Uncertainty modeling of wind power frequency regulation potential considering distributed characteristics of forecast errors

Abstract Large-scale integration of wind power generation decreases the equivalent inertia of a power system, and thus makes frequency stability control challenging. However, given the irregular, nonlinear, and non-stationary characteristics of wind power, significant challenges arise in making wind...
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Autor*in:	Cheng Yan [verfasserIn] Yi Tang [verfasserIn] Jianfeng Dai [verfasserIn] Chenggen Wang [verfasserIn] Shengjun Wu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Inertial response Primary frequency control Error distribution Mixed skew generalized error distribution Uncertainty modeling

Übergeordnetes Werk:	In: Protection and Control of Modern Power Systems - SpringerOpen, 2016, 6(2021), 1, Seite 13
Übergeordnetes Werk:	volume:6 ; year:2021 ; number:1 ; pages:13

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

DOI / URN:	10.1186/s41601-021-00200-3

Katalog-ID:	DOAJ019746822

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520			\|a Abstract Large-scale integration of wind power generation decreases the equivalent inertia of a power system, and thus makes frequency stability control challenging. However, given the irregular, nonlinear, and non-stationary characteristics of wind power, significant challenges arise in making wind power generation participate in system frequency regulation. Hence, it is important to explore wind power frequency regulation potential and its uncertainty. This paper proposes an innovative uncertainty modeling method based on mixed skew generalized error distribution for wind power frequency regulation potential. The mapping relationship between wind speed and the associated frequency regulation potential is established, and key parameters of the wind turbine model are identified to predict the wind power frequency regulation potential. Furthermore, the prediction error distribution of the frequency regulation potential is obtained from the mixed skew model. Because of the characteristics of error partition, the error distribution model and predicted values at different wind speed sections are summarized to generate the uncertainty interval of wind power frequency regulation potential. Numerical experiments demonstrate that the proposed model outperforms other state-of-the-art contrastive models in terms of the refined degree of fitting error distribution characteristics. The proposed model only requires the wind speed prediction sequence to accurately model the uncertainty interval. This should be of great significance for rationally optimizing system frequency regulation resources and reducing redundant backup.
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650		4	\|a Primary frequency control
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700	0		\|a Shengjun Wu \|e verfasserin \|4 aut
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allfields	10.1186/s41601-021-00200-3 doi (DE-627)DOAJ019746822 (DE-599)DOAJ742c863b11ae447f89d365fe4d5b2304 DE-627 ger DE-627 rakwb eng TK3001-3521 TK1001-1841 Cheng Yan verfasserin aut Uncertainty modeling of wind power frequency regulation potential considering distributed characteristics of forecast errors 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Large-scale integration of wind power generation decreases the equivalent inertia of a power system, and thus makes frequency stability control challenging. However, given the irregular, nonlinear, and non-stationary characteristics of wind power, significant challenges arise in making wind power generation participate in system frequency regulation. Hence, it is important to explore wind power frequency regulation potential and its uncertainty. This paper proposes an innovative uncertainty modeling method based on mixed skew generalized error distribution for wind power frequency regulation potential. The mapping relationship between wind speed and the associated frequency regulation potential is established, and key parameters of the wind turbine model are identified to predict the wind power frequency regulation potential. Furthermore, the prediction error distribution of the frequency regulation potential is obtained from the mixed skew model. Because of the characteristics of error partition, the error distribution model and predicted values at different wind speed sections are summarized to generate the uncertainty interval of wind power frequency regulation potential. Numerical experiments demonstrate that the proposed model outperforms other state-of-the-art contrastive models in terms of the refined degree of fitting error distribution characteristics. The proposed model only requires the wind speed prediction sequence to accurately model the uncertainty interval. This should be of great significance for rationally optimizing system frequency regulation resources and reducing redundant backup. Inertial response Primary frequency control Error distribution Mixed skew generalized error distribution Uncertainty modeling Distribution or transmission of electric power Production of electric energy or power. Powerplants. Central stations Yi Tang verfasserin aut Jianfeng Dai verfasserin aut Chenggen Wang verfasserin aut Shengjun Wu verfasserin aut In Protection and Control of Modern Power Systems SpringerOpen, 2016 6(2021), 1, Seite 13 (DE-627)862677181 (DE-600)2860966-9 23670983 nnns volume:6 year:2021 number:1 pages:13 https://doi.org/10.1186/s41601-021-00200-3 kostenfrei https://doaj.org/article/742c863b11ae447f89d365fe4d5b2304 kostenfrei https://doi.org/10.1186/s41601-021-00200-3 kostenfrei https://doaj.org/toc/2367-2617 Journal toc kostenfrei https://doaj.org/toc/2367-0983 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2021 1 13
spelling	10.1186/s41601-021-00200-3 doi (DE-627)DOAJ019746822 (DE-599)DOAJ742c863b11ae447f89d365fe4d5b2304 DE-627 ger DE-627 rakwb eng TK3001-3521 TK1001-1841 Cheng Yan verfasserin aut Uncertainty modeling of wind power frequency regulation potential considering distributed characteristics of forecast errors 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Large-scale integration of wind power generation decreases the equivalent inertia of a power system, and thus makes frequency stability control challenging. However, given the irregular, nonlinear, and non-stationary characteristics of wind power, significant challenges arise in making wind power generation participate in system frequency regulation. Hence, it is important to explore wind power frequency regulation potential and its uncertainty. This paper proposes an innovative uncertainty modeling method based on mixed skew generalized error distribution for wind power frequency regulation potential. The mapping relationship between wind speed and the associated frequency regulation potential is established, and key parameters of the wind turbine model are identified to predict the wind power frequency regulation potential. Furthermore, the prediction error distribution of the frequency regulation potential is obtained from the mixed skew model. Because of the characteristics of error partition, the error distribution model and predicted values at different wind speed sections are summarized to generate the uncertainty interval of wind power frequency regulation potential. Numerical experiments demonstrate that the proposed model outperforms other state-of-the-art contrastive models in terms of the refined degree of fitting error distribution characteristics. The proposed model only requires the wind speed prediction sequence to accurately model the uncertainty interval. This should be of great significance for rationally optimizing system frequency regulation resources and reducing redundant backup. Inertial response Primary frequency control Error distribution Mixed skew generalized error distribution Uncertainty modeling Distribution or transmission of electric power Production of electric energy or power. Powerplants. Central stations Yi Tang verfasserin aut Jianfeng Dai verfasserin aut Chenggen Wang verfasserin aut Shengjun Wu verfasserin aut In Protection and Control of Modern Power Systems SpringerOpen, 2016 6(2021), 1, Seite 13 (DE-627)862677181 (DE-600)2860966-9 23670983 nnns volume:6 year:2021 number:1 pages:13 https://doi.org/10.1186/s41601-021-00200-3 kostenfrei https://doaj.org/article/742c863b11ae447f89d365fe4d5b2304 kostenfrei https://doi.org/10.1186/s41601-021-00200-3 kostenfrei https://doaj.org/toc/2367-2617 Journal toc kostenfrei https://doaj.org/toc/2367-0983 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2021 1 13
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allfieldsSound	10.1186/s41601-021-00200-3 doi (DE-627)DOAJ019746822 (DE-599)DOAJ742c863b11ae447f89d365fe4d5b2304 DE-627 ger DE-627 rakwb eng TK3001-3521 TK1001-1841 Cheng Yan verfasserin aut Uncertainty modeling of wind power frequency regulation potential considering distributed characteristics of forecast errors 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Large-scale integration of wind power generation decreases the equivalent inertia of a power system, and thus makes frequency stability control challenging. However, given the irregular, nonlinear, and non-stationary characteristics of wind power, significant challenges arise in making wind power generation participate in system frequency regulation. Hence, it is important to explore wind power frequency regulation potential and its uncertainty. This paper proposes an innovative uncertainty modeling method based on mixed skew generalized error distribution for wind power frequency regulation potential. The mapping relationship between wind speed and the associated frequency regulation potential is established, and key parameters of the wind turbine model are identified to predict the wind power frequency regulation potential. Furthermore, the prediction error distribution of the frequency regulation potential is obtained from the mixed skew model. Because of the characteristics of error partition, the error distribution model and predicted values at different wind speed sections are summarized to generate the uncertainty interval of wind power frequency regulation potential. Numerical experiments demonstrate that the proposed model outperforms other state-of-the-art contrastive models in terms of the refined degree of fitting error distribution characteristics. The proposed model only requires the wind speed prediction sequence to accurately model the uncertainty interval. This should be of great significance for rationally optimizing system frequency regulation resources and reducing redundant backup. Inertial response Primary frequency control Error distribution Mixed skew generalized error distribution Uncertainty modeling Distribution or transmission of electric power Production of electric energy or power. Powerplants. Central stations Yi Tang verfasserin aut Jianfeng Dai verfasserin aut Chenggen Wang verfasserin aut Shengjun Wu verfasserin aut In Protection and Control of Modern Power Systems SpringerOpen, 2016 6(2021), 1, Seite 13 (DE-627)862677181 (DE-600)2860966-9 23670983 nnns volume:6 year:2021 number:1 pages:13 https://doi.org/10.1186/s41601-021-00200-3 kostenfrei https://doaj.org/article/742c863b11ae447f89d365fe4d5b2304 kostenfrei https://doi.org/10.1186/s41601-021-00200-3 kostenfrei https://doaj.org/toc/2367-2617 Journal toc kostenfrei https://doaj.org/toc/2367-0983 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2021 1 13
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source	In Protection and Control of Modern Power Systems 6(2021), 1, Seite 13 volume:6 year:2021 number:1 pages:13
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topic_facet	Inertial response Primary frequency control Error distribution Mixed skew generalized error distribution Uncertainty modeling Distribution or transmission of electric power Production of electric energy or power. Powerplants. Central stations
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Because of the characteristics of error partition, the error distribution model and predicted values at different wind speed sections are summarized to generate the uncertainty interval of wind power frequency regulation potential. Numerical experiments demonstrate that the proposed model outperforms other state-of-the-art contrastive models in terms of the refined degree of fitting error distribution characteristics. The proposed model only requires the wind speed prediction sequence to accurately model the uncertainty interval. 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callnumber-first	T - Technology
author	Cheng Yan
spellingShingle	Cheng Yan misc TK3001-3521 misc TK1001-1841 misc Inertial response misc Primary frequency control misc Error distribution misc Mixed skew generalized error distribution misc Uncertainty modeling misc Distribution or transmission of electric power misc Production of electric energy or power. Powerplants. Central stations Uncertainty modeling of wind power frequency regulation potential considering distributed characteristics of forecast errors
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topic_title	TK3001-3521 TK1001-1841 Uncertainty modeling of wind power frequency regulation potential considering distributed characteristics of forecast errors Inertial response Primary frequency control Error distribution Mixed skew generalized error distribution Uncertainty modeling
topic	misc TK3001-3521 misc TK1001-1841 misc Inertial response misc Primary frequency control misc Error distribution misc Mixed skew generalized error distribution misc Uncertainty modeling misc Distribution or transmission of electric power misc Production of electric energy or power. Powerplants. Central stations
topic_unstemmed	misc TK3001-3521 misc TK1001-1841 misc Inertial response misc Primary frequency control misc Error distribution misc Mixed skew generalized error distribution misc Uncertainty modeling misc Distribution or transmission of electric power misc Production of electric energy or power. Powerplants. Central stations
topic_browse	misc TK3001-3521 misc TK1001-1841 misc Inertial response misc Primary frequency control misc Error distribution misc Mixed skew generalized error distribution misc Uncertainty modeling misc Distribution or transmission of electric power misc Production of electric energy or power. Powerplants. Central stations
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title	Uncertainty modeling of wind power frequency regulation potential considering distributed characteristics of forecast errors
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title_full	Uncertainty modeling of wind power frequency regulation potential considering distributed characteristics of forecast errors
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title_sort	uncertainty modeling of wind power frequency regulation potential considering distributed characteristics of forecast errors
callnumber	TK3001-3521
title_auth	Uncertainty modeling of wind power frequency regulation potential considering distributed characteristics of forecast errors
abstract	Abstract Large-scale integration of wind power generation decreases the equivalent inertia of a power system, and thus makes frequency stability control challenging. However, given the irregular, nonlinear, and non-stationary characteristics of wind power, significant challenges arise in making wind power generation participate in system frequency regulation. Hence, it is important to explore wind power frequency regulation potential and its uncertainty. This paper proposes an innovative uncertainty modeling method based on mixed skew generalized error distribution for wind power frequency regulation potential. The mapping relationship between wind speed and the associated frequency regulation potential is established, and key parameters of the wind turbine model are identified to predict the wind power frequency regulation potential. Furthermore, the prediction error distribution of the frequency regulation potential is obtained from the mixed skew model. Because of the characteristics of error partition, the error distribution model and predicted values at different wind speed sections are summarized to generate the uncertainty interval of wind power frequency regulation potential. Numerical experiments demonstrate that the proposed model outperforms other state-of-the-art contrastive models in terms of the refined degree of fitting error distribution characteristics. The proposed model only requires the wind speed prediction sequence to accurately model the uncertainty interval. This should be of great significance for rationally optimizing system frequency regulation resources and reducing redundant backup.
abstractGer	Abstract Large-scale integration of wind power generation decreases the equivalent inertia of a power system, and thus makes frequency stability control challenging. However, given the irregular, nonlinear, and non-stationary characteristics of wind power, significant challenges arise in making wind power generation participate in system frequency regulation. Hence, it is important to explore wind power frequency regulation potential and its uncertainty. This paper proposes an innovative uncertainty modeling method based on mixed skew generalized error distribution for wind power frequency regulation potential. The mapping relationship between wind speed and the associated frequency regulation potential is established, and key parameters of the wind turbine model are identified to predict the wind power frequency regulation potential. Furthermore, the prediction error distribution of the frequency regulation potential is obtained from the mixed skew model. Because of the characteristics of error partition, the error distribution model and predicted values at different wind speed sections are summarized to generate the uncertainty interval of wind power frequency regulation potential. Numerical experiments demonstrate that the proposed model outperforms other state-of-the-art contrastive models in terms of the refined degree of fitting error distribution characteristics. The proposed model only requires the wind speed prediction sequence to accurately model the uncertainty interval. This should be of great significance for rationally optimizing system frequency regulation resources and reducing redundant backup.
abstract_unstemmed	Abstract Large-scale integration of wind power generation decreases the equivalent inertia of a power system, and thus makes frequency stability control challenging. However, given the irregular, nonlinear, and non-stationary characteristics of wind power, significant challenges arise in making wind power generation participate in system frequency regulation. Hence, it is important to explore wind power frequency regulation potential and its uncertainty. This paper proposes an innovative uncertainty modeling method based on mixed skew generalized error distribution for wind power frequency regulation potential. The mapping relationship between wind speed and the associated frequency regulation potential is established, and key parameters of the wind turbine model are identified to predict the wind power frequency regulation potential. Furthermore, the prediction error distribution of the frequency regulation potential is obtained from the mixed skew model. Because of the characteristics of error partition, the error distribution model and predicted values at different wind speed sections are summarized to generate the uncertainty interval of wind power frequency regulation potential. Numerical experiments demonstrate that the proposed model outperforms other state-of-the-art contrastive models in terms of the refined degree of fitting error distribution characteristics. The proposed model only requires the wind speed prediction sequence to accurately model the uncertainty interval. This should be of great significance for rationally optimizing system frequency regulation resources and reducing redundant backup.
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title_short	Uncertainty modeling of wind power frequency regulation potential considering distributed characteristics of forecast errors
url	https://doi.org/10.1186/s41601-021-00200-3 https://doaj.org/article/742c863b11ae447f89d365fe4d5b2304 https://doaj.org/toc/2367-2617 https://doaj.org/toc/2367-0983
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author2	Yi Tang Jianfeng Dai Chenggen Wang Shengjun Wu
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doi_str	10.1186/s41601-021-00200-3
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up_date	2024-07-04T00:48:37.157Z
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Uncertainty modeling of wind power frequency regulation potential considering distributed characteristics of forecast errors

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