Review of wind power scenario generation methods for optimal operation of renewable energy systems

•The state-of-the-art scenario generation methods are classified and reviewed comprehensively.•An evaluation framework for scenario generation methods is established.•The applications of scenario generation methods are summarized and discussed.•Limitations and challenges of scenario generation metho...

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Veröffentlicht in:Applied energy Jg. 280; S. 115992
Hauptverfasser: Li, Jinghua, Zhou, Jiasheng, Chen, Bo
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 15.12.2020
Schlagworte:
application methods
Application strategy
energy
methodology
power generation
Scenario generation
stochastic processes
Stochastic programming
Uncertainty
Wind power
Uncertainty
Application strategy
Scenario generation
Wind power
Stochastic programming
ISSN:0306-2619, 1872-9118
Online-Zugang:Volltext
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Zusammenfassung:•The state-of-the-art scenario generation methods are classified and reviewed comprehensively.•An evaluation framework for scenario generation methods is established.•The applications of scenario generation methods are summarized and discussed.•Limitations and challenges of scenario generation methods are discussed. Scenario generation is an effective method for addressing uncertainties in stochastic programming for energy systems with integrated wind power. To comprehensively understand scenario generation and optimize solutions for uncertainties, the various methods and applications of scenario generation are classified and discussed in this work. First, the basic concepts are presented and scenario generation methods for addressing stochastic programming problems are discussed. Second, three categories of scenario generation methods are briefly introduced, along with their derived methods, advantages, and disadvantages. Third, an evaluation framework for these methods is established. Subsequently, applications of the scenario generation methods in power systems are discussed to identify the properties of these methods. Further, a comparative analysis and discussion are presented to show the suitability of each scenario generation method and to help choose the appropriate methods for different practical situations. Finally, the current limitations and future works with regard to scenario generation for stochastic programming in wind-power-integrated systems are highlighted and discussed. The results of this study are expected to provide references for applying scenario generation methods to the optimal operation of renewable energy systems.
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ISSN:0306-2619
1872-9118
DOI:10.1016/j.apenergy.2020.115992