Cooperative trading and optimization scheduling of multi-microgrid considering controllable air conditioning load virtual energy storage

• The comfortable temperature interval prediction method is proposed. • The electric-thermal cooperated scheduling strategy is proposed. • The air conditioning load virtual energy storage model is constructed. • The multi-microgrids energy optimization scheduling strategy is proposed. • The adaptive accelerated algorithm is proposed. Controllable air conditioning load, as a flexible load scheduling resource, has become a crucial component of demand response mechanisms in distribution networks. However, the dynamic variation of the user’s comfort interval and the time-delay effect of air conditioning loads reduce the accuracy and timeliness of the scheduling process. Therefore, this paper proposes a cooperative trading and optimization scheduling approach for multi-microgrids, considering controllable air conditioning load virtual energy storage. Firstly, based on the dynamic variation of the optimal comfort temperature for the human body, the Quantile Regression Neural Network algorithm is employed to predict the dynamic comfort interval of the human body. Secondly, an electric-thermal coordinated scheduling strategy accounting for the synchronous response characteristics of thermal inertia is proposed, and the virtual energy storage model for variable-frequency air conditioning load is established. Subsequently, the multi-microgrids energy optimization scheduling system is constructed. The system consists of two subproblems: Subproblem 1 aims to minimize the total operating cost of the multi-microgrids, while Subproblem 2 seeks to maximize the cooperative benefits of the multi-microgrids. The Adaptive Accelerated Alternating Direction Method of Multipliers algorithm is employed to solve the problem. Finally, a case study simulation is conducted using multi-microgrids in Liaoning Province. The results demonstrate that, compared to traditional methods, the proposed multi-microgrids system reduces operating costs by 45.1 % and improves solution speed by 33.8 %.