scholarly journals Research and Application of Hybrid Wind-Energy Forecasting Models Based on Cuckoo Search Optimization

Energies ◽  
2019 ◽  
Vol 12 (19) ◽  
pp. 3675 ◽  
Author(s):  
Ru Hou ◽  
Yi Yang ◽  
Qingcong Yuan ◽  
Yanhua Chen
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Wind Power ◽  
Large Scale ◽  
Search Algorithm ◽  
Moving Average ◽  
Wind Farms ◽  
Cuckoo Search ◽  
Hybrid Models ◽  
Forecasting Accuracy

Wind energy is crucial renewable and sustainable resource, which plays a major role in the energy mix in many countries around the world. Accurately forecasting the wind energy is not only important but also challenging in order to schedule the wind power generation and to ensure the security of wind-power integration. In this paper, four kinds of hybrid models based on cyclic exponential adjustment, adaptive coefficient methods and the cuckoo search algorithm are proposed to forecast the wind speed on large-scale wind farms in China. To verify the developed hybrid models’ effectiveness, wind-speed data from four sites of Xinjiang Uygur Autonomous Region located in northwest China are collected and analyzed. Multiple criteria are used to quantitatively evaluate the forecasting results. Simulation results indicate that (1) the proposed four hybrid models achieve desirable forecasting accuracy and outperform traditional back-propagating neural network, autoregressive integrated moving average as well as single adaptive coefficient methods, and (2) the parameters of hybrid models optimized by artificial intelligence contribute to higher forecasting accuracy compared with predetermined parameters.

scholarly journals A New Hybrid Approach for Wind Speed Forecasting Applying Support Vector Machine with Ensemble Empirical Mode Decomposition and Cuckoo Search Algorithm

2018 ◽  
Vol 8 (10) ◽  
pp. 1754 ◽  
Author(s):  
Tongxiang Liu ◽  
Shenzhong Liu ◽  
Jiani Heng ◽  
Yuyang Gao
Keyword(s):  
Wind Speed ◽  
Search Algorithm ◽  
Wind Farms ◽  
Cuckoo Search ◽  
Ensemble Empirical Mode Decomposition ◽  
Cuckoo Search Algorithm ◽  
Support Vector ◽  
Wind Speed Forecasting ◽  
Mode Decomposition ◽  
Proposed Model

Wind speed forecasting plays a crucial role in improving the efficiency of wind farms, and increases the competitive advantage of wind power in the global electricity market. Many forecasting models have been proposed, aiming to enhance the forecast performance. However, some traditional models used in our experiment have the drawback of ignoring the importance of data preprocessing and the necessity of parameter optimization, which often results in poor forecasting performance. Therefore, in order to achieve a more satisfying performance in forecasting wind speed data, a new short-term wind speed forecasting method which consists of Ensemble Empirical Mode Decomposition (EEMD) for data preprocessing, and the Support Vector Machine (SVM)—whose key parameters are optimized by the Cuckoo Search Algorithm (CSO)—is developed in this paper. This method avoids the shortcomings of some traditional models and effectively enhances the forecasting ability. To test the prediction ability of the proposed model, 10 min wind speed data from wind farms in Shandong Province, China, are used for conducting experiments. The experimental results indicate that the proposed model cannot only improve the forecasting accuracy, but can also be an effective tool in assisting the management of wind power plants.

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>

Hybrid Forecasting Model Based Data Mining and Cuckoo Search: A Case Study of Wind Speed Time Series

2016 ◽  
Vol 9 (1) ◽  
pp. 65-76 ◽  
Author(s):  
Xiangdong Xu ◽  
Xi Song ◽  
Qian Wang ◽  
Zhiyuan Liu ◽  
Jing Wang ◽  
...  
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Renewable Energy Sources ◽  
Eastern China ◽  
Wind Farms ◽  
Cuckoo Search ◽  
Original Data ◽  
Vital Role ◽  
Forecasting Model ◽  
Hybrid Forecasting

Wind energy has been part of the fastest growing renewable energy sources that is clean and pollution-free, which has been increasingly gaining global attention, and wind speed forecasting plays a vital role in the wind energy field, however, it has been proven to be a challenging task owing to the effect of various meteorological factors. This paper proposes a hybrid forecasting model, which can effectively make a preprocess for the original data and improve forecasting accuracy, the developed model applies cuckoo search(CS) algorithm to optimize the parameters of the wavelet neural network (WNN) model. The proposed hybrid method is subsequently examined on the wind farms of eastern China and the forecasting performance shows that the developed model is better than some traditional models.

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.

scholarly journals Evaluation Method of Wind Speed Time-Shifting Characteristics at Multiple Scales and Its Application in Wind Power System

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Han Wang ◽  
Shuang Han ◽  
Yongqian Liu ◽  
Aimei Lin
Keyword(s):  
Wind Speed ◽  
Power System ◽  
Wind Power ◽  
Large Scale ◽  
Wind Farm ◽  
Evaluation Method ◽  
Multiple Scales ◽  
Wind Farms ◽  
Physical Method ◽  
Wind Power System

The wind speed sequences at different spatial positions have a certain spatiotemporal coupling relationship. It is of great significance to analyze the clustering effect of the wind farm(s) and reduce the adverse impact of large-scale wind power integration if we can grasp this relationship at multiple scales. At present, the physical method cannot optimize the time-shifting characteristics in real time, and the research scope is concentrated on the wind farm. The statistical method cannot quantitatively describe the temporal relationship and the speed variation among wind speed sequences at different spatial positions. To solve the above problems, a quantification method of wind speed time-shifting characteristics based on wind process is proposed in this paper. Two evaluation indexes, the delay time and the decay speed, are presented to quantify the time-shifting characteristics. The effectiveness of the proposed method is verified from the perspective of the correlation between wind speed sequences. The time-shifting characteristics of wind speed sequences under the wind farms scale and the wind turbines scale are studied, respectively. The results show that the proposed evaluation method can effectively achieve the quantitative analysis of time-shifting and could improve the results continuously according to the actual wind conditions. Besides, it is suitable for any spatial scale. The calculation results can be directly applied to the wind power system to help obtain the more accurate output of the wind farm.

scholarly journals Wind-Energy-Powered Electric Vehicle Charging Stations: Resource Availability Data Analysis

2020 ◽  
Vol 10 (16) ◽  
pp. 5654
Author(s):  
Fuad Noman ◽  
Ammar Ahmed Alkahtani ◽  
Vassilios Agelidis ◽  
Kiong Sieh Tiong ◽  
Gamal Alkawsi ◽  
...  
Keyword(s):  
Energy Storage ◽  
Wind Speed ◽  
Wind Energy ◽  
Large Scale ◽  
Wind Farms ◽  
Time Interval ◽  
Electric Vehicle Charging ◽  
Demand Variability ◽  
Charging Stations ◽  
Ev Charging

The integration of large-scale wind farms and large-scale charging stations for electric vehicles (EVs) into electricity grids necessitates energy storage support for both technologies. Matching the variability of the energy generation of wind farms with the demand variability of the EVs could potentially minimize the size and need for expensive energy storage technologies required to stabilize the grid. This paper investigates the feasibility of using the wind as a direct energy source to power EV charging stations. An interval-based approach corresponding to the time slot taken for EV charging is introduced for wind energy conversion and analyzed using different constraints and criteria, including the wind speed averaging time interval, various turbines manufacturers, and standard high-resolution wind speed datasets. A quasi-continuous wind turbine’s output energy is performed using a piecewise recursive approach to measure the EV charging effectiveness. Wind turbine analysis using two years of wind speed data shows that the application of direct wind-to-EV is able to provide sufficient constant power to supply the large-scale charging stations. The results presented in this paper confirm that the potential of direct powering of EV charging stations by wind has merits and that research in this direction is worth pursuing.

scholarly journals Minimize electricity generation cost for large scale wind- thermal systems considering prohibited operating zone and power reserve constraints

2021 ◽  
Vol 11 (3) ◽  
pp. 1905
Author(s):  
Phan Nguyen Vinh ◽  
Bach Hoang Dinh ◽  
Van-Duc Phan ◽  
Hung Duc Nguyen ◽  
Thang Trung Nguyen
Keyword(s):  
Wind Power ◽  
Electricity Generation ◽  
Power Plants ◽  
Large Scale ◽  
Search Algorithm ◽  
Thermal Power ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Working Zone ◽  
Generation Cost

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.

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.

scholarly journals An Ensemble Forecasting Model of Wind Power Outputs Based on Improved Statistical Approaches

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1071 ◽  
Author(s):  
Yeojin Kim ◽  
Jin Hur
Keyword(s):  
Wind Power ◽  
Large Scale ◽  
Moving Average ◽  
Wind Farms ◽  
Ensemble Forecasting ◽  
Support Vector ◽  
Forecasting Model ◽  
Single Model ◽  
Simulation Based ◽  
Power Outputs

The number of wind-generating resources has increased considerably, owing to concerns over the environmental impact of fossil-fuel combustion. Therefore, wind power forecasting is becoming an important issue for large-scale wind power grid integration. Ensemble forecasting, which combines several forecasting techniques, is considered a viable alternative to conventional single-model-based forecasting for improving the forecasting accuracy. In this work, we propose the day-ahead ensemble forecasting of wind power using statistical methods. The ensemble forecasting model consists of three single forecasting approaches: autoregressive integrated moving average with exogenous variable (ARIMAX), support vector regression (SVR), and the Monte Carlo simulation-based power curve model. To apply the methodology, we conducted forecasting using the historical data of wind farms located on Jeju Island, Korea. The results were compared between a single model and an ensemble model to demonstrate the validity of the proposed method.

scholarly journals Assessment of wind energy resources in Nigeria – a case study of north-western region of Nigeria

2017 ◽  
Vol 5 (2) ◽  
pp. 83 ◽  
Author(s):  
Boluwaji Olomiyesan ◽  
Onyedi Oyedum ◽  
Paulinus Ugwuoke ◽  
Matthew Abolarin
Keyword(s):  
Power Generation ◽  
Wind Speed ◽  
Wind Energy ◽  
Wind Power ◽  
Large Scale ◽  
Wind Power Generation ◽  
Energy Resources ◽  
Small Scale ◽  
Wind Potential ◽  
Wind Energy Resources

This study assesses the wind-energyresources in Nigeria by reviewing the existing literature on the subject matter, and also evaluates the wind potential in six locations in the northwest region of the country. Twenty-two years’ (1984 – 2005) wind speed data obtained from the Nigerian Meteorological Agencies (NIMET) were used in this study.Weibull two-parameter and other statistical models were employed in this analysis. Wind speed distribution across Nigeria shows that some locations in the northern part of the country are endowed with higher wind potential than others in the southern part of the country. Moreover, assessment of the wind-energy resources in the study locations reveals that wind energy potential in the region is lowest in Yelwa and highest in Kano; WPD varies from 28.30 Wm-2 to 483.72Wm-2 at 10 m AGL, 45.33 Wm-2 to 775.19 Wm-2 at 30 m AGL and 56.43 Wm-2 to 964.77 Wm-2 at 50 m AGL.Thus Kano, Sokoto and Katsina are suitable for large-scale wind power generation, while Gusau is suitable for small-scale wind power generation; whereas Yelwa and Kaduna may not be suitable for wind power production because of their poor wind potential.

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