Duck Curve Aware Dynamic Pricing and Battery Scheduling Strategy Using Reinforcement Learning

Watari, Daichi; Taniguchi, Ittetsu; Onoye, Takao

doi:https://doi.org/10.1109/TSG.2023.3288355

Duck Curve Aware Dynamic Pricing and Battery Scheduling Strategy Using Reinforcement Learning

Watari, Daichi; Taniguchi, Ittetsu; Onoye, Takao

IEEE Transactions on Smart Grid, 2023, 15(1), 457-471

Number of Access:327（2025-09-03 07:55 Counts）

Identifier to cite or link to this item: https://hdl.handle.net/11094/92478

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Title	Duck Curve Aware Dynamic Pricing and Battery Scheduling Strategy Using Reinforcement Learning
Creator	CreatorName Watari, Daichi
	NRID 1000040551453 1000040551453 ORCID 0000-0002-7843-5907 0000-0002-7843-5907 researchmap permalink ittetsu ittetsu Scopus Author ID 23013657500 23013657500 CreatorName Taniguchi, Ittetsu
	NRID 1000060252590 1000060252590 researchmap permalink read0096872 read0096872 CreatorName Onoye, Takao
Subject	Batteries
	Battery scheduling
	Costs
	Deep reinforcement learning
	Demand response
	Duck curve
	Dynamic pricing
	Heuristic algorithms
	Power generation
	Pricing
	Prosumer
	State of charge
	Vehicle dynamics
Description	Watari D., Taniguchi I., Onoye T.. Duck Curve Aware Dynamic Pricing and Battery Scheduling Strategy Using Reinforcement Learning. IEEE Transactions on Smart Grid , (2023); https://doi.org/10.1109/TSG.2023.3288355.
Abstract	The duck curve is becoming a global problem in energy technology due to the rapid increase in solar power adoption and the rise of prosumers. To address this issue, a resource aggregator (RA) has emerged to provide flexible solutions through aggregating the prosumers and demand response such as dynamic pricing. This paper proposes an optimal strategy for the RA that dispatches dynamic pricing to the prosumers and leverages the battery system at both RA and prosumer levels. The proposed method is based on a model-free deep reinforcement learning (DRL) algorithm to optimize each prosumer’s retail prices and schedule usage of the RA’s battery power station. An objective reward function is used to maximize the RA’s profit, minimize the prosumer’s cost, and maximize the improvement of the duck curve. The performance of the proposed DRL-based strategy was demonstrated by simulation experiments using actual wholesale price, demand, and PV generation data. The results show that the proposed strategy can improve the standard deviation and peak-to-average ratio of net load by up to 57.1% and 23%, respectively.
Publisher	Institute of Electrical and Electronics Engineers Inc.
Source Title	IEEE Transactions on Smart Grid
Volume (Issue)	15(1)
Page	457-471
Date of Issued	2023-06-21
Language	English
Handle URL	https://hdl.handle.net/11094/92478
PISSN	19493053
eISSN	19493061
NCID	AA12479464
Relation.isIdenticalTo	DOI (Publisher Version) https://doi.org/10.1109/TSG.2023.3288355
Access Rights	open access
CopyRight	This article is licensed under a Creative Commons Attribution 4.0 International License.
oaire:version	Version of Record
Category	学術雑誌論文 Journal Article

Resource Type	journal article
Local Type	Journal Article
DCMI Type	Text
DCTERMS.bibliographicCitation	IEEE Transactions on Smart Grid. 2023, 15(1), p. 457-471
DC.title	Duck Curve Aware Dynamic Pricing and Battery Scheduling Strategy Using Reinforcement Learning
DC.creator	Watari, Daichi
	Taniguchi, Ittetsu
	Onoye, Takao
DC.publisher	Institute of Electrical and Electronics Engineers Inc.
DC.language' scheme='DCTERMS.RFC1766	English
DC.type' scheme='DCTERMS.DCMIType	Text
DCTERMS.issued' scheme='DCTERMS.W3CDTF	2023-06-21
DC.identifier	https://doi.org/10.1109/TSG.2023.3288355
DC.subject	Batteries
	Battery scheduling
	Costs
	Deep reinforcement learning
	Demand response
	Duck curve
	Dynamic pricing
	Heuristic algorithms
	Power generation
	Pricing
	Prosumer
	State of charge
	Vehicle dynamics
DC.description	Watari D., Taniguchi I., Onoye T.. Duck Curve Aware Dynamic Pricing and Battery Scheduling Strategy Using Reinforcement Learning. IEEE Transactions on Smart Grid , (2023); https://doi.org/10.1109/TSG.2023.3288355.
DCTERMS.abstract	The duck curve is becoming a global problem in energy technology due to the rapid increase in solar power adoption and the rise of prosumers. To address this issue, a resource aggregator (RA) has emerged to provide flexible solutions through aggregating the prosumers and demand response such as dynamic pricing. This paper proposes an optimal strategy for the RA that dispatches dynamic pricing to the prosumers and leverages the battery system at both RA and prosumer levels. The proposed method is based on a model-free deep reinforcement learning (DRL) algorithm to optimize each prosumer’s retail prices and schedule usage of the RA’s battery power station. An objective reward function is used to maximize the RA’s profit, minimize the prosumer’s cost, and maximize the improvement of the duck curve. The performance of the proposed DRL-based strategy was demonstrated by simulation experiments using actual wholesale price, demand, and PV generation data. The results show that the proposed strategy can improve the standard deviation and peak-to-average ratio of net load by up to 57.1% and 23%, respectively.
DC.rights	This article is licensed under a Creative Commons Attribution 4.0 International License.
DC.format	92478
citation_title	Duck Curve Aware Dynamic Pricing and Battery Scheduling Strategy Using Reinforcement Learning
citation_author	Watari, Daichi
	Taniguchi, Ittetsu
	Onoye, Takao
citation_publisher	Institute of Electrical and Electronics Engineers Inc.
citation_language	English
citation_date	2023-06-21
citation_journal_title	IEEE Transactions on Smart Grid
citation_volume	15
citation_issue	1
citation_firstpage	457
citation_lastpage	471
citation_doi	https://doi.org/10.1109/TSG.2023.3288355
citation_public_url	https://hdl.handle.net/11094/92478

Duck Curve Aware Dynamic Pricing and Battery Scheduling Strategy Using Reinforcement Learning

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