Data Learning-based Frequency Risk Assessment in a High-penetrated Renewable Power System

Jiaxin WEN; Siqi BU; Qiyu CHEN; Bowen ZHOU

doi:10.12096/j.2096-4528.pgt.20105

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Data Learning-based Frequency Risk Assessment in a High-penetrated Renewable Power System

Power System Planning | 更新时间：2025-07-04

- Data Learning-based Frequency Risk Assessment in a High-penetrated Renewable Power System
- Power Generation Technology Vol. 42, Issue 1, Pages: 40-47(2021)
- 作者机构：
  
  1.香港理工大学电机工程学系, 香港特别行政区九龙区 999077
  2.中国电力科学研究院有限公司, 北京市海淀区 100192
  3.东北大学信息科学与工程学院, 辽宁省沈阳市 110819
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(51807171);Hong Kong Research Grants Council(15200418);Hong Kong Research Grants Council(15219619);Natural Science Foundation of Guangdong Province(2019A1515011226)
- DOI：10.12096/j.2096-4528.pgt.20105
  CLC： TK81
- Received：30 September 2020，
  
  Published：28 February 2021
- 稿件说明：
移动端阅览
Jiaxin WEN, Siqi BU, Qiyu CHEN, et al. Data Learning-based Frequency Risk Assessment in a High-penetrated Renewable Power System[J]. Power Generation Technology, 2021, 42(1): 40-47.
DOI：

Jiaxin WEN, Siqi BU, Qiyu CHEN, et al. Data Learning-based Frequency Risk Assessment in a High-penetrated Renewable Power System[J]. Power Generation Technology, 2021, 42(1): 40-47. DOI： 10.12096/j.2096-4528.pgt.20105.

摘要

新能源正在逐步代替传统发电厂为用户提供电能，但同时也为电网的安全运行带来了潜在的风险。因此，在规划阶段需要全面地对最大频率偏差越线风险进行概率评估。基于蒙特卡罗仿真（Monte Carlo simulation，MCS）的规划方法效率很低，而人工神经网络（artificial neural network，ANN）可以通过对数据的学习做出快速有效的预测。为此，提出一种基于MCS-ANN的区域频率概率评估方法，以实现对区域最大频率偏差越线风险的快速评估。首先，产生大量的随机扰动，仅对小部分扰动进行仿真；然后将这部分数据送入ANN进行训练，并将剩余的大部分扰动送入训练好的ANN进行输出预测；重复以上训练和预测的过程，将多次预测结果的平均值作为最终的预测输出，得到各个风险区间的概率分布情况。最后，在IEEE 10机39节点的系统上验证了所提方法的有效性。

Abstract

Renewable energy is gradually replacing traditional power plants to provide electricity for users

but it also brings potential risks to the safe operation of the power grid. Thus

in the planning stage

it is necessary to comprehensively evaluate the probability of violation risk of maximum frequency deviation. Planning method based on Monte Carlo simulation (MCS) is inefficient

while artificial neural network (ANN) can make fast and effective prediction by learning data. Therefore

this paper proposed an MCS-ANN algorithm to realize the rapid assessment of violation risk of regional maximum frequency deviation. Firstly

a large number of stochastic disturbances were generated

and only a small part of disturbances were used for MCS. Then

these data were sent to the neural network for training

and most of the remaining disturbances were sent to the trained neural network for output prediction. The above training and prediction processes were repeated. The average of multiple prediction results was used as the final prediction output

and the probability distribution of each risk interval was obtained. Finally

the effectiveness of the proposed MCS-ANN algorithm was verified on IEEE 10-machine 39-node system.

关键词

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references

LI T, LI A, GUO X P. The sustainable development-oriented development and utilization of renewable energy industry: a comprehensive analysis of MCDM methods[J]. Energy, 2020, 212: 118694.

潘旭东, 黄豫, 唐金锐, 等. 新能源发电发展的影响因素分析及前景展望[J]. 智慧电力, 2019, 47(11): 41-47.

PAN X D, HUANG Y, TANG J R, et al. Influencing factors and prospects for development of renewable energy power generation[J]. Smart Power, 2019, 47(11): 41-47.

LU C, FENGY W, FEI C W, et al. Improved decomposed-coordinated kriging modeling strategy for dynamic probabilistic analysis of multicomponent structures[J]. IEEE Transactions on Reliability, 2020, 69(2): 440-457.

武志军. 综合能源服务下新能源集约化管控评价方法[J]. 华电技术, 2019, 41(12): 68-71.

WU Z J. Evaluation method on new energy intensive management for integrated energy services[J]. Huadian Technology, 2019, 41(12): 68-71.

杜文娟, 卜思齐, 王海风. 考虑并网风电随机波动的电力系统小干扰概率稳定性分析[J]. 中国电机工程学报, 2011, 31(S1): 7-11.

DU W J, BU S Q, WANG H F. Effect of stochastic variation of grid-connected wind generation on power system small-signal probabilistic stability[J]. The Proceedings of the CSEE, 2011, 31(S1): 7-11.

KROPOSKI B. Integrating high levels of variable renewable energy into electric power systems[J]. Journal of Modern Power Systems and Clean Energy, 2017(5): 831-837.

李克勋, 宗明珠, 魏高升. 地热能及与其他新能源联合发电综述[J]. 发电技术, 2020, 41(1): 79-87.

LI K X, ZONG M Z, WEI G S. Overview of geothermal power generation and joint power generation with other new energy sources[J]. Power Generation Technology, 2020, 41(1): 79-87.

朱罡, 王茂春. 含新能源发电的电力系统状态估计研究[J]. 电网与清洁能源, 2020, 36(4): 32-40.

ZHU G, WANG M C. Review on state estimation for power system containing renewable energy generation[J]. Power System and Clean Energy, 2020, 36(4): 32-40.

LI R, GENG H, YANG G. Fault ride-through of renewable energy conversion systems during voltage recovery[J]. Journal of Modern Power Systems and Clean Energy, 2016(4): 28-39.

段建民, 王志新, 王承民. 基于电网聚合模型的新能源接纳能力评价与分析[J]. 分布式能源, 2019, 4(5): 42-49.

DUAN J M, WANG Z X, WANG C M. Evaluation and analysis of the capability of new energy integration based on grid aggregation mode[J]. Distributed Energy, 2019, 4(5): 42-49.

李更丰, 邱爱慈, 黄格超, 等. 电力系统应对极端事件的新挑战与未来研究展望[J]. 智慧电力, 2019, 47(8): 1-11.

LI G F, QIU A C, HUANG G C, et al. New challenges and future research prospects in power system against to extreme events[J]. Smart Power, 2019, 47(8): 1-11.

KUNDUR P. Power system stability and control[M]. McGraw-Hill New York: Spring, 2013.

马世英, 王青. 大规模新能源集中外送系统源网协调风险及仿真评估[J]. 发电技术, 2018, 39(2): 112-117.

MA S Y, WANG Q. Risk and simulation evaluation of large-scale centralized new energy sending system[J]. Power Generation Technology, 2018, 39(2): 112-117.

SHI Q, LI F, CUI H. Analytical method to aggregate multi-machine SFR model with applications in power system dynamic studies[J]. IEEE Transactions on Power Systems, 2018, 33(6): 6355-6367.

BU S, WEN J, LI F. A generic framework foranalytical probabilistic assessment of frequency stability in modern power system operational planning[J]. IEEE Transactions on Power Systems, 2019, 34(5): 3973-3976.

WEN J, BU S, ZHOU B, et al. A fast-algorithmic probabilistic evaluation on regional rate of change of frequency (RoCoF) for operational planning of high renewable penetrated power systems[J]. Energies, 2020, 13: 2780.

FRERIS L, LNFIELD D. Renewable energy in power system[M]. New York: Wiley, 2008.

BERIZZI A, BOVO C, DELFANTI M, et al. A Monte Carlo approach for TTC evaluation[J]. IEEE Transactions on Power Systems, 2007, 22(2): 735-743.

HEYDT G T, GRAF T J. Distribution system reliability evaluation using enhanced samples in a Monte Carlo approach[J]. IEEE Transactions on Power Systems, 2010, 25(4): 2006-2008.

AHMADYAR A, RIAZ A, VERBIC G, et al. A framework for assessing renewable integration limits with respect to frequency performance[J]. IEEE Transactions on Power Systems, 2018, 33(4): 4444-4453.

LOPEZ-GARCIA T B, CORONADO-MENDOZA A, DOMÍNGUEZ-NAVARRO J A. Artificial neural networks in microgrids: a review[J]. Engineering Applications of Artificial Intelligence, 2020, 95: 103894.

MUYEEN S M, HASANIEN H M, AL-DURRA A. Transient stability enhancement of wind farms connected to a multi-machine power system by using an adaptive ANN-controlled SMES[J]. Energy Conversion and Management, 2014, 78: 412-420.

GUIJO-RUBIO D, DURÁN-ROSAL A M, GUTIÉRREZ P A, et al. Evolutionary artificial neural networks for accurate solar radiation prediction[J]. Energy, 2020, 210: 118374.

ZHANG X S, YANG B, YU T, et al. Dynamic surrogate model based optimization for MPPT of centralized thermoelectric generation system under heterogeneous temperature differences[J]. IEEE Transactions on Energy Conversion, 2020, 35(2): 966-976.

BU S Q, DU W, WANG H F, et al. Probabilistic analysis of small-signal stability of large-scale power systems as affected by penetration of wind generation[J]. IEEE Transactions on Power Systems, 2012, 27(2): 762-770.

王成山, 郑海峰, 谢莹华, 等. 计及分布式发电的配电系统随机潮流计算[J]. 电力系统自动化, 2005, 24: 39-44.

WANG C S, ZHENG H F, XIE Y H, et al. Probabilistic power flow containing distributed generation in distribution system[J]. Automation of Electric Power Systems, 2005, 24: 39-44.

KENDALL M. Kendall's advanced theory statistics[M]. New York: Oxford University Press, 1987.

ANDERSON P M, FOUAD A A. Power system control and stability[M]. Ames, IA: Iowa State University Press, 1977.

National Electricity Transmission System Security and Quality of Supply Standards[S].UK: National Grid ESO, 2014.

AGATONOVIC-KUSTRIN S, BERESFORD R. Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research[J]. Journal of Pharmaceutical and Biomedical Analysis, 2000, 22(5): 717-727.

ROGERS G. Power system oscillations[M]. Norwell, MA: Kluwer, 2000.

CHOW J H, CULLUM J, WILLOUGHBY R A. A sparsity-based technique for identifying slow-coherent areas in large power systems[J]. IEEE Transactions on Power Apparatus and Systems, 1984, 103(3): 463-473.

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