Quantile based probabilistic wind turbine power curve model*

Wind turbine power curve is an indicator of wind turbine performance and important input of wind farm design or power prediction, therefore can serve the system planning and operation. However, a good power curve model is difficult to obtain because of the uncertain relationship between wind speed and its power output. Existing works focus on a deterministic model or use probabilistic distribution to represent such uncertain relation, which is not easy to be employed by the following decision-makers. This paper presents a novel concept termed as quantile power curve, which generates a series of power curves under any confidence level. Quantile loss based neural network algorithm is proposed to establish the quantile power curve. Index to measure the wind turbine performance and power generation uncertainty is also proposed based on the quantile power curve. Based on the operational data of a Chinese wind farm, the proposed model and index are validated and employed to estimate the wind energy yield when planning a system with wind, solar and electric vehicle charging loads. The results show that quantile power curve provides more comprehensive information about the uncertainty during the power generation process and helps to improve the renewable supply rate to charging loads.

Automated summary

This paper introduces a novel concept called quantile power curve, which can generate a series of power curves at any confidence level, and proposes a neural network algorithm based on quantile loss to establish this curve. Through validation with operational data from a wind farm in China, it is demonstrated that the quantile power curve provides more comprehensive information about uncertainty during power generation and helps improve the renewable energy supply rate.