Two-Level Scheduling Model for Air-Conditioning Load Aggregators Considering User Conditioning Behavior

As an important resource for flexible load management, air conditioning loads play a crucial role in grid regulation and control through load aggregators, especially during the summer season. However, the current grouping control method falls short in fully harnessing the adjustable potential of air conditioners, which may compromise user comfort. To address this issue, this paper proposes a two-layer optimization model for day-ahead scheduling, focusing on temperature control of user air-conditioning loads from the perspective of load aggregators. In the upper-layer optimization objective, the aim is to maximize the interests of the load aggregator by implementing a well-designed temperature control strategy that allows for flexible load adjustment. In the lower-layer optimization objective, the focus shifts to considering the comfort levels of individual users. The optimization objective is to minimize the overall discomfort level of users, taking into account their varying preferences. The particle swarm integer programming algorithm is employed to solve the set temperature regulation of each individual air-conditioning unit. Simulation results demonstrate that the proposed day-ahead scheduling strategy effectively exploits the adjustable potential of air conditioning loads, while also providing load shaving capabilities to the grid. This ensures that the load aggregator achieves maximum benefits, while simultaneously improving the comfort level of the users.