STUDY ON INTERNAL BENEFIT DISTRIBUTION IN VIRTUAL POWER PLANT BASED ON IMPROVED SHAPLEY

Pan Ting; Zhang Yifan; Dong Houqi; Zeng Ming; Zhang Xiaochun

doi:10.19912/j.0254-0096.tynxb.2024-1005

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (10) : 455-465. DOI: 10.19912/j.0254-0096.tynxb.2024-1005

STUDY ON INTERNAL BENEFIT DISTRIBUTION IN VIRTUAL POWER PLANT BASED ON IMPROVED SHAPLEY

Pan Ting¹, Zhang Yifan², Dong Houqi³, Zeng Ming², Zhang Xiaochun²

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Abstract

This paper proposes a multi-agent benefit distribution method for virtual power plants, incorporating two major improvements to the traditional Shapley value approach. Firstly, a correction factor is introduced to adjust the classical Shapley value by comprehensively accounting for each agent’s resource input, risk exposure, and contribution to emission reduction within the coalition. Secondly, a reinforcement learning-based sampling method is employed to approximate benefit distribution, effectively addressing the “combinatorial explosion” problem caused by a large number of participating agents. The proposed model is applied to a case study of a virtual power plant, demonstrating enhanced computational efficiency without compromising accuracy.

Key words

electric energy storage / economic efficiency / virtual power plants / Shapley value distribution

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Pan Ting, Zhang Yifan, Dong Houqi, Zeng Ming, Zhang Xiaochun. STUDY ON INTERNAL BENEFIT DISTRIBUTION IN VIRTUAL POWER PLANT BASED ON IMPROVED SHAPLEY[J]. Acta Energiae Solaris Sinica. 2025, 46(10): 455-465 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1005

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