Distributed energy individuals generally cannot directly participate in electricity market transactions or provide grid auxiliary services. This issue can be solved through distributed energy aggregation. This paper proposes a new distributed energy polymer optimization method based on multiple energy types, multiple trading markets, and multiple services. The optimization method can be divided into three stages: high?level, middle?level, and low?level optimization. First, in the high?level optimization, linearization techniques are used to overcome the non?convexity of the cross spatiotemporal scheduling problem of aggregates, and the problem is transformed into a mixed integer linear programming problem to obtain the day?ahead unit combination scheme. Based on the unit combination scheme, the middle?level optimization solves the economic dispatch problem by the second?order cone?convex relaxation of the optimal power flow equation to obtain the node voltage and power flow. Deviations from the middle?level optimization set point are then penalized in the low?level optimization, making the results of the low?level optimization get close to the system optimal operating point while having a shorter planning horizon. Finally, in a case study, the optimization method is applied to a multi?energy polymer model including electrolytic hydrogen production, wind power generation, photovoltaic power generation, etc. The results show the advantages of the proposed method in terms of grid ancillary service and aggregate flexibility enhancement. The operational benefits are maximized, which validate the effectiveness of the proposed method.