Abstract: There has been rapid development of distributed resource access technology and the tradeable energy market.Along with this, thermal energy sharing and strong uncertainty of source load for massive heterogeneous multi-energy producers and consumers bring great challenges to energy management of an alliance. Based on this, this paper proposes an energy management strategy for a massive heterogeneous multi-energy consumer alliance based on nucleolar cluster estimation and data-driven optimization of a distributed robust system. With the objective of maximizing the social welfare of the alliance and individuals under the influence of multiple uncertainties, the energy management model of a massive heterogeneous consumer alliance considering the dynamic differences of the electric heating network is established. This is to solve the problem that the scalability, fairness and privacy of alliance energy management are difficult to take into account. In addition, considering the complexity of nucleolar computation and the adverse effects of source load uncertainty, this paper proposes a nucleolar estimation method based on Gaussian mixture clustering and distributed robust optimization model based on data-driven Wasserstein distance, respectively, to achieve a balance between model solving speed and accuracy. The numerical results show that the proposed method effectively improves the social welfare of the producers and consumers alliance under the influence of multiple uncertainties, achieves the balance of scalability, fairness and privacy of the energy management of the alliance, and promotes more producers and consumers to participate in local energy peer-to-peer（P2P） transactions.