Optimal Location and Pricing of Electric Vehicle Charging Stations Using Machine Learning and Stackelberg Game

Adil, M; Mahmud, MAP; Kouzani, AZ; Khoo, SY

Optimal Location and Pricing of Electric Vehicle Charging Stations Using Machine Learning and Stackelberg Game

Adil, M Mahmud, MAP Kouzani, AZ Khoo, SY

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Industry Applications, 2024, 60, (3), pp. 4708-4722
Issue Date:: 2024-05-01

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Field	Value	Language
dc.contributor.author	Adil, M
dc.contributor.author	Mahmud, MAP
dc.contributor.author	Kouzani, AZ
dc.contributor.author	Khoo, SY
dc.date.accessioned	2024-08-21T05:39:12Z
dc.date.available	2024-08-21T05:39:12Z
dc.date.issued	2024-05-01
dc.identifier.citation	IEEE Transactions on Industry Applications, 2024, 60, (3), pp. 4708-4722
dc.identifier.issn	0093-9994
dc.identifier.issn	1939-9367
dc.identifier.uri	http://hdl.handle.net/10453/180499
dc.description.abstract	The widespread adoption of electric vehicles (EVs) requires strategically located and well-priced charging stations (CSs) to facilitate the charging and discharging of EVs. To implement this necessity, a two-stage framework is proposed that involves demand forecasting and an optimization model to optimize the location and pricing of CSs. The first stage employs a Long Short-Term Memory (LSTM) model to forecast the 30-day energy demand for CSs using historical data from New South Wales (NSW), Australia. The energy demand is integrated into a bilevel optimization problem modeled as a Stackelberg game. Considering the energy demand, the leader in the game strategically selects the locations for new CSs, while the followers determine their charging prices at each location to maximize payoffs. In this article, an in-depth theoretical and analytical analysis of the potential new locations is performed to understand the demand and profitability at these locations. Moreover, a detailed game theoretic analysis is presented, considering both static and dynamic games to illustrate their impact on the payoff of CSs. Furthermore, a penalty function is designed at the follower end to limit the charging prices within reasonable bounds to improve the social welfare of the market mechanism. Overall, this paper presents a comprehensive scheme that offers a systematic approach to optimizing the location and pricing decisions for CSs.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Transactions on Industry Applications
dc.relation.isbasedon	10.1109/TIA.2024.3364579
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering
dc.subject.classification	40 Engineering
dc.title	Optimal Location and Pricing of Electric Vehicle Charging Stations Using Machine Learning and Stackelberg Game
dc.type	Journal Article
utslib.citation.volume	60
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Science
pubs.organisational-group	University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
utslib.copyright.status	closed_access	*
dc.date.updated	2024-08-21T05:39:08Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	60
utslib.citation.issue	3

Abstract:

The widespread adoption of electric vehicles (EVs) requires strategically located and well-priced charging stations (CSs) to facilitate the charging and discharging of EVs. To implement this necessity, a two-stage framework is proposed that involves demand forecasting and an optimization model to optimize the location and pricing of CSs. The first stage employs a Long Short-Term Memory (LSTM) model to forecast the 30-day energy demand for CSs using historical data from New South Wales (NSW), Australia. The energy demand is integrated into a bilevel optimization problem modeled as a Stackelberg game. Considering the energy demand, the leader in the game strategically selects the locations for new CSs, while the followers determine their charging prices at each location to maximize payoffs. In this article, an in-depth theoretical and analytical analysis of the potential new locations is performed to understand the demand and profitability at these locations. Moreover, a detailed game theoretic analysis is presented, considering both static and dynamic games to illustrate their impact on the payoff of CSs. Furthermore, a penalty function is designed at the follower end to limit the charging prices within reasonable bounds to improve the social welfare of the market mechanism. Overall, this paper presents a comprehensive scheme that offers a systematic approach to optimizing the location and pricing decisions for CSs.

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http://hdl.handle.net/10453/180499