Given the requirement of time linkage between the medium and long-term contract and spot market trading, power generators’ long-term contracted electricity must be decomposed to a short time scale. To help a generator’s self-decomposition of medium and long-term contracted energy, a novel decomposition model based on distributionally robust optimization is proposed, taking into consideration the serious uncertainty of spot market prices. First, the ambiguity set for the distribution uncertainty of spot market prices is constructed using a Wasserstein metric as the distance measurement between distribution functions. Second, a distributionally robust optimization model for a generator’s contract decomposition is presented, in which the objective is to maximize the generator’s profit in the spot market, and the generator’s operational and contract decomposition constraints are considered. In addition, a dual theory is employed to transform the model into a quadratic programming problem which can be solved efficiently. Finally, case studies are presented and comparisons with the traditional stochastic and robust optimization models are also conducted. It is shown that the proposed model can effectively balance the economics and robustness of the generator’s contract decomposition in the presence of serious uncertainty in spot market prices. This work is supported by the National Natural Science Foundation of China (No. 61773252).