Unit Combination Scheduling Method Considering System Frequency Dynamic Constraints under High Wind Power Share

Power systems with a high wind power share are characterized by low rotational inertia and weak frequency regulation, which can easily lead to frequency safety problems. Providing virtual inertia for large-scale wind turbines to participate in frequency regulation is a solution, but virtual inertia...
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Autor*in:	Qun Li [verfasserIn] Qiang Li [verfasserIn] Chenggen Wang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	frequency safety power system inertia wind power forecasting error two-stage robust optimization affine decision rule

Übergeordnetes Werk:	In: Sustainability - MDPI AG, 2009, 15(2023), 15, p 11840
Übergeordnetes Werk:	volume:15 ; year:2023 ; number:15, p 11840

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/su151511840

Katalog-ID:	DOAJ093672446

Internformat


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245	1	0	\|a Unit Combination Scheduling Method Considering System Frequency Dynamic Constraints under High Wind Power Share
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520			\|a Power systems with a high wind power share are characterized by low rotational inertia and weak frequency regulation, which can easily lead to frequency safety problems. Providing virtual inertia for large-scale wind turbines to participate in frequency regulation is a solution, but virtual inertia is related to wind power output prediction. Due to wind power prediction errors, the system inertia is reduced and there is even a risk of instability. In this regard, this article proposes a unit commitment model that takes into account the constraints of sharp changes in frequency caused by wind power prediction errors. First, the expressions of the equivalent inertia, adjustment coefficient, and other frequency influence parameters of the frequency aggregation model for a high proportion wind power system are derived, revealing the mechanism of the influence of wind power prediction power and synchronous machine start stop status on the frequency modulation characteristics of the system. Second, the time domain expression of the system frequency after the disturbance is calculated by the segment linearization method, and the linear expressions of “frequency drop speed and frequency nadir” constraints are derived to meet the demand of frequency regulation in each stage of the system. Finally, a two-stage robust optimization model based on a wind power fuzzy set is constructed by combining the effects of wind power errors on power fluctuation and frequency regulation capability. The proposed model is solved through affine decision rules to reduce its complexity. The simulation results show that the proposed model and method can effectively improve the frequency response characteristics and increase the operational reliability of high-share wind power systems.
650		4	\|a frequency safety
650		4	\|a power system inertia
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spelling	10.3390/su151511840 doi (DE-627)DOAJ093672446 (DE-599)DOAJe2d7cc56de234c2f8e0cdcba58760864 DE-627 ger DE-627 rakwb eng TD194-195 TJ807-830 GE1-350 Qun Li verfasserin aut Unit Combination Scheduling Method Considering System Frequency Dynamic Constraints under High Wind Power Share 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Power systems with a high wind power share are characterized by low rotational inertia and weak frequency regulation, which can easily lead to frequency safety problems. Providing virtual inertia for large-scale wind turbines to participate in frequency regulation is a solution, but virtual inertia is related to wind power output prediction. Due to wind power prediction errors, the system inertia is reduced and there is even a risk of instability. In this regard, this article proposes a unit commitment model that takes into account the constraints of sharp changes in frequency caused by wind power prediction errors. First, the expressions of the equivalent inertia, adjustment coefficient, and other frequency influence parameters of the frequency aggregation model for a high proportion wind power system are derived, revealing the mechanism of the influence of wind power prediction power and synchronous machine start stop status on the frequency modulation characteristics of the system. Second, the time domain expression of the system frequency after the disturbance is calculated by the segment linearization method, and the linear expressions of “frequency drop speed and frequency nadir” constraints are derived to meet the demand of frequency regulation in each stage of the system. Finally, a two-stage robust optimization model based on a wind power fuzzy set is constructed by combining the effects of wind power errors on power fluctuation and frequency regulation capability. The proposed model is solved through affine decision rules to reduce its complexity. The simulation results show that the proposed model and method can effectively improve the frequency response characteristics and increase the operational reliability of high-share wind power systems. frequency safety power system inertia wind power forecasting error two-stage robust optimization affine decision rule Environmental effects of industries and plants Renewable energy sources Environmental sciences Qiang Li verfasserin aut Chenggen Wang verfasserin aut In Sustainability MDPI AG, 2009 15(2023), 15, p 11840 (DE-627)610604120 (DE-600)2518383-7 20711050 nnns volume:15 year:2023 number:15, p 11840 https://doi.org/10.3390/su151511840 kostenfrei https://doaj.org/article/e2d7cc56de234c2f8e0cdcba58760864 kostenfrei https://www.mdpi.com/2071-1050/15/15/11840 kostenfrei https://doaj.org/toc/2071-1050 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2507 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_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 15 2023 15, p 11840
allfields_unstemmed	10.3390/su151511840 doi (DE-627)DOAJ093672446 (DE-599)DOAJe2d7cc56de234c2f8e0cdcba58760864 DE-627 ger DE-627 rakwb eng TD194-195 TJ807-830 GE1-350 Qun Li verfasserin aut Unit Combination Scheduling Method Considering System Frequency Dynamic Constraints under High Wind Power Share 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Power systems with a high wind power share are characterized by low rotational inertia and weak frequency regulation, which can easily lead to frequency safety problems. Providing virtual inertia for large-scale wind turbines to participate in frequency regulation is a solution, but virtual inertia is related to wind power output prediction. Due to wind power prediction errors, the system inertia is reduced and there is even a risk of instability. In this regard, this article proposes a unit commitment model that takes into account the constraints of sharp changes in frequency caused by wind power prediction errors. First, the expressions of the equivalent inertia, adjustment coefficient, and other frequency influence parameters of the frequency aggregation model for a high proportion wind power system are derived, revealing the mechanism of the influence of wind power prediction power and synchronous machine start stop status on the frequency modulation characteristics of the system. Second, the time domain expression of the system frequency after the disturbance is calculated by the segment linearization method, and the linear expressions of “frequency drop speed and frequency nadir” constraints are derived to meet the demand of frequency regulation in each stage of the system. Finally, a two-stage robust optimization model based on a wind power fuzzy set is constructed by combining the effects of wind power errors on power fluctuation and frequency regulation capability. The proposed model is solved through affine decision rules to reduce its complexity. The simulation results show that the proposed model and method can effectively improve the frequency response characteristics and increase the operational reliability of high-share wind power systems. frequency safety power system inertia wind power forecasting error two-stage robust optimization affine decision rule Environmental effects of industries and plants Renewable energy sources Environmental sciences Qiang Li verfasserin aut Chenggen Wang verfasserin aut In Sustainability MDPI AG, 2009 15(2023), 15, p 11840 (DE-627)610604120 (DE-600)2518383-7 20711050 nnns volume:15 year:2023 number:15, p 11840 https://doi.org/10.3390/su151511840 kostenfrei https://doaj.org/article/e2d7cc56de234c2f8e0cdcba58760864 kostenfrei https://www.mdpi.com/2071-1050/15/15/11840 kostenfrei https://doaj.org/toc/2071-1050 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2507 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_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 15 2023 15, p 11840
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allfieldsSound	10.3390/su151511840 doi (DE-627)DOAJ093672446 (DE-599)DOAJe2d7cc56de234c2f8e0cdcba58760864 DE-627 ger DE-627 rakwb eng TD194-195 TJ807-830 GE1-350 Qun Li verfasserin aut Unit Combination Scheduling Method Considering System Frequency Dynamic Constraints under High Wind Power Share 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Power systems with a high wind power share are characterized by low rotational inertia and weak frequency regulation, which can easily lead to frequency safety problems. Providing virtual inertia for large-scale wind turbines to participate in frequency regulation is a solution, but virtual inertia is related to wind power output prediction. Due to wind power prediction errors, the system inertia is reduced and there is even a risk of instability. In this regard, this article proposes a unit commitment model that takes into account the constraints of sharp changes in frequency caused by wind power prediction errors. First, the expressions of the equivalent inertia, adjustment coefficient, and other frequency influence parameters of the frequency aggregation model for a high proportion wind power system are derived, revealing the mechanism of the influence of wind power prediction power and synchronous machine start stop status on the frequency modulation characteristics of the system. Second, the time domain expression of the system frequency after the disturbance is calculated by the segment linearization method, and the linear expressions of “frequency drop speed and frequency nadir” constraints are derived to meet the demand of frequency regulation in each stage of the system. Finally, a two-stage robust optimization model based on a wind power fuzzy set is constructed by combining the effects of wind power errors on power fluctuation and frequency regulation capability. The proposed model is solved through affine decision rules to reduce its complexity. The simulation results show that the proposed model and method can effectively improve the frequency response characteristics and increase the operational reliability of high-share wind power systems. frequency safety power system inertia wind power forecasting error two-stage robust optimization affine decision rule Environmental effects of industries and plants Renewable energy sources Environmental sciences Qiang Li verfasserin aut Chenggen Wang verfasserin aut In Sustainability MDPI AG, 2009 15(2023), 15, p 11840 (DE-627)610604120 (DE-600)2518383-7 20711050 nnns volume:15 year:2023 number:15, p 11840 https://doi.org/10.3390/su151511840 kostenfrei https://doaj.org/article/e2d7cc56de234c2f8e0cdcba58760864 kostenfrei https://www.mdpi.com/2071-1050/15/15/11840 kostenfrei https://doaj.org/toc/2071-1050 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2507 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_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 15 2023 15, p 11840
language	English
source	In Sustainability 15(2023), 15, p 11840 volume:15 year:2023 number:15, p 11840
sourceStr	In Sustainability 15(2023), 15, p 11840 volume:15 year:2023 number:15, p 11840
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	frequency safety power system inertia wind power forecasting error two-stage robust optimization affine decision rule Environmental effects of industries and plants Renewable energy sources Environmental sciences
isfreeaccess_bool	true
container_title	Sustainability
authorswithroles_txt_mv	Qun Li @@aut@@ Qiang Li @@aut@@ Chenggen Wang @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
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callnumber-first	T - Technology
author	Qun Li
spellingShingle	Qun Li misc TD194-195 misc TJ807-830 misc GE1-350 misc frequency safety misc power system inertia misc wind power forecasting error misc two-stage robust optimization misc affine decision rule misc Environmental effects of industries and plants misc Renewable energy sources misc Environmental sciences Unit Combination Scheduling Method Considering System Frequency Dynamic Constraints under High Wind Power Share
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topic_title	TD194-195 TJ807-830 GE1-350 Unit Combination Scheduling Method Considering System Frequency Dynamic Constraints under High Wind Power Share frequency safety power system inertia wind power forecasting error two-stage robust optimization affine decision rule
topic	misc TD194-195 misc TJ807-830 misc GE1-350 misc frequency safety misc power system inertia misc wind power forecasting error misc two-stage robust optimization misc affine decision rule misc Environmental effects of industries and plants misc Renewable energy sources misc Environmental sciences
topic_unstemmed	misc TD194-195 misc TJ807-830 misc GE1-350 misc frequency safety misc power system inertia misc wind power forecasting error misc two-stage robust optimization misc affine decision rule misc Environmental effects of industries and plants misc Renewable energy sources misc Environmental sciences
topic_browse	misc TD194-195 misc TJ807-830 misc GE1-350 misc frequency safety misc power system inertia misc wind power forecasting error misc two-stage robust optimization misc affine decision rule misc Environmental effects of industries and plants misc Renewable energy sources misc Environmental sciences
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title	Unit Combination Scheduling Method Considering System Frequency Dynamic Constraints under High Wind Power Share
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title_full	Unit Combination Scheduling Method Considering System Frequency Dynamic Constraints under High Wind Power Share
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title_sort	unit combination scheduling method considering system frequency dynamic constraints under high wind power share
callnumber	TD194-195
title_auth	Unit Combination Scheduling Method Considering System Frequency Dynamic Constraints under High Wind Power Share
abstract	Power systems with a high wind power share are characterized by low rotational inertia and weak frequency regulation, which can easily lead to frequency safety problems. Providing virtual inertia for large-scale wind turbines to participate in frequency regulation is a solution, but virtual inertia is related to wind power output prediction. Due to wind power prediction errors, the system inertia is reduced and there is even a risk of instability. In this regard, this article proposes a unit commitment model that takes into account the constraints of sharp changes in frequency caused by wind power prediction errors. First, the expressions of the equivalent inertia, adjustment coefficient, and other frequency influence parameters of the frequency aggregation model for a high proportion wind power system are derived, revealing the mechanism of the influence of wind power prediction power and synchronous machine start stop status on the frequency modulation characteristics of the system. Second, the time domain expression of the system frequency after the disturbance is calculated by the segment linearization method, and the linear expressions of “frequency drop speed and frequency nadir” constraints are derived to meet the demand of frequency regulation in each stage of the system. Finally, a two-stage robust optimization model based on a wind power fuzzy set is constructed by combining the effects of wind power errors on power fluctuation and frequency regulation capability. The proposed model is solved through affine decision rules to reduce its complexity. The simulation results show that the proposed model and method can effectively improve the frequency response characteristics and increase the operational reliability of high-share wind power systems.
abstractGer	Power systems with a high wind power share are characterized by low rotational inertia and weak frequency regulation, which can easily lead to frequency safety problems. Providing virtual inertia for large-scale wind turbines to participate in frequency regulation is a solution, but virtual inertia is related to wind power output prediction. Due to wind power prediction errors, the system inertia is reduced and there is even a risk of instability. In this regard, this article proposes a unit commitment model that takes into account the constraints of sharp changes in frequency caused by wind power prediction errors. First, the expressions of the equivalent inertia, adjustment coefficient, and other frequency influence parameters of the frequency aggregation model for a high proportion wind power system are derived, revealing the mechanism of the influence of wind power prediction power and synchronous machine start stop status on the frequency modulation characteristics of the system. Second, the time domain expression of the system frequency after the disturbance is calculated by the segment linearization method, and the linear expressions of “frequency drop speed and frequency nadir” constraints are derived to meet the demand of frequency regulation in each stage of the system. Finally, a two-stage robust optimization model based on a wind power fuzzy set is constructed by combining the effects of wind power errors on power fluctuation and frequency regulation capability. The proposed model is solved through affine decision rules to reduce its complexity. The simulation results show that the proposed model and method can effectively improve the frequency response characteristics and increase the operational reliability of high-share wind power systems.
abstract_unstemmed	Power systems with a high wind power share are characterized by low rotational inertia and weak frequency regulation, which can easily lead to frequency safety problems. Providing virtual inertia for large-scale wind turbines to participate in frequency regulation is a solution, but virtual inertia is related to wind power output prediction. Due to wind power prediction errors, the system inertia is reduced and there is even a risk of instability. In this regard, this article proposes a unit commitment model that takes into account the constraints of sharp changes in frequency caused by wind power prediction errors. First, the expressions of the equivalent inertia, adjustment coefficient, and other frequency influence parameters of the frequency aggregation model for a high proportion wind power system are derived, revealing the mechanism of the influence of wind power prediction power and synchronous machine start stop status on the frequency modulation characteristics of the system. Second, the time domain expression of the system frequency after the disturbance is calculated by the segment linearization method, and the linear expressions of “frequency drop speed and frequency nadir” constraints are derived to meet the demand of frequency regulation in each stage of the system. Finally, a two-stage robust optimization model based on a wind power fuzzy set is constructed by combining the effects of wind power errors on power fluctuation and frequency regulation capability. The proposed model is solved through affine decision rules to reduce its complexity. The simulation results show that the proposed model and method can effectively improve the frequency response characteristics and increase the operational reliability of high-share wind power systems.
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title_short	Unit Combination Scheduling Method Considering System Frequency Dynamic Constraints under High Wind Power Share
url	https://doi.org/10.3390/su151511840 https://doaj.org/article/e2d7cc56de234c2f8e0cdcba58760864 https://www.mdpi.com/2071-1050/15/15/11840 https://doaj.org/toc/2071-1050
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