scholarly journals Electricity Generation Potential and Energy Cost of Wind Conversion Systems in Ikeja Southwest Nigeria

2020 ◽  
Vol 28 (4) ◽  
Author(s):  
Adetona Tayo Fatigun ◽  
Ebenezer Babatope Faweya ◽  
Funmilola Olusola Ogunlana ◽  
Taiwo Hassan Akande

In this study, the wind electricity generation potential and energy cost at Ikeja were investigated using 31 years wind speed data obtained from Nigeria Meteorological Agency. The study addresses the challenges of inadequate electricity supply and the development of alternative source of electricity. The measured data, captured at 10m height were subjected to 2-parameter Weibull and other statistical analysis. Weibull analysis of wind speed showed good fit between actual data and Weibull predicted data confirming the adequacy of the model. The value of wind speed at 10m height ranged between 3.47m/s and 5.33m/s with annual average of 4.5m/s. Also, the Wind Power Density (WPD) ranged between 116.3 W/m² and 423.3W/m² with annual average value of 257.85W/m². The mean electric power outputs from the model turbines varied between 11KW and 290KW while its Capacity Factor (CF) ranged between 13.8% and 0.36%. Also, the generation cost per kilowatt-hour varied between $0.11 and $2.39 annually. Therefore, the wind energy potential at Ikeja could be adjudged marginal and belonging to wind power class 2. The generation cost of wind electricity is cost-effective in the months of April and August while cost-deficit in the remaining months of the year. The location is considered suitable for small to medium scale wind power generation, but economically infeasible for large scale grid connected wind electricity generation.

2019 ◽  
Vol 38 (1) ◽  
pp. 175-200 ◽  
Author(s):  
Shafiqur Rehman ◽  
Narayanan Natarajan ◽  
Mangottiri Vasudevan ◽  
Luai M Alhems

Wind energy is one of the abundant, cheap and fast-growing renewable energy sources whose intensive extraction potential is still in immature stage in India. This study aims at the determination and evaluation of wind energy potential of three cities located at different elevations in the state of Tamil Nadu, India. The historical records of wind speed, direction, temperature and pressure were collected for three South Indian cities, namely Chennai, Erode and Coimbatore over a period of 38 years (1980-2017). The mean wind power density was observed to be highest at Chennai (129 W/m2) and lowest at Erode (76 W/m2) and the corresponding mean energy content was highest for Chennai (1129 kWh/m2/year) and lowest at Erode (666 kWh/m2/year). Considering the events of high energy-carrying winds at Chennai, Erode and Coimbatore, maximum wind power density were estimated to be 185 W/m2, 190 W/m2 and 234 W/m2, respectively. The annual average net energy yield and annual average net capacity factor were selected as the representative parameters for expressing strategic wind energy potential at geographically distinct locations having significant variation in wind speed distribution. Based on the analysis, Chennai is found to be the most suitable site for wind energy production followed by Coimbatore and Erode.


2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Kasra Mohammadi ◽  
Ali Mostafaeipour ◽  
Yagob Dinpashoh ◽  
Nima Pouya

Currently, wind energy utilization is being continuously growing so that it is regarded as a large contender of conventional fossil fuels. This study aimed at evaluating the feasibility of electricity generation using wind energy in Jarandagh situated in Qazvin Province in north-west part of Iran. The potential of wind energy in Jarandagh was investigated by analyzing the measured wind speed data between 2008 and 2009 at 40 m height. The electricity production and economic evaluation of four large-scale wind turbine models for operation at 70 m height were examined. The results showed that Jarandagh enjoys excellent potential for wind energy exploitation in 8 months of the year. The monthly wind power at 70 m height was in the range of 450.28–1661.62 W/m2, and also the annual wind power was 754.40 W/m2. The highest capacity factor was obtained using Suzlon S66/1.25 MW turbine model, while, in terms of electricity generation, Repower MM82/2.05 MW model showed the best performance with total annual energy output of 5705 MWh. The energy cost estimation results convincingly demonstrated that investing on wind farm construction using all nominated turbines is economically feasible and, among all turbines, Suzlon S66/1.25 MW model with energy cost of 0.0357 $/kWh is a better option.


Author(s):  
Watchara Saeheng ◽  
Piyanut Saengsikhiao ◽  
Juntakan Taweekun

Over the past decades, Wind energy is one of the alternative energy or renewable sources, which has been harvested to produce electricity. Our research aims to study the wind potential of the areas in the Rayong provinces of Thailand. Data from meteorological stations were collected every 10 minutes for of 3 years (2017-2019), with a measuring tower at 10-meter height above ground level (AGL). The annual average wind speeds were investigated in Rayong (2.02 m/s) with Weibull Probability Distribution Function (PDF). The annual average power density in Rayong regions was 13 W/m2. In all locations, wind direction was detected mainly from Southwest (SSW) and the yearly maximum wind power capacity is 94.376 MWh. The capacity factor of 21.5 % was noticed. With relatively low wind speed was noticed in Rayong provinces of Thailand, a small wind turbine installed at 30 meters would be recommended as a cost-effective way to convert wind power to electricity.


Author(s):  
Phan Nguyen Vinh ◽  
Bach Hoang Dinh ◽  
Van-Duc Phan ◽  
Hung Duc Nguyen ◽  
Thang Trung Nguyen

Wind power plants (WPs) play a very important role in the power systems because thermal power plants (TPs) suffers from shortcomings of expensive cost and limited fossil fuels. As compared to other renewable energies, WPs are more effective because it can produce electricity all a day from the morning to the evening. Consequently, this paper integrates the optimal power generation of TPs and WPs to absolutely exploit the energy from WPs and reduce the total electricity generation cost of TPs. The target can be reached by employing a proposed method, called one evaluation-based cuckoo search algorithm (OEB-CSA), which is developed from cuckoo search algorithm (CSA). In addition, conventional particle swarm optimization (PSO) is also implemented for comparison. Two test systems with thirty TPs considering prohibited working zone and power reserve constraints are employed. The first system has one wind power plant (WP) while the second one has two WPs. The result comparisons indicate that OEB-CSA can be the best method for the combined systems with WPs and TPs.


2016 ◽  
Vol 113 (48) ◽  
pp. 13570-13575 ◽  
Author(s):  
Lee M. Miller ◽  
Axel Kleidon

Wind turbines generate electricity by removing kinetic energy from the atmosphere. Large numbers of wind turbines are likely to reduce wind speeds, which lowers estimates of electricity generation from what would be presumed from unaffected conditions. Here, we test how well wind power limits that account for this effect can be estimated without explicitly simulating atmospheric dynamics. We first use simulations with an atmospheric general circulation model (GCM) that explicitly simulates the effects of wind turbines to derive wind power limits (GCM estimate), and compare them to a simple approach derived from the climatological conditions without turbines [vertical kinetic energy (VKE) estimate]. On land, we find strong agreement between the VKE and GCM estimates with respect to electricity generation rates (0.32 and 0.37 Wem−2) and wind speed reductions by 42 and 44%. Over ocean, the GCM estimate is about twice the VKE estimate (0.59 and 0.29 Wem−2) and yet with comparable wind speed reductions (50 and 42%). We then show that this bias can be corrected by modifying the downward momentum flux to the surface. Thus, large-scale limits to wind power use can be derived from climatological conditions without explicitly simulating atmospheric dynamics. Consistent with the GCM simulations, the approach estimates that only comparatively few land areas are suitable to generate more than 1 Wem−2of electricity and that larger deployment scales are likely to reduce the expected electricity generation rate of each turbine. We conclude that these atmospheric effects are relevant for planning the future expansion of wind power.


2011 ◽  
Vol 347-353 ◽  
pp. 3846-3855 ◽  
Author(s):  
Ali Baniyounes ◽  
Gang Liu ◽  
M. G. Rasul ◽  
M. M. K. Khan

In Australia the future demand for energy is predicted to increase rapidly. Conventional energy resources soaring prices and environmental impact have increased the interest in renewable energy technology. As a result of that the Australian government is promoting renewable energy; such as wind, geothermal, solar and hydropower. These types of energy are believed to be cost-effective and environmentally friendly. Renewable energy availability is controlled by climatic conditions such as solar radiation, wind speed and temperature. This paper aims to assess the potential of renewable energy resources, in particular wind and solar energy in an Australian subtropical region (Central and North Queensland) namely, Gladstone, Emerald, Rockhampton, Yeppoon, Townsville, and Cairns. Analysis is done by using the latest statistical state of Queensland energy information, along with measured data history of wind speed, solar irradiations, air temperature, relative humidity, and atmospheric pressure for those sites. This study has also shown that national assessments of solar and wind energy potential can be improved by improving local climatic data assessments using spatial databases of Central and North Queensland areas.


2021 ◽  
Author(s):  
Anasuya Gangopadhyay ◽  
Ashwin K Seshadri ◽  
Ralf Toumi

<p>Smoothing of wind generation variability is important for grid integration of large-scale wind power plants. One approach to achieving smoothing is aggregating wind generation from plants that have uncorrelated or negatively correlated wind speed. It is well known that the wind speed correlation on average decays with increasing distance between plants, but the correlations may not be explained by distance alone. In India, the wind speed diurnal cycle plays a significant role in explaining the hourly correlation of wind speed between location pairs. This creates an opportunity of “diurnal smoothing”. At a given separation distance the hourly wind speeds correlation is reduced for those pairs that have a difference of +/- 12 hours in local time of wind maximum. This effect is more prominent for location pairs separated by 200 km or more and where the amplitude of the diurnal cycle is more than about  0.5 m/s. “Diurnal smoothing” also has a positive impact on the aggregate wind predictability and forecast error. “Diurnal smoothing” could also be important for other regions with diurnal wind speed cycles.</p>


Author(s):  
V. P. Evstigneev ◽  
◽  
N. A. Lemeshko ◽  
V. A. Naumova ◽  
M. P. Evstigneev ◽  
...  

The paper deals with assessing an impact of wind climate change on the wind energy potential of the Azov and Black Sea coast region. A lower estimate of operating time for wind power installation and a potential annual energy output for the region are given for the case of Vestas V117-4.2MW. Calculation has been performed of a long-term mean wind speed for two adjacent climatic periods (1954–1983 and 1984–2013) based on data from meteorological stations of the Black and Azov Sea region. The results show a decrease in wind speed at all meteorological stations except for Novorossiysk. The wind climate change is confirmed by comparing two adjoined 30-year periods and by estimating linear trends of the mean annual wind speed for the period 1954–2013, which are negative and significant for almost all meteorological stations in the region (α = 1 %). The trend values were estimated by the nonparametric method of robust linear smoothing using the Theil – Sen function. In the present study, the uncertainty of wind energy resource induced by a gradual wind climate change is estimated for perspective planning of this branch of energy sector. Despite the observed trends in the wind regime, average wind speeds in the Azov and Black Sea region are sufficient for planning the location of wind power plants.


2015 ◽  
Vol 2 (1) ◽  
pp. 25-36
Author(s):  
Otieno Fredrick Onyango ◽  
Sibomana Gaston ◽  
Elie Kabende ◽  
Felix Nkunda ◽  
Jared Hera Ndeda

Wind speed and wind direction are the most important characteristics for assessing wind energy potential of a location using suitable probability density functions. In this investigation, a hybrid-Weibull probability density function was used to analyze data from Kigali, Gisenyi, and Kamembe stations. Kigali is located in the Eastern side of Rwanda while Gisenyi and Kamembe are to the West. On-site hourly wind speed and wind direction data for the year 2007 were analyzed using Matlab programmes. The annual mean wind speed for Kigali, Gisenyi, and Kamembe sites were determined as 2.36m/s, 2.95m/s and 2.97m/s respectively, while corresponding dominant wind directions for the stations were ,  and  respectively. The annual wind power density of Kigali was found to be  while the power densities for Gisenyi and Kamembe were determined as and . It is clear, the investigated regions are dominated by low wind speeds thus are suitable for small-scale wind power generation especially at Kamembe site.


Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1846 ◽  
Author(s):  
Teklebrhan Negash ◽  
Erik Möllerström ◽  
Fredric Ottermo

This paper presents the wind energy potential and wind characteristics for 25 wind sites in Eritrea, based on wind data from the years 2000–2005. The studied sites are distributed all over Eritrea, but can roughly be divided into three regions: coastal region, western lowlands, and central highlands. The coastal region sites have the highest potential for wind power. An uncertainty, due to extrapolating the wind speed from the 10-m measurements, should be noted. The year to year variations are typically small and, for the sites deemed as suitable for wind power, the seasonal variations are most prominent in the coastal region with a peak during the period November–March. Moreover, Weibull parameters, prevailing wind direction, and wind power density recalculated for 100 m above ground are presented for all 25 sites. Comparing the results to values from the web-based, large-scale dataset, the Global Wind Atlas (GWA), both mean wind speed and wind power density are typically higher for the measurements. The difference is especially large for the more complex-terrain central highland sites where GWA results are also likely to be more uncertain. The result of this study can be used to make preliminary assessments on possible power production potential at the given sites.


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