Wind Farms In Europe Research Articles

Understanding stakeholder attitudes towards low-head pumped hydro storage technology

BackgroundThe share of renewable energy feeding the European grid has been growing over the years, even though the intermittency of some renewable energy sources can induce electric grid instability. Energy storage has proven to be an effective way of reducing grid instability. Various solutions for large-scale energy storage are being researched nowadays. This study focusses on the innovative low-head pumped hydro storage (LH PHS) technology, a large-scale energy storage scheme suitable for shallow seas (5 – 30 m depth). Implementation of renewable energy technologies, such as wind farms in Europe, Asia and North America, has faced public opposition which has delayed or even cancelled the implementation of renewable energy projects. Literature about public perception of projects highlights the importance of involving stakeholders from the early stages of project planning. Considering this, the present study aims to collect stakeholder opinions (via an online survey) to determine what is necessary for a smooth implementation of LH PHS in the North Sea, both from technical and policy points of view.ResultsStakeholders from commercial parties, government authorities and local groups recognized the potential of LH PHS as a means to increase the share of renewable energies within the European power grid. Economics, bureaucratic burden, and structural safety have emerged as primary aspects of concern respecting the implementation of LH PHS. The impression of the respondents is that a low-head pumped hydro station would not have negative effects on their organizations. Furthermore, most of the engineering firms participating in the study communicated that their knowledge and resources could be involved in the construction of such an energy storage facility.ConclusionAs identified stakeholder concerns such as economics and structural safety are currently being researched, effective communication of the findings of this research is paramount to keep stakeholders informed of the ongoing progress. Two-way communication between researchers and stakeholders is recommended to enhance public acceptance of future technologies. Furthermore, is it advisable to undertake an examination of the available energy policies relevant to LH PHS.

Modelling the wind potential energy for metallurgical sector in Albania

The metallurgical industry, in the context of the global energy crisis and the new European green deal, needs urgent investments on energy and resource efficiency. The metallurgical sector, which includes the production of different metals is an energy-intensive industry that requires large amounts of energy for various processes such as smelting, refining, and casting. One of the largest consumptions of energy in Albania comes from the metallurgical sector during the production of iron, steel, chromium and aluminum which corresponds respectively to three private companies called “Kurum International Ltd”, “AlbChorme Ltd” and “Everest Ltd”. During the last three years, these companies have temporary interrupted the production process due to the higher electricity price that come from imports. Based on it, our research work presents the energy efficiency analysis in the Albanian metallurgical sector by focusing on the implementation of wind energy in the above mentioned private metallurgical companies, because adding new generation capacity from Renewable Energy Sources in a context of industrial energy communities, will contribute to improve the security of supply for this industry. The Wind Balkan Atlas, New European Wind Atlas, and Wind Atlas Analysis and Application Program (WAsP) has been used to select the appropriate areas and to develop the wind potential distribution maps, as well as to select the most suitable type of wind turbine based on capacity factors. Two areas were selected close to the metallurgical sectors in the regions of “Vajkal” in Bulqizë and “Selitë e Malit” in Tirana. It has been installed the power of 9 MW for each wind farm, with a capacity factor of 40% and 36.6% respectively, and with a total annual energy production of about 60 GWh/year, these wind farms will cover about 26% of the total annual consumption of companies. Clean Energy Management Software (RETScreen Expert) was used for the detailed economic analysis and environmental impact of proposed wind farms. The economic sensitivity analysis of the proposed wind farms showed that even for the highest installation cost value of 1350 €/kW, for discount rates 5, 7, and 11%, the LCoE values are within the statistically established range for wind farms in Europe.

Market Needs, Opportunities and Barriers for the Floating Wind Industry

This paper reviews the status of floating wind energy expansion, market needs, opportunities, and barriers. Even more expensive than many other generation technologies currently, the floating wind will contribute to the decarbonization of Europe. This document assesses the market strategies available to develop floating wind farms in Europe. The study includes four main phases in addition to the overview of the current state-of-the-art: a technology review, market outlook, opportunities, and commercialization barriers. During its development, the offshore wind has moved from experimentation to a final design (Semisubmersible/barge, Tension Leg Platform, and Spar).

LANDSCAPE IN PAIN: SHETLAND’S INDUSTRIAL SCALE WIND FARM AND SOLASTALGIA

Landscape in Pain is a body of digital art works made in response to the distress, solastalgia (Albrecht), produced by the environmental change to the environment caused by the development of the Viking Energy Wind Farm in the Shetland Islands, the northernmost archipelago in Scotland. The industrial scale wind farm will cover most of Shetland’s north central mainland and be visible from the majority of the islands. With 103 wind turbines, it will become one of the largest onshore wind farms in Europe. While Shetland is anxious to tackle the climate crisis, nonetheless many believe this project is not a green solution. Local objections to the wind farm include its disproportionate scale, destruction of peatlands and negative human impact. How do we achieve balance between sustainable development and care for the communities rich in resources, such as wind and tidal power, being mined by multi-national corporations across the northern region?

Analyzing Europe’s Biggest Offshore Wind Farms: A Data Set with 40 Years of Hourly Wind Speeds and Electricity Production

We provide an open, available, and ready-to-use data set covering 40 years of hourly wind speeds and synthetic hourly production signals for the 29 biggest offshore wind farms in Europe. It enables researchers and industry experts to include realistic offshore time series into their analyses. In particular, we provide data from 1980 to 2019 for wind farms already in operation and those that will be in operation by 2024. We document in detail how the data set was generated from publicly available sources and provide manually collected details on the wind farms, such as the turbine power curves. Correspondingly, the users can easily keep the data set up to date and add further wind farm locations as needed. We give a descriptive analysis of the data and its correlation structure and find a relatively high volatility and intermittency for single locations, with balancing effects across wind farms.

Accurate long-term power generation model for offshore wind farms in Europe using ERA5 reanalysis

Accurate long-term wind speed data is important for understanding the role of offshore wind farms in future energy systems. Meteorological reanalyses, such as ERA5, are relied upon by the wind energy industry and researchers. Being unaffected by onshore topography and surface roughness, the historic generation of offshore wind farms can be accurately predicted using such weather reanalysis.In this work we present a new method for using ERA5 weather data to model long term (>40 year) hourly wind generation for individual offshore wind farms. The model is validated against 57 offshore wind farms in Europe, and reduces the root mean squared error in hourly and daily capacity factor predictions by 10% and 18% respectively when compared to the Renewables Ninja. Further, 40 years (from 1980 to 2019) of ERA5 hourly wind speeds within 200 km of the coast is made easily available for energy system research on our accompanying website (windtlas.xyz).

Analysis of rotorcraft wind turbine wake encounters using piloted simulation

The use of offshore wind farms in Europe to provide a sustainable alternative energy source is now considered normal. Particularly in the North Sea, a large number of wind farms exist with a significant distance from the coast. This is becoming standard practice as larger areas are required to support operations. Efficient transport and monitoring of these wind farms can only be conducted using helicopters. As wind turbines continue to grow in size, there is a need to continuously update operational requirements for these helicopters, to ensure safe operations. This study assesses German regulations for flight corridors within offshore wind farms. A semi-empirical wind turbine wake model is used to generate velocity data for the research flight simulator AVES. The reference offshore wind turbine NREL 5 MW has been used and scaled to represent wind turbine of different sizes. This paper reports result from a simulation study concerning vortex wake encounter during offshore operations. The results have been obtained through piloted simulation for a transport case through a wind farm. Both subjective and objective measures are used to assess the severity of vortex wake encounters.

A novel genetic LSTM model for wind power forecast

Variations of produced power in windmills may influence the appropriate integration in power-driven grids which may disrupt the balance between electricity demand and its production. Consequently, accurate prediction is extremely preferred for planning reliable and effective execution of power systems and to guarantee the continuous supply. For this purpose, a novel genetic long short term memory (GLSTM) framework comprising of long short term memory and genetic algorithm (GA) is proposed to predict short-term wind power. In the proposed GLSTM model, the strength of LSTM is employed due to its capability of automatically learning features from sequential data, while the global optimization strategy of GA is exploited to optimize window size and number of neurons in LSTM layers. Prediction from GLSTM has been compared with actual power, predictions of support vector regressor, and with reported techniques in terms of standard performance indices. It can be evaluated from the comparison that GLSTM and its variants provide accurate, reliable, and robust predictions of wind power of seven wind farms in Europe. In terms of percentage improvement, GLSTM, on average, improves wind power predictions from 6% to 30% as opposed to existing techniques. Wilcoxon signed-rank test demonstrates that GLSTM is significantly different from standard LSTM.

Data investigation of installed and output power densities of onshore and offshore wind turbines worldwide

Differing estimates have emerged about how much land or water area is used by existing wind farms and how much power can be obtained from that area. Whereas, no single unique method exists to define wind farm spacing area, the spacing area (thus installed and output power densities) of a wind farm can be determined in a way to meet specific logical criteria, This study proposes a new, intuitive, data based, automatized method of estimating spacing areas occupied by existing onshore and offshore wind farms worldwide. The method eliminates the erroneous counting of space outside of wind farm boundaries, space between clusters of turbines, and overlapping space that results when assuming a large fixed area around each turbine. At least one of three types of extra space has incorrectly been included in all calculations of wind farm areas to date. Unlike most previous methods, this method also ensures that the addition of a wind turbine to a farm increases the overall required spacing area of the farm. The study then uses data from over 1600 operating wind turbines in 16 onshore and 7 offshore wind farms in 13 countries across 5 continents during the period 2016–2018 to quantify installed and output power densities of these farms. Finally, it compares results with estimates from other studies. Results indicate that the mean (range) installed and output power densities of onshore wind farms in Europe are 19.8 (6.2–46.9) MW/km2 and 6.64 (2.3–8.2) W/m2, respectively; of onshore wind farms outside of Europe are similarly 20.5 (16.5–48) MW/km2 and 6.84 (4.81–11.2) W/m2, respectively; and of offshore wind farms in Europe are 7.2 (3.3–20.2) MW/km2 and 2.94 (1.15–6.32) W/m2, respectively. The mean capacity factors in each case are thus 33.5%, 33.4%, and 40.8%, respectively. These results indicate substantially higher installed and output power densities than previously reported, based simply on different definitions of land area, with no impact on capacity factor. Thus, existing wind turbines may extract more wind power over less land or water than previously thought.

Challenges of decommissioning offshore wind farms: Overview of the European experience

In the coming years, an important number of offshore wind turbines will reach the end of their initially planned service life. In a wind turbine end-of-life scenario, owners can decide between extending the life of the asset, repowering the site or decommissioning. This decision-making process is affected by important sources of uncertainty, especially in offshore environments. The limited experience makes the decommissioning procedure challenging, as it is still largely unexplored. This work assesses the current state of knowledge about the challenges surrounding the decommissioning process of an offshore wind farm. The four main challenges encountered are identified and analysed in detail, namely the regulatory framework, the overall planning of the process, the logistics and vessels’ availability, and the environmental impacts confronted. Ultimately, this paper aims at stimulating the dialogue among stakeholders and raising the awareness of adequately regulating and preparing the upcoming decommissioning of offshore wind farms in Europe.

A method to predict the cyclic loading profiles (one-way or two-way) for monopile supported offshore wind turbines

Monopiles are currently the preferred option for supporting offshore wind turbines (OWTs) in water depths up to about 40 m. Whilst there have been significant advancements in the understanding of the behaviour of monopiles, the guidelines on the prediction of long term tilt (Serviceability Limit State, SLS) under millions of cycles of loads are still limited. Observations and analysis of scaled model tests identify two main parameters that governs the progressive tilt of monopiles: (a) Loading type (one-way or two-way) which can be quantified by the ratio of the minimum to maximum mudline bending moments (Mmin/Mmax); (b) factor of safety against overturning i.e. the ratio of the maximum applied moment (Mmax) to the moment carrying capacity of the pile or Moment of Resistance (MR) and therefore the ratio Mmax/MR. Due to the nature of the environmental loads (wind and wave) and the operating conditions of the turbine, the ratio Mmin/Mmax changes. The aim of this paper is to develop a practical method that can predict the nature of loading for the following governing load cases: Normal Operating Conditions, Extreme Wave Load scenario, and Extreme Wind Load scenario. The proposed method is applied to 15 existing wind farms in Europe where (Mmin/Mmax) and (Mmax/MR) are evaluated. The results show that the loading ratio is sensitive to the water depth and turbine size. Furthermore, under normal operating conditions, most of the wind turbine foundations in shallow waters are subjected to one-way loading and in deeper waters and under extreme conditions the loading is marginally two-way. Predictions for the nature of loading for large wind turbines (8 MW and 10 MW) in deeper waters are also presented. The results from this paper can be used for planning scaled model tests and element tests of the soil.

Assessment of natural frequency of installed offshore wind turbines using nonlinear finite element model considering soil-monopile interaction

A nonlinear finite element model is developed to examine the lateral behaviors of monopiles, which support offshore wind turbines (OWTs) chosen from five different offshore wind farms in Europe. The simulation is using this model to accurately estimate the natural frequency of these slender structures, as a function of the interaction of the foundations with the subsoil. After a brief introduction to the wind power energy as a reliable alternative in comparison to fossil fuel, the paper focuses on concept of natural frequency as a primary indicator in designing the foundations of OWTs. Then the range of natural frequencies is provided for a safe design purpose. Next, an analytical expression of an OWT natural frequency is presented as a function of soil-monopile interaction through monopile head springs characterized by lateral stiffness KL, rotational stiffness KR and cross-coupling stiffness KLR, of which the differences are discussed. The nonlinear pseudo three-dimensional finite element vertical slices model has been used to analyze the lateral behaviors of monopiles supporting the OWTs of different wind farm sites considered. Through the monopiles head movements (displacements and rotations), the values of KL, KR and KLR were obtained and substituted in the analytical expression of natural frequency for comparison. The comparison results between computed and measured natural frequencies showed an excellent agreement for most cases. This confirms the convenience of the finite element model used for the accurate estimation of the monopile head stiffness.

Next generation polymeric high voltage direct current cables—A quantum leap needed?

The attention given to high voltage direct current (HVDC) cables appears to be at an all-time high. In the July/August 2017 issue of IEEE Electrical Insulation Magazine, the focus was on different aspects of the design and testing of HVDC cable systems. There are several reasons for this, one of the most important probably being the worldwide attention on harvesting renewable energy at a scale never seen before. The prices of building large offshore windfarms in Europe as reflected in a recent normalized cost of electricity have unexpectedly dropped in the last year, as illustrated in Figure 1, which will further accelerate the development of HVDC cables to link offshore and onshore substations. The winning bid in 2016 for the Borssele II, 700-MW windfarm in the Netherlands was as low as 54.5€ ($61)/MWh [1].

Oscillation mode analysis considering the interaction between a DFIG-based wind turbine and the grid

Sub-synchronous interactions between wind farms and transmission networks with series compensation have drawn great attention. As most large wind farms in Europe and Asia employ doubly fed induction generator turbines, there has recently been a growing interest in studying this phenomenon. To study the stability of wind turbine with doubly fed induction generator after a small disturbance, a complete small signal system is built in this paper. By using eigenvalue and participation factor analysis, the relation between the modes and state variables can be discovered. Thereafter, the oscillation modes are classified into electrical resonance, sub-synchronous resonance, sub-synchronous oscillation, sub-synchronous control interaction, and low frequency oscillation. To verify the oscillation frequency of each oscillation mode, time-domain simulation based on MATLAB/Simulink is presented. The simulation results justify the effectiveness of the small-signal models.

Closed form solution of Eigen frequency of monopile supported offshore wind turbines in deeper waters incorporating stiffness of substructure and SSI

Offshore wind turbines (OWTs) are dynamically loaded structures and therefore the estimation of the natural frequency is an important design calculation to avoid resonance and resonance related effects (such as fatigue). Monopiles are currently the most used foundation type and are also being considered in deeper waters (>30m) where a stiff transition piece will join the monopile and the tapered tall tower. While rather computationally expensive, high fidelity finite element analysis can be carried to find the Eigen solutions of the whole system considering soil–structure interaction; a quick hand calculation method is often convenient during the design optimisation stage or conceptual design stage. This paper proposes a simplified methodology to obtain the first natural frequency of the whole system using only limited data on the WTG (Wind Turbine Generator), tower dimensions, monopile dimensions and the ground. The most uncertain component is the ground and is characterised by two parameters: type of ground profile (i.e. soil stiffness variation with depth) and the soil stiffness at one monopile depth below mudline. In this framework, the fixed base natural frequency of the wind turbine is first calculated and is then multiplied by two non-dimensional factors to account for the foundation flexibility (i.e. the effect of soil–structure interaction). The theoretical background behind the model is the Euler–Bernoulli and Timoshenko beam theories where the foundation is idealised by three coupled springs (lateral, rocking and cross-coupling). 10 wind turbines founded in different ground conditions from 10 different wind farms in Europe (e.g. Walney, Gunfleet sand, Burbo Bank, Belwind, Barrow, Kentish flat, Blyth, Lely, Thanet Sand, Irene Vorrink) have been analysed and the results compared with the measured natural frequencies. The results show good accuracy (errors below 3.5%). A step by step sample calculation is also shown for practical use of the proposed methodology.

Predictor-weighting strategies for probabilistic wind power forecasting with an analog ensemble

Unlike deterministic forecasts, probabilistic predictions provide estimates of uncertainty, which is an additional value for decision-making. Previous studies have proposed the analog ensemble (AnEn), which is a technique to generate uncertainty information from a purely deterministic forecast. The objective of this study is to improve the AnEn performance for wind power forecasts by developing static and dynamic weighting strategies, which optimize the predictor combination with a brute-force continuous ranked probability score (CRPS) minimization and a principal component analysis (PCA) of the predictors. Predictors are taken from the high-resolution deterministic forecasts of the European Centre for Medium-Range Weather Forecasts (ECMWF), including forecasts of wind at several heights, geopotential height, pressure, and temperature, among others. The weighting strategies are compared at five wind farms in Europe and the U.S. situated in regions with different terrain complexity, both on and offshore, and significantly improve the deterministic and probabilistic AnEn forecast performance compared to the AnEn with 10-m wind speed and direction as predictors and compared to PCA-based approaches. The AnEn methodology also provides reliable estimation of the forecast uncertainty. The optimized predictor combinations are strongly dependent on terrain complexity, local wind regimes, and atmospheric stratification. Since the proposed predictor-weighting strategies can accomplish both the selection of relevant predictors as well as finding their optimal weights, the AnEn performance is improved by up to 20 % at on and offshore sites.

Risk Assessment and Reduction (Best Practices for Driving Down Construction and Operational Costs)

The offshore wind sector has been steadily expanding in recent years, but this has not been without painful lessons learned. This article provides an overview of the main challenges faced, categorised into construction, supply chain and contracting issues. It draws on the author’s experience as Lender’s Technical Advisor in the successful non-recourse financing of a number of offshore wind farms in Europe, supplemented by similar experience with other projects currently seeking financing around the world as well as experience from Owner's Engineer assignments.

大型風力用発電機の技術動向と高温超伝導技術の適用可能性

Development and introduction of wind power generation have recently advanced in the R & D of renewable energies. Scaling-up of wind turbines is one of recent R & D trends because of the scale merits. In the wind farms in Europe, a significant number of 5 MW turbines are being installing. Further increase in the turbine size is expected. There are high expectations for a break-thought technology that will enable to develop light weight and compact wind turbine generators with large capacity of more than 10 MW. Applying high temperature superconducting (HTS) technology to the wind turbine generators can provide the light weight and compact design, since the magnetic field can be higher than that of conventional generators and the iron core can be considerably reduced. Therefore, it is considered that the application of the HTS technology to the wind turbine generator is one of the key issues to overcome the technical power limit of conventional wind turbine generators. This article reviews the recent technical trends of large capacity wind turbine generators and related R & D problems. In the article, the recent studies on the electromagnetic design of the HTS wind turbine generators are reviewed. The features of the design of HTS wind turbine generators are summarized.

Effects of construction and operation of offshore wind farms on seals and small cetaceans.

Extensive expansion in offshore wind energy takes place these years in European waters, with North America following. Concern has been about possible conflicts with marine ecosystems, including marine mammals. During the last 10 years, several impact studies have been conducted during construction and first years of operation of wind farms in Europe and general conclusions begin to emerge. Pronounced effects (deterrence of animals) during construction have been observed in most cases. In particular, pile driving of steel monopiles for foundations has repeatedly been demonstrated to affect porpoise behavior at great distance and effects on seal haul‐out behavior has been observed in a single case. Controlled exposure studies have confirmed the results and demonstrated reactions to pile driving impact noise at levels around 140 re. 1 μPa. Effects of operation are far less pronounced and range from negative (deterrence) over neutral, to positive (attraction). Noise levels from operating turbines are very low, however, and it is unlikely that deterrence can be attributed to the noise. In general, there appears to be little conflict between marine mammals and operating offshore wind farms, but there is reason for continued attention to the construction phase, in particular, regarding pile driving operations.

Conditional Prediction Intervals of Wind Power Generation

A generic method for the providing of prediction intervals of wind power generation is described. Prediction intervals complement the more common wind power point forecasts, by giving a range of potential outcomes for a given probability, their so-called nominal coverage rate. Ideally they inform of the situation-specific uncertainty of point forecasts. In order to avoid a restrictive assumption on the shape of forecast error distributions, focus is given to an empirical and nonparametric approach named adapted resampling. This approach employs a fuzzy inference model that permits to integrate expertise on the characteristics of prediction errors for providing conditional interval forecasts. By simultaneously generating prediction intervals with various nominal coverage rates, one obtains full predictive distributions of wind generation. Adapted resampling is applied here to the case of an onshore Danish wind farm, for which three point forecasting methods are considered as input. The probabilistic forecasts generated are evaluated based on their reliability and sharpness, while compared to forecasts based on quantile regression and the climatology benchmark. The operational application of adapted resampling to the case of a large number of wind farms in Europe and Australia among others is finally discussed.