# Aggregation Dispatch and Control Strategies for Multi-Type Loads in Industrial Parks
## 1 Introduction
[12] summarize the business of foreign aggregation merchants, analyzing the operating mechanisms, dispatch, and control strategies of multiple types of controllable resources such as controllable loads, energy storage devices, and distributed power sources. [PERSON] et al. [13] propose a multi-time scale optimization dispatch model considering wind power uncertainty and demand response, using demand response to reduce control errors caused by wind power uncertainty. [PERSON] et al. [14] propose a decision-making model for electricity retailers considering short-term demand response, achieving the maximization of profits from participating in the electricity market. [PERSON] et al. [15] propose a flexible load management mode based on elastic bands to achieve the aggregation response of a large number of flexible loads. [PERSON] et al. [16] propose an operation strategy for virtual power plants in electricity markets, improving the flexibility of demand response resources and the revenue of virtual power plants. According to [PERSON] et al. [17], the demand response is carried out based on real-time electricity prices, adjusting the total load of users to maximize their profits and alleviate the imbalance between electricity supply and demand. [PERSON] et al. [18] propose a two-layer model with a single leader and multiple followers to optimize the real-time transaction strategy of the demand response aggregator. [PERSON] et al. [19] proposes a load decomposition algorithm to identify different user behaviors and derive the optimal dispatch strategy for the demand response aggregator. The existing literature has studied the uncertainty of the errors generated by the controllable load aggregation response. [PERSON] et al. [20] consider the uncertainty of user load response and construct an optimization model for demand response aggregator contract decisions. [PERSON] et al. [21], based on the characteristics of information physical fusion system of demand response, only consider incentive-based DR, and model the incentive and reliability constraints of the distribution network on the aggregator, the reliability of the user response, and the reliability of the aggregator to study the impact of uncertainty in the user response on the response capability, reliability, and operational economy of the aggregator. [PERSON] et al. [22] propose a demand-response method that considers multiple flexible loads, classifies flexible loads using a physical process analysis and reasoning model, and models the load using an improved WGAN gradient penalty model, effectively reducing modeling errors and improving the responsiveness of the park's load. [PERSON] et al. [23] propose a demand response perception and decision-making method based on dynamic consumer classification, effectively controlling the deviation between response and bidding amounts and improving the aggregator's transaction revenue. [PERSON] et al. [24] uses energy storage devices to handle power deviations due to uncertainty in renewable energy output and user response, effectively reducing the cost of multi-energy aggregators and improving risk management capability. [PERSON] et al. [25] propose a two-layer scheduling model that considers the uncertainty of demand response, design the operating modes of two energy storage systems for energy arbitrage and deviation mitigation, and improve the net profit of the load service entity. [PERSON] et al. [26] propose a distributed robust model predictive control for islanded multi-microgrids, which will reduce the adverse effects of uncertain renewable energy output and improve the robustness of the micro-grid system. The above studies did not consider the control characteristics of multi-type loads in industrial parks, such as control response delay, response speed, and sustainable response time.