Multi-timescale optimal control strategy for energy storage using LSTM prediction–correction in the active distribution network - Wu, Junjian; Chen, Yiwei; Zhou, Jinhui; Jiang, Chengtao; Liu, Wei (Frontiers in Energy Research, 2023)
The daily output of wind power is opposite to the load demand in most situations, which will lead to an increase in peak-to-valley difference and fluctuation. To solve the problem, this study proposes a long short-term memory prediction-correction based multi-time-scale optimal control strategy for energy storage. Firstly, the proposed strategy performs a LSTM prediction on the power of wind power and load. Then, it establishes a predictive planning model to improve the effect of peak shaving and the operating income of energy storage. Finally, it uses an online correction of power lines for peak shaving to further optimize the energy storage power according to the error between the residual energy of energy storage and the planned residual energy in the actual peak shaving process. By comparing with traditional strategies, the proposed strategy is significantly better than the constant power strategy and the power difference strategy in peak shaving effect and operating income.