scholarly journals Corrigendum to “Hourly day-ahead wind power forecasting at two wind farms in northeast Brazil using WRF model” [Energy 230 (2021) 120841]

Energy ◽  
2021 ◽  
pp. 121390
Author(s):  
William Duarte Jacondino ◽  
Ana Lucia da Silva Nascimento ◽  
Leonardo Calvetti ◽  
Gilberto Fisch ◽  
Cesar Augustus Assis Beneti ◽  
...  
Keyword(s):  
Wind Power ◽  
Wrf Model ◽  
Wind Farms ◽  
Northeast Brazil ◽  
Wind Power Forecasting ◽  
Power Forecasting

Study on a Service Mechanism that Wind/Photovoltaic/Energy Storage Power Station Help Wind Farms Tracking Generation Schedule Curve

2013 ◽  
Vol 860-863 ◽  
pp. 1909-1913
Author(s):  
Hai Xiang Xu ◽  
Peng Wang ◽  
Xiao Meng Ren
Keyword(s):  
Energy Storage ◽  
Wind Power ◽  
Wind Farm ◽  
Wind Farms ◽  
Power Station ◽  
Forecasting Accuracy ◽  
Wind Power Forecasting ◽  
Assessment Standard ◽  
The Impact ◽  
Power Forecasting

At present, the technology of wind power forecasting isn‘t mature enough in china, so some grid-connected wind farms will be assessed when theirs power forecasting accuracy cant reach the assessment standard. In response to the situation, combined with the characteristics of WPSPS and wind farms, this paper designs a service mechanism that WPSPS help wind farms tracking generation schedule curve, namely, encouraging WPSPS to supply output compensation service for wind farm by market means to increase the accuracy of wind power forecasting. By this mechanism, not only WPSPS and wind farms will achieve win-win, but also the impact on the grid caused by fluctuations of wind powers output will reduce.

A Wind Power Forecasting System to Optimize Power Integration

2011 ◽  
Author(s):  
Sue Ellen Haupt ◽  
Gerry Wiener ◽  
Yubao Liu ◽  
Bill Myers ◽  
Juanzhen Sun ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Wind Power ◽  
Learning Algorithm ◽  
Wrf Model ◽  
Forecast Accuracy ◽  
The United States ◽  
Processing Technologies ◽  
Wind Power Forecasting ◽  
Forecasting System ◽  
Power Forecasting

The National Center for Atmospheric Research (NCAR) has developed a wind prediction system for Xcel Energy, the power company with the largest wind capacity in the United States. The wind power forecasting system includes advanced modeling capabilities, data assimilation, nowcasting, and statistical post-processing technologies. The system ingests both external model data and observations. NCAR produces a deterministic mesoscale wind forecast of hub height winds on a very fine resolution grid using the Weather Research and Forecasting (WRF) model, run using the Real Time Four Dimensional Data Assimilation (RTFDDA) system. In addition, a 30 member ensemble system is run to both improve forecast accuracy and provide an indication of forecast uncertainty. The deterministic and ensemble model output plus data from various global and regional models are ingested by NCAR’s Dynamic, Integrated, Forecast System (DICast®), a statistical learning algorithm. DICast® produces forecasts of wind speed for each wind turbine. These wind forecasts are then fed into a power conversion algorithm that has been empirically derived for each Xcel power connection node. In addition, a ramp forecasting technology fine-tunes the capability to accurately predict the time, magnitude, and duration of a ramping event. This basic system has consistently improved Xcel’s ability to optimize the economics of incorporating wind energy into their power system.

Multistage Model for Short Term Wind Power Forecasting

2011 ◽  
Author(s):  
J. Shi ◽  
Y. Q. Liu ◽  
Y. P. Yang ◽  
S. Han ◽  
W. J. Lee
Keyword(s):  
Wind Power ◽  
Linear Models ◽  
Wind Farms ◽  
Support Vector ◽  
Climatic Data ◽  
Short Term ◽  
Wind Power Forecasting ◽  
Multistage Model ◽  
National Climatic Data Center ◽  
Power Forecasting

The increased integration of wind power into the electric grid poses new challenges due to its fluctuation and volatility. Short term wind power forecasting is one of the most effective ways to deal with it. Various individual non-linear models are proposed to meet the data requirement to forecast short term wind power. However, as every model has its advantage and weakness, when these models are applied to different wind farms, the forecasting accuracy of every model varies because of distinct data character. This paper analyzes individual forecast models like Wavelet Transform and Support Vector Machine (SVM), and then puts forward a complex-valued forecasting model which is based on Artificial Natural Network in accordance with forecasting data provided by National Climatic Data Center in U.S. The existing individual models are matched and trained according to certain means by Natural Network to propose a multistage model. For variable data from different wind farms, the model can adjust and optimize portion of individual models. Compared with each single model, the multistage model has more robust adaptation and faster calculation speed, which can improve the forecasting precision and have more engineering value.

Study of Wind Power Forecasting Assessment Standard Based on Unit Commitment and Spinning Reserve Optimization Model

2012 ◽  
Vol 608-609 ◽  
pp. 547-552
Author(s):  
Chun Jie Gao ◽  
Peng Wang
Keyword(s):  
Wind Power ◽  
Power Output ◽  
Large Scale ◽  
North China ◽  
Wind Farms ◽  
Spinning Reserve ◽  
Wind Power Forecasting ◽  
Assessment Standard ◽  
North China Area ◽  
Power Forecasting

After large-scale wind power integrate into the system, there is a great impact for the system dispatching operation and the unit maintenance and repair of the wind power , so it's extremely necessary to forecast wind power output and assess its level of forecasting. This paper mainly focusing on the containing wind power system, studies the wind power output fluctuation in the demand for system reserve, and analyse the rationality of the wind power forecasting assessment standard in North China area wind power integration operation management implementing regulations by combining with the status of wind power in North China area, that is, whether the assessment mechanism can promote wind farms raising the forecasting level.

An Advanced Cascaded Method for Regional Wind Power Forecasting

2012 ◽  
Vol 198-199 ◽  
pp. 814-818
Author(s):  
Jin Sha Yuan ◽  
Hong Yang
Keyword(s):  
Wind Power ◽  
Prediction Error ◽  
Wind Farm ◽  
Evaluation Criteria ◽  
Wind Farms ◽  
Practical Method ◽  
Wind Power Forecasting ◽  
Forecasting Models ◽  
Simulation Results ◽  
Power Forecasting

Regional wind power forecasting deals with the prediction of the aggregated power output of wind farms located within a defined region. By architecture, regional forecasting models are classed by three approaches. In these approaches, the cascaded approach is a most practical method although the method has a larger prediction error. This paper provides a systematic review of all these regional forecasting approaches and ultimately proposes an advanced cascaded method to improve forecasting precision. The method use root mean square error as evaluation criteria to adjust every prediction of signal wind farm in a region. The simulation results show the effectiveness of the advanced cascaded method.

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