An aggregator‐based resource allocation in the smart grid using an artificial neural network and sliding time window optimization

Abstract The success of an efficient and effective aggregator‐based residential demand response system in the smart grid relies on the day‐ahead customer incentive pricing (CIP) and the load shifting protocols. An artificial neural network model is designed to generate the day‐ahead CIP for the aggr...

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Autores principales:	Yingying Zheng, Berk Celik, Siddharth Suryanarayanan, Anthony A. Maciejewski, Howard Jay Siegel, Timothy M. Hansen
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/8656e677de0846dca5e718bb49d7085f
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spelling	oai:doaj.org-article:8656e677de0846dca5e718bb49d7085f2021-11-13T03:16:47ZAn aggregator‐based resource allocation in the smart grid using an artificial neural network and sliding time window optimization2515-294710.1049/stg2.12042https://doaj.org/article/8656e677de0846dca5e718bb49d7085f2021-12-01T00:00:00Zhttps://doi.org/10.1049/stg2.12042https://doaj.org/toc/2515-2947Abstract The success of an efficient and effective aggregator‐based residential demand response system in the smart grid relies on the day‐ahead customer incentive pricing (CIP) and the load shifting protocols. An artificial neural network model is designed to generate the day‐ahead CIP for the aggregator based on historical data. Load scheduling is proposed as a day‐ahead optimization problem that is solved using a blocked sliding window technique using parallel computing. With the assumptions made, the proposed algorithm improved the aggregator performance by reducing the overall simulation time from 275 to 45 min and increasing the aggregator forecast profits and customer savings by 11.85% and 35.99% compared to the previous genetic algorithm‐based approach.Yingying ZhengBerk CelikSiddharth SuryanarayananAnthony A. MaciejewskiHoward Jay SiegelTimothy M. HansenWileyarticleElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIET Smart Grid, Vol 4, Iss 6, Pp 612-622 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Electrical engineering. Electronics. Nuclear engineering TK1-9971 Yingying Zheng Berk Celik Siddharth Suryanarayanan Anthony A. Maciejewski Howard Jay Siegel Timothy M. Hansen An aggregator‐based resource allocation in the smart grid using an artificial neural network and sliding time window optimization
description	Abstract The success of an efficient and effective aggregator‐based residential demand response system in the smart grid relies on the day‐ahead customer incentive pricing (CIP) and the load shifting protocols. An artificial neural network model is designed to generate the day‐ahead CIP for the aggregator based on historical data. Load scheduling is proposed as a day‐ahead optimization problem that is solved using a blocked sliding window technique using parallel computing. With the assumptions made, the proposed algorithm improved the aggregator performance by reducing the overall simulation time from 275 to 45 min and increasing the aggregator forecast profits and customer savings by 11.85% and 35.99% compared to the previous genetic algorithm‐based approach.
format	article
author	Yingying Zheng Berk Celik Siddharth Suryanarayanan Anthony A. Maciejewski Howard Jay Siegel Timothy M. Hansen
author_facet	Yingying Zheng Berk Celik Siddharth Suryanarayanan Anthony A. Maciejewski Howard Jay Siegel Timothy M. Hansen
author_sort	Yingying Zheng
title	An aggregator‐based resource allocation in the smart grid using an artificial neural network and sliding time window optimization
title_short	An aggregator‐based resource allocation in the smart grid using an artificial neural network and sliding time window optimization
title_full	An aggregator‐based resource allocation in the smart grid using an artificial neural network and sliding time window optimization
title_fullStr	An aggregator‐based resource allocation in the smart grid using an artificial neural network and sliding time window optimization
title_full_unstemmed	An aggregator‐based resource allocation in the smart grid using an artificial neural network and sliding time window optimization
title_sort	aggregator‐based resource allocation in the smart grid using an artificial neural network and sliding time window optimization
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/8656e677de0846dca5e718bb49d7085f
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An aggregator‐based resource allocation in the smart grid using an artificial neural network and sliding time window optimization

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