Dynamic Energy Management of a Microgrid Using Approximate Dynamic Programming and Deep Recurrent Neural Network Learning
Date of Original Version
This paper focuses on economical operation of a microgrid (MG) in real-time. A novel dynamic energy management system is developed to incorporate efficient management of energy storage system into MG real-time dispatch while considering power flow constraints and uncertainties in load, renewable generation and real-time electricity price. The developed dynamic energy management mechanism does not require long-term forecast and optimization or distribution knowledge of the uncertainty, but can still optimize the long-term operational costs of MGs. First, the real-time scheduling problem is modeled as a finite-horizon Markov decision process over a day. Then, approximate dynamic programming and deep recurrent neural network learning are employed to derive a near optimal real-time scheduling policy. Last, using real power grid data from California independent system operator, a detailed simulation study is carried out to validate the effectiveness of the proposed method.
IEEE Transactions on Smart Grid
Zeng, Peng, Hepeng Li, Haibo He, and Shuhui Li. "Dynamic Energy Management of a Microgrid Using Approximate Dynamic Programming and Deep Recurrent Neural Network Learning." IEEE Transactions on Smart Grid 10, 4 (2019): 4435-4445. doi:10.1109/TSG.2018.2859821.