To address the challenges of source-load uncertainty and privacy preservation in the coordinated operation of multiple virtual power plants (VPPs)

this paper proposes an innovative framework that integrates peer-to-peer (P2P) electricity sharing with distributed optimization. Firstly

an electro-thermal VPP model incorporating a cloud-based energy storage leasing system is established

and a multi-VPP P2P electricity mutual assistance framework is designed. Secondly

to overcome the limitations of high computational complexity and strong conservatism in conventional uncertainty optimization methods

a four-layer three-stage stochastic robust optimization model is constructed that accounts for the probability of worst-case source-load scenarios. This model simultaneously addresses dual uncertainties arising from source-load outputs and scenario probability distributions. At the same time

a modified column-and-constraint generation (C&CG) algorithm that supports parallel computing is proposed to enhance solution efficiency. Finally

a multi-VPP benefit allocation model is established based on Nash bargaining theory

decomposed into cooperative cost-minimization and individual profit-maximization subproblems. A distributed algorithm combining modified C&CG with the alternating direction method of multipliers (ADMM) is adopted to achieve an efficient solution while preserving privacy. Case studies demonstrate: 1) The proposed model significantly improves system economy under uncertain environments; 2) The distributed solution method balances system privacy and computational efficiency; 3) Fair and reasonable profit allocation is realized for each virtual power plant.