Abstract：The compressed air energy storage micro energy network has contributed new ideas to promote the consumption of clean energy and accelerate the transformation of energy structure in the current situation of large-scale development of renewable energy. However, the uncertainty factors such as new energy generation output, user load power and environmental temperature that exist in the compressed air energy storage micro energy network have also added difficulty to the optimization and scheduling of the system. To address the uncertainty issues of new energy output, environmental temperature and load demand in the compressed air energy storage micro energy system, this paper focuses on how to mitigate the adverse effects of uncertain variables on the system. A multi-time scale optimization scheduling method is proposed, including “upper long time scale day ahead optimization” and “lower short time scale day ahead rolling correction”. Simulation research shows that the multi-time scale optimal scheduling method of the compressed air energy storage micro energy system based on model predictive control can weaken the adverse effects caused by uncertainties, reduce economic costs and power fluctuations, and help the compressed air energy storage micro energy network operate smoothly and economically.
林旗斌. 基于模型预测控制的含压缩空气储能微能网多时间尺度优化调度方法[J]. 电气技术, 2023, 24(7): 11-19.
LIN Qibin. Multi-time scale optimal scheduling strategy for micro energy network with compressed air energy storage based on model predictive control. Electrical Engineering, 2023, 24(7): 11-19.
2023, Vol. 24 
