Bayesian Network Based Imprecise Probability Estimation Method for Wind Power Ramp Events

Although wind power ramp events (WPREs) are relatively scarce, they can inevitably deteriorate the stability of power system operation and bring risks to the trading of electricity market. In this paper, an imprecise conditional probability estimation method for WPREs is proposed based on the Bayesi...

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Autores principales: Yuanchun Zhao, Wenli Zhu, Ming Yang, Mengxia Wang
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Lenguaje:EN
Publicado: IEEE 2021
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Acceso en línea:https://doaj.org/article/8ddcef56bf714c4e87e537d7f819e111
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spelling oai:doaj.org-article:8ddcef56bf714c4e87e537d7f819e1112021-11-27T00:01:39ZBayesian Network Based Imprecise Probability Estimation Method for Wind Power Ramp Events2196-542010.35833/MPCE.2019.000294https://doaj.org/article/8ddcef56bf714c4e87e537d7f819e1112021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9096504/https://doaj.org/toc/2196-5420Although wind power ramp events (WPREs) are relatively scarce, they can inevitably deteriorate the stability of power system operation and bring risks to the trading of electricity market. In this paper, an imprecise conditional probability estimation method for WPREs is proposed based on the Bayesian network (BN) theory. The method uses the maximum weight spanning tree (MWST) and greedy search (GS) to build a BN that has the highest fitting degree with the observed data. Meanwhile, an extended imprecise Dirichlet model (IDM) is developed to estimate the parameters of the BN, which quantificationally reflect the ambiguous dependencies among the random ramp event and various meteorological variables. The BN is then applied to predict the interval probability of each possible ramp state under the given meteorological conditions, which is expected to cover the target probability at a specified confidence level. The proposed method can quantify the uncertainty of the probabilistic ramp event estimation. Meanwhile, by using the extracted dependencies and Bayesian rules, the method can simplify the conditional probability estimation and perform reliable prediction even with scarce samples. Test results on a real wind farm with three-year operation data illustrate the effectiveness of the proposed method.Yuanchun ZhaoWenli ZhuMing YangMengxia WangIEEEarticleBayesian network (BN)conditional probabilityimprecise Dirichlet model (IDM)imprecise probabilitywind power ramp eventsProduction of electric energy or power. Powerplants. Central stationsTK1001-1841Renewable energy sourcesTJ807-830ENJournal of Modern Power Systems and Clean Energy, Vol 9, Iss 6, Pp 1510-1519 (2021)
institution DOAJ
collection DOAJ
language EN
topic Bayesian network (BN)
conditional probability
imprecise Dirichlet model (IDM)
imprecise probability
wind power ramp events
Production of electric energy or power. Powerplants. Central stations
TK1001-1841
Renewable energy sources
TJ807-830
spellingShingle Bayesian network (BN)
conditional probability
imprecise Dirichlet model (IDM)
imprecise probability
wind power ramp events
Production of electric energy or power. Powerplants. Central stations
TK1001-1841
Renewable energy sources
TJ807-830
Yuanchun Zhao
Wenli Zhu
Ming Yang
Mengxia Wang
Bayesian Network Based Imprecise Probability Estimation Method for Wind Power Ramp Events
description Although wind power ramp events (WPREs) are relatively scarce, they can inevitably deteriorate the stability of power system operation and bring risks to the trading of electricity market. In this paper, an imprecise conditional probability estimation method for WPREs is proposed based on the Bayesian network (BN) theory. The method uses the maximum weight spanning tree (MWST) and greedy search (GS) to build a BN that has the highest fitting degree with the observed data. Meanwhile, an extended imprecise Dirichlet model (IDM) is developed to estimate the parameters of the BN, which quantificationally reflect the ambiguous dependencies among the random ramp event and various meteorological variables. The BN is then applied to predict the interval probability of each possible ramp state under the given meteorological conditions, which is expected to cover the target probability at a specified confidence level. The proposed method can quantify the uncertainty of the probabilistic ramp event estimation. Meanwhile, by using the extracted dependencies and Bayesian rules, the method can simplify the conditional probability estimation and perform reliable prediction even with scarce samples. Test results on a real wind farm with three-year operation data illustrate the effectiveness of the proposed method.
format article
author Yuanchun Zhao
Wenli Zhu
Ming Yang
Mengxia Wang
author_facet Yuanchun Zhao
Wenli Zhu
Ming Yang
Mengxia Wang
author_sort Yuanchun Zhao
title Bayesian Network Based Imprecise Probability Estimation Method for Wind Power Ramp Events
title_short Bayesian Network Based Imprecise Probability Estimation Method for Wind Power Ramp Events
title_full Bayesian Network Based Imprecise Probability Estimation Method for Wind Power Ramp Events
title_fullStr Bayesian Network Based Imprecise Probability Estimation Method for Wind Power Ramp Events
title_full_unstemmed Bayesian Network Based Imprecise Probability Estimation Method for Wind Power Ramp Events
title_sort bayesian network based imprecise probability estimation method for wind power ramp events
publisher IEEE
publishDate 2021
url https://doaj.org/article/8ddcef56bf714c4e87e537d7f819e111
work_keys_str_mv AT yuanchunzhao bayesiannetworkbasedimpreciseprobabilityestimationmethodforwindpowerrampevents
AT wenlizhu bayesiannetworkbasedimpreciseprobabilityestimationmethodforwindpowerrampevents
AT mingyang bayesiannetworkbasedimpreciseprobabilityestimationmethodforwindpowerrampevents
AT mengxiawang bayesiannetworkbasedimpreciseprobabilityestimationmethodforwindpowerrampevents
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