The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Dynamic Energy Management System for a Smart Microgrid
Abstract
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.
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Cites
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Cited by
Review on the research and practice of deep learning and reinforcement learning in smart grids
China Electric Power Research Institute, Zhang, D., Han, X., Taiyuan University of Technology, Deng, C., & China Electric Power Research Institute (2018) · CSEE Journal of Power and Energy Systems · doi:10.17775/cseejpes.2018.00520 · Cited by 561
Glavic, M., Fonteneau, R., & Ernst, D. (2017) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2017.08.1217 · Cited by 241
Metadata
- Authors
- Venayagamoorthy, G. K., Sharma, R. K., Gautam, P. K., & Ahmadi, A.
- Venue
- IEEE Transactions on Neural Networks and Learning Systems
- Published
- 2016-08
- Volume
- 27
- Issue
- 8
- Pages
- 1643-1656
- ISSN
- 2162-237X (ISSN-L)
- 2162-2388
- OpenAlex
- W2343217767
- Cited by
- 328
- References
- 24
- FWCI
- 24.69
- Type
- journal article
- Language
- en
- Open access
- closed
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