DESIGNING RELIABLY FOR WIND ENERGY

1988 ◽  
Vol 12 (3) ◽  
pp. 173-178 ◽  
Author(s):  
H. ALAWI
Keyword(s):  
Wind Speed ◽  
Probability Density Function ◽  
Wind Energy ◽  
Computer Program ◽  
Probability Density ◽  
Wind Power ◽  
Energy Production ◽  
Storage System ◽  
Power Production ◽  
Production Plant

In this study the methodology for probabilistic modeling of wind speed using computer oriented techniques is presented. This approach makes it possible to simulate the wind speed on a computer thereby allowing for proper planning and reliably designing matters involving wind. The computer program developed is used to simulate power production and to determine probability density function of wind power produced for reliability purposes and idle times of a wind power production plant for determining the back-up storage system needed. An example is given on wind energy production and standby storage requirements for Kuwait during the month of January.

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

2020 ◽  
pp. 014459872092074 ◽  
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.

Impact of weather regimes on wind power variability in western Europe

2020 ◽  
Author(s):  
Ricardo García-Herrera ◽  
Jose M. Garrido-Perez ◽  
Carlos Ordóñez ◽  
David Barriopedro ◽  
Daniel Paredes
Keyword(s):  
High Resolution ◽  
Wind Speed ◽  
Wind Energy ◽  
Wind Power ◽  
Large Scale ◽  
Western Europe ◽  
Power Production ◽  
Large Scale Circulation ◽  
Weather Regimes ◽  
The Uk

<p><span><span>We have examined the applicability of a new set of 8 tailored weather regimes (WRs) to reproduce wind power variability in Western Europe. These WRs have been defined using a substantially smaller domain than those traditionally used to derive WRs for the North Atlantic-European sector, in order to maximize the large-scale circulation signal on wind power in the region of study. Wind power is characterized here by wind capacity factors (CFs) from a meteorological reanalysis dataset and from high-resolution data simulated by the Weather Research and Forecasting (WRF) model. We first show that WRs capture effectively year-round onshore wind power production variability across Europe, especially over northwestern / central Europe and Iberia. Since the influence of the large-scale circulation on wind energy production is regionally dependent, we have then examined the high-resolution CF data interpolated to the location of more than 100 wind farms in two regions with different orography and climatological features, the UK and the Iberian Peninsula. </span></span></p><p><span><span>The use of WRs allows discriminating situations with varied wind speed distributions and power production in both regions. In addition, the use of their monthly frequencies of occurrence as predictors in a multi-linear regression model allows explaining up to two thirds of the month-to-month CF variability for most seasons and sub-regions. These results outperform those previously reported based on Euro-Atlantic modes of atmospheric circulation. The improvement achieved by the spatial adaptation of WRs to a relatively small domain seems to compensate for the reduction in explained variance that may occur when using yearly as compared to monthly or seasonal WR classifications. In addition, our annual WR classification has the advantage that it allows applying a consistent group of WRs to reproduce day-to-day wind speed variability during extreme events regardless of the time of the year. As an illustration, we have applied these WRs to two recent periods such as the wind energy deficit of summer 2018 in the UK and the surplus of March 2018 in Iberia, which can be explained consistently by the different combinations of WRs.</span></span></p>

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

2014 ◽  
Vol 25 (3) ◽  
pp. 2-10 ◽  
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.

Weibull Probability Distribution of Wind Speed for Gaza Strip for 10 Years

2019 ◽  
Vol 892 ◽  
pp. 284-291
Author(s):  
Ahmed S.A. Badawi ◽  
Nurul Fadzlin Hasbullah ◽  
Siti Hajar Yusoff ◽  
Sheroz Khan ◽  
Aisha Hashim ◽  
...  
Keyword(s):  
Wind Speed ◽  
Probability Density Function ◽  
Probability Distribution ◽  
Wind Energy ◽  
Probability Density ◽  
Density Function ◽  
Gaza Strip ◽  
Actual Data ◽  
Average Wind Speed ◽  
Average Wind

The need of clean and renewable energy, as well as the power shortage in Gaza strip with few wind energy studies conducted in Palestine, provide the importance of this paper. Probability density function is commonly used to represent wind speed frequency distributions for the evaluation of wind energy potential in a specific area. This study shows the analysis of the climatology of the wind profile over the State of Palestine; the selections of the suitable probability density function decrease the wind power estimation error percentage. A selection of probability density function is used to model average daily wind speed data recorded at for 10 years in Gaza strip. Weibull probability distribution function has been estimated for Gaza based on average wind speed for 10 years. This assessment is done by analyzing wind data using Weibull probability function to find out the characteristics of wind energy conversion. The wind speed data measured from January 1996 to December 2005 in Gaza is used as a sample of actual data to this study. The main aim is to use the Weibull representative wind data for Gaza strip to show how statistical model for Gaza Strip over ten years. Weibull parameters determine by author depend on the pervious study using seven numerical methods, Weibull shape factor parameter is 1.7848, scale factor parameter is 4.3642 ms-1, average wind speed for Gaza strip based on 10 years actual data is 2.95 ms-1 per a day so the behavior of wind velocity based on probability density function show that we can produce energy in Gaza strip.

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

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

<p>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.</p><p>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.</p><p>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.</p>

Techno-economic analysis of wind energy potential in Kazakhstan

2021 ◽  
pp. 095765092110015
Author(s):  
Hamed H Pourasl ◽  
Vahid M Khojastehnezhad
Keyword(s):  
Wind Speed ◽  
Energy Density ◽  
Wind Energy ◽  
Wind Power ◽  
Energy Production ◽  
Energy Utilization ◽  
Energy Potential ◽  
Average Wind Speed ◽  
Average Wind ◽  
Wind Energy Potential

The use of renewable energy as a future energy source is attracting considerable research interest globally. In particular, there is a significant growth in wind energy utilization during the last few years. This present study through a detailed and systematic literature survey assesses the wind energy potential of Kazakhstan for the first time. Using the Weibull distribution function and hourly wind speed data, the annual power and energy density of the sites are calculated. For the 50 sites considered in this study and at a height of 10 m above the ground, the annual average wind speed, the power density, and energy production of Kazakhstan range from 0.94–5.15 m/s, 4.50–169.34 W/m2 and 39.56–1502.50 kWh/m2/yr, respectively. It was found that Fort Sevcenko, Atbasar, and Akmola are the three best locations for wind turbine installation with wind power densities of 169.34, 135.30, and 111.51 W/m2, respectively. Fort Sevcenko demonstrates the highest potential for wind energy harvesting with an energy density of 1483.46 kWh/m2/yr. For the 15 commercial wind turbines, it was observed that the annual energy production of the selected turbines ranges between 3.8 GWh/yr in Petropavlovsk to 15.4 GWh/yr in Fort Sevcenko among the top six locations. The lowest and highest capacity factors correspond to the same sites with the values of 29.21% and 58.66%, respectively. Overall, it is the intention of this study to constitute a database for the users and developers of wind power in Kazakhstan.

scholarly journals An Isofactorial Change-of-Scale Model for the Wind Speed Probability Density Function

2010 ◽  
Vol 27 (2) ◽  
pp. 257-273 ◽  
Author(s):  
Mark L. Morrissey ◽  
Angie Albers ◽  
J. Scott Greene ◽  
Susan Postawko
Keyword(s):  
Wind Speed ◽  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Wind Power ◽  
Power Density ◽  
Scale Model ◽  
Subgrid Scale ◽  
Wind Power Density ◽  
Time Space

Abstract The wind speed probability density function (PDF) is used in a variety of applications in meteorology, oceanography, and climatology usually as a dataset comparison tool of a function of a quantity such as momentum flux or wind power density. The wind speed PDF is also a function of measurement scale and sampling error. Thus, quantities derived from a function of the wind PDF estimated from measurements taken at different scales may yield vastly different results. This is particularly true in the assessment of wind power density and studies of model subgrid-scale processes related to surface energy fluxes. This paper presents a method of estimating the PDF of wind speed representing a specific scale, whether that is in time, space, or time–space. The concepts used have been developed in the field of nonlinear geostatistics but have rarely been applied to meteorological problems. The method uses an expansion of orthogonal polynomials that incorporates a scaling parameter whose values can be found from the variance of wind speed at the desired scale. Possible uses of this technique are for scale homogenization of model or satellite datasets used in comparison studies, investigations of subgrid-scale processes for development of parameterization schemes, or wind power density assessment.

scholarly journals Comparison of Optimum Spline-Based Probability Density Functions to Parametric Distributions for the Wind Speed Data in Terms of Annual Energy Production

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 3190 ◽  
Author(s):  
Munir Elfarra ◽  
Mustafa Kaya
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Probability Density ◽  
Energy Production ◽  
Optimization Problems ◽  
Feasibility Studies ◽  
Economic Assessment ◽  
Probability Density Functions ◽  
Density Functions ◽  
Wind Speed Data

The common approach to wind energy feasibility studies is to use Weibull distribution for wind speed data to estimate the annual energy production (AEP). However, if the wind speed data has more than one mode in the probability density, the conventional distributions including Weibull fail to fit the wind speed data. This highly affects the technical and economic assessment of a wind energy project by causing crucial errors. This paper presents a novel way to define the probability density for wind speed data using splines. The splines are determined as a solution of constrained optimization problems. The constraints are the characteristics of probability density functions. The proposed method is implemented for different wind speed distributions including multimodal data and compared with Weibull, Weibull and Weibull and Beta Exponentiated Power Lindley (BEPL) distributions. It is also compared with two other nonparametric distributions. The results show that the spline-based probability density functions produce a minimum fitting error for all the analyzed cases. The AEP calculated based on this method is considered to have high fidelity, which will decrease the investment risk.

Twelve Years Energy Production Assessment of Greek State Wind Parks

2003 ◽  
Vol 27 (3) ◽  
pp. 215-226 ◽  
Author(s):  
J.K. Kaldellis ◽  
D.S. Vlachou ◽  
A.G. Paliatsos
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Electricity Market ◽  
Time Variation ◽  
Energy Production ◽  
Power Production ◽  
Power Capacity ◽  
Public Power ◽  
Power Installations ◽  
Energy Exploitation

In 1982, the Greek State started an ambitious wind energy exploitation program for that period, via the State owned Public Power Corporation. Now this 20 year effort faces serious obstacles and drawbacks, with an installed wind power capacity of only 37 MW. This paper analyses the life-long energy production of State owned wind power installations, with respect to the available wind speed data. A significant result is the erratic time-variation of the wind power production, not justified by the corresponding wind speed changes. In addition, the technical availability of most State wind parks has been rather low, despite significant improvement during the last five years. The overall conclusions have importance within the new liberalized European electricity market.

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