# Techno-Economic Analysis of the Peak Shifting Strategy Based on Time-of-Use Tariff for Cold Stores
## 2 Refrigeration System
### Date Collection and Pre-Processing  
The locations of the data collection points in this refrigeration system are identified by the dotted lines shown in Figure 1b. A total of 3 pressure sensors are included, measuring \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Compressor** \\ \hline Refrigerant & R717 \\ Rated motor speed & 2950 r\(\cdot\)min\({}^{-1}\) \\ Nominal cooling capacity & 390 kW \\ Rated power & 164 kW \\ Voltage/Frequency & 380 \(\mathrm{\SIUnitSymbol{V}}\)/3 N\(\cdot\)50 Hz \\ Nominal condensing temperature & +36 \(\mathrm{\SIUnitSymbol{C}}\) \\ Nominal evaporating temperature & \(-\)28 \(\mathrm{\SIUnitSymbol{C}}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Main parameters of the compressors. Figure 1: The refrigeration system of the cold store. (**a**) Overview of the warehouse; (**b**) Schematic diagram of the refrigeration system. the pressure in the suction and discharge of the compressor unit and the centralized high-pressure ammonia storage. Neglecting the flow pressure loss, the suction and discharge pressures of the compressor unit can be regarded as the evaporating and condensing pressures. Several temperature sensors are installed at different sites within the warehouse, and the average temperature is used as the input for calculations. In practical cold stores, power sensors are rarely available due to their high price. The use of Electrical Specification Documents for electrical performance evaluation when there are no power sensors is recommended by [PERSON] et al. [31]. Hence, a constant power factor of 0.9 provided by the motor manufacturer is adopted for calculations. Environmental conditions such as outdoor temperature, humidity, light intensity, and wind speed are also collected to predict the cooling load. The main parameters of the data acquisition system used in this paper are shown in Table 2. To evaluate the accuracy of the collected data, the uncertainty can be calculated by the following equation series (Equations (1)-(3)), where \(Y\) is a function with vector \(x\) as a variable [32; 33]. Accordingly, the maximum uncertainty of the cooling capacity is 1.02%. \[Y=f(x_{1},x_{2},\cdots,x_{n}) \tag{1}\]  
\[u_{Y}=\sqrt{\sum_{i=1}^{n}\left(\frac{\partial Y}{\partial x_{i}}\right)^{2}u _{i}^{2}} \tag{2}\]  
\[\frac{u_{Y}}{Y}=\sqrt{\sum_{i=1}^{n}\left(\frac{\partial Y}{\partial x_{i}} \cdot\frac{u_{i}}{y}\right)^{2}} \tag{3}\]  
Subject to sensor accuracy as well as refrigeration system characteristics, the directly collected data tend to have a high degree of volatility and mutability. In addition, with the purpose of limiting the cost of sensors for the cold store, some key parameters (e.g., cooling load) are not available. Hence, data preprocessing is of great importance. In this paper, a filtering algorithm was used to perform noise reduction of the raw data, and a thermodynamic model was established (see Section 3.2) to realize cooling load data supplementation. Finally, a representative data set could be obtained. The filtering method used in this paper is the wavelet noise reduction algorithm, whose basic idea is to achieve noise removal based on the intensity distribution characteristics of the wavelet decomposition coefficients of the noise and signal in different frequency bands [34; 35]. Compared with other algorithms, the wavelet noise reduction algorithm has a higher model recognition rate and is particularly effective in denoising time-varying and abruptly changing signals. To ensure the reliability of the calculation, all raw data generated by the acquisition needs to be filtered. A comparison of the before and after filtering effect of the compressor's suction and discharge pressure is shown in Figure 2, where it can be seen that some steep rises or drops that do not conform to common sense can be removed well.