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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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) |
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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 |
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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 |
_version_ |
1718409242893877248 |