## 5 The Realization of ATC-Based Distributed Coordinated Optimization
### Simulation Results and Discussion  
In the RMGs level, the optimization problems of energy management are solved in parallel. The optimized result of energy resources scheduling for an arbitrarily specified RMG is shown in Figure 9.  
Figure 8: The 24-h power load curve in the distribution network level and time-of-use (TOU) electricity price. Figure 7: The 24-h load-demand profile for a household. Figure 9: Daily scheduling optimization results for the RMG connected to bus 9. It is obvious that flexible and EVs charging loads can effectively aid the peak clipping and valley filling by increasing the use of power energy during off-peak periods while reducing the amount of electric power used during on-peak periods. According to the optimized results, the flexible loads are put into operation between 01:00 hrs and 06:00 hrs, while the EVs are charged between 02:00 hrs and 06:00 hrs and at 12:00 hrs with lower prices. During peak hours (18:00 hrs to 22:00 hrs), EVs provide power supply to the household loads in the RMG by V2H discharging. During daylight hours, the electric power consumed by household loads is covered from the purchased electricity from the DN and PV generation. The RMG is the aggregate of household prosumers, the optimized scheduling result of EV and flexible loads for a household prosumer within the RMG is shown in Figure 10.  
By comparing Figures 7 and 10, it is obvious that the shiftable loads are allocated during the off-peak hours inline with the lower energy prices, and the EV's battery is also charged when electricity prices are relatively lower (i.e., early morning). Moreover, it can be seen that the EV battery is used as electricity storage for household loads and provides a V2H service during peak load period, i.e., the energy stored in the EV's battery supplies the domestic loads from 18:00 hrs to 22:00 hrs during the peak load period. Moreover, it can be seen that the household prosumer has two hours of autonomous operation, i.e., during 10:00 hrs and 18:00 hrs when RMG is independent of the main grid. The advantage is assisted by rooftop PV generation, demand responses of EV and flexible loads. Energy consumption cost minimization is a major objective for every household prosumer. Table 1 shows the daily energy costs based on TOU electricity price for a typical household in RMG level under the un-optimized case and the proposed energy coordinated mode. The positive values mean the amount of energy cost that a household prosumer is required to pay, and the negative values mean the amount of the cost saved. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Energy Cost** & **Un-Optimized Case** & **Proposed Energy Coordinated Mode** \\ \hline Inflexible loads & 7.7 & 7.7 \\ EV charging + Flexible loads & 9.93 & 3.06 \\ PV Generation & \(-\)0.5 & \(-\)0.5 \\ V2H & 0 & \(-\)5.22 \\ Daily energy cost & 17.13 & 5.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Energy Comparative Costs ($/day) for a Typical Household.  
Figure 10: Optimized daily scheduling results for a typical household prosumer within RMG.  
From Table 1 it can be seen that shifting EV charging and flexible loads from high-demand hours to off-peak hours a significant amount of cost can be saved. The cost went down from $9.93 to $3.06, a reduction in the cost about 69%. The V2H system usage also brings a respectable energy cost saving ($5.22) to individual household prosumer. Figure 11 shows the power scheduling results of controllable DGs in the distribution network from the proposed method in the minimum cost operational solution of ADN. Blue is the cumulate profile under the un-optimized case while red is the cumulate profile under the proposed distributed optimal coordinated operation mode. It is obvious that the output power of all sources in the distribution system level becomes smooth after optimizing since the proposed method allows for optimized scheduling of demand responses to household prosumers in the RMG level so that shifting electric demand from on-peak periods to off-peak periods.