## 1 Introduction  
Renewable energy sources such as wind and photovoltaic power generation have experienced explosive growth, and their natural intermittence brings new challenges for the economic and safe operation of renewable power systems. To handle uncertainties of renewable energy sources [1; 2], an effective scheduling method for a multi-energy complementary generation system [3; 4] is pressingly needed to alleviate the volatility of renewable power generation [5]. At present, there are a few studies for the combined operation of a multiple-source power generation system [6; 7; 8]. A hybrid scheduling optimization model was proposed in [9] to manage the demand response for cascaded hydropower and wind-photovoltaic power stations. An integrated dispatch model with a multi-objective function was presented in [10] for optimizing the wind-photovoltaic-storage power generation output under generalized load fluctuations. In [11], a wind-hydro-thermal unit combination model was established for a day-ahead scheduling plan, and afterward the model was solved by variables separation and a piecewise linear strategy to manage the hydraulic production capacity curve. An optimal operation model for a large-scalewind-photovoltaic power generation system was established in [12] and the influence of dispatch time resolution on system operation cost was analyzed. In [13; 14] a statistical method was used to analyze the power output characteristics of wind-photovoltaic power and provide a solid data support for the combined system optimal operation. Authors of [15] proposed a constraint-based iterative search algorithm for the energy scheduling of a hybrid microgrid system with wind-photovoltaic and storages. The aforementioned references have focused on the combined system optimal scheduling of a single time scale with some promising results; however, they have not fully explored the complementary operation characteristics of multiple-energy sources for multiple time scale optimal scheduling. Wind power and photovoltaic power generation are greatly affected by weather conditions, though the accuracy of renewable power prediction is difficult to be guaranteed, the error of wind-photovoltaic power prediction gradually decreases with the reduced time scale. At the same time, the power generation output characteristics of controllable power sources such as hydropower, thermal power, and energy storage are different at various time scales, and the complementary coordination among multiple energy sources could be explored for multi-time-scale coordinated optimal operation. In [16] a multi-time-scale scheduling for the combined cooling heating and power (CCHP) was proposed. [PERSON] et al. [17] solved an optimal power flow problem with the uncertainty of renewable energy power generation in two stages. The first stage determined the operation mode of the conventional power station, and the second stage relied on demand-side flexibility to search an optimal scheduling of wind-photovoltaic power. In [18], [PERSON] et al. combined photovoltaic power plants with thermal energy storage and electric heaters to develop a daily stochastic two-stage wind-photovoltaic scheduling model in a rolling fashion manner. In [19], a multi-time-scale dispatch model of wind-thermal power was presented and the spinning reserve requirement was optimized to reduce the total power generation cost. In [20] a multi-time-scale active power scheduling model was established for wind-storage combined power generation system, and a dynamic economic dispatching algorithm was adopted to solve the model on the basis of rolling forecast for wind power. A multi-time-scale coordinated optimization model for CCHP was established in [21], and afterwards it was solved by a multi-scenario stochastic programming with a model predictive control method.