Windstorm risk assessment for offshore wind farms in the North Sea

The North Sea is becoming increasingly attractive to wind energy developers and investors, with 38 wind farms belonging to five different countries and representing over€35 billion of assets. Concerns about offshore wind turbines being damaged by extreme windstorms pose a challenge to insurers, investors and regulators. Catastrophe modeling can adequately quantify the risk. In this study, a Monte Carlo simulation approach is used to assess the number of turbines that buckle using maximum wind speeds reaching each wind farm. Damage assessment is undertaken for each wind farm using a log‐logistic damage function and a left‐truncated Weibull distribution. The risk to offshore wind power in the North Sea is calculated using an exceedance probability (EP) curve for the portfolio of wind farms. The European Union Solvency II directive requires insurance companies to hold sufficient capital to guard against insolvency. The solvency capital requirement (SCR) is based on a value‐at‐risk measure calibrated to a 99.5% confidence level over a 1‐year time horizon. The SCR is estimated at €0.049 billion in the case of yawing turbines. Simulations are repeated for different climate change scenarios. If wind speeds grow by 5% and the frequency of storms increases by 40%, the SCR is seen to rise substantially to €0.264 billion. Relative to the total value of assets, the SCR is 0.14% compared with 0.08% for European property, confirming that these wind farm assets represent a relatively high risk. Furthermore, climate change could increase the relative SCR to levels as high as 0.75%.     

基于PR控制的海上风电场并网VSC-HVDC系统

提出了一种新的适用于海上风电场并网的新型高压直流输电(Voltage Source Converter based HVDC,VSC-HVDC)系统的比例谐振(Proportional Resonant,PR)控制策略。该方法充分利用PR控制器能够在αβ坐标系下对交流输入信号无静差控制的特点,将矢量控制策略下的有功电流和无功电流分量转换到αβ坐标系下进行调节,实现风电场和电网侧换流器维持直流电压稳定以及有功、无功功率的解耦控制。与常用的双闭环PI控制相比,该策略无需多次坐标变换和前馈解耦控制,且易于实现对系统谐波电流的补偿,降低了实现难度,提高了系统的鲁棒性和并网电能质量,为海上风电场并网VSC-HVDC系统提供了一种优化的控制方案。     

Resonant overvoltage assessment in offshore wind farms via a parametric black‐box wind turbine transformer model

The protection of offshore wind farms (OWFs) against overvoltages, especially resonant overvoltage, is of paramount importance because of poor accessibility and high repair costs. In this paper, we study how switching overvoltages at the wind turbine transformer (WTT) medium voltage (MV) side can lead to high overvoltages on the low voltage (LV) side. The effect of overvoltage protective devices is analyzed. A detailed model of an OWF row is developed in electromagnetic transients program–alternative transients program (EMTP‐ATP), including interconnecting cables, WTT, surge arresters and resistive–capacitive filters. A parameterized black‐box WTT model is obtained from measurements and is used for investigating the transfer of resonant overvoltages from the MV to the LV side. The model is capable of shifting systematically the frequencies and adjusting the transformer input impedance. Simulation results show that wind turbine energization in an OWF can lead to overvoltages on the LV terminals. The rate of rise of overvoltages (du/dt) is in the range of 300–500 pu/µs. It is found that resistive–capacitive filters should be installed on both MV and LV terminals of WTTs to decrease both resonant overvoltages and du/dt, which is unachievable by surge arrester alone. Copyright © 2014 John Wiley & Sons, Ltd.     

Willingness to pay for reduced visual disamenities from offshore wind farms in Denmark

Expansion of offshore wind power plays a significant role in the energy policies of many EU countries. However, offshore wind farms create visual disamenities. These disamenities can be reduced by siting wind farms at larger distances from the coast—and accepting higher costs per kWh produced. In this paper willingness to pay for reducing the visual disamenities from future offshore wind farms is elicited using the economic valuation method Choice Experiments. The valuation scenario comprises the location of 720 offshore wind turbines (equivalent to 3600 MW) in farms at distances equal to: 12, 18 or 50 km from the shore, relative to an 8 km baseline. Using a fixed effect logit model average willingness to pay amounts were estimated as: 46, 96 and 122 Euros/household/year for having the wind farms located at 12, 18 and 50 km from the coast as opposed to 8 km. The results also reveal that WTP deviates significantly depending on the age of respondents and their experiences with offshore wind farms.     

Experimental verification of computational predictions in power generation variation with layout of offshore wind farms

The optimization of wind farms with respect to spatial layout is addressed experimentally. Wake effects within wind turbine farms are well known to be deleterious in terms of power generation and structural loading, which is corroborated in this study. Computational models are the predominant tools in the prediction of turbine‐induced flow fields. However, for wind farms comprising hundreds of turbines, reliability of the obtained numerical data becomes a growing concern with potentially costly consequences. This study pursues a systematic complementary theoretical, experimental and numerical study of variations in generated power with turbine layout of an 80 turbine large wind farm. Wake effects within offshore wind turbine arrays are emulated using porous discs mounted on a flat plate in a wind tunnel. The adopted approach to reproduce experimentally individual turbine wake characteristics is presented, and drag measurements are argued to correctly capture the variation in power generation with turbine layout. Experimental data are juxtaposed with power predictions using ANSYS WindModeller simulation suite. Although comparison with available wind farm power output data has been limited, it is demonstrated nonetheless that this approach has potential for the validation of numerical models of power loss due to wake effects or even to make a direct physical prediction. The approach has even indicated useful data for the improvement of the physics within numerical models. Copyright © 2014 John Wiley & Sons, Ltd.     

ENDOW (efficient development of offshore wind farms): modelling wake and boundary layer interactions

While experience gained through the offshore wind energy projects currently operating is valuable, a major uncertainty in estimating power production lies in the prediction of the dynamic links between the atmosphere and wind turbines in offshore regimes. The objective of the ENDOW project was to evaluate, enhance and interface wake and boundary layer models for utilization offshore. The project resulted in a significant advance in the state of the art in both wake and marine boundary layer models, leading to improved prediction of wind speed and turbulence profiles within large offshore wind farms. Use of new databases from existing offshore wind farms and detailed wake profiles collected using sodar provided a unique opportunity to undertake the first comprehensive evaluation of wake models in the offshore environment. The results of wake model performance in different wind speed, stability and roughness conditions relative to observations provided criteria for their improvement. Mesoscale model simulations were used to evaluate the impact of thermal flows, roughness and topography on offshore wind speeds. The model hierarchy developed under ENDOW forms the basis of design tools for use by wind energy developers and turbine manufacturers to optimize power output from offshore wind farms through minimized wake effects and optimal grid connections. The design tools are being built onto existing regional‐scale models and wind farm design software which was developed with EU funding and is in use currently by wind energy developers. Copyright © 2004 John Wiley & Sons, Ltd.     

Redefinition power curve for more accurate performance assessment of wind farms

The lack of accurate methods for assessment of the productive capacity of wind power plants is becoming a bottleneck in an increasingly commercialized wind power industry. In this article the inherent components of performance assessment are identified and analysed and ways of minimizing uncertainties on the components are investigated. The main components are identified as ‘site calibration’, ‘wind turbine sensitivity to flow variables’, ‘plant blockage effects’ and ‘uncertainty analysis’. Site calibration is the action of estimating the flow variables at the wind turbine position from measurements of these quantities at another (reference) position. The purpose of sensitivity analysis is to clarify which and how flow variables influence power output. Plant blockage effects refer to the power plant's influence on the reference measurements of flow variables. Finally, the component uncertainties and in turn the integrated uncertainty on the average productive capacity of the wind power plant are investigated. It is found that uncertainties can be reduced (1) by including several more flow variables in addition to hub‐height wind speed, (2) by carrying out site calibration with utmost care and by inclusion of more variables, (3) by taking plant blockage into consideration, (4) by aiming at ‘plant‐average’ power instead of looking only at individual machines and, possibly, (5) by introduction of remote‐sensing anemometer techniques. Copyright © 2000 John Wiley & Sons, Ltd.     

A comparison of methods for assessing power output in non‐uniform onshore wind farms

Wind resource assessments are used to estimate a wind farm's power production during the planning process. It is important that these estimates are accurate, as they can impact financing agreements, transmission planning, and environmental targets. Here, we analyze the challenges in wind power estimation for onshore farms. Turbine wake effects are a strong determinant of farm power production. With given input wind conditions, wake losses typically cause downstream turbines to produce significantly less power than upstream turbines. These losses have been modeled extensively and are well understood under certain conditions. Most notably, validation of different model types has favored offshore farms. Models that capture the dynamics of offshore wind conditions do not necessarily perform equally as well for onshore wind farms. We analyze the capabilities of several different methods for estimating wind farm power production in 2 onshore farms with non‐uniform layouts. We compare the Jensen model to a number of statistical models, to meteorological downscaling techniques, and to using no model at all. We show that the complexities of some onshore farms result in wind conditions that are not accurately modeled by the Jensen wake decay techniques and that statistical methods have some strong advantages in practice.     

Review of recent offshore wind power developments in china

Rapid wind power development in China has attracted worldwide attention. The huge market potential and fast development of wind turbine manufacturing capacity are making China a world leader in wind power development. In 2010, with the newly installed wind power capacity and the cumulative installed capacity, China was ranked first in the world. In 2009, China also constructed and commissioned its first large offshore wind farm near Shanghai. Following earlier papers reviewing the state of China's onshore wind industry, this paper presents a broader perspective and up‐to‐date survey of China's offshore wind power development, making comparisons between the developments in the rest of the world and China, to draw out similarities and differences and lessons for the China offshore wind industry. The paper highlights six important aspects for China's offshore wind development: economics, location, Grid connection, technological development, environmental adaptation and national policies. The authors make recommendations for mitigating some outstanding issues in these six aspects for the future development of China's offshore wind resource. Copyright © 2012 John Wiley & Sons, Ltd.     

Fatigue distribution optimization for offshore wind farms using intelligent agent control

A novel control approach is proposed to optimize the fatigue distribution of wind turbines in a large‐scale offshore wind farm on the basis of an intelligent agent theory. In this approach, each wind turbine is considered to be an intelligent agent. The turbine at the farm boundary communicates with its neighbouring downwind turbines and organizes them adaptively into a wind delivery group along the wind direction. The agent attributes and the event structure are designed on the basis of the intelligent agent theory by using the unified modelling language. The control strategy of the intelligent agent is studied using topology models. The reference power of an individual wind turbine from the wind farm controller is re‐dispatched to balance the turbine fatigue in the power dispatch intervals. In the fatigue optimization, the goal function is to minimize the standard deviation of the fatigue coefficient for every wind turbine. The optimization is constrained such that the average fatigue for every turbine is smaller than what would be achieved by conventional dispatch and such that the total power loss of the wind farm is restricted to a few percent of the total power. This intelligent agent control approach is verified through the simulation of wind data from the Horns Rev offshore wind farm. The results illustrate that intelligent agent control is a feasible way to optimize fatigue distribution in wind farms, which may reduce the maintenance frequency and extend the service life of large‐scale wind farms. Copyright © 2012 John Wiley & Sons, Ltd.     

A framework for data integration of offshore wind farms

Operation and maintenance play an important role in maximizing the yield and minimizing the downtime of wind turbines, especially offshore wind farms where access can be difficult due to harsh weather conditions for long periods. It contributes up to 25–30% to the cost of energy generation. Improved operation and maintenance (O&M) practices are likely to reduce the cost of wind energy and increase safety. In order to optimize the O&M, the importance of data exchange and knowledge sharing within the offshore wind industry must be realized. With more data available, it is possible to make better decisions, and thereby improve the recovery rates and reduce the operational costs. This article describes the development of a framework for data integration to optimize the remote operations of offshore wind farms.     

Scenarios for offshore wind power production for Central California Call Areas

In response to the growing interest in offshore wind energy development in California, the U.S. Bureau of Ocean Energy Management delineated three Call Areas for potential leasing. This study provides a comprehensive characterization and comparison of offshore wind power potential within the two Central California Call Areas (Diablo Canyon and Morro Bay) using 12- and 15-MW turbines under different inter-turbine spacing and wind farm size scenarios. Our analysis shows similar daily and seasonal patterns of wind power produced within the Call Areas, which peak in spring and during evening hours. Per-turbine power production is higher in the Morro Bay Call Area due to slightly higher hub-height wind speeds, whereas total power production is higher in the Diablo Canyon Call Area due to its larger size. Turbine type had a negligible impact on average power production per-unit-area because while larger turbines produce more power, they require greater inter-turbine spacing. Combined power production from the two fully built out Call Areas could equal nearly a quarter of California's current annual electrical energy production. A commercial-scale wind farm with a realized power output of 960 MW would require a footprint of at least half of the Morro Bay Call Area or at least a quarter of the Diablo Canyon Call Area. These results provide guidance on offshore wind development over the Central California Coast, and the framework demonstrated here could be applied to other wind data sets in other regions.     

Reactive power management and control of distant large-scale grid-connected offshore wind power farms

Reactive power management and control of distant large-scale offshore wind power farms connected to the grid through high-voltage alternating current (HVAC) transmission cable are presented in this paper. The choice of the transmission option is based on the capacity of the considered wind farm (WF) and the distance to the onshore grid connection point. The WF is made up of identical doubly-fed induction generators (DFIGs). Modelling and improved analysis of the effective reactive power capability of DFIGs as affected by various operational constraints are provided. In addition, modelling and analysis of the reactive power demands, balance, and control are presented. The minimum capacity and reactive power settings for reactive power compensation required for the system are determined. Possibility of unity power factor operation suggested by the German electricity association (VDEW) is investigated. A summary of the main outcomes of the work presented in this paper is provided in the conclusions section.     

Visual impact assessment of offshore wind farms and prior experience

Energy planners have shifted their attention towards offshore wind power generation and the decision is supported by the public in general, which in the literature has a positive attitude towards offshore wind generation. However, globally only a few offshore wind farms are operating. As more wind farms start operating and more people become experienced with especially the visual impacts from offshore wind farms, the public positive attitude could change if the experienced impacts are different from the initially perceived visual interference. Using a binary logit model, the present paper investigates the relation between different levels of prior experience with visual disamenities from offshore wind farms and perception of visual impacts from offshore wind farms. The differences in prior experience are systematically controlled for sampling respondents living in the areas close to the large scale offshore wind farms Nysted and Horns Rev and by sampling the a group of respondents representing the Danish population, which has little experience with offshore wind farms. Compared to previous results in the literature, the present paper finds that perception of wind power generation is influenced by prior experience. More specifically, the results show that people with experience from offshore wind farms located far from the coast have a significant more positive perception of the visual impacts from offshore wind farms than people with experience from wind farms located closer to the coast. These results are noteworthy on two levels. First of all, the results show that perceptions of offshore wind generation are systematically significantly influenced by prior experience with offshore wind farms. Secondly, and in a policy context, the results indicate that the future acceptance of future offshore wind farms is not independent of the location of existing and new offshore wind farms. This poses for caution in relation to locating offshore wind farms too close to the coast.     

Modeling space‐time correlations of velocity fluctuations in wind farms

An analytical model for the streamwise velocity space‐time correlations in turbulent flows is derived and applied to the special case of velocity fluctuations in large wind farms. The model is based on the Kraichnan‐Tennekes random sweeping hypothesis, capturing the decorrelation in time while including a mean wind velocity in the streamwise direction. In the resulting model, the streamwise velocity space‐time correlation is expressed as a convolution of the pure space correlation with an analytical temporal decorrelation kernel. Hence, the spatiotemporal structure of velocity fluctuations in wind farms can be derived from the spatial correlations only. We then explore the applicability of the model to predict spatiotemporal correlations in turbulent flows in wind farms. Comparisons of the model with data from a large eddy simulation of flow in a large, spatially periodic wind farm are performed, where needed model parameters such as spatial and temporal integral scales and spatial correlations are determined from the large eddy simulation. Good agreement is obtained between the model and large eddy simulation data showing that spatial data may be used to model the full spatiotemporal structure of fluctuations in wind farms.     

Modelling and measurements of power losses and turbulence intensity in wind turbine wakes at Middelgrunden offshore wind farm

Understanding of power losses and turbulence increase due to wind turbine wake interactions in large offshore wind farms is crucial to optimizing wind farm design. Power losses and turbulence increase due to wakes are quantified based on observations from Middelgrunden and state‐of‐the‐art models. Observed power losses due solely to wakes are approximately 10% on average. These are relatively high for a single line of wind turbines due in part to the close spacing of the wind farm. The wind farm model Wind Analysis and Application Program (WAsP) is shown to capture wake losses despite operating beyond its specifications for turbine spacing. The paper describes two methods of estimating turbulence intensity: one based on the mean and standard deviation (SD) of wind speed from the nacelle anemometer, the other from mean power output and its SD. Observations from the nacelle anemometer indicate turbulence intensity which is around 9% higher in absolute terms than those derived from the power measurements. For comparison, turbulence intensity is also derived from wind speed and SD from a meteorological mast at the same site prior to wind farm construction. Despite differences in the measurement height and period, overall agreement is better between the turbulence intensity derived from power measurements and the meteorological mast than with those derived from data from the nacelle anemometers. The turbulence in wind farm model indicates turbulence increase of the order 20% in absolute terms for flow directly along the row which is in good agreement with the observations. Copyright © 2007 John Wiley & Sons, Ltd.     

新型海上风力发电及其关键技术研究

回顾国外海上风力发电场的发展,针对随着海水深度增加导致海上风力机成本急剧上升的矛盾,引入海上漂浮式风力机概念,并详细介绍其结构和特点,通过系统介绍海上漂浮式风力机组成部分和设计制造中的关键技术,最后得出海上漂浮式风机是一种潜力巨大的新型风力发电技术,值得进一步深入研究。同时,针对我国陆、海资源的具体情况,较为系统地提出了海上漂浮式风力机研究的需要关注的关键问题,指出了该研究所具有的巨大社会经济价值。     

The impact of wind resource spatial variability on floating offshore wind farms finance

Wind resource availability determines the financial performance of wind farms as it is directly related to production. Offshore wind developers require great investments to design, build, operate and dismantle offshore wind farms. Furthermore, the investments in the offshore floating wind sector are expected to increase in the future. Because of that, the assessment of the variability of the investments, mainly because of the wind resource variability, seems to be a crucial step in the design methodology. Consequently, a flexible methodology for supporting offshore floating wind farm optimal location assessment is presented in this paper. The proposed methodology is focused on including the offshore wind resource variability and its influence on the power production of floating wind farms, as well as on the main financial indicators (internal rate of return, net present value, pay‐back period and cost of energy). The methodology is applied to the north coast of Spain, and it allows to identify the most promising sites for offshore wind farms deployment. Differences on the cost of energy up to 100% can be found in the area under study. The methodology proposed has been conceived to be site‐independent and applied at any spatial and time horizon. Copyright © 2017 John Wiley & Sons, Ltd.     

Reliability-based topology optimization for offshore wind farm collection system

An optimization framework for global optimization of the cable layout topology for offshore wind farm (OWF) is presented. The framework designs and compares closed-loop and radial layouts for the collection system of OWFs. For the former, a two-stage stochastic optimization program based on a mixed integer linear programming (MILP) model is developed, while for the latter, a hop-indexed full binary model is used. The purpose of the framework is to provide a common base for assessing both designs economically, using the same underlying contingency treatment. A discrete Markov model is implemented for calculating the cable failure probability, useful for estimating the time under contingency for multiple power generation scenarios. The objective function supports simultaneous optimization of (i) initial investment (network topology and cable sizing), (ii) total electrical power loss costs and (iii) operation costs due to energy curtailment from cable failures. Constraints are added accounting for common engineering aspects. The applicability of the full method is demonstrated by tackling three differently sized real-world OWFs. Results show that (i) the profitability of either topology type depends strongly on the project size and wind turbine rating. Closed loop may be a competitive solution for large-scale projects where large amounts of energy are potentially curtailed. (ii) The stochastic model presents low tractability to tackle large-scale instances, increasing the required computing time and memory resources. (iii) Strategies must be adopted in order to apply stochastic optimization for modern OWFs, intending analytically or numerically simplification of mathematical models.     

Service reliability of offshore wind turbines

A probabilistic formulation is proposed to assess the performance of the support structure of offshore wind turbines based on their probability and expected time of exceeding specified drift thresholds. To this end, novel probabilistic models are developed to predict the mean and standard deviation of the drift ratio response of wind turbine support structures operating under day-to-day loads as a function of the wind turbine geometry and material properties, and loading conditions. The proposed models are assessed using a database of virtual experiments generated using detailed three-dimensional (3D) nonlinear finite element (FE) models of a set of representative wind turbine configurations. The developed models are then used in a random vibration formulation to estimate the probability and expected time of exceeding specified drift thresholds. As an example, the probability and expected time of exceeding specified drift thresholds are estimated for a typical offshore wind turbine at different wind speeds. A comparison is made between the results obtained based on the proposed models, those obtained using simulators commonly used in practice and detailed 3D nonlinear FE analyses.