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.