scholarly journals A Hybrid Wind Speed Forecasting Method and Wind Energy Resource Analysis Based on a Swarm Intelligence Optimization Algorithm and an Artificial Intelligence Model

2018 ◽  
Vol 10 (11) ◽  
pp. 3913 ◽  
Author(s):  
Tonglin Fu ◽  
Chen Wang
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Wind Energy ◽  
Wind Power ◽  
Wind Farm ◽  
Operating Cost ◽  
Energy Resource ◽  
Wind Farms ◽  
Wind Speed Forecasting ◽  
Analysis System

Wind power has the most potential for clean and renewable energy development. Wind power not only effectively solves the problem of energy shortages, but also reduces air pollution. In recent years, wind speed time series analyses have increasingly become a concern of administrators and power grid dispatchers searching for a reasonable way to reduce the operating cost of wind farms. However, analyzing wind speed in detail has become a difficult task, because the traditional models sometimes fail to capture data features due to the randomness and intermittency of wind speed. In order to analyze wind speed series in detail, in this paper, an effective and practical analysis system is studied and developed, which includes a data analysis module, a data preprocessing module, a parameter optimization module, and a wind speed forecasting module. Numerical results show that the wind time series analysis system can not only assess wind energy resources of a wind farm, but also master future changes of wind speed, and can be an effective tool for wind farm management and decision-making.

The Application of Wind Speed Numerical Simulation in Wind Power Generation

2013 ◽  
Vol 380-384 ◽  
pp. 3370-3373 ◽  
Author(s):  
Li Yang Liu ◽  
Jun Ji Wu ◽  
Shao Liang Meng
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Wind Power ◽  
Wind Farm ◽  
Power Grid ◽  
Wind Farms ◽  
Stable Operation ◽  
Wind Gust ◽  
Gradual Change ◽  
Wake Effect

With the massive development and application of wind energy, wind power is having an increasing proportion in power grid. The changes of the wind speed in a wind farm will lead to fluctuations in the power output which would affect the stable operation of the power grid. Therefore the research of the characteristics of wind speed has become a hot topic in the field of wind energy. In the paper, the wind speed at the wind farm was simulated in a combination of wind speeds by which wind speed was decomposed of four components including basic wind, gust wind, stochastic wind and gradient wind which denote the regularity, the mutability, the gradual change and the randomness of a natural wind respectively. The model is able to reflect the characteristics of a real wind, easy for engineering simulation and can also estimate the wind energy of a wind farm through the wind speed and wake effect model. This paper has directive significance in the estimation of wind resource and the layout of wind turbines in wind farms.

Wind Energy Resource Assessment for Wind Farm

2011 ◽  
Vol 130-134 ◽  
pp. 1295-1297
Author(s):  
Hui Qun Ma ◽  
Qi Feng Wang
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Weibull Distribution ◽  
Wind Power ◽  
Wind Farm ◽  
Energy Resource ◽  
Resource Assessment ◽  
Important Process ◽  
Wind Resources ◽  
Resources Assessment

In feasible research of wind farm construction, wind resources assessment is an important process. The grade of wind resources is the crucial qualification in the construction. It determines whether this wind farm is profitable or not. his paper introduces the theory of wind energy resource assessment firstly, including: wind power density, wind speed correction and Weibull distribution. Then take Yishui wind farm as example to calculate the wind energy resource assessment.

scholarly journals Performance Enhancement of Proposed Namaacha Wind Farm by Minimising Losses Due to the Wake Effect: A Mozambican Case Study

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4291
Author(s):  
Paxis Marques João Roque ◽  
Shyama Pada Chowdhury ◽  
Zhongjie Huan
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Wind Turbines ◽  
Power Output ◽  
Wind Farm ◽  
Wind Farms ◽  
Operating Conditions ◽  
Wake Effect ◽  
Wind Generators ◽  
Wind Potential

District of Namaacha in Maputo Province of Mozambique presents a high wind potential, with an average wind speed of around 7.5 m/s and huge open fields that are favourable to the installation of wind farms. However, in order to make better use of the wind potential, it is necessary to evaluate the operating conditions of the turbines and guide the independent power producers (IPPs) on how to efficiently use wind power. The investigation of the wind farm operating conditions is justified by the fact that the implementation of wind power systems is quite expensive, and therefore, it is imperative to find alternatives to reduce power losses and improve energy production. Taking into account the power needs in Mozambique, this project applied hybrid optimisation of multiple energy resources (HOMER) to size the capacity of the wind farm and the number of turbines that guarantee an adequate supply of power. Moreover, considering the topographic conditions of the site and the operational parameters of the turbines, the system advisor model (SAM) was applied to evaluate the performance of the Vestas V82-1.65 horizontal axis turbines and the system’s power output as a result of the wake effect. For any wind farm, it is evident that wind turbines’ wake effects significantly reduce the performance of wind farms. The paper seeks to design and examine the proper layout for practical placements of wind generators. Firstly, a survey on the Namaacha’s electricity demand was carried out in order to obtain the district’s daily load profile required to size the wind farm’s capacity. Secondly, with the previous knowledge that the operation of wind farms is affected by wake losses, different wake effect models applied by SAM were examined and the Eddy–Viscosity model was selected to perform the analysis. Three distinct layouts result from SAM optimisation, and the best one is recommended for wind turbines installation for maximising wind to energy generation. Although it is understood that the wake effect occurs on any wind farm, it is observed that wake losses can be minimised through the proper design of the wind generators’ placement layout. Therefore, any wind farm project should, from its layout, examine the optimal wind farm arrangement, which will depend on the wind speed, wind direction, turbine hub height, and other topographical characteristics of the area. In that context, considering the topographic and climate features of Mozambique, the study brings novelty in the way wind farms should be placed in the district and wake losses minimised. The study is based on a real assumption that the project can be implemented in the district, and thus, considering the wind farm’s capacity, the district’s energy needs could be met. The optimal transversal and longitudinal distances between turbines recommended are 8Do and 10Do, respectively, arranged according to layout 1, with wake losses of about 1.7%, land utilisation of about 6.46 Km2, and power output estimated at 71.844 GWh per year.

Assessment of wind energy potential and characteristics in Qatar for clean electricity generation

2021 ◽  
pp. 0309524X2110438
Author(s):  
Carlos Méndez ◽  
Yusuf Bicer
Keyword(s):  
Wind Energy ◽  
Wind Power ◽  
Power Density ◽  
Wind Farm ◽  
Wind Farms ◽  
Wind Rose ◽  
Energy Potential ◽  
Wind Power Density ◽  
Wind Speeds ◽  
Wind Energy Potential

The present study analyzes the wind energy potential of Qatar, by generating a wind atlas and a Wind Power Density map for the entire country based on ERA-5 data with over 41 years of measurements. Moreover, the wind speeds’ frequency and direction are analyzed using wind recurrence, Weibull, and wind rose plots. Furthermore, the best location to install a wind farm is selected. The results indicate that, at 100 m height, the mean wind speed fluctuates between 5.6054 and 6.5257 m/s. Similarly, the Wind Power Density results reflect values between 149.46 and 335.06 W/m2. Furthermore, a wind farm located in the selected location can generate about 59.7437, 90.4414, and 113.5075 GWh/y electricity by employing Gamesa G97/2000, GE Energy 2.75-120, and Senvion 3.4M140 wind turbines, respectively. Also, these wind farms can save approximately 22,110.80, 17,617.63, and 11,637.84 tons of CO2 emissions annually.

Effect of Air Density on Output Power of Wind Turbine

2013 ◽  
Vol 336-338 ◽  
pp. 1114-1117 ◽  
Author(s):  
Ying Zhi Liu ◽  
Wen Xia Liu
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Output Power ◽  
Power Output ◽  
Wind Farm ◽  
Wind Farms ◽  
Power Curve ◽  
Air Density ◽  
Wind Farm Design ◽  
Farm Design

This paper elaborates the effect of wind speed on the output power of the wind farms at different locations. It also describes the correction of the power curve and shows the comparison chart of the standard power curve and the power curve after correction. In China's inland areas, wind farms altitude are generally higher, the air density is much different from the standard air density. The effect of air density on wind power output must be considered during the wind farm design.

A Probabilistic Model for Simulating Long-Term Wind-Power Output

2003 ◽  
Vol 27 (3) ◽  
pp. 167-181 ◽  
Author(s):  
Scott Kennedy ◽  
Peter Rogers
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Wind Power ◽  
Power Output ◽  
Energy Resource ◽  
Autoregressive Process ◽  
Resource Planning ◽  
Offshore Wind ◽  
Arbitrary Length

This paper describes a chronological wind-plant simulation model for use in long-term energy resource planning. The model generates wind-power time series of arbitrary length that accurately reproduce short-term (hourly) to long-term (yearly) statistical behaviour. The modelling objective and methodology differ from forecasting models, which focus on minimizing prediction error. In the present analysis, periodic cycles are isolated from historical wind-speed data from a known local site and combined with a first-order autoregressive process to produce a wind-speed time series model. Corrections for negative wind-speed values and spatial smoothing for geographically disperse wind turbines are discussed. The resulting model is used to simulate the output from a hypothetical offshore wind-plant south of Long Island, New York. Modelled differences of power output between individual turbines result from wind speed variability; wake effects are not considered in this analysis.

scholarly journals Method for Determining the Maximum Allowable Capacity of Wind Farm Based on Box Set Robust Optimization

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Lihui Guo ◽  
Hao Bai
Keyword(s):  
Wind Speed ◽  
Robust Optimization ◽  
Wind Power ◽  
Optimization Model ◽  
Wind Farm ◽  
Optimization Theory ◽  
Wind Farms ◽  
Bilevel Optimization ◽  
Grid Connection ◽  
Node Voltage

With the increasing penetration of wind power, the randomness and volatility of wind power output would have a greater impact on safety and steady operation of power system. In allusion to the uncertainty of wind speed and load demand, this paper applied box set robust optimization theory in determining the maximum allowable installed capacity of wind farm, while constraints of node voltage and line capacity are considered. Optimized duality theory is used to simplify the model and convert uncertainty quantities in constraints into certainty quantities. Under the condition of multi wind farms, a bilevel optimization model to calculate penetration capacity is proposed. The result of IEEE 30-bus system shows that the robust optimization model proposed in the paper is correct and effective and indicates that the fluctuation range of wind speed and load and the importance degree of grid connection point of wind farm and load point have impact on the allowable capacity of wind farm.

Ensemble-Based Data Assimilation for Wind Forecasting – Application to Wind Farm

2020 ◽  
Author(s):  
Yang-Ming Fan
Keyword(s):  
Wind Speed ◽  
Natural Gas ◽  
Data Assimilation ◽  
Wind Energy ◽  
Wind Farm ◽  
Initial Conditions ◽  
Wind Farms ◽  
Weather Conditions ◽  
Energy Output ◽  
Combining Data

<p>The purpose of this study is to develop an ensemble-based data assimilation method to accurately predict wind speed in wind farm and provide it for the use of wind energy intelligent forecasting platform. As Taiwan government aimed to increase the share of renewable energy generation to 20% by 2025, among them, the uncertain wind energy output will cause electricity company has to reserve a considerable reserve capacity when dispatching power, and it is usually high cost natural gas power generation. In view of this, we will develop wind energy intelligent forecasting platform with an error of 10% within 72 hours and expect to save hundred millions of dollars of unnecessary natural gas generators investment. Once the wind energy can be predicted more accurately, the electricity company can fully utilize the robustness and economy of smart grid supply. Therefore, the mastery of the change of wind speed is one of the key factors that can reduce the minimum error of wind energy intelligent forecasting.</p><p>There are many uncertainties in the numerical meteorological models, including errors in the initial conditions or defects in the model, which may affect the accuracy of the prediction. Since the deterministic prediction cannot fully grasp the uncertainty in the prediction process, so it is difficult to obtain all possible wind field changes. The development of ensemble-based data assimilation prediction is to make up for the weakness of deterministic prediction. With the prediction of 20 wind fields as ensemble members, it is expected to include the uncertainty of prediction, quantify the uncertainty, and integrate the wind speed observations of wind farms as well to provide the optimal prediction of wind speed for the next 72 hours. The results show that the prediction error of wind speed within 72 hours is 6% under different weather conditions (excluding typhoons), which proves that the accuracy of wind speed prediction by combining data assimilation technology and ensemble approach is better.</p>

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.

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