# Aggregation Dispatch and Control Strategies for Multi-Type Loads in Industrial Parks
## 4 Case Study
### Scenario Description for the Case Study  
The peak-valley-flat electricity price of a certain industrial park in Guangdong Province is shown in Table 1.  
The photovoltaic pre-forecast power curve, intra-day ultra-short-term power forecast curve, and actual power generation curve of a certain operation day in the park are shown in Figure 4, and the pre-forecast power curve and actual rigid load power curve are shown in Figure 5. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Time Period** & **Electricity Price** \\  & & **(CYN/kWh)** \\ \hline Peak & 10:00–12:00, 14:00–19:00 & 1.1767 \\ Flat & 08:00–10:00, 12:00–14:00, 19:00–24:00 & 0.7036 \\ Valley & 00:00–08:00 & 0.2845 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Peak–valley–flat electricity price of a certain industrial park in Guangdong Province. Figure 4: Day-ahead and intra-day power forecasting curves and actual power generation curves of photovoltaic. The industrial park includes various types of controllable load resources with different characteristic parameters, which are shown in Table 2. \begin{table}
\begin{tabular}{c c} \hline Controllable load object AL0: & \\ \hline \(a_{i}\) & \{0, 0.444, 0.889, 1\} \\ \(b_{i}\) & \{0, 0.583, 1\} \\ \(c_{i}\) & \{1, 0.556, 0.111, 0\} \\ Range of operating power & [500, 900] \\ Single continuous operating time & 4 \\ Daily operating time constraint & [4, 10] \\ Daily start–stop frequency limit & 1 \\ Daily operating time interval constraint & [14 : 00, 24 : 00] \\ Controllable load object AL1: & \\ \(a_{i}\) & \{0, 0.5, 0.875, 1\} \\ \(b_{i}\) & \{0, 0.5625, 1\} \\ \(c_{i}\) & \{1, 0.5, 0.125, 0\} \\ Range of operating power & [600, 1200] \\ Single continuous operating time & 2 \\ Daily operating time constraint & [8, 18] \\ Daily start–stop frequency limit & 2 \\ Daily operating time interval constraint & [00 : 00, 18 : 00] \\ Controllable load object AL2: & \\ \(a_{i}\) & \{0, 0, 0.64, 1\} \\ \(b_{i}\) & \{0, 0. 0.62, 1\} \\ \(c_{i}\) & \{1, 1, 0.36, 0\} \\ Range of operating power & [200, 500] \\ Single continuous operating time & 10 \\ Daily operating time constraint & [10, 24] \\ Daily start–stop frequency limit & 2 \\ Daily operating time interval constraint & [00 : 00, 24 : 00] \\ Controllable load object AL3: & \\ \(a_{i}\) & \{0, 0, 0.562, 0.938, 1\} \\ \(b_{i}\) & \{0, 0, 0.545, 1\} \\ \(c_{i}\) & \{1, 1, 0.438, 0.062, 0\} \\ Range of operating power & [600, 1200] \\ Single continuous operating time & 2.5 \\ Daily operating time constraint & [2.5, 8] \\ Daily start–stop frequency limit & 1 \\ Daily operating time interval constraint & [12 : 00, 20 : 00] \\ \hline \end{tabular}
\end{table} 
Table 2: Characteristic parameters of controllable loads. Figure 5: Day-ahead and intra-day power forecasting curves and actual power curves of rigid loads. To analyze the impact of different control strategies on the operating cost of the industrial park, the following four scenarios are set:  
* Scenario 1: The day-ahead optimal dispatch strategy of the industrial park is used as the execution strategy, and no further optimization control is performed on the controllable loads within the day, ignoring the response characteristics of the controllable loads. * Scenario 2: Based on the day-ahead optimal dispatch plan of the industrial park, considering the forecast errors of photovoltaic power generation and rigid loads, the controllable loads are continuously rolling optimized and scheduled within the day, ignoring the response characteristics of the controllable loads. * Scenario 3: Based on the intra-day rolling optimization dispatch plan in an industrial park, using a neural network model [27] to perform real-time control of controllable loads, and the specific steps are as follows: Firstly, based on the control inputs and the actual power output of controllable loads, the sample data are constructed to train the neural network model in order to describe the control-response characteristics of controllable loads.