Optimal Scheduling of Isolated Microgrids Using Automated Reinforcement Learning-Based Multi-Period Forecasting

• Published in: IEEE transactions on sustainable energy Vol. 13; no. 1; pp. 159 - 169
• Main Authors: Li, Yang, Wang, Ruinong, Yang, Zhen
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Automated reinforcement learning; Automation; Deep learning; Distributed generation; Errors; Forecasting; Integer programming; Learning; Linear programming; Load modeling; Mathematical models; microgrid; Microgrids; Mixed integer; Operating costs; optimal scheduling; Predictions; Predictive models; Reinforcement; Reinforcement learning; Renewable energy; Renewable energy sources; Scheduling; sequence operation theory; single-step multi-period forecasting; Uncertainty; uncertainty handling

In order to reduce the negative impact of the uncertainty of load and renewable energies outputs on microgrid operation, an optimal scheduling model is proposed for isolated microgrids by using automated reinforcement learning-based multi-period forecasting of renewable power generations and loads....