## 6 Simulation Analysis  
### Basic Data  
The improved IEEE 14-bus system with four thermal power units was used as the simulation system [28]. A WPP, a PV, an HS, and ESDs were connected with the simulation system to analyze the complementarity of the different power sources. The rated capacities of the WPP, the PV, and the HS were 900 MW, 400 MW, and 600 MW, respectively. Moreover, 400 MW ESDs were considered to analyze the optimal effects in the OEE and LLC modes. The rated discharge-charge power of the ESDs was 100 MW, the maximum discharge-charge power was 150 MW, and the initial energy storage was zero. The total maximum and minimum loads on a typical load day were 18,500 MW and 1400 MW, respectively, in summer and 1800 MW and 1350 MW, respectively, in winter. The loss of the tested system was 5.5%. The operating parameters and pollutant emission parameters of thermal power units can be found in Reference [25]. Currently, most thermal power tariffs have already included SO\({}_{2}\), NO\({}_{\text{x}}\), soot, and other pollutant costs in China; thus, the carbon cost was primarily considered in this study, and the price of CO\({}_{2}\) emission was set to Y55/ton [29]. Table 2 shows the operation parameters of the TPPs. The grid connection prices of the WPP, PV, HS, and TPPs were \(\mathbb{V}610/\text{MW}\cdot\text{h}\), \(\mathbb{V}1000/\text{MW}\cdot\text{h}\), \(\mathbb{V}320/\text{MW}\cdot\text{h}\), and \(\mathbb{V}450/\text{MW}\cdot\text{h}\), respectively [26]. The average price of customer power consumption was \(\mathbb{V}590/\text{MW}\cdot\text{h}\)[8]. It was assumed that the ESDs enjoyed real-time pricing. The division of the peak-valley period and peak-valley electricity price can be found in Reference [10]. It was also assumed that the coal price was \(\mathbb{V}500/\text{tce}\). Load demand curves for a typical summer day and a typical winter day were chosen as the input data, and the water resources situation on a typical load day was chosen as the HS's input data [21]. Figure 4 shows the load demand and the HS's available output on a typical load day. The parameters of the wind turbine were \(v_{in}=3\,\text{m}/s\) and \(v_{rated}=14\,\text{m}/s\)[21]. The shape parameter and scale parameter on a typical load day were 3.8 and 9.4, respectively, in summer, and 5.2 and 11.8, respectively, in winter. In accordance with the literature [26], the illumination intensity parameters \(\alpha\) and \(\beta\) on a typical load day were 0.5 and 9.12, respectively, in summer, and 0.3 and 8.54, respectively, in winter. Then, the continuous probability distribution curve of wind speed and solar irradiance was obtained. As this study was primarily concerned with the complementarity of different power sources and because the exact method for selecting the number of discrete points was irrelevant here, ten discrete points were used for both curves. The proposed uncertainty simulation method was applied to obtain 50 groups of simulation scenarios for wind speed and solar irradiance. Then, the improved K-medoids method was used to reduce the number of scenarios, and the outputs of ten typical scenarios were calculated. Subsequently, the average outputs of the different scenarios were taken as the input data. Figure 5 shows the simulation scenarios for WPP and PV on a typical summer day. Figure 6 shows the average output of the WPP and the PV on typical load days. After this basic data had been input, the model was solved by the GAMS software application using the CPLEX 11.0 linear solver from the ILOG_solver. The proposed model involved a nonlinear objective function and constraints belonging to the class of mixed integer nonlinear programming problems. As GAMS is better suited to mixed integer programming problems, the objective function and the constraints were linearized [28]. Figure 7 shows the optimization flowchart for the MECS scheduling model. Figure 5: The ten simulation scenarios of WPPs and PVs on a typical load day (summer).