# Aggregation Dispatch and Control Strategies for Multi-Type Loads in Industrial Parks
## 1 Introduction  
As the scale of renewable energy construction continues to increase, the random fluctuations in its power generation have a huge impact on the power system. Power systems with a high proportion of renewable energy require large-scale flexible dispatchable and controllable resources to balance and regulate the system. Industrial parks, as the concentration areas of China's industrial enterprises, have a diverse range of high-power loads that provide diverse power regulation capability. Fully tapping into the power regulation capability of the multi-types loads in the industrial park and making them into low-cost flexible controllable resources through lightweight modification is an effective approach to build the power regulation capability of the new power system on a large scale. However, the types of loads in industrial parks are varied, and some equipment cannot be regulated due to process limitations. The regulation capability of controllable equipment also varies greatly, and their control delay characteristics, response speed, and sustainable response time are different. The control response process is also restricted by the production plans of park enterprises. Therefore, it is necessary to comprehensively consider the production plans of each enterprise and the control characteristics of each load to achieve precise control of the multi-type loads in the industrial park. Otherwise, it will cause significant deviations in the control execution and have adverse effects on the operation of the power system. The research on load dispatch and control strategies, both domestically and internationally, mostly focuses on the classification modeling of controllable loads and the analysis of control benefits for various types of loads. Regarding the classification modeling of controllable loads, [PERSON] et al. [1] study classify controllable loads and establish various steady-state control models for controllable loads. [PERSON] et al. [2] study the classification method of temperature-controlled loads and establishes steady-state control models for air-conditioning loads and electric water heaters, proposing an optimized scheduling strategy for temperature-controlled load clusters to participate in demand response. [PERSON] et al. [3] study the characteristics of residential load participation in demand response and establishes an aggregated dispatch model for residential loads. [PERSON] et al. [4] studies the characteristics of coordinating multiple controllable loads to participate in the demand response. [PERSON] et al. [5] propose a management method for using multiple demand-side resources to respond to renewable energy power fluctuations, to improve the speed of the demand response. The study by [PERSON] et al. [6] separately conducts the model for curtailable loads and deferrable loads, but it does not provide descriptions of response characteristics such as response delay and response speed. [PERSON] et al. [7] and [PERSON] et al. [8] establish a greenhouse load control model, including an artificial lighting model, a heating load model, and a load shifting model. A novel optimal operation strategy for rural microgrid and clean energy systems is proposed considering greenhouse load control. Regarding the dispatch and control strategies of various controllable loads, [PERSON] et al. [9] consider linear and nonlinear response models and proposes a dynamic economic dispatch strategy combined with demand response. [PERSON] et al. [10] propose an interruptible load dispatch model considering user subsidy rates, which effectively reduces peak loads and operating costs. [PERSON] et al. [11] establish a revenue model for controllable load aggregation merchants, comprehensively considering the game relationship between users and aggregation merchants, and providing the optimal compensation pricing strategy for aggregation merchants. [PERSON] et al.