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Abstract The aerodynamic design of small wind turbines for the urban setting attracts increasing interest within the scientific community, but the adoption of a proper control strategy may be just as important, especially in high turbulent winds, where such energy conversion devices should ideally operate. As a matter of fact, the mere rotor efficiency is meaningless unless the system has also the capability of rapidly changing its angular speed in case of a sudden variation of the wind velocity, to reach a new optimal operating condition. This work will attempt neither to develop dynamic simulation models nor to examine possible turbine control strategies, being the focus much broader, namely, the investigation of operational contexts where the peculiar inertial characteristics of wind turbines would compromise any form of robust control. Inertial and operational data of commercially available turbines (characterized by both horizontal and vertical-axis architectures), as well as the results disseminated in various literature sources, operational experiences and design best practices, are here collected under one cover and compared, thus deriving some basic and fundamental relations between rotor inertia and angular acceleration, highlighting how, in several cases, a control strategy based on the continuous tracking of the optimal operating condition is most unlikely. Such considerations raise the question of whether the problem of inertia renders futile many prevailing theories about small wind turbine operation and plans for implementing new control strategies, especially as far as vertical axis architectures are concerned. On the other hand, some constructive advice is also presented, in the form of a means to compare the effective performances of different turbines, in a given installation site, with respect to their nominal (i.e. steady state, or wind tunnel) behaviour. As a final result, a new bound for a reliable estimation of the amount of energy a wind turbine will generate in a specific site is suggested, based on the comparison between a representative time scale of the installation site and the response time of the candidate wind turbine.

Abstract Mediterranean islands have the advantage of favourable climatic conditions to use different marine renewable energy sources. Remote sensing can provide data to determine wind energy production potential and observational activity to identify, assess and detect suitable points in large marine areas. In this paper, a new combined model has been developed to integrate wind speed assessment, mapping and forecasting using Sentinel 1 satellite data through images processing and Adaptive Neuro-Fuzzy Inference System and the Bat algorithm. Synthetic Aperture Radar (SAR) satellite images from the Sentinel 1 satellite have been used in order to detect offshore and nearshore wind potential. Particularly, Sentinel 1 images have been analysed by means of the SNAP software. Then, to extract data about wind speed and direction, a GIS software for mapping the wind climate has been used. This new methodology has been applied to the North-Central coasts of Sardinia Island and then focused on six main small islands of La Maddalena archipelago. Furthermore, ten Hot Spots (HSs) have been identified as interesting because of their high-energy potential and the possibility to be considered as sites for future implementation of Wind Turbine Generators (WTGs). Finally, the ten identified HS have been used as input data to train and test the proposed forecast model.

Abstract This study presents an evaluation of the error induced by neglecting the effect of air compressibility in modelling Oscillating Water Column (OWC) wave energy converters. A compressible two phases CFD model in the open-source software package OpenFOAM® is validated and then used to simulate a fixed OWC device, detached from the sea bottom. A comparative analysis of the results obtained by simulating the device at full-scale (1:1) and at four smaller scales (1:50, 1:25, 1:10 and 1:5) is performed in order to assess the scale effects associated with air compressibility. Indeed, for the air pressure levels considered in the simulations (up to 350 Pa at model scale 1:50), the effect of neglecting the air compressibility results in an overestimation up to about 15% for the air pressure in the OWC chamber and the subsequent air volume flux, but less than 10% for the capture width ratio. This overestimation increases with increasing pressure level. Results are analysed in terms of dimensionless parameters and a new parameter, strongly related to the compressible effects, is proposed and used to provide generalized equations delivering correction factors.

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.