iron and steel industry (ISI) towards low-carbon manufacturing is crucial for the European Union (EU)’s 2050 climate neutrality objective. One emerging solution is electrification by using hydrogen (H2) as iron ore reductant, which increases specific electricity use per tonne of steel up to 35 times compared to the conventional, most adopted coal-based technology. This study develops three scenarios, encompassing a moderate to an accelerated ISI transition, to evaluate the impact of the ISI decarbonisation on the power system CO2 emissions, generation mix and volume, and marginal prices in 2030. The study first estimates future electricity and H2 demand by considering country-specific technologies deployment and energy intensities. Then, these estimates serves as input to the model METIS to simulate European power system operations through a unit commitment and economic dispatch problem. The study shows that the power system can accommodate a transition of the ISI that substitutes 28% of the coal-based production with low carbon technologies, mainly based on H2. This leads to a 25% reduction in direct CO2 emissions and a demand increase of 20 TWh of electricity and 40 TWhHHV of H2. Furthermore, a 50% reduction in indirect power system emissions is achieved, compared to 2018, thanks to the substantial renewable power capacity deployment foreseen in the coming years. The study also demonstrates that a reduction of indirect CO2 emissions by over 85% can be achieved by deploying 1.2 and 2.7 GW of renewable power generators, and 200 and 400 MW of electrolyser capacity for each million tonne of steel produced annually with low-carbon technologies. Additional renewable capacity that ensures green steel production is also key to maintaining stable electricity prices.

The global energy sector currently is in turmoil because of different and often conflicting drivers and reasons: growing energy demand from emerging economy countries, the global economic crises, climate change policies, peak oil phenomena, the sudden increase of shale oil and shale gas production in the United States, geopolitical tensions, the demise of nuclear energy and last but not least the plummeting costs of renewable energy technologies. Global energy scenarios from established organisations like the International Energy Agency (IEA), World Energy Council and the big oil firms however give comparable expectations about probable future energy systems. They have one thing in common: they will all lead to a higher global temperature increase than the 2 degrees Celsius seen as the acceptable limit by climate scientists and as a consequence are not environmentally sustainable. Normative scenarios from the IEA, but also from NGOs such as Greenpeace, that take the CO2-emissions reductions needed as a starting point, show that a clean energy future that fulfils expected global energy demand is technically and economically possible. However, except for power production from renewables, the development of the clean energy technologies needed is not (yet) on track. Starting from this observation the author develops guidelines for the development of a clean energy future which basically consists of a combination of an accelerated direct and indirect electrification of energy demand combined with an accelerated shift to power production from renewables against a background of continuing energy efficiency improvements, including efficient use of waste heat flows. Also the main consequences and challenges of this development are discussed, including the development needed of new control and management strategies which will need smart ICT solutions