## 1 Introduction
[14] used a fixed threshold and dynamic threshold method to calculate the discharge threshold power to control the discharge power of the storage system. However, due to the high cost of the electric energy storage system, it is difficult to use it on a large scale. The systematic charge and discharge control of electric vehicles can make up for the shortcomings of the electric energy storage system. [PERSON] et al. [15] introduced the basic models of the charging and discharging process of electric vehicles, and [PERSON] et al., [16] put forward two charging and discharging control strategies based on the spatial-temporal model. [PERSON] et al. [17] studied the optimal power flow of the power-natural gas hybrid system. Considering the volatility of renewable energy sources, how to optimize and coordinate the natural gas and power systems is researched in reference [18]. In reference [19], a method of equipment selection and capacity planning for the IES is presented with the consideration of the coupling among power, heat and gas. Aiming at the problems in equipment selection and capacity planning, an optimization model is set up with the minimum annual comprehensive cost as the objective function, and the mixed integer linear programming (MILP) algorithm is adopted to optimize the types and capacities of the equipment simultaneously. The goal of energy Internet operation scheduling is to reduce the total generation cost of distributed power supply while ensuring the overall real-time power balance of the energy Internet [9], which is equivalent to converting the economic scheduling problem into the consistency of incremental cost in the power distribution process [20; 21]. Therefore, it is of great significance to study the real-time power allocation in the operation scheduling of energy Internet. Modeling and solving of IES optimization scheduling problems are two closely related processes. Generally, the solving methods of optimization problems are divided into two categories: heuristic methods and mathematical optimization methods. The typical heuristic method is the intelligent algorithm, such as particle swarm optimization (PSO), genetic algorithm (GA) [22] and simulated annealing algorithm. The mathematical optimization method is the most widely used mathematical programming method, such as interior point method [23], the simplex method [24], recursive least squares method [25] and so on. Based on the above background, from the perspective of energy Internet, the optimal operation scheduling of an urban power grid with high clean energy penetrance is studied. A multi-energycoordinated intelligent management method which takes an internal equipment operation system and network stable operation constraints into consideration is also proposed. Based on load forecasting and renewable energy output forecasting data, an urban multi-energy day-ahead scheduling optimization model is established. After that, this paper used alternating direction method of multipliers (ADMM) to solve the model. Finally, the validity of the method is verified by a real-time power grid example. The main contributions of this paper are as follows:  
(1) To solve the scheduling and the grid connection problems of multi-type new energy, this paper presented a coordinated scheduling method for urban multi-time scale heat-electricity systems. The method ensured the robustness of the scheduling scheme through coordinating the output of controllable generators, electric boilers, storage devices, etc., as well as using multiple time scales (day-ahead, days, real-time) to gradually reduce the impact of renewable energy forecast deviation on the grid. (2) Considering the coupling and complementarity between different energy systems, the quasi-steady-state model of the equipment is modified and the balance between electrical energy and thermal energy is ensured. (3) The model solving method of ADMM was proposed.