Long-term climate change effects in the European offshore wind energy

Mapping Intimacies ◽

10.5194/egusphere-egu21-7671 ◽

2021 ◽

Author(s):

Stefano Susini ◽

Melisa Menendez

Keyword(s):

Climate Change ◽

Wind Speed ◽

Wind Energy ◽

Wind Power ◽

Power Density ◽

Climate Change Impact ◽

Energy Production ◽

Offshore Wind ◽

Change Impact

Climate change and offshore renewable energy sector are connected by a double nature link. Even though energy generation from clean marine sources is one of the strategies to reduce climate change impact within next decades, it is expected that large scale modification of circulation patterns will have in turn an impact on the spatial and temporal distribution of the wind fields. Under the WINDSURFER project of the ERA4CS initiative, we analyse the climate change impact on marine wind energy resource for the European offshore wind energy sector. Long-term changes in specific climate indicators are evaluated over the European marine domain (e.g. wind power density, extreme winds, operation hours) as well as local indicators (e.g. gross energy yield, capacity factor) at several relevant operating offshore wind farms.Adopting an ensemble approach, we focus on the climate change greenhouse gases scenario RCP8.5 during the end of the century (2081-2100 period) and analyze the changes and uncertainty of the resulting multi-model from seven high resolution Regional Climate Models (RCM) realized within Euro-Cordex initiative (EUR-11, ~12.5km). ERA5 reanalysis and in-situ offshore measurements are the historical data used in present climate.Results indicate a small decrease of wind energy production, testified by reduction of the climatological indicators of wind speed and wind power density, particularly in the NW part of the domain of study. The totality of the currently operating offshore windfarms is located in this area, where a decrease up to 20% in the annual energy production is expected by the end of the century, accompanied by a reduction of the operation hours between 5 and 8%. Exceptions are represented by Aegean and Baltic Sea, where these indicators are expected to slightly increase. Extreme storm winds however show a different spatial pattern of change. The wind speed associated to 50 years return period decreases within western Mediterranean Sea and Biscay Bay, while increases in the remaining part of the domain (up to 15% within Aegean and Black Sea). Finally, the estimated variations in wind direction are relevant on the Biscay Bay region.

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Climate change impacts on wind power density over southeastern Mediterranean

10.5194/egusphere-egu2020-1546 ◽

2020 ◽

Author(s):

Chris G. Tzanis ◽

Kostas Philippopoulos ◽

Constantinos Cartalis ◽

Konstantinos Granakis ◽

Anastasios Alimissis ◽

...

Keyword(s):

Climate Change ◽

Wind Speed ◽

Wind Energy ◽

Wind Power ◽

Power Density ◽

Energy Production ◽

Energy Potential ◽

Wind Power Density ◽

Climatic Scenarios ◽

Wind Energy Potential

Energy production from the utilization of wind energy potential depends on the variability of the wind field as determined by the interaction of natural processes on different scales. Global climate change can cause alterations in the surface wind and thus it may affect the geographical distribution and the wind energy potential variability. Wind energy production is sensitive to wind speed changes, especially in the upper percentile of the wind speed distributions, where energy production is more effective. The importance of wind energy production changes is enhanced by the fact that wind energy investments are long-term and are characterized by high initial costs and low operating costs. In the present study, these changes are examined for the southeastern Mediterranean region, based on simulations of the Regional Climate Model ALADIN 5.2 extracted from the Med-CORDEX database for the climatic scenarios RCP4.5 and RCP8.5. The results indicate a wind power density increase over the Aegean Sea, the Ionian Sea, the Dardanelles and the Black Sea, with similar levels of increase for both climatic scenarios. In contrast, during the winter period there is a decline across the southeastern Mediterranean, which is more significant in the case of the RCP8.5 scenario. Finally, for most areas of eastern Greece, there is a reduction in the number of wind speed cases for both below and above cut-in and cut-out wind speeds, while there is an increase in the number of wind speed cases that wind turbines operate at their maximum power. The results are expected to reduce the uncertainty associated with the impact of climate change on wind energy production.&#160;

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Wind potential estimation and proposed energy production in Southern Punjab using Weibull probability density function and surface measured data

Energy Exploration & Exploitation ◽

10.1177/0144598720920748 ◽

2020 ◽

pp. 014459872092074 ◽

Cited By ~ 2

Author(s):

Muhammad Sumair ◽

Tauseef Aized ◽

Syed Asad Raza Gardezi ◽

Syed Ubaid Ur Rehman ◽

Syed Muhammad Sohail Rehman

Keyword(s):

Wind Speed ◽

Energy Density ◽

Wind Energy ◽

Wind Power ◽

Power Density ◽

Energy Production ◽

Economic Assessment ◽

Weibull Model ◽

Wind Power Density ◽

Wind Potential

Current work focusses on the wind potential assessment in South Punjab. Eleven locations from South Punjab have been analyzed using two-parameter Weibull model (with Energy Pattern Factor Method to estimate Weibull parameters) and five years (2014–2018) hourly wind data measured at 50 m height and collected from Pakistan Meteorological Department. Techno-economic analysis of energy production using six different turbine models was carried out with the purpose of presenting a clear picture about the importance of turbine selection at particular location. The analysis showed that Rahim Yar Khan carries the highest wind speed, highest wind power density, and wind energy density with values 4.40 ms−1, 77.2 W/m2 and 677.76 kWh/m2/year, respectively. On the other extreme, Bahawalnagar observes the least wind speed i.e. 3.60 ms−1 while Layyah observes the minimum wind power density and wind energy density as 38.96 W/m2 and 352.24 kWh/m2/year, respectively. According to National Renewable Energy Laboratory standards, wind potential ranging from 0 to 200 W/m2 is considered poor. Economic assessment was carried out to find feasibility of the location for energy harvesting. Finally, Polar diagrams drawn to show the optimum wind blowing directions shows that optimum wind direction in the region is southwest.

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A case study of climate variability effects on wind resources in South Africa

Journal of Energy in Southern Africa ◽

10.17159/2413-3051/2014/v25i3a2652 ◽

2014 ◽

Vol 25 (3) ◽

pp. 2-10 ◽

Cited By ~ 1

Author(s):

Lynette Herbst ◽

Jörg Lalk

Keyword(s):

Climate Change ◽

Wind Speed ◽

Wind Energy ◽

Wind Turbine ◽

Wind Power ◽

Energy Production ◽

Energy Sector ◽

Speed Increase ◽

Wind Resources ◽

Wind Speed Increase

The wind energy sector is one of the most prominent sectors of the renewable energy industry. However, its dependence on meteorological factors subjects it to climate change. Studies analysing the impact of climate change on wind resources usually only model changes in wind speed. Two elements that have to be calculated in addition to wind speed changes are Annual Energy Production (AEP) and Power Density (PD). This is not only because of the inherent variability between wind speed and wind power generated, but also because of the relative magnitudes of change in energy potentially generated at different areas under varied wind climates. In this study, it was assumed that two separate locations would experience a 10% wind speed increase after McInnes et al. (2010). Given the two locations’ different wind speed distributions, a wind speed increase equal in magnitude is not equivalent to similar magnitudes of change in potential energy production in these areas. This paper demonstrates this fact for each of the case studies. It is of general interest to the energy field and is of value since very little literature exists in the Southern African context on climate change- or variability-effects on the (wind) energy sector. Energy output is therefore dependent not only on wind speed, but also wind turbine characteristics. The importance of including wind power curves and wind turbine generator capacity in wind resource analysis is emphasised.

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Seasonal Correction of Offshore Wind Energy Potential due to Air Density: Case of the Iberian Peninsula

Sustainability ◽

10.3390/su11133648 ◽

2019 ◽

Vol 11 (13) ◽

pp. 3648 ◽

Cited By ~ 7

Author(s):

Alain Ulazia ◽

Gabriel Ibarra-Berastegi ◽

Jon Sáenz ◽

Sheila Carreno-Madinabeitia ◽

Santos J. González-Rojí

Keyword(s):

Wind Energy ◽

Iberian Peninsula ◽

Wind Power ◽

Power Density ◽

Energy Production ◽

Offshore Wind ◽

Power Curve ◽

Energy Potential ◽

Air Density ◽

Wind Energy Potential

A constant value of air density based on its annual average value at a given location is commonly used for the computation of the annual energy production in wind industry. Thus, the correction required in the estimation of daily, monthly or seasonal wind energy production, due to the use of air density, is ordinarily omitted in existing literature. The general method, based on the implementation of the wind speed’s Weibull distribution over the power curve of the turbine, omits it if the power curve is not corrected according to the air density of the site. In this study, the seasonal variation of air density was shown to be highly relevant for the computation of offshore wind energy potential around the Iberian Peninsula. If the temperature, pressure, and moisture are taken into account, the wind power density and turbine capacity factor corrections derived from these variations are also significant. In order to demonstrate this, the advanced Weather Research and Forecasting mesoscale Model (WRF) using data assimilation was executed in the study area to obtain a spatial representation of these corrections. According to the results, the wind power density, estimated by taking into account the air density correction, exhibits a difference of 8% between summer and winter, compared with that estimated without the density correction. This implies that seasonal capacity factor estimation corrections of up to 1% in percentage points are necessary for wind turbines mainly for summer and winter, due to air density changes.

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Framework for assessment of climate change impact on offshore wind energy

Meteorological Applications ◽

10.1002/met.1673 ◽

2017 ◽

Vol 25 (1) ◽

pp. 94-104 ◽

Cited By ~ 4

Author(s):

Sumeet Kulkarni ◽

M. C. Deo ◽

Subimal Ghosh

Keyword(s):

Climate Change ◽

Wind Energy ◽

Climate Change Impact ◽

Offshore Wind ◽

Offshore Wind Energy ◽

Change Impact

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Climate change impact on Northwestern African offshore wind energy resources

Environmental Research Letters ◽

10.1088/1748-9326/ab5731 ◽

2019 ◽

Vol 14 (12) ◽

pp. 124065 ◽

Cited By ~ 2

Author(s):

Pedro M M Soares ◽

Daniela C A Lima ◽

Alvaro Semedo ◽

William Cabos ◽

Dmitry V Sein

Keyword(s):

Climate Change ◽

Wind Energy ◽

Climate Change Impact ◽

Energy Resources ◽

Offshore Wind ◽

Offshore Wind Energy ◽

Change Impact ◽

Wind Energy Resources

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Evaluation of the Technical Wind Energy Potential of Kisii Region Based on the Weibull and Rayleigh Distribution Models

Journal of Energy ◽

10.1155/2021/6627509 ◽

2021 ◽

Vol 2021 ◽

pp. 1-17

Author(s):

N. Laban Ongaki ◽

Christopher M. Maghanga ◽

Joash Kerongo

Keyword(s):

Wind Speed ◽

Wind Energy ◽

Wind Power ◽

Power Density ◽

Series Data ◽

Energy Potential ◽

Distribution Models ◽

Wind Power Density ◽

Wind Speeds

Background. Global warming is a growing threat in the world today mainly due to the emission of CO2 caused by the burning of fossil fuel. Consequently, countries are being forced to seek potential alternative sources of energy such as wind, solar, and photovoltaic among many others. However, the realization of their benefits is faced with challenges. Though wind stands a chance to solve this problem, the lack of adequate site profiles, long-term behavioural information, and specific data information that enables informed choice on site selection, turbine selection, and expected power output has remained a challenge to its exploitation. In this research, Weibull and Rayleigh models are adopted. Wind speeds were analyzed and characterized in the short term and then simulated for a long-term measured hourly series data of daily wind speeds at a height of 10 m. The analysis included daily wind data which was grouped into discrete data and then calculated to represent the mean wind speed, diurnal variations, daily variations, and monthly variations. To verify the models, statistical tools of Chi square, RMSE, MBE, and correlational coefficient were applied. Also, the method of measure, correlate, and predict was adopted to check for the reliability of the data used. The wind speed frequency distribution at the height of 10 m was found to be 2.9 ms-1 with a standard deviation of 1.5. From the six months’ experiments, averages of wind speeds at hub heights of 10 m were calculated and found to be 1.7 m/s, 2.4 m/s, and 1.3 m/s, for Ikobe, Kisii University, and Nyamecheo stations, respectively. The wind power density of the region was found to be 29 W/m2. By a narrow margin, Rayleigh proves to be a better method over Weibull in predicting wind power density in the region. Wind speeds at the site are noted to be decreasing over the years. The region is shown as marginal on extrapolation to 30 m for wind energy generation hence adequate for nongrid connected electrical and mechanical applications. The strong correlation between the site wind profiles proves data reliability. The gradual decrease of wind power over the years calls for attention.

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Analysis and Distribution of Wind Speed in Saudi Arabia

10.21203/rs.3.rs-153916/v1 ◽

2021 ◽

Author(s):

Y. Al-Douri ◽

S. A. Waheeb

Keyword(s):

Saudi Arabia ◽

Wind Speed ◽

Wind Energy ◽

Wind Power ◽

Power Density ◽

Frequency Distribution ◽

North West ◽

Wind Power Density ◽

Wind Speeds

Abstract Background: the purpose of this study is to estimate the winds, its erosion and wind power of Kingdom of Saudi Arabia. The additional value is to details wind energy in Saudi Arabia indulging updated wind speed analysis, wind speed frequency distribution and mean wind power density variation to present novel work could be added to the literature providing recent data helping for future researches and studies. Results: the updated analysis and distribution of wind energy are presented in six sites; Al Jouf, Hafar Al Batin, Riyadh, Al Wajh, Jeddah South and Sharurah of Saudi Arabia. The winds and wind energy are elaborated. The long-term annual mean values of wind speeds are found to vary between year 2000 and 2020. The annual values of wind power density are varied between year 2006 and 2020. Also, the wind speeds are researched over the entire geography of Saudi Arabia. The percent frequency distribution at different wind speeds for the mentioned six sites at 12 m for two decades is displayed. Conclusions: the long-term values of wind speed were found between 3.3 m/s in 2000 and 5 m/s in 2020. The annual wind power density values were varied between 44 W/m2 in 2006 and 88 W/m2 in 2020. In addition, the wind speeds were researched over the entire Saudi Arabia for east, north, west and south. The deduced percent frequency distribution was less than 18% of the time at 12 m.

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