Dynamic Energy Management of a Microgrid Using Approximate Dynamic Programming and Deep Recurrent Neural Network Learning
Document Type
Article
Date of Original Version
7-1-2019
Abstract
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.
Publication Title, e.g., Journal
IEEE Transactions on Smart Grid
Volume
10
Issue
4
Citation/Publisher Attribution
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.
