A Fast Power Market Clearing Method Based on Active Constraints Identification by Deep Learning

doi:10.11930/j.issn.1004-9649.202005028

Abstract

Abstract: The day-ahead power market clearing needs to solve the security-constrained economic dispatch (SCED) problem. Although the SCED problem is a linear programming (LP) model, the model size is too large to be effectively solved because the massive security constraints in the N–1 scenarios need to be considered. Therefore, this paper proposes a fast clearing method for day-ahead power market based on the deep neural network. Firstly, a computation framework for SCED model based on deep neural network is designed, and embeds deep learning technology into the existing day-ahead power market clearing framework, which can effectively improve the solving speed of the SCED model without compromising precision. Secondly, a deep learning strategy is proposed for identification of active constraint sets, which can provide technical support for deep neural networks to effectively identify the active constraints of SCED from two aspects: feature vector design and efficient processing of the results of deep neural network. Finally, the effectiveness of the proposed method is verified in the IEEE 30 standard test system with renewable energy sources. The deep neural network is used to pre-identify the active constraints of the SCED model, which is beneficial to reduce the complexity of the model and improve the calculation efficiency of market clearing.

Key words: security-constrained economic dispatch, active constraints identification, deep learning technology, day-ahead power market clearing

WU Yunliang, ZHANG Jianxin, LI Bao, LI Peng, LI Zhiyong, ZHOU Xin, YANG Yan, LAI Xiaowen. A Fast Power Market Clearing Method Based on Active Constraints Identification by Deep Learning[J]. Electric Power, 2020, 53(9): 90-97,207.

Add to citation manager EndNote|Ris|BibTeX

URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202005028

https://www.electricpower.com.cn/EN/Y2020/V53/I9/90

References

[1] 陈政, 张翔, 马子明, 等. 引导电力供需长期有效均衡的容量市场设计[J]. 中国电力, 2020, 53(8): 164–172.
CHEN Zheng, ZHANG Xiang, MA Ziming, et al. A capacity market design guiding the long-term effective balance of power supply and demand[J]. Electric Power, 2020, 53(8): 164–172.
[2] 房欣欣, 杨知方, 余娟, 等. 节点电价的理论剖析与拓展[J]. 中国电机工程学报, 2020, 40(2): 379–390
FANG Xinxin, YANG Zhifang, YU Juan, et al. Theoretical analysis and extension of locational marginal price[J]. Proceedings of the CSEE, 2020, 40(2): 379–390
[3] CAPITANESCU F, GLAVIC M, ERNST D, et al. Contingency filtering techniques for preventive security-constrained optimal power flow[J]. IEEE Transactions on Power Systems, 2007, 22(4): 1690–1697.
[4] ALLEN J W, BRUCE F W. Power generation operation and control[M]. 2nd edition. Beijing: Tsinghua University Press, 1996.
[5] BRANDWAJN V. Efficient bounding method for linear contingency analysis[J]. IEEE Transactions on Power Systems, 1988, 3(1): 38–43.
[6] MADANI R, LAVAEI J, BALDICK R. Constraint screening for security analysis of power networks[J]. IEEE Transactions on Power Systems, 2017, 32(3): 1828–1838.
[7] ZHAI Q Z, GUAN X H, CHENG J H, et al. Fast identification of inactive security constraints in SCUC problems[J]. IEEE Transactions on Power Systems, 2010, 25(4): 1946–1954.
[8] YANG Y F, GUAN X H, ZHAI Q Z. Fast grid security assessment with N-k contingencies[J]. IEEE Transactions on Power Systems, 2017, 32(3): 2193–2203.
[9] ARDAKANI A J, BOUFFARD F. Identification of umbrella constraints in DC-based security-constrained optimal power flow[J]. IEEE Transactions on Power Systems, 2013, 28(4): 3924–3934.
[10] HUA B W, BIE Z H, LIU C, et al. Eliminating redundant line flow constraints in composite system reliability evaluation[J]. IEEE Transactions on Power Systems, 2013, 28(3): 3490–3498.
[11] TEJADA-ARANGO D A, SANCHEZ-MARTIN P, RAMOS A. Security constrained unit commitment using line outage distribution factors[J]. IEEE Transactions on Power Systems, 2018, 33(1): 329–337.
[12] XAVIER Á S, QIU F, WANG F Y, et al. Transmission constraint filtering in large-scale security-constrained unit commitment[J]. IEEE Transactions on Power Systems, 2019, 34(3): 2457–2460.
[13] XAVIER Á S, QIU F, AHMED S. Learning to solve large-scale security-constrained unit commitment problems[EB/OL]. (2019-12-28) [2020-04-29]. https://arxiv.org/abs/1902.01697.
[14] NG Y, MISRA S, ROALD L A, et al. Statistical learning for DC optimal power flow[C]//2018 Power Systems Computation Conference (PSCC). Dublin, Ireland. IEEE, 2018: 1–7.
[15] DEKA D, MISRA S. Learning for DC -OPF: Classifying active sets using neural nets. [EB/OL]. (2019-02-14)[2020-04-29]. https://arxiv.org/abs/1902.05607.
[16] 杨晓楠, 孙博, 郎燕生. 基于深度学习的特高压直流闭锁故障智能调度决策[J]. 中国电力, 2020, 53(6): 8–17
YANG Xiaonan, SUN Bo, LANG Yansheng. Intelligent dispatch decision-making for UHVDC blocking fault based on deep learning[J]. Electric Power, 2020, 53(6): 8–17
[17] SHAHAM U, CLONINGER A, COIFMAN R R. Provable approximation properties for deep neural networks[J]. Applied and Computational Harmonic Analysis, 2018, 44(3): 537–557.
[18] 邓韦斯, 吴云亮, 孙宇军, 等. 面向现货市场出清的发电计划校正决策方法[J]. 电力系统自动化: 1–14[2020-08-20].http://kns.cnki.net/kcms/detail/32.1180.TP.20200807.1442.002.html.
DENG Weisi, WU Yunliang, SUN Yujun, et al. Decision-making method of generation scheduling correction oriented to electricity spot market clearing[J]. Automation of Electric Power Systems: 1–14[2020-08-20].http://kns.cnki.net/kcms/detail/32.1180.TP.20200807.1442.002.html.
[19] 吴云亮, 李豹, 罗会洪, 等. 面向现货市场出清的条件断面约束模型化处理方法[J]. 电网技术, 2020, 44(8): 2819–2831
WU Yunliang, LI Bao, LUO Huihong, et al. Approach for conditional section constraints modeling based on spot market clearing[J]. Power System Technology, 2020, 44(8): 2819–2831
[20] 余娟, 杨燕, 杨知方, 等. 基于深度学习的概率能量流快速计算方法[J]. 中国电机工程学报, 2019, 39(1): 22–30, 317
YU Juan, YANG Yan, YANG Zhifang, et al. Fast probabilistic energy flow analysis based on deep learning[J]. Proceedings of the CSEE, 2019, 39(1): 22–30, 317
[21] TIELEMAN T, HINTON G. Rmsprop: Divide the gradient by a running average of its recent magnitude[EB/OL]. (2012-10-11)[2020-04-29]. https://amara.org/en/videos/vrXNiLBHyW92/en/180511/.
[22] QIN Z L, LI W Y, XIONG X F. Incorporating multiple correlations among wind speeds, photovoltaic Powers and bus loads in composite system reliability evaluation[J]. Applied Energy, 2013, 110: 285–294.
[23] RASKUTTI G, WAINWRIGHT M J, YU B. Early stopping and non-parametric regression: an optimal data-dependent stopping rule[J]. Journal of Machine Learning Research, 2014, 15(1): 335–366.