Multi-time scale energy management framework for smart PV systems mixing fast and slow dynamics

Watari, Daichi; Taniguchi, Ittetsu; Goverde, Hans; Manganiello, Patrizio; Shirazi, Elham; Catthoor, Francky; Onoye, Takao

doi:https://doi.org/10.1016/j.apenergy.2021.116671

Multi-time scale energy management framework for smart PV systems mixing fast and slow dynamics

Watari, Daichi; Taniguchi, Ittetsu; Goverde, Hans et al.

Applied Energy, 2021, 289, 116671

アクセス数:429件（2025-10-26 05:30 集計）

固定URL: https://hdl.handle.net/11094/92479

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タイトル	Multi-time scale energy management framework for smart PV systems mixing fast and slow dynamics
著者	著者名 Watari, Daichi
	NRID 1000040551453 1000040551453 ORCID 0000-0002-7843-5907 0000-0002-7843-5907 researchmap permalink ittetsu ittetsu Scopus Author ID 23013657500 23013657500 著者名 Taniguchi, Ittetsu
	著者名 Goverde, Hans
	著者名 Manganiello, Patrizio
	著者名 Shirazi, Elham
	著者名 Catthoor, Francky
	NRID 1000060252590 1000060252590 researchmap permalink read0096872 read0096872 Scopus Author ID 7007073634 7007073634 著者名 Onoye, Takao
キーワード等	Battery
	Energy management system
	Multi-time scale
	PV forecasting
	Shiftable appliance
内容	Watari D., Taniguchi I., Goverde H., et al. Multi-time scale energy management framework for smart PV systems mixing fast and slow dynamics. Applied Energy 289, 116671 (2021); https://doi.org/10.1016/j.apenergy.2021.116671.
抄録	We propose a multi-time scale energy management framework for a smart photovoltaic (PV) system that can calculate optimized schedules for battery operation, power purchases, and appliance usage. A smart PV system is a local energy community that includes several buildings and households equipped with PV panels and batteries. However, due to the unpredictability and fast variation of PV generation, maintaining energy balance and reducing electricity costs in the system is challenging. Our proposed framework employs a model predictive control approach with a physics-based PV forecasting model and an accurately parameterized battery model. We also introduce a multi-time scale structure composed of two-time scales: a longer coarse-grained time scale for daily horizon with 15-minutes resolution and a shorter fine-grained time scale for 15-minutes horizon with 1-second resolution. In contrast to the current single-time scale approaches, this alternative structure enables the management of a necessary mix of fast and slow system dynamics with reasonable computational times while maintaining high accuracy. Simulation results show that the proposed framework reduces electricity costs up 48.1% compared with baseline methods. The necessity of a multi-time scale and the impact on accurate system modeling in terms of PV forecasting and batteries are also demonstrated.
出版者	Elsevier Ltd
収録物名	Applied Energy
巻(号)	289
ページ	116671
刊行年月	2021-05-01
言語	英語
ハンドルURL	https://hdl.handle.net/11094/92479
PISSN	03062619
NCID	AA00543227
関連情報 (isIdenticalTo)	DOI (出版社版) https://doi.org/10.1016/j.apenergy.2021.116671
アクセス権	open access
権利情報	This article is licensed under a Creative Commons Attribution 4.0 International License.
出版タイプ	Version of Record
カテゴリ	学術雑誌論文 Journal Article

資源タイプ	学術雑誌論文
ローカル資源タイプ	学術雑誌論文
DCMI資源タイプ	text
DCTERMS.bibliographicCitation	Applied Energy. 2021, 289, p. 116671
DC.title	Multi-time scale energy management framework for smart PV systems mixing fast and slow dynamics
DC.creator	Watari, Daichi
	Taniguchi, Ittetsu
	Goverde, Hans
	Manganiello, Patrizio
	Shirazi, Elham
	Catthoor, Francky
	Onoye, Takao
DC.publisher	Elsevier Ltd
DC.language' scheme='DCTERMS.RFC1766	英語
DC.type' scheme='DCTERMS.DCMIType	text
DCTERMS.issued' scheme='DCTERMS.W3CDTF	2021-05-01
DC.identifier	https://doi.org/10.1016/j.apenergy.2021.116671
DC.subject	Battery
	Energy management system
	Multi-time scale
	PV forecasting
	Shiftable appliance
DC.description	Watari D., Taniguchi I., Goverde H., et al. Multi-time scale energy management framework for smart PV systems mixing fast and slow dynamics. Applied Energy 289, 116671 (2021); https://doi.org/10.1016/j.apenergy.2021.116671.
DCTERMS.abstract	We propose a multi-time scale energy management framework for a smart photovoltaic (PV) system that can calculate optimized schedules for battery operation, power purchases, and appliance usage. A smart PV system is a local energy community that includes several buildings and households equipped with PV panels and batteries. However, due to the unpredictability and fast variation of PV generation, maintaining energy balance and reducing electricity costs in the system is challenging. Our proposed framework employs a model predictive control approach with a physics-based PV forecasting model and an accurately parameterized battery model. We also introduce a multi-time scale structure composed of two-time scales: a longer coarse-grained time scale for daily horizon with 15-minutes resolution and a shorter fine-grained time scale for 15-minutes horizon with 1-second resolution. In contrast to the current single-time scale approaches, this alternative structure enables the management of a necessary mix of fast and slow system dynamics with reasonable computational times while maintaining high accuracy. Simulation results show that the proposed framework reduces electricity costs up 48.1% compared with baseline methods. The necessity of a multi-time scale and the impact on accurate system modeling in terms of PV forecasting and batteries are also demonstrated.
DC.rights	This article is licensed under a Creative Commons Attribution 4.0 International License.
DC.format	92479
citation_title	Multi-time scale energy management framework for smart PV systems mixing fast and slow dynamics
citation_author	Watari, Daichi
	Taniguchi, Ittetsu
	Goverde, Hans
	Manganiello, Patrizio
	Shirazi, Elham
	Catthoor, Francky
	Onoye, Takao
citation_publisher	Elsevier Ltd
citation_language	英語
citation_date	2021-05-01
citation_journal_title	Applied Energy
citation_volume	289
citation_firstpage	116671
citation_doi	https://doi.org/10.1016/j.apenergy.2021.116671
citation_public_url	https://hdl.handle.net/11094/92479

Multi-time scale energy management framework for smart PV systems mixing fast and slow dynamics

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