Low-carbon optimal scheduling of park-integrated energy system based on bidirectional Stackelberg-Nash game theory

Wang, Yi, Jin, Zikang, Liang, Jing, Li, Zhongwen, Dinavahi, Venkata and Liang, Jun ORCID: https://orcid.org/0000-0001-7511-449X 2024. Low-carbon optimal scheduling of park-integrated energy system based on bidirectional Stackelberg-Nash game theory. Energy 305 , 132342. 10.1016/j.energy.2024.132342 Item availability restricted.

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Abstract

Multi-stakeholder participation is crucial in facilitating the development of park-integrated energy systems (PIES). Balancing the diverse interests of various stakeholders, each with its distinct requirements presents a notable challenge. Concurrently, the model's complexity increases due to the engagement of various stakeholders, posing challenges to its resolution through traditional methods. In this context, this paper aims to investigate an optimal scheduling model that incorporates shared energy storage (SES) system, microgrids operator (MGO), electric vehicles station (EVS), and user aggregator (UA) with multiple prosumers. To comprehensively address the interests of all stakeholders, this study introduces a tri-level optimization model. The proposed model integrates a bidirectional Stackelberg-Nash game framework, in which the SES acts as the leader, the MGO acts as the secondary leader, and the UA-EVS acts as the followers while allocating benefits based on the asymmetric Nash bargaining theory. The Stackelberg game model between MGO and UA-EVS is analyzed using the Karush-Kuhn-Tucker (KKT) condition, while the Stackelberg game model between SES and MGO is resolved using the bisection method. Meanwhile, the Nash bargaining method among users is solved using the alternating direction method of multipliers (ADMM) technique. The analysis indicates that the proposed strategy can reduce PIES's costs and carbon emissions, yielding a win-win situation for all stakeholders.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Publisher:	Elsevier
ISSN:	0360-5442
Date of First Compliant Deposit:	12 August 2024
Date of Acceptance:	5 July 2024
Last Modified:	13 Aug 2024 07:21
URI:	https://orca.cardiff.ac.uk/id/eprint/170677

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