Abstract: The utilization of large-scale distributed energy resources (DER) can effectively alleviate the environmental pollution and energy crisis brought by fossil energy. As to how to determine the location and capacity size of DER, a comprehensive optimal allocation model of DER is built by considering uncertainties factor. In this model, the minimum total programming cost is taken as the objective, the system security and operation requirements are regarded as constraints, and such factors as the construction cost of low-carbon and DER, running cost, maintenance cost and carbon emission cost as well as the optimization of regional energy efficiency are considered, in which chance constrained programming is used to handle such uncertainties as uncertain load growth, output of wind turbines and photovoltaic cells. On the basis of determining objective function, a Monte Carlo simulation based embedded quantum particle swarm optimization algorithm approach is developed to solve the NP-hard problem, which greatly improve the searching efficiency and ability by comparing with particle swarm algorithm. Finally, through analysis on the IEEE 37-bus distribution test system, results show that proposed model can solve the locating and sizing problems of regional DER.