Coordinated DSO-VPP operation framework with energy and reserve integrated from shared energy storage: A mixed game method

Lin, Mohan, Liu, Jia, Tang, Zao, Zhou, Yue ORCID: https://orcid.org/0000-0002-6698-4714, Jiang, Biao, Zeng, Pingliang and Zhou, Xinghua 2025. Coordinated DSO-VPP operation framework with energy and reserve integrated from shared energy storage: A mixed game method. Applied Energy 379 , 125006. 10.1016/j.apenergy.2024.125006 Item availability restricted.

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Abstract

Virtual power plants (VPPs) contribute to the flexibility and economy of distributed system by leveraging integrated distributed renewable resources, optimizing energy production and consumption patterns, and facilitating dynamic grid management. However, challenges arise when multi-VPPs coordinated operate, including complex spatial and temporal correlation characteristics and conflicting interests in multi-agent decision-making. Shared energy storage (SES), as a product of the sharing economy, can be more flexible to help VPPs consume power generation from distributed renewable resources. Hence, focusing on the complementary problems and conflicts of interest between VPPs and improving the utilization of distributed renewable resources, this paper proposes an energy-reserve coordinated optimization model led by the distribution system operator (DSO) and involves the participation of both multi-VPPs and SES. Firstly, a Stackelberg-cooperative mixed game (SC-mixed game) framework is proposed for the DSO-VPP system, which leverages the DSO as the leader. The transactional electricity price between DSO and VPPs is optimized via the Stackelberg game, and the operation strategies for multi-VPPs can be calculated by the Cooperative game. Besides, an energy-reserve model is proposed for SES, which considers six reserve modes for further exploring the reserve potential of SES and guarantee the reserve provision ability. Additionally, a tailored alternating direction method of multipliers (ADMM), integrating a bisection method, is proposed to solve the SC-mixed game model efficiently. Finally, several case studies are conducted to validate the effectiveness of the proposed model.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Publisher:	Elsevier
ISSN:	0306-2619
Date of First Compliant Deposit:	7 January 2025
Date of Acceptance:	22 November 2024
Last Modified:	07 Jan 2025 14:45
URI:	https://orca.cardiff.ac.uk/id/eprint/174552

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