• Energy Research
• 2021-2025close
• Restricted close
• Open Source close
• Embargo close
• Renewable Energy close

In this paper, we provide an in-depth analysis of factors determining energy import demand of EU countries. We suggest a novel approach to study the role of renewable versus non renewable energy sources in shaping the demand for energy imports. The aim of the paper is twofold. First, we provide a comprehensive analysis of the EU energy market structure using country-by-country I–O tables to show the rate of interdependency between EU member states in terms of renewable and non renewable energy flows. Second, we investigate on the role of renewable energy sources in reducing energy dependency in EU countries. The econometric analysis uses data from 26 EU countries observed between 2007 and 2016 available from the Eurostat energy statistics database. The descriptive analysis of I–O electricity tables shows some degree of heterogeneity between countries in terms of energy in- and out-flows. Such heterogeneous market structure suggests the use of panel models in the econometric analysis. Moreover, a lasso regression method has been employed for variables selection to avoid the collinearity. The results show that benefits may arise from replacing energy imports with domestic energy production and from reducing energy dependency rate. Moreover, if import substitution occurs with domestic renewable energy sources additional positive effects are produced in terms of either energy dependency, energy security and sustainable development.

Editorial paper of the Renewable Energy's special issue "Integrated biorefinery for the planet's future"

Recent European Community directives introduce Renewable Energy Communities (REC) and Jointly Acting Renewable Self-Consumers (JARSC). Both entities are constituted by communities of residential and/or non-residential prosumers, located in proximity of renewable generators and Electrical Storage Systems (ESS) owned and managed by the REC/JARSCs. These aggregations of prosumers are aimed at providing environ-mental and economic benefits by maximizing their global self-consumption. In this frame, it is relevant to introduce a control strategy which considers the whole system represented by the REC/JARSCs and performs optimal management of energy production, storage and consumption. The present paper proposes a Model Predictive Control (MPC) based control design, targeted at the minimization of electricity cost and equivalent CO2 emissions, considering the whole ensemble of loads included in the REC/JARSCs over a 24-h prediction horizon. To exploit the MPC ability of including forecasts in the optimization problem, predictors including Artificial Neural Networks (ANN) are developed for solar irradiance, air temperature, electricity price and carbon intensity. The proposed control performance is evaluated considering a case study located in Milan, Italy, and its advantages with respect to traditional control algorithms are highlighted by comprehensive numerical simula-tions. Lastly, an economic evaluation of the considered system is presented.

Rotor imbalances present a serious problem for wind turbines. In particular, for offshore wind turbines, aerodynamic imbalance can have a severe impact because of the large rotor size. In this study, the impact of the aerodynamic imbalance is investigated. A novel framework for detecting aerodynamic imbalance is proposed. Firstly, a model of a 3MW direct-driven wind turbine was developed. The signals were acquired to test and verify the impact of aerodynamic imbalance. Secondly, a method based on optimized maximum correlated kurtosis deconvolution was proposed for the primary detection. The intrinsic mode functions of nacelle vibration were adopted as the input variable. The weak unbalanced signals could be discerned. Moreover, the azimuth of rotor allows the unbalanced blades to be obtained. Thirdly, a convolutional neural network with a new structure was used to determine the magnitudes of aerodynamic imbalances. The first layer of the convolutional neural network is sufficiently wide for improving feature extraction, it could make nacelle acceleration as the input. This structure exhibits accuracy and robustness satisfactorily. Finally, the framework was demonstrated in a high-fidelity simulation environment. Different scenarios of aerodynamic imbalance were tested, the results demonstrate the satisfactory performance of the proposed framework.

Abstract Volumetric solar receivers are essential components in high-temperature concentrated solar power plants. Their optical design is crucial for achieving efficient photon-thermal energy conversion; however, their three-dimensional geometry complicates a reliable and accurate optical characterization. This work proposes a new methodology for the 3D optical-performance analysis of the volumetric absorber having an open hierarchical structure. Firstly, the solar absorber is optically characterized using a test bench that mainly comprises a solar simulator and a customized instrument equipped with single-photon avalanche diodes, which holds the absorber and measures light flux on its external surface. Then experimental measurements are compared with a Monte Carlo ray-tracing numerical model. The results are consequently employed to understand light propagation in the absorber. This procedure is successfully applied to characterize a complex three-dimensional self-similar structure manufactured by Selective Laser Melting. The proposed experimental technique is a promising candidate for becoming a robust in operando method to characterize the radiation propagation within the complex porous structures employed for volumetric receivers.

The accurate training of a wind power forecasting (WPF) model for a newly built wind farm is difficult because of limited historical data. This study established a multitask learning architecture wherein the WPF in different wind farms represents an independent task. Subsequently, a novel short-term WPF model based on a multitask learning architecture was proposed. In this model, a multitask Gaussian process is used to capture the intertask conjunction, which contributed to the training of each task. The proposed methodological framework employs dependencies from other tasks wherein older wind farms contain substantial historical data to enhance the performance of tasks in which there is a newly built wind farm. Several numerical experiments were conducted using datasets from seven independent wind farms in Australia. The results show that the proposed scheme not only obtains improved point forecasting results but also produces better probabilistic forecasting results, thus demonstrating the superiority of the proposed method.

Abstract Offshore Wind (OW) speed assessment is a key aspect for the development of new wind farms at sea. Satellites can be used to globally obtain ocean and sea distribution, compensating limited in-situ measurements. In this study, a new methodology to estimate the wind’s speed potential is here proposed. Preliminary, Sentinel-1 (S-1) images have been analyzed by means of the Sentinel Application Platform (SNAP) software, extrapolating wind speed data for each cell pixel size of a testing area. Then GIS (Geographic Information System) software has been used to map wind data and find the best pixel location comparing these data with in-situ data. Furthermore, wind speed has been analyzed using the ERA-Interim reanalysis dataset for areas within 11 km and 40 km from the Lillgrund OW farm in the Baltic Sea to better understand wind regimes. Finally, wind speed parameters obtained by S-1 in Sea Surface Water (SSW) with the 10 m standard high have been compared with wind speed recorded by Supervisory Control and Data Acquisition (SCADA) systems of two turbine using wind profile formula. Obtained results show the comparison accuracy of wind speed assessment for each center of the pixels by S-1 satellite images and in-situ (SCADA) measurements. Data actually depends on the distance between the selected center pixel and the location of the turbines. The obtained wind speed differences (0.26 m/s - RMSE = 1.38 and 0.92 m/s - RMSE = 1.82) pinpointed the direct effect of the distance between the selected pixel center and the in-situ measurements location in the S-1 imagery for wind speed potential assessment. Obtained results proved an improvement of the OW assessment accuracy using multiple satellite observations, demonstrating that SAR wind maps can support OW speed sites assessment by introducing observations in different phases of an OW farm project.

Super-hot rock geothermal is an emerging source of renewable and carbon-free energy. This paper is the first attempt to explore fluid and heat flow dynamics in the reservoir-wellbore coupled system, to assess the power generation performance of a super-hot (>450 °C) enhanced geothermal system (EGS). We developed a high-performance code and built a 3-D wellbore-reservoir coupled model based on data from a recently completed deep-drilling project at Larderello, Italy. The general pattern of the super-hot EGS is characterized by a significant temperature plummet (>60 °C), after which the production fluid evolves from steam to a two-phase mixture till the end of the operation period. Reservoir pressure emerges as a key parameter to determine the temperature of the two-phase mixture. By realistically capturing phase transitions driven by coupled thermo-hydraulic processes during operations, our numerical model predicts a lower power generation efficiency compared to previous attempts based on ultra-simplified models. Although finalized at assessing the thermodynamic viability of a specific system, this modeling approach provides general information on fundamental thermo-hydraulic processes in the Earth crust that might be applied for the design of similar EGS projects elsewhere.