Stochastic Scenario Generation Methods for Uncertainty in Wind and Photovoltaic Power Outputs: A Comprehensive Review

Kun Zheng, Zhiyuan Sun (), Yi Song, Chen Zhang, Chunyu Zhang, Fuhao Chang, Dechang Yang and Xueqian Fu
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Kun Zheng: Electric Power Research Institute of Guangxi Power Grid Co., Ltd., Nanning 650214, China
Zhiyuan Sun: Electric Power Research Institute of Guangxi Power Grid Co., Ltd., Nanning 650214, China
Yi Song: Electric Power Research Institute of Guangxi Power Grid Co., Ltd., Nanning 650214, China
Chen Zhang: College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
Chunyu Zhang: College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
Fuhao Chang: College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
Dechang Yang: College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
Xueqian Fu: College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China

Energies, 2025, vol. 18, issue 3, 1-31

Abstract: This paper reviews scenario generation techniques for modeling uncertainty in wind and photovoltaic (PV) power generation, a critical component as renewable energy integration into power systems grows. Scenario generation enables the simulation of variable power outputs under different weather conditions, serving as essential inputs for robust, stochastic, and distributionally robust optimization in system planning and operation. We categorize scenario generation methods into explicit and implicit approaches. Explicit methods rely on probabilistic assumptions and parameter estimation, which enable the interpretable yet parameterized modeling of power variability. Implicit methods, powered by deep learning models, offer data-driven scenario generation without predefined distributions, capturing complex temporal and spatial patterns in the renewable output. The review also addresses combined wind and PV power scenario generation, highlighting its importance for accurately reflecting correlated fluctuations in multi-site, interconnected systems. Finally, we address the limitations of scenario generation for wind and PV power integration planning and suggest future research directions.

Keywords: wind power; PV power; scenario generation; implicit methods; explicit methods; deep generative models (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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