Application of wind speed forecasting to the integration of wind energy into a large scale power system

1994 ◽  
Vol 141 (4) ◽  
pp. 357 ◽  
Author(s):  
S.J. Watson
Keyword(s):  
Wind Speed ◽  
Power System ◽  
Wind Energy ◽  
Large Scale ◽  
Wind Speed Forecasting

Short Term Wind Speed Forecasting Based on Bayesian Model Averaging Method

2009 ◽  
Author(s):  
Gong Li ◽  
Jing Shi ◽  
Junyi Zhou
Keyword(s):  
Wind Speed ◽  
Power System ◽  
Wind Energy ◽  
Statistical Models ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Short Term ◽  
Goodness Of Fit Test ◽  
Wind Speed Forecasting

Wind energy has been the world’s fastest growing source of clean and renewable energy in the past decade. One of the fundamental difficulties faced by power system operators, however, is the unpredictability and variability of wind power generation, which is closely connected with the continuous fluctuations of the wind resource. Good short-term wind speed forecasting methods and techniques are urgently needed since it is important for wind energy conversion systems in terms of the relevant issues associated with the dynamic control of the wind turbine and the integration of wind energy into the power system. This paper proposes the application of Bayesian Model Averaging (BMA) method in combining the one-hour-ahead short-term wind speed forecasts from different statistical models. Based on the hourly wind speed observations from one representative site within North Dakota, four statistical models are built and the corresponding forecast time series are obtained. These data are then analyzed by using BMA method. The goodness-of-fit test results show that the BMA method is superior to its component models by providing a more reliable and accurate description of the total predictive uncertainty than the original elements, leading to a sharper probability density function for the probabilistic wind speed predictions.

scholarly journals Multiperiod-Ahead Wind Speed Forecasting Using Deep Neural Architecture and Ensemble Learning

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Lei Chen ◽  
Zhijun Li ◽  
Yi Zhang
Keyword(s):  
Wind Speed ◽  
Ensemble Learning ◽  
Analysis Of Variance ◽  
Large Scale ◽  
Fuzzy Inference ◽  
Feature Learning ◽  
Denoising Autoencoder ◽  
Inference System ◽  
Wind Speed Forecasting ◽  
Learning Machine

Accurate forecasting of wind speed plays a fundamental role in enabling reliable operation and planning for large-scale integration of wind turbines. It is difficult to obtain the accurate wind speed forecasting (WSF) due to the intermittent and random nature of wind energy. In this paper, a multiperiod-ahead WSF model based on the analysis of variance, stacked denoising autoencoder (SDAE), and ensemble learning is proposed. The analysis of variance classifies the training samples into different categories. The stacked denoising autoencoder as a deep learning architecture is later built for unsupervised feature learning in each category. The ensemble of extreme learning machine (ELM) is applied to fine-tune the SDAE for multiperiod-ahead wind speed forecasting. Experimental results are made to demonstrate that the proposed model has the best performance compared with the classic WSF methods including the single SDAE-ELM, ELMAN, and adaptive neuron-fuzzy inference system (ANFIS).

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>

A Study on Wind - Energy Storage Hybrid System

2011 ◽  
Vol 187 ◽  
pp. 97-102 ◽  
Author(s):  
Liang Liang ◽  
Jian Lin Li ◽  
Dong Hui
Keyword(s):  
Renewable Energy ◽  
Energy Storage ◽  
Power System ◽  
Wind Energy ◽  
Wind Power ◽  
Large Scale ◽  
Renewable Energy Sources ◽  
Storage System ◽  
Energy Storage System ◽  
Wind Power System

Recently, more and more people realize the importance of environment protection. Electric power generation systems using renewable energy sources have an advantage of no greenhouse effect gas emission. Among all the choices, wind power can offer an economic and environmentally friendly alternative to conventional methods of power supply. As a result, wind energy generation, utilization and its grid penetration in electrical grid is increasing world wide. The wind generated power is always fluctuating due to its time varying nature and causing stability problem. Inserting energy storage system into large scale wind farm to eliminate the fluctuation becomes a solution for developing large scale renewable energy system connected with grid. The topology diagram and control strategy are presented in this paper. According to the simulation result, it could be indicated that embedding energy storage system into wind power system could improve the access friendly and extend system functions. This paper shows that integrating energy storage system into wind power system will build a more reliable and flexible system for power grid.

The impact of atmospheric circulation on wind energy resource using self-organizing maps

2021 ◽  
Author(s):  
Kostas Philippopoulos ◽  
Chris G. Tzanis
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Atmospheric Circulation ◽  
Large Scale ◽  
Synoptic Climatology ◽  
Energy Potential ◽  
Self Organizing Maps ◽  
Wind Energy Potential ◽  
The Impact ◽  
Self Organizing

<p>The sensitivity of wind to the Earth’s energy budget and the changes it causes in the climate system has a significant impact on the wind energy sector. The scope of this work is to examine the association of atmospheric circulation with the wind speed distribution characteristics on different timescales over Greece. Emphasis is given to the effect of specific regimes on the wind speed distributions at different locations. The work is based on using synoptic climatology as a tool for providing information regarding wind variability. This approach allows a more detailed description of the effect of changes in large-scale atmospheric circulation on wind energy potential. The atmospheric classification methodology, upon the selection of relevant atmospheric variables and domains, includes a Principal Components Analysis for dimension reduction purposes and subsequently, the classification is performed using an artificial neural network and in particular self-organizing maps. In the resulting feature map, the neighboring nodes are inter-connected and each one is associated with the composites of the selected large-scale variables. Upon the assignment and the characterization of each day in one of the resulting patterns, a daily catalog is constructed and frequency analysis is performed. In the context of estimating wind energy potential variability for each atmospheric pattern, the fit of multiple probability functions to the surface wind speed frequency distributions is performed. The most suitable function is selected based on a set of difference and correlation statistical measures, along with the use of goodness-of-fit statistical tests. The study employs the ERA5 reanalysis dataset with a 0.25° spatial resolution from 1979/01/01 up to 2019/12/31 and the wind field data are extracted at the 10m and the 100m levels. The approach could be valuable to the wind energy industry and can provide the required scientific understanding for the optimal siting of Wind Energy Conversion Systems considering the atmospheric circulation and the electricity interconnection infrastructure in the region. Considering the emerging issue of energy safety, accurate wind energy production estimates can contribute towards the establishment of wind as the primary energy source and in meeting the increasing energy demand.</p>

scholarly journals The Forecasting Procedure for Long-Term Wind Speed in the Zhangye Area

2010 ◽  
Vol 2010 ◽  
pp. 1-17 ◽  
Author(s):  
Zhenhai Guo ◽  
Yao Dong ◽  
Jianzhou Wang ◽  
Haiyan Lu
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Alternative Energy ◽  
Energy System ◽  
Support Vector ◽  
Ancillary Service ◽  
Average Wind Speed ◽  
Index Method ◽  
Wind Speed Forecasting

Energy crisis has made it urgent to find alternative energy sources for sustainable energy supply; wind energy is one of the attractive alternatives. Within a wind energy system, the wind speed is one key parameter; accurately forecasting of wind speed can minimize the scheduling errors and in turn increase the reliability of the electric power grid and reduce the power market ancillary service costs. This paper proposes a new hybrid model for long-term wind speed forecasting based on the first definite season index method and the Autoregressive Moving Average (ARMA) models or the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) forecasting models. The forecasting errors are analyzed and compared with the ones obtained from the ARMA, GARCH model, and Support Vector Machine (SVM); the simulation process and results show that the developed method is simple and quite efficient for daily average wind speed forecasting of Hexi Corridor in China.

scholarly journals An Experimental Investigation of FNN Model for Wind Speed Forecasting Using EEMD and CS

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Jianzhou Wang ◽  
Haiyan Jiang ◽  
Bohui Han ◽  
Qingping Zhou
Keyword(s):  
Wind Speed ◽  
Power System ◽  
Wind Power ◽  
Wind Farm ◽  
Frequency Noise ◽  
Wind Speed Forecasting ◽  
System A ◽  
Wind Power System ◽  
And Performance ◽  
Using Data

With depletion of traditional energy and increasing environmental problems, wind energy, as an alternative renewable energy, has drawn more and more attention internationally. Meanwhile, wind is plentiful, clean, and environmentally friendly; moreover, its speed is a very important piece of information needed in the operations and planning of the wind power system. Therefore, choosing an effective forecasting model with good performance plays a quite significant role in wind power system. A hybrid CS-EEMD-FNN model is firstly proposed in this paper for multistep ahead prediction of wind speed, in which EEMD is employed as a data-cleaning method that aims to remove the high frequency noise embedded in the wind speed series. CS optimization algorithm is used to select the best parameters in the FNN model. In order to evaluate the effectiveness and performance of the proposed hybrid model, three other short-term wind speed forecasting models, namely, FNN model, EEMD-FNN model, and CS-FNN model, are carried out to forecast wind speed using data measured at a typical site in Shandong wind farm, China, over three seasons in 2011. Experimental results demonstrate that the developed hybrid CS-EEMD-FNN model outperforms other models with more accuracy, which is suitable to wind speed forecasting in this area.

Sign in / Sign up

Export Citation Format

Share Document