Wind power forecast optimization by integration of CFD and Kalman filtering

ABSTRACT

In this work, an integrated wind power forecasting method has been carried out to analyze the spatial dynamics of wind power in a micro-scale wind farm, located in a gully region of the loess plateau, by integrating computational fluid mechanics (CFD) model and Kalman filter method. The CFD model was used to simulate the wind-speed distribution characteristics for obtaining six different wind zones. Then, we corrected the wind speed and wind power in each wind zone at periods I and II, respectively, using Kalman filter and theoretical wind power curve, with an hourly forecasting product of BJ-RUC system during in June 2014. Furthermore, we assessed the effects of correcting results and actual outputs in wind speed and wind power separately, for acquiring the uncertainties' results of the integrated model. The capacity factor and curtailment rate of wind power generation were also assessed to reflect the potential economic benefit under current conditions in the wind farm. The result showed that the integrated model used in the study could improve the wind power forecast accuracy to 97.55% and 80.79% at periods I and II, respectively. The integrated method in the case study can be used to optimize the forecast on wind speed and wind power in similar wind farms, which is helpful for the local managers to make decisions on sustainable wind power planning.

KEYWORDS:

• Wind energy
• BJ-RUC
• CFD
• Kalman filter
• capacity factor
• wind curtailment rate

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

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Additional information

Funding

This study was supported by the National Natural Science Foundation of China (41601184). We thank the anonymous reviewers for their valuable comments.

Notes on contributors

Lijun Liu

Lijun Liu is a Ph.D. candidate at the Wuhan University of Technology. Her academic interest is focused on the integrated modeling of ship emission processes and extreme events under climate change.

Youjia Liang

Youjia Liang is presently serving as an Associate Professor in the Wuhan University of Technology. He has worked in various areas of sustainable development, particularly in exploring the future uncertainties in the complex social-ecological systems, utilizing scenario-based integrated modeling exercises that aim to formulate robust spatial policies and optimization of ecosystem services.