Deep-Q-network-based energy management of multi-resources in limited power micro-grid
To overcome the shortage of power supply to the rural area, a hybrid connected mode micro-grid (MG) is proposed. It is suggested to include a diesel generator (DG) and renewable energy resources (RER) with a limited power of utility grid. To ensure the availability of fuel supply, the take-or-pay method is employed. In this paper, a smart energy management system (EMS) has been proposed to control the operation of hybrid MG, in addition to ensuring complete fuel disbursement under the scheduling of fuel supply. To facilitate the construction of EMS, a free model-based reinforcement learning (RL) algorithm has been employed for this purpose, in which the design of this algorithm depends on deep Q-network (DQN). The simulation of the algorithm has been achieved by MATLAB to validate the proposed system; the results showed a good performance of the technique compared with the performance achieved by improved particle swarm optimisation (IPSO) algorithm.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/17424267
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Supplemental Notes:
- Copyright © 2023 Inderscience Enterprises Ltd.
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Authors:
- Aklo, Nabil Jalil
- Rashid, Mofeed Turky
- Publication Date: 2023
Language
- English
Media Info
- Media Type: Web
- Pagination: pp 25-53
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Serial:
- International Journal of Powertrains
- Volume: 12
- Issue Number: 1
- Publisher: Inderscience Enterprises Limited
- ISSN: 1742-4267
- EISSN: 1742-4275
- Serial URL: https://www.inderscience.com/jhome.php?jcode=ijpt
Subject/Index Terms
- TRT Terms: Algorithms; Electric power supply; Grids (Transmission lines); Machine learning; Neural networks; Plug-in hybrid vehicles
- Subject Areas: Data and Information Technology; Energy; Highways; Vehicles and Equipment;
Filing Info
- Accession Number: 01881130
- Record Type: Publication
- Files: TRIS
- Created Date: Apr 25 2023 9:49AM