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Global energy consumption is growing rapidly, with the frequency and intensity of extreme events constantly increasing, posing a long-term threat to power supply and consumption. Therefore, analyzing the spatiotemporal characteristics of electricity consumption and quantitatively assessing the impact of extreme events on electricity consumption are of great significance. Based on fine-grained electricity consumption data from Europe for the years 2019–2022, this paper employs a data mining perspective and four methods including Z-score, Isolation Forest, Local Outlier Factor, and Autoencoder to detect abnormal electricity consumption during extreme events. Additionally, it combines indicators such as elastic loss, vulnerability, and duration to measure the impact of extreme events on electricity consumption. It is found that low temperatures could lead to abrupt changes in electricity consumption, with Northern Europe being more significantly affected by low temperatures. The COVID-19 pandemic had the most significant impact on electricity consumption in Europe, with the middle part of Europe being the hardest hit during the first wave of the pandemic. Electricity anomalies during the pandemic period were related to national pandemic control policies and exhibited some lag. High temperatures persisted for a longer duration in the middle part of Europe. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) can play an important role in improving the European Union's (EU)'s energy supply security, reducing the environmental impact of transport, and increasing EU competitiveness. The EU aims at fostering the synchronised deployment of EVs and necessary recharging infrastructure. There is currently a lack of studies in the literature for analysing the societal impacts of EV and infrastructure deployment at continental scale. In our paper, we analyse the likely impact of related plans of the EU member states (MSs). With the help of qualitative and quantitative analyses, we study the impact of plans on recharging infrastructure deployment, contributions to the EU climate and energy goals, air quality objectives, and reinforcement of the EU's competitiveness and job creation. We soft-link a fleet impact model with a simplified source receptor relationship model, and propose a new model to calculate job impacts. The results overall show modest impacts by 2020, as most member states' plans are not very ambitious. According to our analysis of the plans, a reduction of CO2 emissions by 0.4%, NOx emissions by 0.37%, and PM2.5 emissions by 0.44%, as well as a gross job creation of more than 8000 jobs will be achieved by 2020. The member state plans are very divergent. For countries with more ambitious targets up to 2020, such as Austria, France, Germany, and Luxemburg, the climate, energy, and air quality impacts are significant and show what would be achievable if the EU would increase its pace of EV and infrastructure deployment. We conclude that more ambitious efforts by the member states' to deploy electric vehicles could accelerate the reduction of CO2 emissions and lead to less dependence on fossil oil-based fuels, along with air quality improvements, while at the same time creating new job opportunities in Europe. In regards to the ratio of publicly accessible recharging points (RPs) per EV, we conclude that member states have to come up with more ambitious targets for recharging point deployment, as the current plans will lead to only one recharging point per every 20 EVs by 2020 across the EU. This paper can serve as useful input to the further the planning of EV and recharging infrastructure deployment in the EU and elsewhere. Our study highlights that the different strategies that are followed in the EU member states can be a fertile ground to identify best practices. It remains a challenge to quantify how different support policies impact EV deployment. In terms of further research needs, we identify that more detailed studies are required to determine an appropriate level of infrastructure deployment, including fast chargers. [ABSTRACT FROM AUTHOR]

Building integrated photovoltaics is one of the key technologies when it comes to electricity generation in buildings, districts or urban areas. However, the potential of building façades for the BIPV system, especially in urban areas, is often neglected. Façade-mounted building integrated photovoltaics could contribute to supply the energy demand of buildings in dense urban areas with economic feasibility where the availability of suitable rooftop areas is low. This paper deals with the levelised cost of electricity (LCOE) of building integrated photovoltaic systems (BIPV) in the capitals of all the European member state countries plus Norway and Switzerland and presents a metric to investigate a proper subsidy or incentive for BIPV systems. The results showed that the average LCOE of the BIPV system as a building envelope material for the entire outer skin of buildings in Europe is equal to 0.09 Euro per kWh if its role as the power generator is considered in the economic calculations. This value will be 0.15 Euro per kWh if the cost corresponding to its double function in the building is taken into the economic analysis (while the average electricity price is 0.18 Euro per kWh). The results indicate that the BIPV generation cost in most case studies has already reached grid parity. Furthermore, the analysis reveals that on average in Europe, the BIPV system does not need a feed-in tariff if the selling price to the grid is equal to the purchasing price from the grid. Various incentive plans based on the buying/selling price of electricity from/to the main grid together with LCOE of the BIPV systems is also investigated. [ABSTRACT FROM AUTHOR]

In light of the advancing energy transition and an increasing amount of intermittent renewable energy to be integrated, flexibility from distributed energy resources will be key. In this paper, the Italian UVAM (Unità Virtuali Abilitate Miste, i.e., virtually aggregated mixed units) project, one of the biggest pilots in Europe to serve this purpose, is critically reviewed and mapped after two years of operation. The pilot is analyzed on a global level as well as the individual participant level. Based on the extensive analysis of actual market data, different strategies of participating companies to obtain capacity in accordance with the pilot project's design are identified. Furthermore, the specific bidding strategies of individual participating units on the balancing market are outlined. Alongside this, the overall pilot project's market integration, in terms of offered and accepted bids, is depicted. The thorough data analysis, therefore, serves as an input and fundamental building block for future electricity market modeling. Comprehending specific data from the coronavirus disease 2019 (COVID-19) pandemic, provides insights for future high renewable-energy scenarios. Based on the analysis findings, valuable deliverables are devised for both policy-makers and decision-makers who aim to leverage the flexibility potential of distributed resources. [ABSTRACT FROM AUTHOR]

An effective energy oversight represents a major concern throughout the world, and the problem has become even more stringent recently. The prediction of energy load and consumption depends on various factors such as temperature, plugged load, etc. The machine learning and deep learning (DL) approaches developed in the last decade provide a very high level of accuracy for various types of applications, including time-series forecasting. Accordingly, the number of prediction models for this task is continuously growing. The current study does not only overview the most recent and relevant DL for energy supply and demand, but it also emphasizes the fact that not many recent methods use parameter tuning for enhancing the results. To fill the abovementioned gap, in the research conducted for the purpose of this manuscript, a canonical and straightforward long short-term memory (LSTM) DL model for electricity load is developed and tuned for multivariate time-series forecasting. One open dataset from Europe is used as a benchmark, and the performance of LSTM models for a one-step-ahead prediction is evaluated. Reported results can be used as a benchmark for hybrid LSTM-optimization approaches for multivariate energy time-series forecasting in power systems. The current work highlights that parameter tuning leads to better results when using metaheuristics for this purpose in all cases: while grid search achieves a coefficient of determination ( R 2 ) of 0.9136, the metaheuristic that led to the worst result is still notably better with the corresponding score of 0.9515. [ABSTRACT FROM AUTHOR]

Off-grid power systems are often used to supply electricity to remote households, cottages, or small industries, comprising small renewable energy systems, typically a photovoltaic plant whose energy supply is stochastic in nature, without electricity distributions. This approach is economically viable and conforms to the requirements of the European Green Deal and the Fit for 55 package. Furthermore, these systems are associated with a lower short circuit power as compared with distribution grid traditional power plants. The power quality parameters (PQPs) of such small-scale off-grid systems are largely determined by the inverter's ability to handle the impact of a device; however, this makes it difficult to accurately forecast the PQPs. To address this issue, this work compared prediction models for the PQPs as a function of the meteorological conditions regarding the off-grid systems for small-scale households in Central Europe. To this end, seven models—the artificial neural network (ANN), linear regression (LR), interaction linear regression (ILR), quadratic linear regression (QLR), pure quadratic linear regression (PQLR), the bagging decision tree (DT), and the boosting DT—were considered for forecasting four PQPs: frequency, the amplitude of the voltage, total harmonic distortion of the voltage ( T H D u ), and current ( T H D i ). The computation times of these forecasting models and their accuracies were also compared. Each forecasting model was used to forecast the PQPs for three sunny days in August. As a result of the study, the most accurate methods for forecasting are DTs. The ANN requires the longest computational time, and conversely, the LR takes the shortest computational time. Notably, this work aimed to predict poor PQPs that could cause all the equipment in off-grid systems to respond in advance to disturbances. This study is expected to be beneficial for the off-grid systems of small households and the substations included in existing smart grids. [ABSTRACT FROM AUTHOR]

In the EU, electricity suppliers are obliged to disclose to their customers the energy origin and environmental impacts of sold electricity. To this end, guarantees of origin (GOs) are used to explicitly track electricity generation attributes to individual electricity consumers. When part of a reliable electricity disclosure system, GOs deliver an important means for consumers to participate in the support of renewable power. In order to be considered reliable, GOs require the support of an implicit disclosure system, a residual mix, which prevents once explicitly tracked attributes from being double counted in a default energy mix. This article outlines the key problems in implicit electricity disclosure: (1) uncorrected generation statistics used for implicit disclosure; (2) contract-based tracking; (3) uncoordinated calculation within Europe; (4) overlapping regions for implicit disclosure; (5) active GOs. The improvements achieved during the RE-DISS project (04/2010-10/2012) with regard to these problems have reduced the total implicit disclosure error by 168 TWh and double counting of renewable generation attributes by 70 TWh, in 16 selected countries. Quantitatively, largest individual improvements were achieved in Norway, Germany and Italy. Within the 16 countries, a total disclosure error of 75 TWh and double counting of renewable generation attributes of 36 TWh still reside after the end of the project on national level. Regarding the residual mix calculation methodology, the article justifies the implementation of a shifted transaction-based method instead of a production year-based method. [ABSTRACT FROM AUTHOR]

We conducted the Life Cycle Analysis (LCA) of energy production from biogas for maize and three types of wetland biomass: reed Phragmites australis, sedges Carex elata, and Carex gracilis, and "grassy vegetation" of wet meadows (WM). Biogas energy produced from maize reached over 90 GJ ha−1, which was more than four times higher than that gained from wetland biomass. However, an estimation of energy efficiency (EE) calculated as a ratio of energy input to the energy produced in a biogas plant showed that the wet fermentation (WF) of maize was similar to the values obtained for dry fermentation (DF) of sedge biomass (~0.30 GJ GJ−1). The greenhouse gases (GHG) emissions released during preparation of the feedstock and operation of the biogas plant were 150 g CO2 eq. kWhel.−1 for DF of sedges and 262 g CO2 eq. kWhel.−1 for WF of Phragmites. Compared to the prevailing coal-based power generation in Central Europe, anaerobic digestion (AD) of wetland biomass could contribute to a reduction in GHG emissions by 74% to 85%. However, calculations covering the GHG emissions during the entire process "from field to field" seem to disqualify AD of conservation biomass as valid low-GHG energy supply technology. Estimated emissions ranged between 795 g CO2 eq. kWhel.−1 for DF of Phragmites and 2738 g CO2 eq. kWhel.−1 for the WM and, in most cases, exceeded those related to fossil fuel technologies. [ABSTRACT FROM AUTHOR]

The global energy matrix is going to embrace more and more renewable-based combined energy systems. Therefore, multi-generation energy systems, like CHPs (combined heat and power) could be extremely beneficial for such integrated energy systems. Also, the trend is toward 100% sustainable production where both renewable and waste energy sources are of special value. Especially, in Europe, waste incineration has received special attention over the past decades, as not only it is a smart method of waste disposal, but also a measure of cheap and environmentally friendly energy production. This study proposes a municipal waste-driven tri-generation (cold, heat, and power) system and assesses how this solution helps for easier integration of energy sectors and having a more sustainable chain of energy supply. Then, the solution is comprehensively analyzed over thorough thermodynamic, thermoeconomic, and thermoenvironmental investigations. The results of the assessments show that the proposed trigeneration system may effectively operate in any energy systems with simultaneous cold, heat, and power demands. Thermal, exergetic, fuel-to-power, fuel-to-heat, and fuel-to-cold efficiencies are found to be 83.28, 25.69, 23.49, 47.41, and 12.38%, respectively, while the payback period of 6 years is obtained based on the net present method. [ABSTRACT FROM AUTHOR]

Hydro power can provide a source of dispatchable low-carbon electricity and a storage solution in a climate-dependent energy mix with high shares of wind and solar production. Therefore, understanding the effect climate has on hydro power generation is critical to ensure a stable energy supply, particularly at a continental scale. Here, we introduce a framework using climate data to model hydro power generation at the country level based on a machine learning method, the random forest model, to produce a publicly accessible hydro power dataset from 1979 to present for twelve European countries. In addition to producing a consistent European hydro power generation dataset covering the past 40 years, the specific novelty of this approach is to focus on the lagged effect of climate variability on hydro power. Specifically, multiple lagged values of temperature and precipitation are used. Overall, the model shows promising results, with the correlation values ranging between 0.85 and 0.98 for run-of-river and between 0.73 and 0.90 for reservoir-based generation. Compared to the more standard optimal lag approach the normalised mean absolute error reduces by an average of 10.23% and 5.99%, respectively. The model was also implemented over six Italian bidding zones to also test its skill at the sub-country scale. The model performance is only slightly degraded at the bidding zone level, but this also depends on the actual installed capacity, with higher capacities displaying higher performance. The framework and results presented could provide a useful reference for applications such as pan-European (continental) hydro power planning and for system adequacy and extreme events assessments. [ABSTRACT FROM AUTHOR]