New extractive frontiers in Ireland and the moebius strip of wind/data

2020 ◽  
pp. 251484862097012
Author(s):  
Patrick Bresnihan ◽  
Patrick Brodie
Keyword(s):  
Wind Farms ◽  
Business Environment ◽  
Wind Data ◽  
Data Systems ◽  
Environment Friendly ◽  
Energy Futures ◽  
Data Infrastructures ◽  
Moebius Strip ◽  
Automated Methods ◽  
Data Centres

This article maps the interconnections between two emergent resource frontiers in Ireland: wind and data. Adding to literature about extraction and extractivism, we account for how these expanded extractive frontiers are mobilised within self-sustaining and automated formations. In Ireland, digital infrastructures such as data centres are developed by multinational tech companies to avail of a naturally cool climate and business environment friendly to their investment, part of a wider extractive system by which data are made valuable for their expansive operations. Wind farms similarly make use of Ireland’s climate to generate energy, often used to power digital infrastructures, and are increasingly embedded within ‘smart’ energy and data systems. Wind and data are seen discretely as ‘abundant’ resources, their infrastructures built on terra or (offshore) mare nullius, and their operations ‘green’. However, their infrastructures are entangled with non-renewable energy systems and tax evasive capital, and built across existing communities and environments through policy, planning logics and increasingly automated methods of maintenance and optimisation. Through what we call ‘the moebius strip of wind/data’, wind and data infrastructures are increasingly formidable in dictating our energy futures. In this article, we articulate how they are connected and how we can disentangle them, especially in their operation across urban and rural geographies.

scholarly journals Offshore Wind Energy Resource Assessment across the Territory of Oman: A Spatial-Temporal Data Analysis

2021 ◽  
Vol 13 (5) ◽  
pp. 2862
Author(s):  
Amer Al-Hinai ◽  
Yassine Charabi ◽  
Seyed H. Aghay Kaboli
Keyword(s):  
Data Analysis ◽  
Wind Energy ◽  
Wind Turbines ◽  
Energy Resource ◽  
Wind Farms ◽  
Offshore Wind ◽  
Wind Data ◽  
Offshore Wind Energy ◽  
Offshore Wind Turbines ◽  
Wind Potential

Despite the long shoreline of Oman, the wind energy industry is still confined to onshore due to the lack of knowledge about offshore wind potential. A spatial-temporal wind data analysis is performed in this research to find the locations in Oman’s territorial seas with the highest potential for offshore wind energy. Thus, wind data are statistically analyzed for assessing wind characteristics. Statistical analysis of wind data include the wind power density, and Weibull scale and shape factors. In addition, there is an estimation of the possible energy production and capacity factor by three commercial offshore wind turbines suitable for 80 up to a 110 m hub height. The findings show that offshore wind turbines can produce at least 1.34 times more energy than land-based and nearshore wind turbines. Additionally, offshore wind turbines generate more power in the Omani peak electricity demand during the summer. Thus, offshore wind turbines have great advantages over land-based wind turbines in Oman. Overall, this work provides guidance on the deployment and production of offshore wind energy in Oman. A thorough study using bankable wind data along with various logistical considerations would still be required to turn offshore wind potential into real wind farms in Oman.

scholarly journals MIDAS: A Benchmarking Multi-Criteria Method for the Identification of Defective Anemometers in Wind Farms

Energies ◽  
2018 ◽  
Vol 12 (1) ◽  
pp. 28 ◽  
Author(s):  
Arkaitz Rabanal ◽  
Alain Ulazia ◽  
Gabriel Ibarra-Berastegi ◽  
Jon Sáenz ◽  
Unai Elosegui
Keyword(s):  
Wind Farm ◽  
Principal Component ◽  
Wind Farms ◽  
General Purpose ◽  
Wind Data ◽  
Kolmogorov Smirnov ◽  
Clear Identification ◽  
Novel Method ◽  
Verification Techniques ◽  
First Time

A novel multi-criteria methodology for the identification of defective anemometers is shown in this paper with a benchmarking approach: it is called MIDAS: multi-technique identification of defective anemometers. The identification of wrong wind data as provided by malfunctioning devices is very important, because the actual power curve of a wind turbine is conditioned by the quality of its anemometer measurements. Here, we present a novel method applied for the first time to anemometers’ data based on the kernel probability density function and the recent reanalysis ERA5. This estimation improves classical unidimensional methods such as the Kolmogorov–Smirnov test, and the use of the global ERA5’s wind data as the first benchmarking reference establishes a general method that can be used anywhere. Therefore, adopting ERA5 as the reference, this method is applied bi-dimensionally for the zonal and meridional components of wind, thus checking both components at the same time. This technique allows the identification of defective anemometers, as well as clear identification of the group of anemometers that works properly. After that, other verification techniques were used versus the faultless anemometers (Taylor diagrams, running correlation and R M S E , and principal component analysis), and coherent results were obtained for all statistical techniques with respect to the multidimensional method. The developed methodology combines the use of this set of techniques and was able to identify the defective anemometers in a wind farm with 10 anemometers located in Northern Europe in a terrain with forests and woodlands. Nevertheless, this methodology is general-purpose and not site-dependent, and in the future, its performance will be studied in other types of terrain and wind farms.

Technical and Economical Investigation of Wind Power Potential in Kuwait

2015 ◽  
Author(s):  
Yousef A. Gharbia ◽  
Haytham Ayoub
Keyword(s):  
Large Scale ◽  
Wind Farm ◽  
Wind Farms ◽  
Wind Data ◽  
Electricity Production ◽  
Specific Power ◽  
Wind Rose ◽  
Average Wind Speed ◽  
Prevailing Wind Direction ◽  
Power Curves

The State of Kuwait is considering diversifying its energy sector and not entirely depend on oil. This desire is motivated by Kuwait commitment to reducing its share of pollution, as a result of burning fossil fuel, and to extend the life of its oil and gas reserves. The potential for solar energy in Kuwait is quite obvious; however, it is not the case when it comes to wind energy. The aim of this work was to analyze wind data from several sites in Kuwait and assess their suitability for building large-scale wind farms. The analysis of hourly averaged wind data showed that some sites can have an average wind speed as high as 5.3 m/s at 10 m height. The power density using Weibull distribution function was calculated for the most promising sites. The prevailing wind direction for these sites was also determined using wind-rose charts. The power curves of several Gamesa turbines were used in order to identify the best turbine model in terms of specific power production cost. The results showed that the area of Abraq Al-Habari has the highest potential for building a large-scale wind farm. The payback period of investments was found to be around 7 years and the cost of electricity production was around US Cent 4/kWh.

scholarly journals Creating Synthetic Wind Speed Time Series for 15 New Zealand Wind Farms

2011 ◽  
Vol 50 (12) ◽  
pp. 2394-2409 ◽  
Author(s):  
Richard Turner ◽  
Xiaogu Zheng ◽  
Neil Gordon ◽  
Michael Uddstrom ◽  
Greg Pearson ◽  
...  
Keyword(s):  
Time Series ◽  
New Zealand ◽  
Wind Speed ◽  
Wind Farm ◽  
Central Government ◽  
Wind Farms ◽  
Practical Method ◽  
Wind Data ◽  
Regulatory Oversight ◽  
Short Period

AbstractWind data at time scales from 10 min to 1 h are an important input for modeling the performance of wind farms and their impact on many countries’ national electricity systems. Planners need long-term realistic (i.e., meteorologically spatially and temporally consistent) wind-farm data for projects studying how best to integrate wind power into the national electricity grid. In New Zealand, wind data recorded at wind farms are confidential for commercial reasons, however, and publicly available wind data records are for sites that are often not representative of or are distant from wind farms. In general, too, the public sites are at much lower terrain elevations than hilltop wind farms and have anemometers located at 10 m above the ground, which is much lower than turbine hub height. In addition, when available, the mast records from wind-farm sites are only for a short period. In this paper, the authors describe a novel and practical method to create a multiyear 10-min synthetic wind speed time series for 15 wind-farm sites throughout the country for the New Zealand Electricity Commission. The Electricity Commission (known as the Electricity Authority since 1 October 2010) is the agency that has regulatory oversight of the electricity industry and that provides advice to central government. The dataset was constructed in such a way as to preserve meteorological realism both spatially and temporally and also to respect the commercial secrecy of the wind data provided by power-generation companies.

Atmospheric Wind Data Collection and Wind Turbine Analysis in UAE

2009 ◽  
Author(s):  
Isam Janajreh ◽  
Rana Qudaih ◽  
Ilham Talab ◽  
Zaki Al Nahari
Keyword(s):  
Wind Energy ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Ecological Impact ◽  
Wind Farms ◽  
Offshore Wind ◽  
Wind Data ◽  
Gulf Region ◽  
Electrical Grid ◽  
Capital Costs

Wind turbine technology has improved dramatically in the last two decades as demonstrated by their plummeting capital costs ($0.08/KW), the enhanced reliability, and the increased efficiency. Large-scale wind turbines and wind farms provided 94.1GW of electrical grid capacity in 2007, and are expected to reach 160 GW by 2010 according to WWEA. Wind energy is plentiful and sustainable energy source with an estimated potential capacity of 72 TW. In Denmark the inland and offshore implementation of wind energy generation adds 1/5 of their electrical grid capacity. In Germany, it is forecasted to attain 12.5% by early 2010. Offshore wind farms have lower ecological impact due to lack of land mortgage, easier transportation, and low perception of noise issue. In the gulf region, the generated power can fulfill the power needs of UAE’s islands, while the excess capacity can be channeled to the inland grids fulfilling the peak demand. In this work we will investigate the implementation of low-turning moment wind turbines in the UAE, suited for low wind speeds (∼3–5m/s) and that consists of two research components: (i) Collection of wind data, analysis, recommendation for implementation strategies, and using Masdar wind data to assess its characteristics and its fit for wind turbine implementation; (ii) Carry out flow analysis on a downwind, two-bladed, horizontal-axes wind turbine to investigate the flow lift, drag and wake characteristics on the tower blade interaction. The interaction is studied utilizing Arbitrary Lagrangian Eulerian method. Downwind turbines are self-aligned, pass up yaw mechanisms and its needed power, and have fewer moving parts that necessitate regular maintenance. These factors however play in favor of wind turbine that is subjected to low wind speed.

scholarly journals ASSESSMENT OF IEC 61400-1 NORMAL TURBULENCE MODEL FOR WIND CONDITIONS IN TAIWAN WEST COAST AREAS

2014 ◽  
Vol 34 ◽  
pp. 1460382 ◽  
Author(s):  
TZONG-SHYNG LEU ◽  
JUI-MING YO ◽  
YI-TING TSAI ◽  
JIU-JIH MIAU ◽  
TA-CHUNG WANG ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Turbulence Model ◽  
West Coast ◽  
Wind Farms ◽  
Offshore Wind ◽  
Wind Data ◽  
Model Parameters ◽  
Observation Data ◽  
Offshore Wind Farms ◽  
Proposed Model

This paper studies the applicability of Normal Turbulence Model (NTM) in IEC61400-1 for wind conditions in Taiwan west coast area where future offshore wind farms are planning in the nearby areas. The parameters for the standard deviation of wind fluctuating [Formula: see text] are presented and compared with IEC Normal Turbulence Model. It is found that the trend of turbulence standard deviation [Formula: see text] based on the observation data agreed qualitatively well with IEC Normal Turbulence Model. However, IEC Normal Turbulence Model results in rather small [Formula: see text] compared to surveillance wind data in Taiwan. In this paper, model parameters for [Formula: see text] and [Formula: see text] based on the two-year observation wind data are proposed. The proposed model parameters a, b, α and β are 0.9125, 2.4345, 0.097 and 2.1875.

scholarly journals Optimal Configuration of Wind Farms in Radial Distribution System Using Particle Swarm Optimization Technique

2018 ◽  
Vol 7 (2) ◽  
pp. 286-293 ◽  
Author(s):  
Mahesh Kumar ◽  
Bhagwan Das ◽  
Perumal Nallagownden ◽  
Irraivan Elamvazuthi ◽  
Sadia Ali Khan
Keyword(s):  
Particle Swarm Optimization ◽  
Radial Distribution ◽  
Distribution System ◽  
Particle Swarm ◽  
Wind Farms ◽  
Optimal Location ◽  
Optimal Configuration ◽  
Wind Data ◽  
Swarm Optimization ◽  
Radial Distribution System

Recently, a wide range of wind farm based distributed generations (DGs) are being integrated into distribution systems to fulfill energy demands and to reduce the burden on transmission corridors. The non-optimal configuration of DGs could severely affect the distribution system operations and control. Hence, the aim of this paper is to analyze the wind data in order to build a mathematical model for power output and pinpoint the optimal location. The overall objective is minimization of power loss reduction in distribution system. The five years of wind data was taken from 24o 44’ 29” North, 67o 35’ 9” East coordinates in Pakistan. The optimal location for these wind farms were pinpointed via particle swarm optimization (PSO) algorithm using standard IEEE 33 radial distribution system. The result reveals that the proposed method helps in improving renewable energy near to load centers, reduce power losses and improve voltage profile of the system. Moreover, the validity and performance of the proposed model were also compared with other optimization algorithms.

scholarly journals A Casual Study on Impact of Green HRM Practices on Organisation Sustainability

2020 ◽  
Vol 19 (4) ◽  
pp. 19-26
Author(s):  
Swarnalatha V
Keyword(s):  
Business Environment ◽  
Review Of The Literature ◽  
Industrial Accidents ◽  
Environment Friendly ◽  
Business World ◽  
Hrm Practices ◽  
Financial Perspective ◽  
The World ◽  
Excessive Consumption ◽  
General Business

Nowadays, our business environment has changed itsview from a traditional financial perspective tocompetency based and strategic based perspective. Thegreen movement across the world has given birth to theconcept of Green HRM. The concept became popularowing to the issues in the present business world, rangingfrom excessive consumption of natural resources, carboncredits, global warming, to pollution. All these results inserious industrial accidents decrease in life span, changesin climate, etc. In general, Green HR deals with practicingall HR activities with environment-friendly concerns.This, in turn, helps in the sustainability of businesses aswell as the employees. Nevertheless, there is a wide crackbetween the HRM and environmental facets. Thereforethere is an emerging requirement for research inarticulating green HR approaches and their suggestionson general business execution. The reason behind thisinvestigation is to talk about the ideas of green HRM andits impact on organisation sustainability with the help ofextensive review of the literature.

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