Data-driven Robust Unit Commitment Based on the Generalized Convex Hull Uncertainty Set

被引：0

作者：

Zhang Y. ^{[1
]}

Ai X. ^{[1
]}

Fang J. ^{[1
]}

Zhang M. ^{[1
]}

Yao W. ^{[1
]}

Wen J. ^{[1
]}

机构：

[1] State Key Laboratory of Advanced Electromagnetic Engineering and Technolog, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan, 430074, Hubei Province

来源：

Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering | 2020年 / 40卷 / 02期

基金：

中国国家自然科学基金; 国家重点研发计划;

关键词：

Data-driven; Generalized convex hull; Renewable energy; Robust unit commitment; Temporal and spatial correlations;

D O I：

10.13334/j.0258-8013.pcsee.180910

中图分类号：

学科分类号：

摘要：

In the robust unit commitment, how to rationally describe the uncertain power of renewable energy sources plays an important role in determining the conservativeness of the model. In this paper, the data-driven generalized convex hull uncertainty set was proposed and applied to the two-stage robust unit commitment. This model can effectively consider the temporal and spatial correlations of renewable energy sources, thus reducing the conservativeness of the decision and improve the economy. Firstly, the historical data of the renewable energies was used to construct the data-adaptive uncertainty set and exact the extreme scenarios. Then, the two-stage robust unit commitment model based on the extracted extreme scenarios was introduced. Furthermore, the column and constraint generation algorithm was employed to solve the robust model. Finally, the proposed model and solution technique were tested on a 14-bus and a modified IEEE RTS-79 system. The numerical results showed the effectiveness of the proposed model and solution method. © 2020 Chin. Soc. for Elec. Eng.

引用

页码：477 / 486

页数：9

共 24 条

[1] Wind power grid operation in 2017, (2018)
[2] China's annual photovoltaic power generation exceeded 100 billion volts, (2018)
[3] Morales-Espana G., Baldick R., Garcia-Gonzalez J., Et al., Power-capacity and ramp-capability reserves for wind integration in power-based UC, IEEE Transactions on Sustainable Energy, 7, 2, pp. 614-624, (2016)
[4] Sun X., Chen J., Duan X., Et al., Probabilistic load flow calculation based on stochastic response surface method considering high-dimension stochastic variables, Proceedings of the CSEE, 38, 9, pp. 2551-2560, (2018)
[5] Ai X., Wen J., Wu T., Et al., A discrete point estimate method for probabilistic load flow based on the measured data of wind power, IEEE Transactions on Industry Applications, 49, 5, pp. 2244-2252, (2013)
[6] Zhao S., Wang Y., Xu Y., Et al., Coordinated dispatch of large scale energy storage system and thermal generation in high wind power penetration level system based on chance constrained goal programming, Proceedings of the CSEE, 36, 4, pp. 969-977, (2016)
[7] Wang Q., Guan Y., Wang J., A chance-constrained two-stage stochastic program for unit commitment with uncertain wind power output, IEEE Transactions on Power Systems, 27, 1, pp. 206-215, (2012)
[8] Guan Y., Wang J., Uncertainty sets for robust unit commitment, IEEE Transactions on Power Systems, 29, 3, pp. 1439-1440, (2014)
[9] Jabr R.A., Robust transmission network expansion planning with uncertain renewable generation and loads, IEEE Transactions on Power Systems, 28, 4, pp. 4558-4567, (2013)
[10] Jiang R., Wang J., Guan Y., Robust unit commitment with wind power and pumped storage hydro, IEEE Transactions on Power Systems, 27, 2, pp. 800-810, (2012)

← 1 2 3 →