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

被引:149
作者
Li, Jinghua [1 ]
Zhou, Jiasheng [1 ]
Chen, Bo [1 ]
机构
[1] Guangxi Univ, Sch Elect Engn, Nanning 530004, Peoples R China
基金
中国国家自然科学基金;
关键词
Scenario generation; Stochastic programming; Wind power; Uncertainty; Application strategy; TRANSMISSION EXPANSION; PREDICTION INTERVALS; UNIT COMMITMENT; ELECTRIC-POWER; LOAD; OPTIMIZATION; UNCERTAINTY; ALGORITHM; DISPATCH; FRAMEWORK;
D O I
10.1016/j.apenergy.2020.115992
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
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.
引用
收藏
页数:18
相关论文
共 125 条
[51]   A clustering-based scenario generation framework for power market simulation with wind integration [J].
Li, Binghui ;
Sedzro, Kwami ;
Fang, Xin ;
Hodge, Bri-Mathias ;
Zhang, Jie .
JOURNAL OF RENEWABLE AND SUSTAINABLE ENERGY, 2020, 12 (03)
[52]  
Li J, 2014, ELECTRICITY, P41
[53]   A Scenario-Based Robust Transmission Network Expansion Planning Method for Consideration of Wind Power Uncertainties [J].
Li, Jinghua ;
Ye, Liu ;
Zeng, Yan ;
Wei, Hua .
CSEE JOURNAL OF POWER AND ENERGY SYSTEMS, 2016, 2 (01) :11-18
[54]   Combination of moment-matching, Cholesky and clustering methods to approximate discrete probability distribution of multiple wind farms [J].
Li, Jinghua ;
Zhu, Dunlin .
IET RENEWABLE POWER GENERATION, 2016, 10 (09) :1450-1458
[55]   A Scenario Optimal Reduction Method for Wind Power Time Series [J].
Li, Jinghua ;
Lan, Fei ;
Wei, Hua .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2016, 31 (02) :1657-1658
[56]   Generating wind power time series based on its persistence and variation characteristics [J].
Li JingHua ;
Li JiaMing ;
Wen JinYu ;
Cheng ShiJie ;
Xie HaiLian ;
Yue ChengYan .
SCIENCE CHINA-TECHNOLOGICAL SCIENCES, 2014, 57 (12) :2475-2486
[57]  
Li Jinghua, 2014, Proceedings of the CSEE, V34, P2544
[58]   Risk-constrained bidding strategy with stochastic unit commitment [J].
Li, Tao ;
Shahidehpour, Mohammad ;
Li, Zuyi .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2007, 22 (01) :449-458
[59]   Optimal Stochastic Operation of Integrated Low-Carbon Electric Power, Natural Gas, and Heat Delivery System [J].
Li, Yong ;
Zou, Yao ;
Tan, Yi ;
Cao, Yijia ;
Liu, Xindong ;
Shahidehpour, Mohammad ;
Tian, Shiming ;
Bu, Fanpeng .
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, 2018, 9 (01) :273-283
[60]  
Lin CF, 2017, 2017 IEEE 7TH INTERNATIONAL CONFERENCE ON POWER AND ENERGY SYSTEMS (ICPES), P90, DOI 10.1109/ICPESYS.2017.8215927