## 3 Multi-Energy Coordination and Optimization Dispatching Strategy of Urban Power Grid  
### Integrated Day-Ahead Coordination and Dispatching Model with Large-Scale Intermittent Energy  
In the coordinated operation of the power system with large-scale intermittent renewable energy generators, the inherent uncertainty of renewable energy cannot be eliminated, which means other power sources have to make up for it [29,30]. When the intermittent power supply with a high proportion is suddenly increased or absent, the operating cost and efficiency of other units will be affected [31,32]. Under the current development of a new energy power situation, it is generally believed that renewable energy should be prioritized during system operation. Due to its good adjustability, the hydropower can balance the random fluctuation of intermittent energy power and undertake the task of peak shifting and valley filling, and the residual capacity bears part of the load. Conventional thermal power generating units should avoid frequent output adjustment (daily generation scheme does not consider start-stop) and maintain high efficiency of power generation. The energy storage device is charged at the load trough period and discharged at the load peak period and when the load increases sharply [28]. Heat pump, heat storage boiler and other devices can also achieve the regulation of electricity through heat storage [33]. Different from the traditional method of simply increasing the rotary reserve capacity to absorb the fluctuation of new energy, the method proposed in this paper will make full use of the regulating capacity of adjustable generator set and  
Figure 1: The multi-energy coordination system architecture of an urban power grid. heat storage equipment to absorb the fluctuation of renewable new energy, and meet the robustness requirements of various extreme scenarios through reasonable resource allocation. (1) Objective function  
The optimization scheduling model of IES considering multi-energy complementarity constructed in this paper takes the minimum cost of the system as the objective function, which is as follows:  
\[\min L\Big{(}Q_{\text{PB}}^{t},Q_{\text{HB}}^{t},Q_{\text{PG}}^{t},Q_{\text{HG }}^{t}\Big{)}=C_{\text{PB}}+C_{\text{PG}}+C_{\text{HB}}+C_{\text{HG}} \tag{1}\]  
where, \(Q_{\text{PB}}^{t}\) and \(Q_{\text{HB}}^{t}\) are respectively the quantities of electricity and heat purchased outside the system in time period \(t\). \(Q_{\text{PG}}^{t}\) and \(Q_{\text{HG}}^{t}\) are respectively the quantities of electricity and heat produced by the equipment in the system in time period \(t\). \(C_{\text{PB}}\) is the purchase cost of the power grid. \(C_{\text{PG}}\) is the power generation cost of its own equipment. \(C_{\text{HB}}\) is the heat purchase cost of the system. \(C_{\text{HG}}\) is the heating cost of its own equipment. Each cost is a function of \(Q_{\text{PB}}^{t}\), \(Q_{\text{HB}}^{t}\), \(Q_{\text{PG}}^{t}\) and \(Q_{\text{HG}}^{t}\). (1) Power and heat purchase costs of the system:  
\[C_{\text{PB}}=\sum_{t=1}^{T}\rho_{\text{PB}}^{t}Q_{\text{PB}}^{t} \tag{2}\]  
\[C_{\text{HB}}=\sum_{t=1}^{T}\rho_{\text{HB}}^{t}Q_{\text{HB}}^{t} \tag{3}\]  
where \(\rho_{\text{PB}}^{t}\) and \(\rho_{\text{HB}}^{t}\) are the prices of electricity and heat purchased outside the system in time period \(t\). Under normal circumstances, the price of electricity varies due to the imbalance between the supply and demand of renewable energy and load. \(T\) is the number of time periods in the scheduling cycle. For day-ahead scheduling, 60 min is usually taken as one time period, what is more, \(T=24\).