Existing Wind Farms Research Articles

“Wind Theft” from Onshore Wind Turbine Arrays: Sensitivity to Wind Farm Parameterization and Resolution

AbstractHigh-resolution simulations are conducted with the Weather Research and Forecasting Model to evaluate the sensitivity of wake effects and power production from two wind farm parameterizations [the commonly used Fitch scheme and the more recently developed Explicit Wake Parameterization (EWP)] to the resolution at which the model is applied. The simulations are conducted for a 9-month period for a domain encompassing much of the U.S. Midwest. The two horizontal resolutions considered are 4 km × 4 km and 2 km × 2 km grid cells, and the two vertical discretizations employ either 41 or 57 vertical layers (with the latter having double the number in the lowest 1 km). Higher wind speeds are observed close to the wind turbine hub height when a larger number of vertical layers are employed (12 in the lowest 200 m vs 6), which contributes to higher power production from both wind farm schemes. Differences in gross capacity factors for wind turbine power production from the two wind farm parameterizations and with resolution are most strongly manifest under stable conditions (i.e., at night). The spatial extent of wind farm wakes when defined as the area affected by velocity deficits near to wind turbine hub heights in excess of 2% of the simulation without wind turbines is considerably larger in simulations with the Fitch scheme. This spatial extent is generally reduced by increasing the horizontal resolution and/or increasing the number of vertical levels. These results have important applications to projections of expected annual energy production from new wind turbine arrays constructed in the wind shadow from existing wind farms.

Wind farm layout optimization for wake effect uniformity

The basic objective of wind farm layout optimization is to maximize the energy produced by wind farms. However, when wind turbines are arranged in a limited space like an onshore wind farm, specific wind turbines may have greater wake exposure than other wind turbines. This phenomenon can be conspicuous in a mixed layout that consists of turbines with different capacities and hub heights. In this study, we developed and tested a new objective function to increase wind farm energy output while making the wake loss of each wind turbine uniform. The purpose of this function is to adjust the wake effects of all of the wind turbines on a wind farm to similar levels, thereby promoting the operational stability of all of the wind turbines. Layout optimization was performed using a simulated annealing algorithm, which is a heuristic method, with actual wind conditions for an existing wind farm in operation. Then, the results obtained using the proposed method were compared with those yielded by layout optimization for energy maximization. The layout generated using the proposed objective function had lower energy output than that obtained by energy maximization. However, this difference was small and the proposed method prevented wake effect concentration on specific turbines by making the wake effect levels uniform.

Wind Farm Layout Upgrade Optimization

The problem of optimally increasing the size of existing wind farms has not been investigated in the literature. In this paper, a proposed wind farm layout upgrade by adding different (in type and/or hub height) commercial turbines to an existing farm is introduced and optimized. Three proposed upgraded layouts are considered: internal grid, external grid, and external unstructured. The manufacturer’s power curve and a general representation for thrust coefficient are used in power and wake calculations, respectively. A simple field-based model is implemented and both offshore and onshore conditions are considered. A genetic algorithm is used for the optimization. The trade-off range between energy production and cost of energy is investigated by considering three objective functions, individually: (1) annual energy production; (2) cost of added energy; and (3) cost of total energy. The proposed upgraded layouts are determined for the Horns Rev 1 offshore wind farm. The results showed a wide range of suitable upgrade scenarios depending on the upgraded layout and the optimization objective. The farm energy production is increased by 190–336% with a corresponding increase in the total cost by 147–720%. The external upgrade offers more energy production but with much more cost. The unstructured layouts showed clear superiority over the grid ones by providing much lower cost of energy.

Estimation of the Daily Variability of Aggregate Wind Power Generation in Alberta, Canada

This paper describes a hierarchy of increasingly complex statistical models for wind power generation in Alberta applied to wind power production data that are publicly available. The models are based on combining spatial and temporal correlations. We apply the method of Gaussian random fields to analyze the wind power time series of the 19 existing wind farms in Alberta. Following the work of Gneiting et al., three space-time models are used: Stationary, Separability, and Full Symmetry. We build several spatio-temporal covariance function estimates with increasing complexity: separable, non-separable and symmetric, and non-separable and non-symmetric. We compare the performance of the models using kriging predictions and prediction intervals for both the existing wind farms and a new farm in Alberta. It is shown that the spatial correlation in the models captures the predominantly westerly prevailing wind direction. We use the selected model to forecast the mean and the standard deviation of the future aggregate wind power generation of Alberta and investigate new wind farm siting on the basis of reducing aggregate variability.

From SCADA to lifetime assessment and performance optimization: how to use models and machine learning to extract useful insights from limited data

A common challenge in the decision making process regarding operation and life extension of existing wind farms is the lack of accurate information about the actual dynamic states of the turbines in terms of its operation from inception. SCADA records normally contain limited number of channels, and are not necessarily kept for the entire operating period of the wind farm; design and site data may be outdated or inaccessible. Nevertheless, as long as a minimum amount of information is available, statistical analysis and augmentation with artificial intelligence based simulation can be used to supplement the information. In the present study, we delineate a combination of data analysis, physical modelling and machine learning, that produces a detailed assessment of the operating conditions experienced by a wind farm and establishes the corresponding power performance, loads and fatigue damage accumulation.

Wind energy deployment in wind farm aging context. Appraising an onshore wind farm enlargement project: A contingent valuation study in the Center of Italy

In many European countries, the most suitable onshore sites for wind installations are almost fully engaged. Furthermore, the existing onshore wind farm capacity should be replaced in the next 10 years because the existing wind power plants are progressively aging in terms of depreciation, degradation and technological obsolescence. Without an adequate policy intervention, the Italian installed wind power capacity would return to the size of 2011 by 2030. In this scenario, the main opportunity for further growth in wind energy generation is wind farm enlargement. The aim of this paper is to assess the welfare change due to the proposed enlargement project. First, we investigate whether existing wind farms affect respondents' attitude and perception toward the potential enlargement of the wind farm by using the contingent valuation method. Second, we investigate the perception of the risk associated with the enlargement of a wind farm. In this case, we explicitly consider respondents' heterogeneity in perceiving the new project externalities. To achieve this objective, we use willingness to pay and willingness to accept measure to appraise welfare change due to the enlargement project. Each of these measures has been elicited jointly regarding the respective appraised externality impact perceived by the respondents. The individual and aggregate findings of this research can offer useful insights for planning and design enlargement schemes to achieve further growth in wind energy generation.

Layout optimization of a wind farm using geometric pattern-based approach

Abstract Wind farm design deals with the optimal placement of turbines in a wind farm to maximizes the expected power output and increase the overall efficiency of a wind farm. In the present study, a novel procedure known as a geometrical pattern inspired placement methodology is introduced to position wind turbines to maximize the total power output of a wind farm located at the Kutch district at the west cost of the Gujarat province of India. The enhanced passing vehicle search (PVSe) algorithm is applied to rectangular wind farm considering the multi-directional variable wind velocity. The numerical results are compared with the existing placement of turbines at a wind farm. The numerical results indicate that the power output obtained by the geometrical pattern based approach is improved by 4.29 percentages compared to the existing wind farm layout at Kutch district.

Моделирование динамики частиц в планетарном пограничном слое и в модельном ветропарке

Currently, Russia is actively building new wind farms. The issues of studying physical processes are relevant, as the existing wind farms have an impact on the microclimate and ecology. In wind farms, the appearance and movement of liquid and solid particles is possible. In the study of two-phase flows containing a suspension of aerosol particles (dispersed phase) in the carrier medium (dispersion medium) in the atmosphere, it is important to choose the main parameters that determine the system correctly and adequately describe the real process using the formulated mathematical model. The work is devoted to the development of new solvers based on the SOWFA library as part of the open-source OpenFOAM 2.4.0 package for the study of particle dynamics modeling in the atmospheric boundary layer and in the model wind farm. The Euler-Lagrangian approach is used to describe particle dynamics. Two new solvers based on ABLSolver and pisoTurbineFoam.ALM have been developed to simulate particle dynamics. The calculation results for the case of a neutral boundary layer and a model wind farm with 14 model wind turbines are presented. Graphs for the distribution of particles with different diameters in height are given. Between 72 and 96 cores were used to calculate one example.

Wind energy planning for a sustainable transition to a decarbonized generation scenario based on the opportunity cost of the wind energy: Spanish Iberian Peninsula as case study

Most countries in Europe are going under a significant transformation process from carbonized power energy sources to renewable and sustainable ones to feed their increasing demands. European Union targets on its energy roadmap states that by 2030, a reduction of 40% of CO2 emissions regarding the 1990 levels must be done and full transition must be completed by 2050. Thus, EU member countries, such Spain, are carrying out lots of investments to transform their power generation structure by promoting the installation of renewable energy power plants, with a significant visual and environmental impact. In the case of Spain, it is planned that, by 2030, 27% of final power consumption must be renewable, with approximately 31 GW of installed wind energy (8 GW must be installed in the next 10 years). Wind energy is supposed to be one of the generation leaders in this transformation context due to its high performance, high availability and fast decreasing costs.Nevertheless, wind farms have been installed on-shore with great success since the decade of the 90s covering the locations with the best wind resource. Thus, many of the installed wind turbines have overcome the half part of their expected lifespan and their payback periods, but the learning curve of the wind energy technology has improved exponentially since then. This paper is focused on the true valorization of an existing wind farm and the efficacy of the exploitation of the site’s wind resource. A novel repowering decision support model based on the opportunity cost analysis between repowering the current wind farm with the latter technologies and full depletion of the wind farm is proposed and applied to the Spanish Iberian Peninsula case study. Results show that the repowering option is significantly attractive in most cases and its proper application could have, not only a positive environmental effect, but also an economic impact on the sustainability of this source of energy.

Costs and consequences of wind turbine wake effects arising from uncoordinated wind energy development

Optimal wind farm locations require a strong and reliable wind resource and access to transmission lines. As onshore and offshore wind energy grows, preferred locations become saturated with numerous wind farms. An upwind wind farm generates ‘wake effects’ (decreases in downwind wind speeds) that undermine a downwind wind farm’s power generation and revenues. Here we use a diverse set of analysis tools from the atmospheric science, economic and legal communities to assess costs and consequences of these wake effects, focusing on a West Texas case study. We show that although wake effects vary with atmospheric conditions, they are discernible in monthly power production. In stably stratified atmospheric conditions, wakes can extend 50+ km downwind, resulting in economic losses of several million dollars over six years for our case study. However, our investigation of the legal literature shows no legal guidance for protecting existing wind farms from such significant impacts. Wakes from upwind wind farms can reduce energy generation at downwind farms. Here, using power production data and atmospheric simulations, researchers quantify the economic impacts of wakes, explain the physics of wake variability and highlight that no legal framework exists to protect downwind farms.

Using Web-GIS technology as a smart tool for resiliency management to monitor wind farms performances (Ganjeh site, Iran)

Considering the wide spread locations of wind farms in Iran, it is important to develop a suitable decision support system (DSS) to fulfill proper management of wind farms. Extensive literature survey indicates that there are no integrated forms of DSS to manage a set of wind farms. The existing wind farms are performing independently, and there is no practical method for exchanging the online data. DSS can contribute to optimal operation of wind farms, operation and maintenance scheduling, pricing policy, etc. In this study, a geographic information system and RETSCREEN software were linked to the designed DSS to achieve a more suitable result. Also, a huge number of data are constantly collected and fitted into the DSS to predict future performance of the wind farms, support the decision for pricing policy, and develop next generation of wind farms in the studied area. The developed software, called Monitoring Wind Turbines Performance, was applied to a wind farm located in Ganjeh at the northern part of Iran to ensure the capability of the DSS model. The results show that application of this model can lead to both minimal costs imposed to the wind farm owners and considerable margin of economic gain. Setting up such a network and smart technologies can be considered as a useful tool for energy resiliency management in mega cities at the national level.

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.

Predicting bird collisions with wind turbines: Comparison of the new empirical Flux Collision Model with the SOSS Band model

Collision of birds with wind turbines is an important negative effect of wind energy generation. Assessments of the potential numbers of bird collisions are required prior to the construction of wind farms. Collision rate models (CRMs) are used as a tool to estimate numbers of collision victims for wind farm initiatives.In the past couple of decades various CRMs have been developed. These models are all based on the theoretical calculation of collision probabilities (theoretical models).In this paper we introduce an empirical model, the Flux Collision Model (FCM), in which actual knowledge of species (group)-specific collision probabilities collected in existing wind farms on land is used to calculate collision rates for planned wind farms.An important quality of the FCM is that it provides a means to use empirical information to assess collision rates in Environmental Impact Assessments (EIAs) for wind farm initiatives. In addition, no detailed information on bird behaviour close to the rotor is needed, as this information is already incorporated in the empirical collision probability.In two case studies, one offshore and one on land, we compare and discuss the use and performance of the empirical FCM and the theoretical SOSS Band model for predicting collision rates of birds at wind farm initiatives. To date, no actual collision rates are known for the offshore situation. Accordingly, in the FCM, collision probabilities derived from wind farms on land were used. Nevertheless, in the offshore case study, the results of the FCM were comparable with those of the SOSS Band model.Basic sensitivity analyses for both the FCM and the SOSS Band model showed that purely theoretically both models are equally sensitive to changes in avoidance rates. However, because lower values for avoidance are applied in the FCM (wind farm avoidance) than in the SOSS Band model (overall avoidance), in practice the effect of realistic variation in avoidance rates on the resulting collision rates is much smaller for the FCM than for the SOSS Band model.Our results show that the FCM provides a valuable addition to the existing suite of (theoretical) CRMs. The predictive value of the theoretical SOSS Band model is constrained by the limited availability of knowledge on species (group)-specific (wind turbine) avoidance rates, which is not the case for the FCM. By contrast, the reliability of the empirical FCM is determined only by variation in the availability and quality of information on species (group)-specific collision probabilities.The choice of which CRM to use (theoretical or empirical) seems not to depend on the location of the wind farm initiative as being offshore or on land, but on the availability and reliability of species (group)-specific information in existing wind farms. The availability of a reliable collision probability supports the use of the FCM, while the availability of information on (overall) avoidance rates in the absence of a species (group)-specific collision probability supports the use of a theoretical CRM like the SOSS Band model.Synthesis and applications. The predictive power of collision rate models relies in the first place on the quality of the input information, and second on the theoretical details of the model calculations. Although the FCM is less dependent on measurements of avoidance rates, an urgent need remains to obtain information on actual collision rates and corresponding collision probabilities as well as avoidance rates in existing wind farms both offshore and on land in order to accurately determine the impact of wind energy on bird populations.

Wind power potential for energy sustainability and climate change mitigation: A case study in Taiwan

According to the latest Taiwan’s energy plan, nuclear power that provides approximately 16% of total electricity will be replaced by renewable energy sources by 2025. Wind power is of particular interest because Taiwan’s maritime climate and constant monsoons make it a feasible alternative that potentially generate a considerable amount of electricity. To better understand how wind power can provide stable electricity output and sequester CO2 emissions, this study employs the Weibull distribution with a threshold regression model to estimate the electricity potential for 370 scheduled wind farm sites and refine electricity estimation according to observed data from all existing wind farms. The results show that, compared to the theoretical estimation models, our proposed refinement method can, in average, reduce estimating error by 87%. The results indicate that construction of all scheduled sites are not a cost-effective approach, and the government may focus on construction of stations that can generate electricity of more than 12 million kWh per year, if capital rationing do exist. Our insightful results thus convey constructive suggestions regarding sites selection, stability of wind speed, and electricity potential of each site, all of which can be helpful in decision making. It is also noteworthy to point out that unless future climate is far deviated from the observed data, wind power can be an effective substitute of nuclear power.

Optimal Selection of Power Converter in DFIG Wind Turbine With Enhanced System-Level Reliability

With the increasing penetration of wind power, reliable and cost-effective wind energy production is of more and more importance. As one of the common configurations, the doubly fed induction generator based partial-scale wind power converter is still dominating in the existing wind farms, and its reliability assessment is studied considering the annual wind profile. According to an electro-thermal stress evaluation, the time-to-failure of the key power semiconductors is predicted by using lifetime models and Monte Carlo based variation analysis. Aiming for the system-level reliability analysis, a reliability block diagram can be used based on Weibull distributed component-level reliability. A case study of a 2 MW wind power converter shows that the optimal selection of power module may be different seen from the reliability perspective compared to the electrical stress margin. It can also be seen that the B1 lifetimes of the grid-side converter and the rotor-side converter deviate a lot by considering the electrical stresses, while they become more balanced by using an optimized design strategies. Thus, the system-level lifetime increases significantly with an appropriate design of the back-to-back power converters.

Development of a stochastic computational fluid dynamics approach for offshore wind farms

In this paper, a method for stochastic analysis of an offshore wind farm using computational fluid dynamics (CFD) is proposed. An existing offshore wind farm is modelled using a steady-state CFD solver at several deterministic input ranges and an approximation model is trained on the CFD results. The approximation model is then used in a Monte-Carlo analysis to build joint probability distributions for values of interest within the wind farm. The results are compared with real measurements obtained from the existing wind farm to quantify the accuracy of the predictions. It is shown that this method works well for the relatively simple problem considered in this study and has potential to be used in more complex situations where an existing analytical method is either insufficient or unable to make a good prediction.

Hurricane risk assessment of offshore wind turbines

Abstract A barrier to the development of the offshore wind resource along the U.S. Atlantic coast is a lack of quantitative measures of the risk to offshore wind turbines (OWTs) from hurricanes. The research presented in this paper quantifies the risk of failure of OWTs to hurricane-induced wind and waves by developing and implementing a risk assessment framework that is adapted from a well-established framework in performance-based earthquake engineering. Both frameworks involve the convolution of hazard intensity measures (IMs) with engineering demand parameters (EDPs) and damage measures (DMs) to estimate probabilities of damage or failure. The adapted framework in this study is implemented and applied to a hypothetical scenario wherein portions of nine existing Wind Farm Areas (WFAs), spanning the U.S. Atlantic coast, are populated with ∼7000 5 MW OWTs supported by monopiles. The IMs of wind and wave are calculated with a catalog representing 100,000 years of simulated hurricane activity for the Atlantic basin, the EDPs are calculated with 24 1-h time history simulations, and a fragility function for DM is estimated by combining variability observed in over one hundred flexural tests of hollow circular tubes found in the literature. The results of the study are that, for hurricane-induced wind and wave, the mean lifetime (i.e., 20-year) probability of structural failure of the tower or monopile of OWTs installed within the nine WFAs along the U.S. Atlantic coast ranges between 7.3 × 10−10 and 3.4 × 10−4 for a functional yaw control system and between 1.5 × 10−7 and 1.6 × 10−3 for a non-functional yaw control system.

The resilience of Australian wind energy to climate change

The Paris Agreement limits global average temperature rise to 2 °C and commits to pursuing efforts in limiting warming to 1.5 °C above pre-industrial levels. This will require rapid reductions in the emissions of greenhouse gases and the eventual decarbonisation of the global economy. Wind energy is an established technology to help achieve emissions reductions, with a cumulative global installed capacity of ~486 GW (2016). Focusing on Australia, we assess the future economic viability of wind energy using a 12-member ensemble of high-resolution regional climate simulations forced by Coupled Model Intercomparison Project (CMIP) output. We examine both near future (around 2030) and far future (around 2070) changes. Extractable wind power changes vary across the continent, though the most spatially coherent change is a small but significant decrease across southern regions. The cost of future wind energy generation, measured via the Levelised Cost of Energy (LCOE), increases negligibly in the future in regions with significant existing installed capacity. Technological developments in wind energy generation more than compensate for projected small reductions in wind, decreasing the LCOE by around 30%. These developments ensure viability for existing wind farms, and enhance the economic viability of proposed wind farms in Western Australian and Tasmania. Wind energy is therefore a resilient source of electricity over most of Australia and technological innovation entering the market will open new regions for energy production in the future.

Wind power prediction in new stations based on knowledge of existing Stations: A cluster based multi source domain adaptation approach

Historical wind power production figures are not available when a new wind farm goes into power production. It is thus difficult to forecast power productions of such wind farms that is required for demand management. Wind power is a function of weather variables and it is likely that weather patterns of the new station is similar to some existing operational wind farms. It will thus be interesting to investigate how the forecast/prediction models of the existing wind farms can be adapted to generate a prediction model for new stations. On this regard, we explore a particular branch of machine learning called Multi Source Domain Adaptation (MSDA). MSDA approaches identify a weighing mechanism to fuse the predictions from the source models (i.e. existing stations) to produce a prediction for the target (i.e. new station). The weights are computed based on similarity of data distributions between source and target. Conventional MSDA approaches utilise an instance based weighting scheme and we identified that fails to capture the data distribution of wind data sets appropriately. We thus propose a novel cluster based MSDA approach that captures wind data distribution in terms of natural groups that exist within data and compute distribution similarity (and source weight) in terms of cluster distributions. Experimental results demonstrate that cluster based MSDA approach can reduce regression error by 20.63% over instance based MSDA approach.

Wind farms and rural tourism: A Portuguese case study of residents’ and visitors’ perceptions and attitudes

AbstractResidents’ and visitors’ perceptions of and attitudes towards existing wind farms, as well as the perceived impact of wind farms on tourism, are examined in this article with reference to a built heritage site in the Portuguese countryside. Based on a set of semi-structured interviews, the paper sheds light on the positive impact that the community’s or local actors’ involvement in the constitution, management and decision-making processes has on the residents’ perceptions and attitudes regarding wind farms, and also on the trade-off with the perceived effect of wind farms on local tourism. Moreover, it shows that although most visitors criticised the proximity of wind turbines to medieval architecture, a clear majority of them accepted their presence and virtually all of them stated that these facilities had no impact on their choice of destination.