## 5 Case Study
### Fifth Scenario (Mode5: Hybrid Energy Supply, All of the Factors Considered)  
In this mode, heat load supply is the same as Mode 2, and wind and solar power ought to supply load in the micro grid, while the residual is supplied by energy storage, EVs and the power grid system, with demand response. At this time, the daily operation cost is 2625 RMB; the purchase of electricity from the grid is not involved; the gas purchase volume is 989 m\({}^{3}\), costing 2470 RMB; the cost of battery charging and discharging is 128 RMB; and the cost of EV charging and discharging is 28 RMB. Due to the adoption of the DSM, the system load has been partly transferred, while the system operating expenses have decreased slightly. The system output is shown in Table 2.  
As is shown in the above results, the operation cost is unrelated to the operation mode in a micro grid system once DERs, including wind turbines and PV arrays, are constructed. Wind and solar power ought to be given the priority to supply loads in the micro grid. It can be seen from a comparison between the total operation cost of the cogeneration mode and the separate supply mode that the former has a better economic efficiency, while the efficiency of the hybrid supply system can be increased by 11.36%. The operation cost of the micro grid will be further reduced with the installation of energy-storing devices (Mode 3), yet the construction cost will be increased. Hence, the construction capacity of energy storing devices requires an integrated consideration of both economic efficiency and reliability. EVs that are not schedulable are essentially electric load when connected to the micro grid, while schedulable ones are essentially storage batteries. After taking the EV-connected micro grid operation mode (Mode 4), the operation cost increased by 8 RMB. It is worth noting that the electric demand increased by 27 kWh as three non-schedulable EVs are added to the grid, while the power supply cost of the system reduced from 0.63 RMB/KWh to 0.62 RMB/KWh with a decrease ratio of 1.6%. A part of the peak load is transferred to the valley load after DSM is taken into consideration. The peak-valleydifference is reduced from 179.2 kW (a peak of 265 kW at 21:00 and a valley of 86.4 kW at 5:00) to 128.3 kW (a maximum of 242 kW at 21:00 and a minimum of 114 kW at 5:00). The operation cost is reduced to 2625 RMB, with a 2.2% decrease compared to Mode 4. The operation costs in all of the modes are shown in Figure 3.  
The operation profit of the power supplier in the micro grid under each operation mode is illustrated in Figure 4. The profit in Mode 2 is improved greatly over Mode 1 from 2761 RMB to 3270 RMB. In Mode 3, the system operation cost is reduced, and the system profit increases by 80 RMB, reaching 3350 RMB with a growth rate of 2.45%, because the added storage batteries efficiently adjust the peak-valley load ratio of the system. In Mode 4, the decreasing rate of operation costs is relatively low, since EVs have a much smaller capacity compared to the system load, and the system profit increases slightly by 28 RMB. In Mode 5, the system load is effectively transferred after the DSM is adopted, and the operation cost is further reduced. Since the peak load is reduced, the supplier profit is decreased under this mode. However, the users gain an obvious benefit, with the electricity bill decreasing from 4426 RMB to 4215 RMB. This indicates that the adoption of DSM provides the supplier in the micro grid with no profit from the operation perspective, but the storage device investment can be saved as the load is effectively transferred. The load profile with and without DSM is shown in Figure 5.  
Figure 4: The micro grid profits in different modes. Figure 3: The micro grid operation costs in different modes. As to the solving of energy dispatch optimization, the MTPSO algorithm introduced in this paper has a better performance in optimizing capability than the ordinary PSO algorithm.