As the main body of urban energy consumption, the low carbon and efficient operation of intelligent buildings has great significance to achieve the goal of "peak carbon dioxide emissions and carbon neutrality". In order to enhance building economics while improving building energy sharing and distributed energy resources consumption, a distributed optimal scheduling model for buildings while considering building characteristics and energy trading was proposed. At the building optimization level, a multi-objective building operation optimization model considering economic and temperature comfort requirements was established. At the energy sharing level, a peer to peer buildings trading market was established, and a new continues double auction trading mechanism combining building optimization results and market risks was proposed. Then through feeding back the results of market transactions to the optimization level of each building to achieve the buildings iterative optimization and the energy sharing within buildings. Finally, robust optimization was used to test the effectiveness of the model in various uncertainty scenarios. Simulation results show that the distributed optimal scheduling model of buildings can optimize building economics while enhancing buildings energy complementarity and distributed energy resources consumption in various scenarios.