@article{WANG2026, 
author = {Chenyi WANG and Junteng WANG and Tianran LI and Zhenya JI and Yue QIU},
title = {Multi-uncertainty-aware hybrid game optimization for shared energy storage and multi-microgrid operation},
year = {2026},
journal = {Electric Power Engineering Technology},
volume = {45},
number = {9},
pages = {104-117},
keywords = {shared energy storage, integrated regional energy microgrid (IREM), hybrid game, peer-to-peer (P2P) electricity trading, Nash bargaining, distributionally robust optimization},
url = {https://www.sciopen.com/article/10.12158/j.2096-3203.2026.09.010},
doi = {10.12158/j.2096-3203.2026.09.010},
abstract = {To address the problem of multi-agent collaborative power optimization between shared energy storage operator (SEO) and integrated regional energy microgrid (IREM) alliances under the dual-carbon background, a hybrid game-based optimization strategy considering multiple uncertainties is proposed. Firstly, a bi-level optimization model based on hybrid games is established, in which the upper level focuses on maximizing the operational profit of SEO, while the lower level aims to maximize the overall benefit of the IREM alliance. Secondly, cooperative games among IREM members are introduced, and fair benefit allocation is achieved through asymmetric Nash bargaining. To cope with multiple uncertainties arising from electricity price fluctuations and the intermittency of renewable energy output in IREM, robust optimization is adopted to address price uncertainty, and a data-driven distributionally robust optimization model is developed for IREM scheduling. Finally, the column-and-constraint generation (C&CG) method is employed to solve the bi-level game model, and the alternating direction method of multipliers (ADMM) is adopted to solve the cooperative game model. Case study results show that the proposed strategy enhances SEO profits, effectively promotes peer-to-peer (P2P) energy trading within the IREM alliance, reduces the overall operating cost and carbon emissions, and achieves fair benefit allocation among multiple agents.}
}