scholarly journals A New Hybrid Forecasting Strategy Applied to Mean Hourly Wind Speed Time Series

2014 ◽  
Vol 2014 ◽  
pp. 1-9
Author(s):  
Stylianos Sp. Pappas ◽  
Dimitrios Ch. Karamousantas ◽  
George E. Chatzarakis ◽  
Christos Sp. Pappas
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Space Form ◽  
Renewable Energy Sources ◽  
Arma Model ◽  
Power Production ◽  
Technical Problems ◽  
Forecasting Method ◽  
Hybrid Forecasting ◽  
Different Order

An alternative electric power source, such as wind power, has to be both reliable and autonomous. An accurate wind speed forecasting method plays the key role in achieving the aforementioned properties and also is a valuable tool in overcoming a variety of economic and technical problems connected to wind power production. The method proposed is based on the reformulation of the problem in the standard state space form and on implementing a bank of Kalman filters (KF), each fitting an ARMA model of different order. The proposed method is to be applied to a greenhouse unit which incorporates an automatized use of renewable energy sources including wind speed power.

An Efficient Hybrid Forecasting Approach for Wind Speed Time Series

2017 ◽  
Vol 7 (9) ◽  
pp. 13
Author(s):  
Bhargavi Munnaluri ◽  
K. Ganesh Reddy
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Wind Speed ◽  
Fossil Fuels ◽  
Renewable Energy Sources ◽  
Support Vector ◽  
Hybrid Forecasting ◽  
Artificial Neural ◽  
Future Utilization

Wind forecasting is one of the best efficient ways to deal with the challenges of wind power generation. Due to the depletion of fossil fuels renewable energy sources plays a major role for the generation of power. For future management and for future utilization of power, we need to predict the wind speed.  In this paper, an efficient hybrid forecasting approach with the combination of Support Vector Machine (SVM) and Artificial Neural Networks(ANN) are proposed to improve the quality of prediction of wind speed. Due to the different parameters of wind, it is difficult to find the accurate prediction value of the wind speed. The proposed hybrid model of forecasting is examined by taking the hourly wind speed of past years data by reducing the prediction error with the help of Mean Square Error by 0.019. The result obtained from the Artificial Neural Networks improves the forecasting quality.

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>

Control of BESS Using Frequency-Linked Pricing with High Wind Penetration

2014 ◽  
Vol 511-512 ◽  
pp. 1099-1102
Author(s):  
Zhen Bao Sun
Keyword(s):  
Wind Speed ◽  
Real Time ◽  
Wind Power ◽  
Renewable Energy Sources ◽  
Storage System ◽  
Energy Storage System ◽  
Test System ◽  
High Wind ◽  
Battery Energy ◽  
The Impact

Recently, many countries have been pushing for a higher share of renewable energy sources, especially wind, in their generation mix. However, the intermittent and uncertain nature of wind power imposes a limit on the extent it can replace the conventional generation resources. In a high wind penetration scenario, the Battery Energy Storage System (BESS) offers a solution to the grid operation problems. The purpose of this paper is to evaluate the merits of price-based operation of BESS in a real-time market with high wind penetration using frequency-linked pricing. The authors propose a real-time market in which real-time prices are based on the grid frequency. A model for real-time price-based operation of a conventional generator and a BESS is presented. Simulations for different wind penetration scenarios are carried out on an isolated area test system. Wind speed sequence is generated using composite wind speed model. A simplified model of wind speed to power conversion is adopted to observe the impact of increase in wind power generation on the grid frequency and the real-time prices.

scholarly journals A Hybrid Forecasting Method for Wind Power Ramp Based on Orthogonal Test and Support Vector Machine (OT-SVM)

2017 ◽  
Vol 8 (2) ◽  
pp. 451-457 ◽  
Author(s):  
Yongqian Liu ◽  
Ying Sun ◽  
David Infield ◽  
Yu Zhao ◽  
Shuang Han ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Wind Power ◽  
Orthogonal Test ◽  
Support Vector ◽  
Forecasting Method ◽  
Hybrid Forecasting

scholarly journals Features of operation of wind power stations as an supplementary source of electricity for non-traction consumers of railway electric mains

2019 ◽  
pp. 36-41
Author(s):  
Kachan Yu ◽  
Kuznetsov V
Keyword(s):  
Renewable Energy ◽  
Wind Speed ◽  
Wind Power ◽  
Wind Turbines ◽  
Power Supply ◽  
Renewable Energy Sources ◽  
Wind Farms ◽  
Energy Sources ◽  
Railway Transport ◽  
Supply Systems

Purpose. Identify the features of operation of wind farms as an auxiliary supplier of electricity for non-traction consumers of railway networks and analyze the main factors that directly affect the use of wind farms due to the random nature of wind flow and additional factors due to the above conditions in different climates. The research methodology is based on modern methods of computational mathematics, statistics and information analysis using modern computer technology. Findings. The need to use renewable energy sources in the power supply systems of non-traction consumers of railway transport is obvious. Given the constant growth of prices and tariffs for electricity in Ukraine, more and more attention is paid to its savings and the search for the cheapest and most affordable alternative sources. The authors consider issues related to the possibility of using additional generation of electricity in the power supply systems of railway transport through the use of wind turbines, including for non-traction consumers. The analysis of wind flow features in some regions of Ukraine was carried out, and the measurement of wind speed in Zaporizhia and Dnipropetrovsk regions was obtained with the help of a compact wind speed sensor manufactured by Micro-Step-MIS LLC (Russia). The obtained values of wind speed were recorded and stored digitally. The received information of the above device was processed. The authors conclude that in the case of using wind turbines as an additional power source in the networks of non-traction consumers of railway power supply systems it is economically advantageous to connect them directly to these networks and fully use all electricity produced by them, reducing its consumption from this power supply system. The originality is that the use of renewable energy sources in the power supply systems of non-traction consumers of railway transport, in particular wind turbines, is proposed. Practical implications. Introduction of wind power plants as an auxiliary supplier of electricity for non-traction consumers of railway power grids in order to minimize electricity costs. Keywords: renewable energy sources, quality of electric energy, wind power plant, power supply networks of railway transport, non-traction consumers of railway electric networks, electricity production, wind speed.

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.

scholarly journals Evaluation of ERA5, MERRA-2, COSMO-REA6, NEWA and AROME to simulate wind power production over France

2020 ◽  
Vol 17 ◽  
pp. 63-77 ◽  
Author(s):  
Bénédicte Jourdier
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Wind Farm ◽  
Regional Scale ◽  
Power Production ◽  
Local Scale ◽  
Electric System ◽  
Wind Speeds ◽  
Diurnal Cycles ◽  
Nwp Model

Abstract. As variable renewable energies are developing, their impacts on the electric system are growing. To anticipate these impacts, prospective studies may use wind power production simulations in the form of 1 h or 30 min time series that are often based on reanalysis wind-speed data. The purpose of this study is to assess how several wind-speed datasets are performing when used to simulate wind-power production at the local scale, when no observation is available to use bias correction methods. The study evaluates two global reanalysis (MERRA-2 from NASA and ERA5 from ECMWF), two high-resolution models (COSMO-REA6 reanalysis from DWD, AROME NWP model from Météo-France) and the New European Wind Atlas mesoscale data. The study is conducted over continental France. In a first part, wind-speed measurements (between 55 and 100 m above ground) at eight locations are directly compared to modelled wind speeds. In a second part, 30 min wind-power productions are simulated for every wind farm in France and compared to two open datasets of observed production published by the distribution and transmission system operators, either at the local scale in terms of annual bias, or aggregated at the regional scale, in terms of bias, correlations and diurnal cycles. ERA5 is very skilled, despite its low resolution compared to the regional models, but it underestimates wind speeds, especially in mountainous areas. AROME and COSMO-REA6 have better skills in complex areas and have generally low biases. MERRA-2 and NEWA have large biases and overestimate wind speeds especially at night. Several problems affecting diurnal cycles are detected in ERA5 and COSMO-REA6.

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