好文推荐克拉克森大学吴磊等一种基于机会约束的风能量化方法在鲁棒机组组合优化中动态地决定不确定性集合

A chance-constrained wind range quantification approach to robust SCUC by determining dynamic uncertainty intervals

Aditi Upadhyay, Bingqian Hu, Jie Li, Lei Wu

Clarkson University

1

Brief Introduction of Authors and Their Team

李洁，Clarkson大学助理教授，IEEE member。分别于2003年和2006年获得西安交通大学学士学位和硕士学位，2012年获得Illinois Institute of Technology博士学位。2012-2013在General Electric Energy Management担任应用工程师，2006-2008在IBM中国研究中心担任科研工程师。目前主要从事电力市场和竞标策略等相关领域研究。

Jie Li received the B.S. degree in information engineering from Xi’an Jiaotong University in 2003 and the M.S. degree in system engineering from Xi’an Jiaotong University, China in 2006, and the Ph.D. degree from the Illinois Institute of Technology (IIT), Chicago, in 2012. She was an Application Engineer with General Electric Energy Management from 2012 to 2013. She worked as a Research Engineer with IBM China Research Lab from 2006 to 2008.

Presently, she is an Assistant Professor in the Electrical and Computer Engineering Department at Clarkson University. Her research interests include power systems restructuring and bidding strategy.

吴磊，Clarkson大学副教授，IEEE Senior member。分别于2001年和2004年获得西安交通大学学士学位和硕士学位，2008年获得Illinois Institute of Technology博士学位。2012年在NYISO访问咨询。2008-2012在IIT Robert W. Galvin Center担任高级研究员。目前主要从事电力系统优化和经济运营等相关领域研究.

Lei Wu received his BS in electrical engineering and M.S. in systems engineering from Xi’an Jiaotong University, China, in 2001 and 2004, respectively, and Ph.D. in EE from Illinois Institute of Technology (IIT), USA, in 2008. He worked as a Visiting Faculty at NYISO in 2012. He was a senior research associate at the Robert W. Galvin Center for Electricity Innovation at IIT during 2008-2010.

Presently, he is an Associate Professor in the ECE Department at Clarkson University, Potsdam, NY, USA. His research interests include power systems optimization and economics.

2Research Background

随着风能的逐渐增多，风电的不确定性对电力系统的运营变得更加重要。在此背景下，亟需有效的方法衡量风电的不确定性，以更好地保证电力系统的安全运营。

本文提出了一个有效的方法准确地度量风电输出范围，以保证实际的风电输出落在此范围外的概率非常小。此方法进一步用来确定动态的风电不确定性集合，并以此建立鲁棒机组组合优化模型得到更经济的机组组合结果。

With increasing penetration of wind energy, the variability and uncertainty of wind resources have become important factors for power systems operation. In particular, an effective method is required for identifying the stochastic range of wind power output, in order to better guide the operational security of power systems.

This paper proposes a metric to determine accurate wind power output ranges so that the probability of actual wind power outputs being out of the range would be less than a small pre-defined value. Furthermore, the derived optimal wind power output range is used to construct dynamic uncertainty intervals for the robust SCUC model, which could derive effective robust solutions for managing wind power uncertainties and guaranteeing operational security and economics of power systems.

3

Main Creative Point and Advanced Part

（1）建立了一个系统的混合整数规划模型，确定有效的动态风电输出不确定性范围。

The proposed wind power output range model quantifies wind power variability at different timeframes, which is rigorously solved via an efficient MILP-based chance- constrained approach.

（2）动态风电输出不确定性范围应用在鲁棒机组组合优化中，并和传统的鲁棒机组组合优化模型比较。

The derived wind power output ranges are incorporated into the robust SCUC model as dynamic uncertainty intervals. Its effectiveness is illustrated by comparing with the deterministic SCUC and the traditional robust SCUC models in terms of operation cost and load shedding quantities.

4Brief Introduction of Method, Experiment and so on

论文基于NYISO的实际数据完成了大量仿真和实验测试。

Numerical case studies on the real wind power data in the NYISO market are performed to verify the proposed metric for quantifying wind power ranges

如图1 和2所示，在中间的出力水平和长的时间间隔下，风电的不确定性更加显著。

From these figures, it can be seen that the wind power variability is higher at a mid-level production level and with a larger  .                          

图3展示了24小时的风电预测值，以及静态的和动态的风电不确定性集合。和确定性机组组合以及使用静态风电不确定性集合的鲁棒机组组合模型相比较，实验测试显示使用动态的风电不确定性集合可以得到更经济的机组组合结果并且保证相似的鲁棒性。

Fig. 3 shows hourly wind forecasts over the 24 hours, as well as static and dynamic wind power intervals. Numerical tests show that the robust SCUC model with dynamic uncertainty intervals from the proposed wind power ranges outperforms the deterministic SCUC and robust SCUC with static uncertainty intervals in literature. Specifically, derives more economical and less conservative SCUC solutions with a similar level of robustness.

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