# Aggregation Dispatch and Control Strategies for Multi-Type Loads in Industrial Parks
## 4 Case Study
### Simulation Analysis
#### 4.2.4 Analysis of Scenario Four  
Based on Scenario Two, real-time predictive control of controllable loads is performed considering their response characteristics in accordance with the intra-day adjustable load dispatch instructions. The adjustable load dispatch instructions and the actual executed power curve for the industrial park are shown in Figure 12.  
By using a real-time predictive control model, the actual power curve of the controllable load can only deviate from the intra-day dispatch plan in a few time periods,  
Figure 11: The day-ahead pre-dispatch plan curve(base), the optimized dispatch curve(expected), and the actual operating curve(actual) for the interconnection line of Scenario Three. Figure 12: Dispatch plan and the actual power curve of the controllable load in industrial park with considering the control-response characteristics. while in most time periods, the actual power curve can accurately follow the intra-day dispatch plan. Taking into account the ultra-short-term forecast error of photovoltaics and rigid loads, as well as the response execution deviation of controllable loads, the expected operation curve and the actual curve of the industrial park's interconnection line are shown in Figure 13.  
By using a real-time predictive control model, the actual power curve of the controllable load can better follow the intra-day dispatch plan, which minimizes the error between the expected power and actual power of the interconnection line, and results in lower assessment costs. After the real-time predictive control of controllable loads, the expected operating cost of the industrial park is consistent with Scenario Two. The actual cost of purchasing electricity during the day is S41 = CYN 390,995.81, and the assessment cost is S42 = CYN 62,826.08. The total cost is S4 = CYN 453,821.90. In summary, a comparative analysis of operating costs for an industrial park in Guangdong Province under three scenarios is shown in Table 3.  
From the simulation comparison of the three scenarios, it can be seen that:  
1. Based on the ultra-short-term power forecast results of photovoltaics and rigid loads, rolling optimization can be performed during the day to make the interconnection line power of the industrial park more accurately follow the optimized dispatch plan, reducing the execution deviation assessment and lowering the overall operating cost of the park. 2. The real-time control model using neural networks cannot effectively reduce the execution deviation of controllable load units, industrial parks still need to bear significant assessment costs, and they cannot significantly reduce the overall operation costs of industrial parks. Figure 13: The day-ahead pre-dispatch plan curve(base), the optimized dispatch curve(expected), and the actual operating curve(actual) for the interconnection line of Scenario Four. \begin{table}
\begin{tabular}{l l l l l} \hline \hline  & **Scenario One** & **Scenario Two** & **Scenario Three** & **Scenario Four** \\  & **(CYN)** & **(CYN)** & **(CYN)** & **(CYN)** \\ \hline Expected cost of purchasing electricity & 398,618.06 & 399,991.38 & 399,991.38 & 399,991.38 \\ Actual cost of purchasing electricity & 403,184.11 & 402,262.37 & 386,169.39 & 3  
3. Considering the response characteristics (delay, response speed, etc.) of controllable loads and using minimum variance as the control objective, real-time predictive control of controllable loads can reduce the execution deviation of the controllable load unit and make the interconnection line power of the industrial park more accurately follow the rolling optimization dispatch plan during the day, further reducing the overall operating cost of the industrial park. Controllable loads in industrial parks exhibit complex operational characteristics due to the influence of production processes and operating conditions. The model of controllable load proposed in this paper is a non-parametric model, in which the response characteristic curve is obtained through fitting with measured data.