When the electricity

gas and heat systems of integrated energy system (IES) belong to different operating subjects

the coordination and scheduling of heterogeneous energy within IES will lead to serious conflicts of interests among various subjects

which is not conducive to maintaining the stability of the joint operation mechanism of IES multi-energy system. Therefore

it is necessary to solve the key problems

such as the collaborative operation pricing mechanism of IES internal multi-energy system

the collaborative optimization scheduling of IES internal multi-energy system under this mechanism

and the joint operation optimization scheduling between IES and active distribution network (ADN)

so as to promote the local consumption of high proportion of new energy in IES and accomplish the win-win interests of multiple subjects. For this reason

this paper proposes a paid transaction mode of surplus electric energy within IES

that is

the power system converts the surplus electric energy into gas and heat energy

which will be the basis that the electric power operator formulates the energy sales

and then guides the gas and heat operators to purchase. All of above is to achieve the aim of promoting the local consumption of new energy through the regulation and scheduling of gas and heat space-time migration. In this regard

this paper constructs an IES double-layer optimal scheduling model: the outer layer is a master-slave game model between ADN and IES

which optimizes the electricity price and IES electric transaction volume；the inner layer is the master-slave game model between the electric power operator and the gas and heat operators

which optimizes the bidding strategy of the electric power operator and the energy purchase strategy of the gas and heat operators. Finally

the PSO algorithm and CPLEX solver are combined to solve the problem

and the internal and external double-layer models are alternately iteratively optimized to obtain the optimal purchase and sale price strategy of ADN and the optimal energy scheduling strategy of electricity

gas and heat. Meanwhile

the effectiveness of the proposed method is verified by multi-scenario examples.