scholarly journals Electricity Generation and Energy Cost Estimation of Large-Scale Wind Turbines in Jarandagh, Iran

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Kasra Mohammadi ◽  
Ali Mostafaeipour ◽  
Yagob Dinpashoh ◽  
Nima Pouya
Keyword(s):  
Wind Energy ◽  
Wind Power ◽  
Cost Estimation ◽  
Energy Cost ◽  
Electricity Generation ◽  
Large Scale ◽  
Wind Farm ◽  
Energy Utilization ◽  
Electricity Production ◽  
Energy Output

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.

scholarly journals Optimizing operation of a large-scale pumped storage hydropower system coordinated with wind farm by means of genetic algorithms

2019 ◽  
Keyword(s):  
Genetic Algorithms ◽  
Renewable Energy ◽  
Wind Energy ◽  
Large Scale ◽  
Wind Farm ◽  
Closed Loop ◽  
Open Loop ◽  
Energy Output ◽  
Reservoir System ◽  
Pumped Storage

<p>Due to the intermittent and fluctuating nature of wind and other renewable energy sources, their integration into electricity systems requires large-scale and flexible storage systems to ensure uninterrupted power supply and to reduce the percentage of produced energy that is discarded or curtailed. Storage of large quantities of electricity in the form of dynamic energy of water masses by means of coupled reservoirs has been globally recognized as a mature, competitive and reliable technology; it is particularly useful in countries with mountainous terrain, such as Greece. Its application may increase the total energy output (and profit) of coupled wind-hydroelectric systems, without affecting the availability of water resources. Optimization of such renewable energy systems is a very complex, multi-dimensional, non-linear, multi modal, nonconvex and dynamic problem, as the reservoirs, besides hydroelectric power generation, serve many other objectives such as water supply, irrigation and flood mitigation. Moreover, their function should observe constraints such as environmental flow. In this paper we developed a combined simulation and optimization model to maximize the total benefits by integrating wind energy production into a pumped-storage multi-reservoir system, operating either in closed-loop or in open-loop mode. In this process, we have used genetic algorithms as the optimization tool. Our results show that when the operation of the reservoir system is coordinated with the wind farm, the hydroelectricity generation decreases drastically, but the total economical revenue of the system increases by 7.02% when operating in closed-loop and by 7.16% when operating in open-loop mode. We conclude that the hydro-wind coordination can achieve high wind energy penetration to the electricity grid, resulting in increase of the total benefits of the system. Moreover, the open-loop pumped-storage multi-reservoir system seems to have better performance, ability and flexibility to absorb the wind energy decreasing to a lesser extent the hydroelectricity generation, than the closed-loop.</p>

scholarly journals Potential of Electricity Generation on the Western Coast of Mediterranean Sea in Egypt

2008 ◽  
Vol 45 (5) ◽  
pp. 26-38
Author(s):  
A. Ahmed Shata ◽  
S. Abdelaty ◽  
R. Hanitsch
Keyword(s):  
Wind Turbine ◽  
Mediterranean Sea ◽  
Power Law ◽  
Wind Turbines ◽  
Electricity Generation ◽  
Wind Farm ◽  
Electricity Production ◽  
Energy Output ◽  
Western Coast ◽  
Wind Potential

Potential of Electricity Generation on the Western Coast of Mediterranean Sea in EgyptA technical and economic assessment has been made of the electricity generation by wind turbines located at three promising potential wind sites: Sidi Barrani, Mersa Matruh and El Dabaa in the extreme northwest of Egypt along the Mediterranean Sea. These contiguous stations along the coast have an annual mean wind speed greater than 5.0 m/s at a height of 10 m. Weibull's parameters and the power law coefficient for all seasons have been estimated and used to describe the distribution and behavior of seasonal winds at these stations. The annual values of wind potential at the heights of 70-100 m above the ground level were obtained by extrapolation of the 10 m data from the results of our previous work using the power law. The three stations have a high wind power density, ranging from 340-425 to 450-555 W/m2at the heights of 70-100 m, respectively. In this paper, an analysis of the cost per kWh of electricity generated by two different systems has been made: one using a relatively large single 2 MW wind turbine and the other - 25 small wind turbines (80 kW, total 2 MW) arranged in a wind farm. The yearly energy output of each system at each site was determined, and the electricity generation costs in each case were also calculated and compared with those at using diesel oil, natural gas and photovoltaic systems furnished by the Egyptian Electricity Authority. The single 2 MW wind turbine was found to be more efficient than the wind farm. For all the three considered stations the electricity production cost was found to be less than 2 ϵ cent/kWh, which is about half the specific cost of the wind farm.

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
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Energy Cost ◽  
Electricity Generation ◽  
Large Scale ◽  
Cost Effective ◽  
Energy Potential ◽  
Annual Average ◽  
Alternative Source ◽  
Generation Cost

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/m&#178; and 423.3W/m&#178; with annual average value of 257.85W/m&#178;. 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.

scholarly journals Analysis of Hong Kong’s Wind Energy: Power Potential, Development Constraints, and Experiences from Other Countries for Local Wind Energy Promotion Strategies

2019 ◽  
Vol 11 (3) ◽  
pp. 924 ◽  
Author(s):  
Xiaoxia Gao ◽  
Lu Xia ◽  
Lin Lu ◽  
Yonghua Li
Keyword(s):  
Hong Kong ◽  
Renewable Energy ◽  
Wind Energy ◽  
Wind Power ◽  
Electricity Market ◽  
Wind Farm ◽  
Energy Development ◽  
Energy Utilization ◽  
Offshore Wind ◽  
Wind Energy Development

The wind energy utilization in Hong Kong is limited, although its potential has proven to be significant. The lack of effective policy for wind energy development is the main constraint. In this paper, the wind power potential in Hong Kong is analyzed, and the wind power potential assessment is conducted based on one-year field measured wind data using Light Detection & Ranging (LiDAR) technology in a proposed offshore wind farm. Results show that the offshore wind power potential in Hong Kong was 14,449 GWh which occupied 32.20% of electricity consumption in 2017. In addition, the electricity market and power structure in Hong Kong are also reviewed with the existing policies related to renewable energy development. Conclusions can be made that the renewable energy target in Hong Kong is out of date and until now there have been no specific effective policies on wind energy. In order to urge Hong Kong, catch up with other countries/regions on wind energy development, the histories and evolution of wind energy policies in other countries, especially in Denmark, are reviewed and discussed. Suggestions are provided in the aspects of economics, public attitude, and political factors which can stimulate wind power development in Hong Kong.

Wind energy

2007 ◽  
Author(s):  
Bill Leithead
Keyword(s):  
Wind Energy ◽  
Wind Turbine ◽  
Wind Power ◽  
Electricity Generation ◽  
Wind Farm ◽  
Power Converter ◽  
Offshore Wind ◽  
Renewable Sources ◽  
Near Term ◽  
The Uk

A wind turbine or even a wind farm, i.e. a group of wind turbines, is becoming an increasingly familiar sight in the countryside today. The wind turbine converts the power in the wind to electrical power and consists of a tower, rotor, typically with three blades as in Fig. 5.1, and a nacelle containing the power converter. From its rebirth in the early 1980s, wind power has experienced a dramatic development. Today, other than hydropower, it is the most important of the renewable sources of power. With an installed capacity equivalent to that required to provide electricity for over 19,000,000 average European homes and annual turnover greater than £5,500,000,000, wind energy has exceeded its year-on-year targets over the last decade. This growth in the contribution to electricity generation from wind power in Europe is likely to continue over the next few years, since the EU Commission has set a European target for 2010 of 12% of electricity generation from renewable sources. In the long term, the achievable limit to the contribution of wind power is estimated to be30%of the total European demand, an amount almost equal to the installed nuclear capacity. In the UK, wind power is the fastest growing energy sector. Over 4,000 people are employed by companies working in the wind sector , and it is estimated by the UK Department of Trade and Industry (DTI) that the next round of offshore wind development could generate a further 20,000 jobs. In a 2003 Energy White Paper, the UK government aspired to achieving a 60% reduction in UK CO2 emissions by 2050. In order to do so, it has set targets for UK electricity generation from renewable sources of 10% of electricity demand by 2010 and20% by 2015. Since it is the most mature of the renewable energies, much of these near term targets must be met by wind power . Irrespective of whether these targets are achieved, the potential for increase in the UK is substantial. The prospects for wind power development in the UK are dependent on the available wind resource, public acceptance, and technical development. Each of these issues is discussed below.

The Impact of Large Scale Wind Power Integration on a Regional Power Grid - A Case Study

2014 ◽  
Vol 472 ◽  
pp. 219-225
Author(s):  
Hui Ren ◽  
Dan Xia Yang ◽  
David Watts ◽  
Xi Chen
Keyword(s):  
Power System ◽  
Wind Energy ◽  
Wind Power ◽  
Large Scale ◽  
Wind Farm ◽  
Reactive Power ◽  
Energy Integration ◽  
Wind Power Integration ◽  
Wind Energy Integration ◽  
The Impact

Renewable Energy especially wind energy integration has attained profound growth across the worldwide power system. Wind energy integration at large scale comes up with the challenge on voltages and reactive power management at power system level. The research work presented in this paper has analyzed the impact of wind energy on reactive power reserve with special reference to Hebei Southern Power System. The maximum wind power integration capacity is calculated, and the effect of increasing wind power integration on voltage profiles is studied. Possible controls from system sides and its effects on wind power integration are explored. Study shows that with the increase of the wind power integration capacity, the intermittency and variation will bring more serious problems to the system frequency regulation, reserve service and voltage control. These problems also become the limiting factors for further increase of large-scale wind power integration. In order to make a better use of wind power resources in Heibei province and maintain system safety at the same time, further research should be performed on exploring the reactive and active power regulation and control of the wind farm and the methods to decrease the variability of wind farm outputs.

scholarly journals Numerical Weather Prediction and Artificial Neural Network Coupling for Wind Energy Forecast

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 338
Author(s):  
Lorenzo Donadio ◽  
Jiannong Fang ◽  
Fernando Porté-Agel
Keyword(s):  
Machine Learning ◽  
Wind Energy ◽  
Wind Power ◽  
Numerical Weather Prediction ◽  
Wind Farm ◽  
Weather Prediction ◽  
Wind Power Forecasting ◽  
Numerical Weather ◽  
Artificial Neural ◽  
Power Forecasting

In the past two decades, wind energy has been under fast development worldwide. The dramatic increase of wind power penetration in electricity production has posed a big challenge to grid integration due to the high uncertainty of wind power. Accurate real-time forecasts of wind farm power outputs can help to mitigate the problem. Among the various techniques developed for wind power forecasting, the hybridization of numerical weather prediction (NWP) and machine learning (ML) techniques such as artificial neural networks (ANNs) are attracting many researchers world-wide nowadays, because it has the potential to yield more accurate forecasts. In this paper, two hybrid NWP and ANN models for wind power forecasting over a highly complex terrain are proposed. The developed models have a fine temporal resolution and a sufficiently large prediction horizon (>6 h ahead). Model 1 directly forecasts the energy production of each wind turbine. Model 2 forecasts first the wind speed, then converts it to the power using a fitted power curve. Effects of various modeling options (selection of inputs, network structures, etc.) on the model performance are investigated. Performances of different models are evaluated based on four normalized error measures. Statistical results of model predictions are presented with discussions. Python was utilized for task automation and machine learning. The end result is a fully working library for wind power predictions and a set of tools for running the models in forecast mode. It is shown that the proposed models are able to yield accurate wind farm power forecasts at a site with high terrain and flow complexities. Especially, for Model 2, the normalized Mean Absolute Error and Root Mean Squared Error are obtained as 8.76% and 13.03%, respectively, lower than the errors reported by other models in the same category.

scholarly journals A Compound Wind Power Forecasting Strategy Based on Clustering, Two-Stage Decomposition, Parameter Optimization, and Optimal Combination of Multiple Machine Learning Approaches

Energies ◽  
2019 ◽  
Vol 12 (18) ◽  
pp. 3586 ◽  
Author(s):  
Sizhou Sun ◽  
Jingqi Fu ◽  
Ang Li
Keyword(s):  
Wind Power ◽  
Large Scale ◽  
Wind Farm ◽  
Search Algorithm ◽  
Interpolation Method ◽  
Least Square ◽  
Support Vector ◽  
Two Stage ◽  
Wind Power Forecasting ◽  
Power Forecasting

Given the large-scale exploitation and utilization of wind power, the problems caused by the high stochastic and random characteristics of wind speed make researchers develop more reliable and precise wind power forecasting (WPF) models. To obtain better predicting accuracy, this study proposes a novel compound WPF strategy by optimal integration of four base forecasting engines. In the forecasting process, density-based spatial clustering of applications with noise (DBSCAN) is firstly employed to identify meaningful information and discard the abnormal wind power data. To eliminate the adverse influence of the missing data on the forecasting accuracy, Lagrange interpolation method is developed to get the corrected values of the missing points. Then, the two-stage decomposition (TSD) method including ensemble empirical mode decomposition (EEMD) and wavelet transform (WT) is utilized to preprocess the wind power data. In the decomposition process, the empirical wind power data are disassembled into different intrinsic mode functions (IMFs) and one residual (Res) by EEMD, and the highest frequent time series IMF1 is further broken into different components by WT. After determination of the input matrix by a partial autocorrelation function (PACF) and normalization into [0, 1], these decomposed components are used as the input variables of all the base forecasting engines, including least square support vector machine (LSSVM), wavelet neural networks (WNN), extreme learning machine (ELM) and autoregressive integrated moving average (ARIMA), to make the multistep WPF. To avoid local optima and improve the forecasting performance, the parameters in LSSVM, ELM, and WNN are tuned by backtracking search algorithm (BSA). On this basis, BSA algorithm is also employed to optimize the weighted coefficients of the individual forecasting results that produced by the four base forecasting engines to generate an ensemble of the forecasts. In the end, case studies for a certain wind farm in China are carried out to assess the proposed forecasting strategy.

Design of a Magnetic Wall-Climbing Car

2014 ◽  
Vol 526 ◽  
pp. 211-216
Author(s):  
Qiong Ying Lv ◽  
Yu Shi Mei ◽  
Xi Jia Tao
Keyword(s):  
Renewable Energy ◽  
Wind Energy ◽  
Wind Power ◽  
Large Scale ◽  
Magnetic Force ◽  
Mechanical Analysis ◽  
Heavy Duty ◽  
Magnetic Wall ◽  
Force Curves

As the trend of large-scale wind Power, People pay more attention to wind energy, which as a clean, renewable energy. Traditional unarmed climbing and crane lifting has been unable to meet the requirements of the equipment maintenance. Magnetic climb car can automatically crawl along the wall of the steel tower, the maintenance equipment and personnel can be sent to any height of the tower. The quality of the magnetic wall-climbing car is 550kg, which can carry 1.3 tons load. In this paper completed the magnetic wall-climbing car design and modeling, mechanical analysis in static and dynamic, obtained with the air gap and Magnetic Force curves. The application shows that the magnetic wall-climbing car meets the reliable adsorption, heavy-duty operation, simple operation etc..

Sign in / Sign up

Export Citation Format

Share Document