Dynamic Energy Management System for a Smart Microgrid

This paper presents the development of an intelligent dynamic energy management system (I-DEMS) for a smart microgrid. An evolutionary adaptive dynamic programming and reinforcement learning framework is introduced for evolving the I-DEMS online. The I-DEMS is an optimal or near-optimal DEMS capable...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems 2016-08, Vol.27 (8), p.1643-1656
Main Authors: Venayagamoorthy, Ganesh Kumar, Sharma, Ratnesh K., Gautam, Prajwal K., Ahmadi, Afshin
Format: Article
Language:English
Subjects:
Adaptive dynamic programming
Batteries
Chlorine
Control systems
Distributed generation
dynamic energy management system (DEMS)
Dynamic programming
Dynamical systems
Dynamics
Electric utilities
Energy management
Energy management systems
Energy storage
evolutionary computing
Learning
microgrid
Microgrids
neural networks
Real-time systems
reinforcement learning
Renewable energy
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Summary:This paper presents the development of an intelligent dynamic energy management system (I-DEMS) for a smart microgrid. An evolutionary adaptive dynamic programming and reinforcement learning framework is introduced for evolving the I-DEMS online. The I-DEMS is an optimal or near-optimal DEMS capable of performing grid-connected and islanded microgrid operations. The primary sources of energy are sustainable, green, and environmentally friendly renewable energy systems (RESs), e.g., wind and solar; however, these forms of energy are uncertain and nondispatchable. Backup battery energy storage and thermal generation were used to overcome these challenges. Using the I-DEMS to schedule dispatches allowed the RESs and energy storage devices to be utilized to their maximum in order to supply the critical load at all times. Based on the microgrid's system states, the I-DEMS generates energy dispatch control signals, while a forward-looking network evaluates the dispatched control signals over time. Typical results are presented for varying generation and load profiles, and the performance of I-DEMS is compared with that of a decision tree approach-based DEMS (D-DEMS). The robust performance of the I-DEMS was illustrated by examining microgrid operations under different battery energy storage conditions.
ISSN:2162-237X
2162-2388
DOI:10.1109/TNNLS.2016.2514358