A probabilistic power flow prediction and situation awareness method based on long and short term memory network
Article No.: 87, Pages 1 - 8
Abstract
With the massive integration of renewable energy into the power system, the randomness and volatility of power generation in the power system are increasing day by day. These characteristics have a great impact on the direction and size of power flow in the power grid. This paper presents a probabilistic power flow prediction and situation awareness method based on long and short term memory network. This paper first introduces the probability model based on wind power generation, photovoltaic power generation, demand side load, electric vehicle charging, generator set; Secondly, based on the NATAF transformation method, several probability models are transformed by de-correlation standard normal distribution, and a probability scheduling model with minimum cost of multi-party coordination and complementarity is established. Then, a probabilistic power flow solution based on long short-term memory network is proposed for the probabilistic scheduling model. Finally, an actual power grid is taken as an example to verify and compare the proposed algorithms, and the results prove the effectiveness of the proposed methods.
References
[1]
H. Yuan, F. Li, Y. Wei, Novel Linearized Power Flow and Linearized OPF Models for Active Distribution Networks With Application in Distribution LMP[J]. IEEE Transactions on Smart Grid, 2018, 9(1): 438-448.
[2]
Z. Yang, K. Xie, J. Yu, A General Formulation of Linear Power Flow Models: Basic Theory and Error Analysis[J]. IEEE Transactions on Power Systems, 2019, 34(2): 1315-1324.
[3]
ZHANG Li, LIU Junyong, LIU Youbo, Probabilistic Forecast of Transmission Congestion With Wind Speed Correlation[J]. Power System Technology, 2016, 40(6):1696-1702.
[4]
SU Chenbo, LIU Chongru, LI Zhiyu, Bayesian Theory Based Calculation of Probabilistic Power Flow Considering Correlation Between Multi-dimensional Wind Speed[J]. Automation of Electric Power Systems, 2020, 11(24):1-8.
[5]
Pai L I, Liu C, Huang Y, Modeling Correlated Power Time Series of Multiple Wind Farms Based on Hidden Markov Model[J]. Proceedings of the CSEE, 2019.
[6]
XIE Z Q, JI T Y, LI M S, et al. Quasi-Monte Carlo based probabilistic optimal power flow considering the correlation of wind speeds using Copula function[J]. IEEE Transactions on Power Systems, 2018, 33(2):2239-2247.
[7]
Liu Z, Wei Z, Sun G, Probabilistic Power Flow Calculation of Power System With Wind Farms Considering Fuzzy Parameters[J]. Power System Technology, 2017.
[8]
Ye L, Zhang Y, Ju Y, Gaussian Mixture Model for Probabilistic Power Flow Calculation of System Integrated Wind Farm[J]. Proceedings of the Csee, 2017, 37(15):4379-4387.
[9]
Wei W U, Wang K, Guojie L I . Probabilistic Load Flow Calculation Method Based on Multiple Integral Method Considering Correlation of Photovoltaic Generation[J]. Proceedings of the Csee, 2015, 35(3):568-575.
[10]
Zhao L, Zhao S, Yongqiang H U . Probabilistic Load Flow Calculation Considering Correlativity of Photovoltaic Generation[J]. Journal of North China Electric Power University(Natural Science Edition), 2017.
[11]
LI Yunyu, LIANG Youzhen, ZHEN Wenxi, Probabilistic Forecast of Transmission Congestion With Wind Speed Correlation[J].Power System and Clean Energy, 2019, 35(6):69-75.
[12]
Huang W, Yang S, Liangjun G E . Operation Strategy for Source and Load Coordinated Optimization of Active Distribution Network Based on Probabilistic Load Flow[J]. Automation of Electric Power Systems, 2017, 41(13):39-44 and 61.
[13]
Zhou J, Wang K, Shi F, Probabilistic power flow algorithm responses[J]. Electric Power Automation Equipment, 2016.
[14]
Kun H E, Xiaoyuan X U, Yan Z, Probabilistic Power Flow Calculation of Islanded Microgrid Based on Sparse Polynomial Chaos Expansion[J]. Automation of Electric Power Systems, 2019.
[15]
Wang H, Yan Z, Xiaoyuan X U, Probabilistic Power Flow Calculation of Islanded Microgrid Considering Uncertainty of Renewable Energy[J]. Automation of Electric Power Systems, 2018.
[16]
YIN W L, WU X, RUI X M. Adaptive robust back stepping control of the speed regulating differential mechanism for wind turbines[J]. IEEE Transactions on Sustainable Energy, 2019, 10(3):1311-1318.
[17]
Considering Correlated Photovoltaic Power Output Chinese Full Text CHEN Can;WU Wenchuan;ZHANG Boming, Probabilistic Load Flow of Distribution Network Considering Correlated Photovoltaic Power Output[J].Automation of Electric Power Systems, 2015, 39(9):41-47.
[18]
Xiao Qing.Evaluating correlation coefficient for Nataf transformation[J].Probabilistic Engineering Mechanics, 2014, 37:1-6.
[19]
Hohenbichler M, Rackwitz R.Zon-normal dependent vectors in structural safety[J].Journal of the Engineering Mechanics Division, 1981, 107(6):1227-1238.
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Published In
December 2022
770 pages
ISBN:9781450398336
DOI:10.1145/3579654
Copyright © 2022 ACM.
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Association for Computing Machinery
New York, NY, United States
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Published: 14 March 2023
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ACAI 2022
ACAI 2022: 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence
December 23 - 25, 2022
Sanya, China
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Overall Acceptance Rate 173 of 395 submissions, 44%
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