Wind Turbine Installation for High Elevations

2006 ◽  
Author(s):  
Lorenzo Battisti ◽  
Ambra Giovannelli
Keyword(s):  
Wind Energy ◽  
Wind Turbine ◽  
Wind Farm ◽  
Wind Farms ◽  
Cost Effective ◽  
Cold Climate ◽  
Depth Analysis ◽  
External Conditions ◽  
A Site ◽  
High Elevations

The strong drive to exploit wind energy has recently led to new types of location for wind turbine installations being considered, including mountain regions and, to be more specific, areas at elevations coming between 800 and 2,500 m asl. Authoritative sources, such as the European Wind Energy Association (EWEA), have estimated that 20–25% of the approximately 60,000 MW expected to be installed in Europe between now and 2010 will be situated in cold-climate areas, and a part of them will be on hills and mountains. The installation of wind farms in the mountains consequently demands an in-depth analysis, in the design of such plant, into both the methods for assessing the resource and the more or less direct transfer of procedures and technologies developed for conventional sites. For the time being, the IEC standards (originally developed to provide a reference picture relating to conventional sites) fail to provide recommendations on this type of site, where the structure of the flow field is substantially more complex in terms of its effect on the stresses involved. The present work outlines the main features of mountain wind farm sites and discusses the effects of some of said features on the structural assessment of the turbines destined for such installations in the light of the IEC standard requirements. The work illustrate that the installation of wind turbines in mountain sites must consider different site-related features from those used to develop the requirements of the IEC standards. The examples given here indicate that, based on the standards, these features influence both energy generation and the turbine’s working life. Only an adequate understanding of these features can lead to a cost-effective sizing of the turbines. This type of approach can lead to a site-specific design concept, and only certain components are structurally adequate for the stress characteristics of a given site. These procedures will then have to be transferred to the standards, overcoming the conflict between the minimum standard requirements specifying the fundamental elements to consider in the project and the set of parameters describing the external conditions that demand a turbine of equivalent sturdiness in comparable applications.

scholarly journals Inter-annual variability of wind climates and wind turbine annual energy production

2018 ◽  
Author(s):  
Sara C. Pryor ◽  
Tristan J. Shepherd ◽  
Rebecca J. Barthelmie
Keyword(s):  
Standard Deviation ◽  
Wind Energy ◽  
Wind Turbine ◽  
Energy Production ◽  
Wind Farm ◽  
Wind Farms ◽  
Wind Speeds ◽  
Annual Variability ◽  
Eastern Usa

Abstract. Inter-annual variability (IAV) of expected annual energy production (AEP) from proposed wind farms plays a key role in dictating project financing. IAV in pre-construction projected AEP and the difference in 50th and 90th percentile (P50 and P90) AEP derives in part from variability in wind climates. However, the magnitude of IAV in wind speeds at/close to wind turbine hub-heights is poorly constrained and maybe overestimated by the 6 % standard deviation of annual mean wind speeds that is widely applied within the wind energy industry. Thus there is a need for improved understanding of the long-term wind resource and the inter-annual variability therein in order to generate more robust predictions of the financial value of a wind energy project. Long-term simulations of wind speeds near typical wind turbine hub-heights over the eastern USA indicate median gross capacity factors (computed using 10-minute wind speeds close to wind turbine hub-heights and the power curve of the most common wind turbine deployed in the region) that are in good agreement with values derived from operational wind farms. The IAV of annual mean wind speeds at/near to typical wind turbine hub-heights in these simulations is lower than is implied by assuming a standard deviation of 6 %. Indeed, rather than in 9 in 10 years exhibiting AEP within 0.9 and 1.1 times the long-term mean AEP, results presented herein indicate that over 90 % of the area in the eastern USA that currently has operating wind turbines simulated AEP lies within 0.94 and 1.06 of the long-term average. Further, IAV of estimated AEP is not substantially larger than IAV in mean wind speeds. These results indicate it may be appropriate to reduce the IAV applied to pre-construction AEP estimates to account for variability in wind climates, which would decrease the cost of capital for wind farm developments.

scholarly journals Optimization of Wind Turbine Interconnections in an Offshore Wind Farm Using Metaheuristic Algorithms

2020 ◽  
Vol 12 (14) ◽  
pp. 5761 ◽  
Author(s):  
Chakib El Mokhi ◽  
Adnane Addaim
Keyword(s):  
Wind Energy ◽  
Wind Turbine ◽  
Wind Farm ◽  
Renewable Energy Sources ◽  
Wind Farms ◽  
Optimization Methods ◽  
Network Routing ◽  
Metaheuristic Algorithms ◽  
Offshore Wind ◽  
Offshore Wind Farms

Wind energy is currently one of the fastest-growing renewable energy sources in the world. For this reason, research on methods to render wind farms more energy efficient is reasonable. The optimization of wind turbine positions within wind farms makes the exploitation of wind energy more efficient and the wind farms more competitive with other energy resources. The investment costs alone for substation and electrical infrastructure for offshore wind farms run around 15–30% of the total investment costs of the project, which are considered high. Optimizing the substation location can reduce these costs, which also minimizes the overall cable length within the wind farm. In parallel, optimizing the cable routing can provide an additional benefit by finding the optimal grid network routing. In this article, the authors show the procedure on how to create an optimized wind farm already in the design phase using metaheuristic algorithms. Besides the optimization of wind turbine positions for more energy efficiency, the optimization methods of the substation location and the cable routing for the collector system to avoid cable losses are also presented.

scholarly journals Techno-Economic Analysis of On-Site Energy Storage Units to Mitigate Wind Energy Curtailment: A Case Study in Scotland

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1691
Author(s):  
Seda Canbulat ◽  
Kutlu Balci ◽  
Onder Canbulat ◽  
I. Safak Bayram
Keyword(s):  
Renewable Energy ◽  
Energy Storage ◽  
Economic Analysis ◽  
Wind Energy ◽  
Wind Farm ◽  
Lithium Ion ◽  
Wind Farms ◽  
Storage Size ◽  
Site Energy ◽  
Depth Analysis

Wind energy plays a major role in decarbonisation of the electricity sector and supports achieving net-zero greenhouse gas emissions. Over the last decade, the wind energy deployments have grown steadily, accounting for more than one fourth of the annual electricity generation in countries like the United Kingdom, Denmark, and Germany. However, as the share of wind energy increases, system operators face challenges in managing excessive wind generation due to its nondispatchable nature. Currently, the most common practice is wind energy curtailment in which wind farm operators receive constraint payments to reduce their renewable energy production. This practice not only leads to wastage of large volumes of renewable energy, but also the associated financial cost is reflected to rate payers in the form of increased electricity bills. On-site energy storage technologies come to the forefront as a technology option to minimise wind energy curtailment and to harness wind energy in a more efficient way. To that end, this paper, first, systematically evaluates different energy storage options for wind energy farms. Second, a depth analysis of curtailment and constraint payments of major wind energy farms in Scotland are presented. Third, using actual wind and market datasets, a techno-economic analysis is conducted to examine the relationship between on-site energy storage size and the amount of curtailment. The results show that, similar to recent deployments, lithium-ion technology is best suited for on-site storage. As case studies, Whitelee and Gordon bush wind farms in Scotland are chosen. The most suitable storage capacities for 20 years payback period is calculated as follows: (i) the storage size for the Gordonbush wind farm is 100 MWh and almost 19% of total curtailment can be avoided and (ii) the storage size for the Whitlee farm is 125 MWh which can reduce the curtailment by 20.2%. The outcomes of this study will shed light into analysing curtailment reduction potential of future wind farms including floating islands, seaports, and other floating systems.

scholarly journals Chosen threats from wind farms located at sea

2018 ◽  
Vol 19 (10) ◽  
pp. 57-61
Author(s):  
Jerzy Herdzik
Keyword(s):  
European Union ◽  
Wind Energy ◽  
Wind Turbine ◽  
Wind Farm ◽  
Wind Farms ◽  
Present Knowledge ◽  
Energy Utilization ◽  
Wind Power Plant ◽  
Sea Wind ◽  
Energy Plants

Paper discussed the problem of threats from wind farm located at sea. It was presented the possibilities (depends on wind energy) of wind energy utilization on the Europe area. It was mentioned the conditions and perspectives of construction the sea wind energy plants in Europe and Poland. It was performed the wind turbines characteristics used on sea shelf. An example of planned investments on Polish economical area and territorial waters was mentioned. It was stayed focused on chosen threats, articulated through groups of people staying in opposite to wind turbine lobby, trying to stem the wind turbine development through localization limitations. The lobby of companies constructed the wind farms (having large funds) and legislation actions e.g. European Union preferring the energy obtained from renewable sources, stands in the opposition of people groups living on the areas in which the wind farms would be located and some groups of ecologists. It is a problem for neutral opinion when the arguments are different from these groups being in accordance with present knowledge and science achievements. Undoubtedly it is a necessary to undertake the compromise actions, allowing for wind power plant development.

scholarly journals Interannual variability of wind climates and wind turbine annual energy production

2018 ◽  
Vol 3 (2) ◽  
pp. 651-665 ◽  
Author(s):  
Sara C. Pryor ◽  
Tristan J. Shepherd ◽  
Rebecca J. Barthelmie
Keyword(s):  
Wind Energy ◽  
Wind Turbine ◽  
Interannual Variability ◽  
Energy Production ◽  
Wind Farm ◽  
Wind Farms ◽  
Power Curve ◽  
Wind Speeds ◽  
Eastern Usa

Abstract. The interannual variability (IAV) of expected annual energy production (AEP) from proposed wind farms plays a key role in dictating project financing. IAV in preconstruction projected AEP and the difference in 50th and 90th percentile (P50 and P90) AEP derive in part from variability in wind climates. However, the magnitude of IAV in wind speeds at or close to wind turbine hub heights is poorly defined and may be overestimated by assuming annual mean wind speeds are Gaussian distributed with a standard deviation (σ) of 6 %, as is widely applied within the wind energy industry. There is a need for improved understanding of the long-term wind resource and the IAV therein in order to generate more robust predictions of the financial value of a wind energy project. Long-term simulations of wind speeds near typical wind turbine hub heights over the eastern USA indicate median gross capacity factors (computed using 10 min wind speeds close to wind turbine hub heights and the power curve of the most common wind turbine deployed in the region) that are in good agreement with values derived from operational wind farms. The IAV of annual mean wind speeds at or near typical wind turbine hub heights in these simulations and AEP computed using the power curve of the most commonly deployed wind turbine is lower than is implied by assuming σ=6 %. Indeed, rather than 9 out of 10 years exhibiting AEP within 0.9 and 1.1 times the long-term mean AEP as implied by assuming a Gaussian distribution with σ of 6 %, the results presented herein indicate that in over 90 % of the area in the eastern USA that currently has operating wind turbines, simulated AEP lies within 0.94 and 1.06 of the long-term average. Further, the IAV of estimated AEP is not substantially larger than IAV in mean wind speeds. These results indicate it may be appropriate to reduce the IAV applied to preconstruction AEP estimates to account for variability in wind climates, which would decrease the cost of capital for wind farm developments.

scholarly journals Foundation Types for Land and Offshore Sustainable Wind Energy Turbine Towers

2020 ◽  
Vol 184 ◽  
pp. 01094
Author(s):  
C Lavanya ◽  
Nandyala Darga Kumar
Keyword(s):  
Wind Energy ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Wind Farm ◽  
Wind Farms ◽  
Offshore Wind ◽  
Offshore Wind Farms ◽  
Wind Speeds ◽  
Deep Waters ◽  
Wind Turbine Towers

Wind energy is the renewable sources of energy and it is used to generate electricity. The wind farms can be constructed on land and offshore where higher wind speeds are prevailing. Most offshore wind farms employ fixed-foundation wind turbines in relatively shallow water. In deep waters floating wind turbines have gained popularity and are recent development. This paper discusses the various types of foundations which are in practice for use in wind turbine towers installed on land and offshore. The applicability of foundations based on depth of seabed and distance of wind farm from the shore are discussed. Also, discussed the improvement methods of weak or soft soils for the foundations of wind turbine towers.

Layout optimization for a large offshore wind farm using Genetic Algorithm

2020 ◽  
Author(s):  
K Narender Reddy ◽  
S Baidya Roy
Keyword(s):  
Genetic Algorithm ◽  
Wind Energy ◽  
Wind Farm ◽  
Selection Process ◽  
Wind Farms ◽  
Cost Effective ◽  
Layout Optimization ◽  
Offshore Wind ◽  
Previous Generation ◽  
Large Wind

<p>Wind Farm Layout Optimization Problem (WFLOP) is an important issue to be addressed when installing a large wind farm. Many studies have focused on the WFLOP but only for a limited number of turbines (10 – 100 turbines) and idealized wind speed distributions. In this study, we apply the Genetic Algorithm (GA) to solve the WFLOP for large wind farms using real wind data.</p><p>The study site is the Palk Strait located between India and Sri Lanka. This site is considered to be one of the two potential hotspots of offshore wind in India. An interesting feature of the site is that the winds here are dominated by two major monsoons: southwesterly summer monsoon (June-September) and northeasterly winter monsoon (November to January). As a consequence, the wind directions do not drastically change, unlike other sites which can have winds distributed over 360<sup>o</sup>. This allowed us to design a wind farm with a 5D X 3D spacing, where 5D is in the dominant wind direction and 3D is in the transverse direction (D- rotor diameter of the turbine - 150 m in this study).</p><p>Jensen wake model is used to calculate the wake losses. The optimization of the layout using GA involves building a population of layouts at each generation. This population consists of, the best layouts of the previous generation, crossovers or offspring from the best layouts of the previous generation and few mutated layouts. The best layout at each generation is assessed using the fitness or objective functions that consist of annual power production by the layout, cost incurred by layout per unit power produced, and the efficiency of the layout. GA mimics the natural selection process observed in nature, which can be summarised as survival of the fittest. At each generation, the layouts performing the best would enter the next generation where a new population is created from the best performing layouts.</p><p>GA is used to produce 3 different optimal layouts as described below. Results show that:</p><p>A ~5GW layout – has 738 turbines, producing 2.37 GW of power at an efficiency of 0.79</p><p>Layout along the coast – has 1091 turbines, producing 3.665 GW of power at an efficiency of 0.82.</p><p>Layout for the total area – has 2612 turbines, producing 7.82 GW of power at an efficiency of 0.74.</p><p>Thus, placing the turbines along the coast is more efficient as it makes the maximum use of the available wind energy and it would be cost-effective as well by placing the turbines closer to the shores.</p><p>Wind energy is growing at an unprecedented rate in India. Easily accessible terrestrial resources are almost saturated and offshore is the new frontier. This study can play an important role in the offshore expansion of renewables in India.</p>

scholarly journals A survey of modelling methods for high-fidelity wind farm simulations using large eddy simulation

2017 ◽  
Vol 375 (2091) ◽  
pp. 20160097 ◽  
Author(s):  
S.-P. Breton ◽  
J. Sumner ◽  
J. N. Sørensen ◽  
K. S. Hansen ◽  
S. Sarmast ◽  
...  
Keyword(s):  
Wind Energy ◽  
Wind Turbine ◽  
Wind Farm ◽  
Wind Farms ◽  
High Fidelity ◽  
Atmospheric Conditions ◽  
Eddy Simulation ◽  
Current State ◽  
Terrain Effects ◽  
Large Eddy

Large eddy simulations (LES) of wind farms have the capability to provide valuable and detailed information about the dynamics of wind turbine wakes. For this reason, their use within the wind energy research community is on the rise, spurring the development of new models and methods. This review surveys the most common schemes available to model the rotor, atmospheric conditions and terrain effects within current state-of-the-art LES codes, of which an overview is provided. A summary of the experimental research data available for validation of LES codes within the context of single and multiple wake situations is also supplied. Some typical results for wind turbine and wind farm flows are presented to illustrate best practices for carrying out high-fidelity LES of wind farms under various atmospheric and terrain conditions. This article is part of the themed issue ‘Wind energy in complex terrains’.

scholarly journals Contesting facts about wind farms in Australia and the legitimacy of adverse health effects

2017 ◽  
Vol 22 (4) ◽  
pp. 337-355 ◽  
Author(s):  
Shannon Clark ◽  
Linda Courtenay Botterill
Keyword(s):  
Wind Energy ◽  
Wind Turbine ◽  
Health Effects ◽  
Wind Farm ◽  
Wind Farms ◽  
Health Impacts ◽  
Health Complaints ◽  
Discursive Psychology ◽  
Adverse Health Effects ◽  
Research Interviews

The development of wind energy in Australia has been subject to ongoing public debate and has been characterised by concerns over the health impacts of wind turbines. Using discursive psychology, we examine ‘wind turbine syndrome’ as a contested illness and analyse how people build and undermine divergent arguments about wind-farm health effects. This article explores two facets of the dispute. First, we consider how participants construct ‘facts’ about the health effects of wind farms. We examine rhetorical resources used to construct wind farms as harmful or benign. Second, we examine the local negotiation of the legitimacy of health complaints. In the research interviews examined, even though interviewees treat those who report experiencing symptoms from wind farms as having primary rights to narrate their own experience, this epistemic primacy does not extend to the ability to ‘correctly’ identify symptoms’ cause. As a result, the legitimacy of health complaints is undermined. Wind turbine syndrome is an example of a contested illness that is politically controversial. We show how stake, interest and legitimacy are particularly relevant for participants’ competing descriptions about the ‘facts’ of wind turbine health effects.

The Effect of Wind and Wave Misalignment on the Response of a Wind Turbine at Bockstigen

2006 ◽  
Author(s):  
Jenny M. V. Trumars ◽  
Johan O. Jonsson ◽  
Lars Bergdahl
Keyword(s):  
Wind Energy ◽  
Wind Turbine ◽  
Wind Farm ◽  
Wind Wave ◽  
Wind Farms ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Wave Load ◽  
Design Requirements ◽  
The Waves

The aim of this work is to evaluate data from the offshore wind farm Bockstigen in order to study the effect of directional spreading of waves and wind wave misalignment on the response of the structure. The development of offshore wind energy has led to wind farms at sites with water depths ranging from approximately 6 to 30 m. The change of location from land to sea changes the design requirements of wind energy converters. In addition to wind loads, the wave load on the structure has to be taken into account. Since a wind turbine is highly damped in the inline direction as compared to the crosswise direction, the effect of directional spreading of waves on the response is studied. Depending on the dynamics of the structure the crosswise force could give a larger response than the corresponding inline force. In this study the influence of the directional spreading of the waves on the response is not clear, however the effect of wind and wave misalignment is clear.

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