Extraction of Basic Features and Typical Operating Conditions of Wind Power Generation for Sustainable Energy Systems

Yongtao Sun, Qihui Yu (), Xinhao Wang, Shengyu Gao and Guoxin Sun
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Yongtao Sun: The School of Mechanical Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China
Qihui Yu: The School of Mechanical Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China
Xinhao Wang: The School of Mechanical Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China
Shengyu Gao: Guoneng Hebei Cangdong Power Generation Co., Ltd., Cangzhou 061113, China
Guoxin Sun: The School of Mechanical Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China

Sustainability, 2025, vol. 17, issue 14, 1-21

Abstract: Accurate extraction of representative operating conditions is crucial for optimizing systems in renewable energy applications. This study proposes a novel framework that combines the Parzen window estimation method, ideal for nonparametric modeling of wind, solar, and load datasets, with a game theory-based time scale selection mechanism. The novelty of this work lies in integrating probabilistic density modeling with multi-indicator evaluation to derive realistic operational profiles. We first validate the superiority of the Parzen window approach over traditional Weibull and Beta distributions in estimating wind and solar probability density functions. In addition, we analyze the influence of key meteorological parameters such as wind direction, temperature, and solar irradiance on energy production. Using three evaluation metrics, the main result shows that a 3-day representative time scale offers optimal accuracy when determined through game theory methods. Validation with real-world data from Inner Mongolia confirms the robustness of the proposed method, yielding low errors in wind, solar, and load profiles. This study contributes a novel 3-day typical profile extraction method validated on real meteorological data, providing a data-driven foundation for optimizing energy storage systems under renewable uncertainty. This framework supports energy sustainability by ensuring realistic modeling under renewable intermittency.

Keywords: Parzen window estimation; time scale; wind and solar; probability density; game theory methods (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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