Data-Driven Repeated-Feedback Adjustment Strategy for Smart Grid Pricing

Applying the optimal problem, we get the optimal power supply and price. However, how to make the real power consumption close to the optimal power supply is still worth studying. This paper proposes a novel data-driven inverse proportional function-based repeated-feedback adjustment strategy to con...

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Autores principales:	Bingjie He, Qiaorong Dai, Aijuan Zhou, Jinxiu Xiao
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/f511c7eb1c7846d2ae1842488bd229b9
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spelling	oai:doaj.org-article:f511c7eb1c7846d2ae1842488bd229b92021-11-08T02:35:55ZData-Driven Repeated-Feedback Adjustment Strategy for Smart Grid Pricing2314-478510.1155/2021/7477314https://doaj.org/article/f511c7eb1c7846d2ae1842488bd229b92021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/7477314https://doaj.org/toc/2314-4785Applying the optimal problem, we get the optimal power supply and price. However, how to make the real power consumption close to the optimal power supply is still worth studying. This paper proposes a novel data-driven inverse proportional function-based repeated-feedback adjustment strategy to control the users’ real power consumption. With the repeated-feedback adjustment, we adjust the real-time prices according to changes in the power discrepancy between the optimal power supply and the users’ real power consumption. If and only if the power discrepancy deviates the preset range, the real power consumption in different periods will be adjusted through the change of the price, so the adjustment times is the least. Numerical results on real power market show that the novel inverse proportional function-based repeated-feedback adjustment strategy brought forward in the article achieves better effect than the linear one, that is to say, the adjustments times and standard error of the residuals are less. Meanwhile, profit and whole social welfare are more. The proposed strategy can obtain more steady and dependable consumption load close to the optimal power supply, which is conducive to the balanced supply of electric energy.Bingjie HeQiaorong DaiAijuan ZhouJinxiu XiaoHindawi LimitedarticleMathematicsQA1-939ENJournal of Mathematics, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Mathematics QA1-939
spellingShingle	Mathematics QA1-939 Bingjie He Qiaorong Dai Aijuan Zhou Jinxiu Xiao Data-Driven Repeated-Feedback Adjustment Strategy for Smart Grid Pricing
description	Applying the optimal problem, we get the optimal power supply and price. However, how to make the real power consumption close to the optimal power supply is still worth studying. This paper proposes a novel data-driven inverse proportional function-based repeated-feedback adjustment strategy to control the users’ real power consumption. With the repeated-feedback adjustment, we adjust the real-time prices according to changes in the power discrepancy between the optimal power supply and the users’ real power consumption. If and only if the power discrepancy deviates the preset range, the real power consumption in different periods will be adjusted through the change of the price, so the adjustment times is the least. Numerical results on real power market show that the novel inverse proportional function-based repeated-feedback adjustment strategy brought forward in the article achieves better effect than the linear one, that is to say, the adjustments times and standard error of the residuals are less. Meanwhile, profit and whole social welfare are more. The proposed strategy can obtain more steady and dependable consumption load close to the optimal power supply, which is conducive to the balanced supply of electric energy.
format	article
author	Bingjie He Qiaorong Dai Aijuan Zhou Jinxiu Xiao
author_facet	Bingjie He Qiaorong Dai Aijuan Zhou Jinxiu Xiao
author_sort	Bingjie He
title	Data-Driven Repeated-Feedback Adjustment Strategy for Smart Grid Pricing
title_short	Data-Driven Repeated-Feedback Adjustment Strategy for Smart Grid Pricing
title_full	Data-Driven Repeated-Feedback Adjustment Strategy for Smart Grid Pricing
title_fullStr	Data-Driven Repeated-Feedback Adjustment Strategy for Smart Grid Pricing
title_full_unstemmed	Data-Driven Repeated-Feedback Adjustment Strategy for Smart Grid Pricing
title_sort	data-driven repeated-feedback adjustment strategy for smart grid pricing
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/f511c7eb1c7846d2ae1842488bd229b9
work_keys_str_mv	AT bingjiehe datadrivenrepeatedfeedbackadjustmentstrategyforsmartgridpricing AT qiaorongdai datadrivenrepeatedfeedbackadjustmentstrategyforsmartgridpricing AT aijuanzhou datadrivenrepeatedfeedbackadjustmentstrategyforsmartgridpricing AT jinxiuxiao datadrivenrepeatedfeedbackadjustmentstrategyforsmartgridpricing
_version_	1718443247676686336

Data-Driven Repeated-Feedback Adjustment Strategy for Smart Grid Pricing

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