# A Decentralized, Flat-Structured Control System for Chiller Plants +
## 4 Case Study Based on New System
### Results and Analysis  
From 19 September 2018, the system started to operate the energy-saving optimization strategy. The comparison of the operation effects of the optimization control is as follows, among which, from September 16 to 18, the larger chiller was turned on, the water pump and cooling tower was operating at a constant frequency of 50 HZ, and the number of units was manually adjusted by the user. From September 19 to 21, the chiller was automatically selected according to the terminal load. The \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Name** & **a1** & **a 2** & **a 3** & **a 4** & **a 5** & **a6** & **a7** & **a8** & **a 9** & **a 10** \\ \hline Chiller1-3 & 14,317 & 9.593 & 0.413 & \(-\)0.531 & \(-\)12.038 & 0.013 & 0.003 & \(-\)0.088 & 0.217 & \(-\)0.009 \\ \hline Chiller4 & 8.918 & 15,723 & 0.534 & \(-\)0.575 & \(-\)11.767 & 0.006 & 0.006 & 0.048 & 0.074 & \(-\)0.014 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Chiller model parameters. Figure 8: (**a**) Decentralized control cabinet; (**b**) Intelligent node (CPN). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Name** & **a1** & **a 2** & **a 3** & **a 4** & **a 5** & **a6** & **a7** & **a8** & **a 9** & **a 10** \\ \hline Chiller1-3 & 14,317 & 9.593 & 0.413 & \(-\)0.531 & \(-\)12.038 & 0.013 & 0.003 & \(-\)0.088 & 0.217 & \(-\)0.009 \\ \hline Chiller4 & 8.918 & 15,723 & 0.534 & \(-\)0.575 & \(-\)11.767 & 0.006 & 0.006 & 0.048 & 0.074 & \(-\)0.014 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Pump model parameters. frequency and number of water pumps and cooling towers were automatically adjusted by optimizing control APP. To analyze the energy efficiency of the decentralized control system, the energy use of the main equipment before and after the installation of the control system is calculated, which can be seen in Figure 9. Only 6-day energy use records are selected, mainly because the start time of optimal control operation is September 19, and there is little difference in meteorological conditions for these 6 consecutive days. Specific meteorological parameters are shown in the Table 5. The highest and lowest temperatures in these six days are 27 \({}^{\circ}\)C and 14 \({}^{\circ}\)C, respectively. And the weather is mainly cloudy and sunny. Condenser water pumps and chilled water pumps were adjusted by number and frequency conversion, with energy saving rates of 20.9% and 19.6% respectively. At the same time, the original system had all cooling tower water valves fully opened. Under the automatic control strategy, the cooling tower water valves were switched on and off according to the cooling water volume and the operation of the cooling tower fan, avoiding the water mixing phenomenon of the cooling tower, which reduced energy use by 63.4%. The average temperature of condenser water decreased more than 1 \({}^{\circ}\)C, and the energy efficiency of the chiller was relatively improved 10%. The comparison of the energy use of the main device under traditional control and decentralized control can be seen in Figure 10. Totally, the energy saving rates of the whole system reached to 15.4%, which can be seen in Table 6. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Category**} & **16** & **17** & **18** & **19** & **20** & **21** \\  & **September.** & **September** & **September** & **September** & **September** & **September** \\ \hline \(T_{\text{max}}\) (\({}^{\circ}\)C) & 25 & 27 & 25 & 25 & 27 & 25 \\ \(T_{\text{min}}\) (\({}^{\circ}\)C) & 14 & 16 & 17 & 17 & 16 & 14 \\ Weather & Fine & Cloudy & Cloudy & Cloudy & Cloudy & Fine \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Specific meteorological parameters of analysis time period. Figure 9: Energy use of the main device in statistical time period. In order to further reflect the energy conservation of the method proposed in this paper, I calculated the monthly system energy use of the chiller plant system from starting up in May to shutting down at the end of September.