## 5 Experiment Results and Analysis
### Considering Practical Factors Including Flexibility and Action Delays  
The above experiments are based on the ideal scenario that the datacenter can act as soon as possible to adjust the power consumption of itself to the target value exactly. In a practical environment, the power adjustment range of a datacenter will be constrained, which means that the datacenter cannot vary the power consumption as much as possible to reach the target value. We defined \"flexibility\" to describe such characteristics of the datacenter. Furthermore, the datacenter has to spend some time (which we defined as \"action delay\") to dynamically change its power consumption through the combination of multiple methods, such as load shedding or cooling temperature adjustment. Hereafter, we also conducted a series of experiments to study the impact of practical factors. Since the results of sunny or cloudy scenarios are similar, we have only shown the results of data from the sunny day in this subsection for clarity.  
To examine the impact of the flexibility of the datacenter, the adjustable range of the datacenter can be expressed as follows:  
\[[(1-e)\times p(t),(1+e)\times p(t)] \tag{8}\]  
wherein \(e\) represents the flexibility of the datacenter, and the \(p(t)\) means the power of the datacenter in the last time interval. We specify that if the power of the datacenter in the next interval is out of  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scenario** & **(1)** & **(2)** & **(3)** \\ \hline Average Total loss (MW) & 19.548 & 19.492 & 19.19 \\ percentage of loss/generation & 8.52\% & 8.39\% & 8\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistical results with three datacenters.  
Figure 4: Comparison of loss values over three scenarios. (**a**) sunny day; (**b**) cloudy/snowy day.  
the allowed range, the upper or lower limits of the adjustable range will be taken as the power of datacenter in the next interval. On the other hand, to evaluate the impact of the action delay, we simulated different scenarios with a delay of 5, 10, 15 and 20 min respectively.  
Figure 5 shows the impact of different flexibility values, where the abscissa represents different flexibility values and the ordinate represents the mean power losses (MW). We focused on the average loss under different flexibility values, where \(e=0\) means the datacenter kept a constant load, and in such scenario, the loss value was remarkably large. This shows that the adjustments made to the datacenter loads can help to effectively reduce the total power losses.  
The impact of different action delay times on the total power losses of the grid can be seen in Figure 6. As mentioned, we tested different delay time settings from 0 to 20 min and the average loss values were recorded under each condition. From the figure, we can see that the loss value increases proportionally with the delay, which has a great impact on the action delay on the power loss of the entire grid. Besides, comparing the results with Figure 5, we can see that when the flexibility value is equal to or greater than 0.3, the loss value does not change substantially anymore and the value is close to the result in Figure 6 when there is no delay. This implies that even an adjustable range of \(\pm\)30% of the datacenter power consumption can lead to fairly satisfactory results.