• Energy Research
• 8. Economic growth close
• BE close
• Energies close

Renewable Energy Communities consist in an emerging decentralized market mechanism which allows local energy exchanges between end-users, bypassing the traditional wholesale/retail market structure. In that configuration, local consumers and prosumers gather in communities and can either cooperate or compete towards a common objective, such as the minimization of the electricity costs and/or the minimization of greenhouse gas emissions for instance. This paper proposes data analytics modules which aim at helping the community members to schedule the usage of their resources (generation and consumption) in order to minimize their electricity bill. A day-ahead local wind power forecasting algorithm, which relies on state-of-the-art Machine Learning techniques currently used in worldwide forecasting contests, is in that way proposed. We develop furthermore an original method to improve the performance of neural network forecasting models in presence of abnormal wind power data. A technique for computing representative profiles of the community members electricity consumption is also presented. The proposed techniques are tested and deployed operationally on a pilot Renewable Energy Community established on an Medium Voltage network in Belgium, involving 2.25MW of wind and 18 Small and Medium Enterprises who had the possibility to freely access the results of the developed data modules by connecting to a dedicated web platform. We first show that our method for dealing with abnormal wind power data improves the forecasting accuracy by 10% in terms of Root Mean Square Error. The impact of the developed data modules on the consumption behaviour of the community members is then quantified, by analyzing the evolution of their monthly self-consumption and self-sufficiency during the pilot. No significant changes in the members behaviour, in relation with the information provided by the models, were observed in the recorded data. The pilot was however perturbed by the COVID-19 crisis which had a significant impact on the economic activity of the involved companies. We conclude by providing recommendations for the future set up of similar communities.

A complex method developed to assess quality within a proposed framework and at a certain scope of measurement for circular economy (CE) quality indicators is presented. This was used to compare three different scenarios for municipal waste management systems: 1—incineration; 2—recycling and reuse of separated municipal waste and the transformation of the organic fraction into biodiesel and bio-coal; and 3—an upgraded Scenario 2 including decreased recycling of waste streams and the bioprocessing of paper/cardboard and processing the non-recycled fraction into bio-diesel, bio-coal, and second-generation biofuel. For the evaluation of the CE quality indicator, a set of technical, environmental, economic, and social elements was selected by a panel of experts, who also assigned them a qualitative assessment and weighting on the basis of the factors identified. The calculated Relative Increase in the CE indicator for the scenarios analyzed showed that Scenarios 3 and 2 are much more beneficial than Scenario 1 in technical, environmental, economic and social terms.

Energy imports and the transition to renewable energy sources are of critical importance in the current geopolitical context, which necessitates concrete actions to tackle the energy crisis at the European Union level. This study aimed to explore the impact of imported non-renewable energy resources on the EU-27 economy. It examined the correlations and causal relationships between the GDP, the GVA, R&D investments, and energy imports from 2000 to 2021. Data normality was assessed using the Shapiro–Wilk test, while Pearson’s test identified correlations between variables. Linear and multiple regression analyses were conducted to determine the effects of changes in independent variables on dependent variables. The study found a strong association between natural gas imports and the GDP, with increases in GDP leading to a more-than-fourfold rise in imports. Furthermore, a multiple regression analysis indicated that a 1% increase in R&D investments results in a 2.21% decrease in fossil fuel imports in 91.7% of cases. This suggests that R&D investments contribute to improved efficiency and the use of renewable energy sources.

This study examines the long-term relationship between carbon emissions and a number of researchers engaged in Research and Development (R&D), economic development, foreign capital inflows, renewable energy and population growth in 26 countries between 1995 and 2015. Pedroni’s panel cointegration test confirms the cointegrating relationship between the variables. Long-term elasticities are derived from FMOLS regression. Researchers in R&D and renewable energy are negatively and significantly related to carbon emissions. There is a positive and significant long-term relationship between GDPs per capita and CO2 and between the FDI and CO2. Dumitrescu and Hurlin’s panel causality test revealed unidirectional causality running from economic development to carbon emissions and feedback hypotheses between the FDI and CO2 and between renewable energy and CO2.

This study evaluates the environmental profile of a real biomass-based hydrogen production small-scale (1 MWth) system composed of catalytic candle indirectly heated steam gasifier coupled with zinc oxide (ZnO) guard bed, water gas shift (WGS) and pressure swing absorber (PSA) reactors. Environmental performance from cradle-to-gate was investigated by life cycle assessment (LCA) methodology. Biomass production shows high influence over all impact categories. In the syngas production process, the main impacts observed are global warming potential (GWP) and acidification potential (AP). Flue gas emission from gasifier burner has the largest proportion of total GWP. The residual off gas use in internal combustion engine (ICE) leads to important environmental savings for all categories. Hydrogen renewability score is computed as 90% due to over 100% decline in non-renewable energy demand. Sensitivity analysis shows that increase in hydrogen production efficiency does not necessarily result in decrease in environmental impacts. In addition, economic allocation of environmental charges increases all impact categories, especially AP and photochemical oxidation (POFP).