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The wind energy‐bat conflict is well documented for the onshore sector, with high numbers of casualties, specifically for migratory bat species. Offshore wind turbines might be a threat to bats as well, yet offshore bat migration is poorly documented. Accordingly, potential conflicts between bat conservation and offshore wind energy production are difficult to evaluate. Here, we used automated radio‐telemetry to track 50 km continuous offshore movements of two Nathusius' pipistrelles (Pipistrellus nathusii) within the Motus network. After crossing the marine waterbody, tagged bats traveled over several hundred kilometers along the coastline from Germany towards the Netherlands and Belgium. Our study highlights the possibility for migratory bats to collide with offshore and coastal wind turbines. Therefore, we plead for implementing pre‐ and post‐construction surveys and adequate mitigation schemes at offshore wind turbines in sensitive areas of the North and Baltic Sea if not already practised. [ABSTRACT FROM AUTHOR]

The wind energy‐bat conflict is well documented for the onshore sector, with high numbers of casualties, specifically for migratory bat species. Offshore wind turbines might be a threat to bats as well, yet offshore bat migration is poorly documented. Accordingly, potential conflicts between bat conservation and offshore wind energy production are difficult to evaluate. Here, we used automated radio‐telemetry to track 50 km continuous offshore movements of two Nathusius' pipistrelles (Pipistrellus nathusii) within the Motus network. After crossing the marine waterbody, tagged bats traveled over several hundred kilometers along the coastline from Germany towards the Netherlands and Belgium. Our study highlights the possibility for migratory bats to collide with offshore and coastal wind turbines. Therefore, we plead for implementing pre‐ and post‐construction surveys and adequate mitigation schemes at offshore wind turbines in sensitive areas of the North and Baltic Sea if not already practised. [ABSTRACT FROM AUTHOR]

Renewable offshore wind electricity is as one of the major renewable energy sources on our path towards carbon neutrality. As for all energy technologies, offshore wind farms (OWFs) will have both local and global negative and positive impacts. Understanding and quantifying these burdens and benefits requires a holistic sustainability assessment. This study tests and applies a novel (socio-) environmental impact assessment framework to quantify the monetized (socio-) environmental footprint and handprint of an offshore wind farm located in the Belgian Continental Shelf. This framework consists of a combination of two ways of integrating Life Cycle Assessment (LCA) and Ecosystem Services Assessment (ESA) to quantify both the site-specific and site-generic impacts on ecosystem services (ESs) over the lifetime of a human intervention. For the operation and maintenance stage of the OWF, impacts on three local ESs were quantified, i.e. offshore wind energy provisioning, nursery and habitat maintenance and aesthetic value, while for the other life cycle stages site-generic impacts on multiple ESs were calculated. A comprehensive list of data was inventoried to conduct both the LCA and ESA studies. The monetized impact results were then aggregated and monetized at the level of three areas of protection, i.e. human health and well-being, natural resources and ecosystem quality. The results show that the OWF has a net handprint of +€85,196, mainly due to electricity production, while the absolute footprint (−€4039) consists largely of impacts associated to the supply chain of materials to manufacture the offshore windfarm. Furthermore, this study compares the (socio-) environmental performance of an OWF with nuclear energy, which is used as benchmark because of its high importance for electricity supply in Belgium. This study is a first step towards a valuable contribution to understanding the multi-scale burdens and benefits of offshore wind energy, which can support decision- and policy-making. [Display omitted] • Merging two ways to integrate LCA and ESA. • Application of integrated LCA and ESA frameworks to an offshore wind farm in Belgium. • Site-specific and site-generic impacts on ecosystem services are considered. • Quantification of the (socio-) environmental handprint and footprint. • Results show the offshore wind farm has a large handprint compared to its footprint. [ABSTRACT FROM AUTHOR]

Improving the accuracy of wind power prediction is important to maintain power system stability. However, wind power prediction is a difficult task due to non-stationary and high volatility characteristics. This study applies a hybrid algorithm that combines ensemble empirical mode decomposition (EEMD) and support vector regression (SVR) to develop a prediction model for wind power prediction. Ensemble empirical mode decomposition (EEMD) is employed to decompose original data into several Intrinsic Mode Functions (IMFs). Finally, a prediction model using support vector regression is built for each IMF individually, and the prediction result of all IMFs is combined to obtain an aggregated output of wind power. Numerical testing demonstrated that the proposed method could accurately predict the wind power in Belgium. [ABSTRACT FROM AUTHOR]

In the face of accelerating climate change, the transition towards a nonnuclear renewable energy system represents a key political challenge, which can be aggravated by the increasing energy supply uncertainty created by the shift away from fossil fuels. In this article, we conduct a comparison of the expansion of renewable energy sources in Austria, Belgium, and Germany at the level of their subnational units (federal states), thereby covering three economically very important central European federal European Union members. We consider potentially influential factors in a fuzzy-set qualitative comparative analysis: In addition to state-specific socioeconomic and geographical characteristics, political factors, such as parties in government, and specific energy-related policy instruments are included in the analysis. We find that a high potential for renewable electricity expansion in combination with low financial prosperity is most likely to lead to a successful expansion of renewable electricity production from wind and photovoltaics. [ABSTRACT FROM AUTHOR]

• Simulating wind power systems at 10 min resolution results in remarkable errors. • The total self-sufficiency is overestimated with an error range of 0.06–3.6%. • The battery utilization is underestimated with an error range of 7.5–44.7%. • The higher the battery capacity, the lower the total self-sufficiency error. • The C rate limit is the most decisive factor impacting the battery utilization error. In simulating renewable energy systems (RES), researchers tend to focus on the modeling methodology, but less on the impact of data on the obtained results and associated conclusions. Yet, results may be particularly dependent on the data resolution. RES studies on that impact of data resolution mostly consider households with PV systems, leaving industrial sites and wind power systems out of scope. In this paper, we consider a system comprising a medium-sized wind turbine, a high power battery and an industrial load, connected to a distribution grid. Wind and load power were measured concurrently in real time at 1 s resolution for 2 months in summer 2017, at two locations in Belgium. We performed two simulations, one using the real time data at 1 s resolution and the other using the same power data averaged over 10 min intervals. We compared both simulations to calculate three metrics: total self-sufficiency error, battery utilization error and instantaneous self-sufficiency error. We repeated the analysis using different settings in terms of battery capacity, battery C rate limit and load ratio. We conclude that: (i) total self-sufficiency, in all cases, is overestimated, ranging between 0.06 and 3.6%, with errors decreasing for higher battery capacity, (ii) battery utilization is always underestimated with errors ranging between 7.5 and 44.7%, primarily depending on the C rate limit, and (iii) there is a clear correlation between instantaneous self-sufficiency error and instantaneous ratio of the averaged load and wind power, with errors exceeding 100% when the ratio ranges in 0.5–2. [ABSTRACT FROM AUTHOR]

Abstract: Wind power can have considerable impacts on the operation of electricity generation systems. Energy from wind power replaces other forms of electricity generation, thereby lowering overall fuel costs and greenhouse gas (GHG) emissions. However, the intermittency of wind power, reflected in its variability and relative unpredictability restrains the full potential benefits of wind power. The variable nature of wind power requires power plants to be ready for bridging moments of low wind power output. The occurrence of forecast errors for wind speed necessitates sufficient reserve capacity in the system, which cannot be used for other useful purposes. These forecast errors inevitably cause efficiency losses in the operation of the system. To analyse the extent of these impacts, the Belgian electricity generation system is taken as a case and investigated on different aspects such as technical limitations for wind power integration and cost and GHG emissions’ reduction potential of wind power under different circumstances. [Copyright &y& Elsevier]

The Silvertop Towers project is a comprehensive redevelopment project of three high-rise residential buildings in the city of Antwerp (Flanders, Belgium). As part of this project, pedestrian passages will be constructed through each of the towers to improve the accessibility and to increase social control on the site. Experience, wind tunnel modeling and numerical modeling have indicated that wind conditions in passages are often unfavorable. This paper presents the assessment of wind climate and especially the study that has been performed to modify the wind climate in the passages of the Silvertop Towers. The assessment of wind climate is conducted with the aid of numerical modeling with Computational Fluid Dynamics. The wind climate is assessed to be highly unacceptable. Remedial action is imperative. Various traditional remedial measures to improve the wind climate are contemplated. These solutions either provide an insufficient improvement of wind climate or conflict with the envisaged architectural design. Therefore a rather unconventional solution has been selected. We have designed and analyzed an automatic control system to modify the wind climate in the passages. The actuators of the system are sliding doors that are mounted at both ends of the passage. The opening and closing of the doors is controlled by a decision algorithm based on local wind measurements. At least one of the doors will be closed when the control system senses that the threshold wind speed in the passage is exceeded. The control system will ensure an adequate wind climate without conflicting with the original architectural design. The passages are estimated to be opened for about 50–70% of the time. Estimates for the fraction of time that door control is imperfect (doors are closed instead of open and vice versa) are also given. [Copyright &y& Elsevier]

Renewable Energy Sources (RES) have drawn significant attention in the past years to make the transition towards low carbon emissions. On the one hand, the intermittent nature of RES, resulting in variable power generation, hinders their high-level penetration in the power system. On the other hand, RES can aid not only to supply much more eco-friendly energy but also it allows the power system to enhance its stability by ancillary service provision. This article reviews the challenges related to the most intermittent RES utilised in Belgium, that is, wind energy and solar energy. Additionally, wind speed and solar irradiance variations, which are the cause of wind and solar intermittency, are studied. Then, recent techniques to forecast their changes, and approaches to accommodate or mitigate their impacts on the power system, are discussed. Finally, the latest statistics and future situation of RES in the Belgian power system are evaluated. [ABSTRACT FROM AUTHOR]

• Proposing optimization model for gas and power systems. • Incorporating resiliency analysis algorithm into the integrated operation of gas and power systems. • Proposing a novel possibilistic approach to deal with uncertainty. • Investigating the role of demand response and electrical vehicles. Gas-fired power plants are commonly employed to deal with the intermittency of renewable energy resources due to their flexible characteristics. Therefore, the intermittency in the power system transmits to the gas system through the gas-fired power plants, which makes the operation of these systems even more interdependent. This study proposes a novel possibilistic model for the integrated operation of gas and power systems in the presence of electric vehicles and demand response. The model takes into account uncertainty in demand prediction and output power of wind farms, which is based on possibility and necessity theories in fuzzy logic through modeling uncertain parameters by Gaussian membership function. Moreover, a contingency analysis algorithm based on maximin optimization is developed to enhance the resiliency in the integrated operation of these systems by finding the worst-case scenario for the outage of components. The proposed model is implemented on a Belgium gas network and IEEE 24-bus electricity network. It is demonstrated that the possibilistic model allows the gas network to respond to the demand variations by providing a sufficient level of linepack within the pipelines. As a result, gas-fired power plants are supposed to commit more efficiently to cope with the intermittency of wind farms, which reduce the wind curtailment by 26%. Furthermore, it is quantified that through applying the contingency analysis algorithm in presence of demand response and electrical vehicles, the costs of operation and load shedding is reduced up to 17% and 83%, respectively. [ABSTRACT FROM AUTHOR]

• Wind power prediction problem is formulated as a min-max game based on GAN. • Semi-supervised regression is used for the point prediction of wind power. • A double objective model is designed for data augmentation and feature extraction. • A self-tuning strategy is proposed for forecasting of wind power ramp events. Short-term predictions of wind power and its ramp events play a critical role in economic operation and risk management of smart grid. This paper proposes a hybrid forecasting model based on semi-supervised generative adversarial network (GAN) to solve the short-term wind power outputs and ramp event forecasting problems. In the proposed model, the original time series of wind energy data can be decomposed into several sub-series characterized by intrinsic mode functions (IMFs) with different frequencies, and the semi-supervised regression with label learning is employed for data augmentation to extract non-linear and dynamic behaviors from each IMF. Then, the GAN generative model is used to obtain unlabeled virtual samples for capturing data distribution characteristics of wind power outputs, while the discriminative model is redesigned with a semi-supervised regression layer to perform the point prediction of wind power. These two GAN models form a min-max game so as to improve the sample generation quality and reduce forecasting errors. Moreover, a self-tuning forecasting strategy with multi-label classifier is proposed to facilitate the forecasting of wind power ramp events. Finally, the real data of a wind farm from Belgium is collected in the case study to demonstrate the superior performance of the proposed approach compared with other forecasting algorithms. [ABSTRACT FROM AUTHOR]

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 2.25 M W 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. [ABSTRACT FROM AUTHOR]

The development of the wind power market has led various countries to begin shifting the construction of wind farms from land to offshore. Accurately predicting the short-term wind power of an offshore wind farm is significant for preventive control and scheduling. This paper proposes a novel two-stage hybrid model to predict short-term wind power. First, complete ensemble empirical mode decomposition with adaptive noise is used to preprocess the raw data and make it smoother. Second, an improved grey-box model is utilized to predict the wind power, where a multi-objective grey wolf optimizer is used to optimize the kernel-based nonlinear extension of the Arps decline model to ensure both prediction stability and accuracy. The historical wind power data for offshore wind farms in Belgium are taken as a case study, and the results indicate that the proposed model has higher prediction accuracy and stability than those of six benchmark models. Three critical issues related to this work are also considered. The following conclusions are reached: (1) Utilizing a denoising method and multi-objective optimizer effectively improves the prediction accuracy and stability of the original model. Specifically, the multi-objective optimizer improves the prediction performance; (2) The proposed model performs best at predicting wind power in autumn; (3) Because of the great variation in dimensions for the wind power time series, the mean absolute percentage error and root mean squared percentage error are not suitable as error evaluation indicators. • Two-stage hybrid model for predicting wind power of offshore wind farms. • Multi-objective algorithm optimizes the traditional kernel-based model. • Decomposition algorithm preprocesses raw data. • Rationality of the error indicators for the prediction results discussed. [ABSTRACT FROM AUTHOR]

Accurate and reliable quantification of uncertainty in wind power forecasting is critical to the economic operation and real time control of the electric power and energy system. To this end, this paper proposes a novel direct method for probabilistic wind power forecasting based on spiking neural network. In this method, a new forecasting framework is firstly formulated to simultaneously calculate the coverage probability and sharpness with associated confidence levels. Then, group search optimizer is introduced and re-designed to optimize the parameters of the forecasting framework and directly generate the prediction intervals, so that the prediction reliability and stability are ensured. The main advantage of the proposed probabilistic forecasting method is that it does not involve any distribution assumption of the prediction errors required by most existing forecasting methods. The wind power datasets from real wind farms in Belgium and China are used in the case studies. Traditional back-propagation neural network (BPNN), support vector machine (SVM) and extreme learning machine (ELM) are selected as the benchmarking algorithms. The simulation results show that the average coverage of the proposed method is improved by 72.0%, 54.9% and 51.3% respectively, when compared to BPNN, SVM and ELM. The improvement rates of sharpness index are 43.1%, 28.1% and 21.0%, respectively. These statistical results show that the proposed method outperforms BPNN, SVM and ELM in terms of forecasting accuracy, demonstrating that this method has high practical applications in real power systems. • Spiking neural network (SNN) is used for wind power forecasting. • SNN based direct probabilistic wind power forecasting framework is proposed. • Group search optimizer is used to optimize the framework parameters. • The feasibility of the proposed framework is verified via real wind farms. [ABSTRACT FROM AUTHOR]

• Use secondary decomposition-model selection method. • Use non-agnostic uncertainty sampling-active learning-sample selection strategy. • An advanced multi-objective optimization is used. • Offer both deterministic forecasting and probabilistic forecasting information. • Multi-step forecasting is considered. Wind power forecasting plays a significant role to ensure the safe operation of power systems. However, due to the stochastic nature and dynamic uncertainty of wind power, accurate and stable forecasting faces challenges. In this study, a modified combined structure with secondary decomposition-model selection and non-agnostic uncertainty sampling-active learning-sample selection strategy is proposed for multi-step deterministic and probabilistic wind power forecasting. Secondary decomposition-model selection strategy is used before experiments, selecting better forecasting models from the initial model space to the selected model space, and secondary decomposition methods are applied for decreasing the interference in initial data. Non-agnostic uncertainty sampling-active learning-sample selection strategy is employed to accelerate the sample selection process and enhance the testing efficiency, indirectly promoting the final performance. To further promote the forecasting precision and stability simultaneously, an advanced multi-objective optimization is applied, automatically selecting the top best models into the combined models from the selected model space. Except deterministic forecasting, uncertainty estimation is vital, offering different aspects of forecasting information for risk management, as the grid system absorbs large-scale wind power generation, therefore, probabilistic forecasting is considered too. Multi-step forecasting is also considered. Eight datasets from Elia, Belgium, are applied to assess the forecasting performance of secondary decomposition-model selection and the proposed combined models. According to results, compared with comparison models, the proposed combined models obtain higher accuracy and stability of multi-step deterministic forecasting (improving the forecasting skill by more than 90% in accuracy and stability for 1-step to 3-step forecasting) and also offer multi-step probabilistic information. [ABSTRACT FROM AUTHOR]

• Echo state network is used for point forecasting of wind power. • Wavelet transform is used to decompose the original signal into several sub-signals. • Ensemble technique is applied to reduce the uncertainty in wind power data. • The effectiveness of the proposed method is validated on two real wind farms. Accurate and reliable wind power forecasting is of great significance for the economic and safe operation of electric power and energy systems. However, due to the negative factors such as inaccurate numerical weather prediction and frequent ramping events, previous wind power prediction methods are difficult to meet actual needs for practical applications. To this end, this paper originally proposes a new wind power prediction method with high accuracy based on echo state network. This method is a hybrid of wavelet transform, echo state network and ensemble technique. Wavelet transform is used to decompose raw wind power time series data into different frequencies with better outliers and behaviors. Then, we adopt the echo state network to automatically learn the nonlinear mapping relationship between input and output in each frequency. Later, ensemble technique is applied to deal with the model misspecification and data noise problems, which are common in wind power prediction, thereby reducing the uncertainty of wind power forecasting and improving prediction accuracy. Finally, we use wind power data from real wind farms in Belgium and China to verify the feasibility and effectiveness of the proposed method. The simulation results show that the proposed method is superior to other benchmark algorithms in prediction accuracy, which indicates that the method has high potentials for practical application in real electric power and energy systems. [ABSTRACT FROM AUTHOR]

Wind power could supply 12% of global electricity demand by 2020, according to a report by the European Wind Energy Association and Greenpeace called “Wind Force 12” launched recently in Brussels. This strategic blueprint outlines how wind capacity can grow from current levels of 0.4%, mostly placed in Europe and the US with India, Brazil and China also having strong growth potential.This is a short news story only. Visit www.re-focus.net for the latest renewable energy news. [Copyright &y& Elsevier]