The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Intelligent Multi-Microgrid Energy Management Based on Deep Neural Network and Model-Free Reinforcement Learning
Abstract
In this paper, an intelligent multi-microgrid (MMG) energy management method is proposed based on deep neural network (DNN) and model-free reinforcement learning (RL) techniques. In the studied problem, multiple microgrids are connected to a main distribution system and they purchase power from the distribution system to maintain local consumption. From the perspective of the distribution system operator (DSO), the target is to decrease the demand-side peak-to-average ratio (PAR), and to maximize the profit from selling energy. To protect user privacy, DSO learns the MMG response by implementing a DNN without direct access to user's information. Further, the DSO selects its retail pricing strategy via a Monte Carlo method from RL, which optimizes the decision based on prediction. The simulation results from the proposed data-driven deep learning method, as well as comparisons with conventional model-based methods, substantiate the effectiveness of the proposed approach in solving power system problems with partial or uncertain information.
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Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225
On-Line Building Energy Optimization Using Deep Reinforcement Learning
Mocanu, E., Mocanu, D. C., Nguyen, P. H., Liotta, A., Webber, M. E., Gibescu, M., & Slootweg, J. G. (2019) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2018.2834219 · Cited by 649
Model-Free Real-Time EV Charging Scheduling Based on Deep Reinforcement Learning
Wan, Z., Li, H., He, H., & Prokhorov, D. (2019) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2018.2879572 · Cited by 548 · cited 1×
“The authors in [23] proposed the application of deep RL to optimize the real-time electric vehicle charging schedule with the consideration of future electricity price.”
BackgroundPlug-In Electric Vehicle to Home (V2H) Operation Under a Grid Outage
Shin, H., & Baldick, R. (2017) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2016.2603502 · Cited by 175
Cited by
Reinforcement Learning and Its Applications in Modern Power and Energy Systems: A Review
Cao, D., Hu, W., Zhao, J., Zhang, G., Zhang, B., Liu, Z., Chen, Z., & Blaabjerg, F. (2020) · Journal of Modern Power Systems and Clean Energy · doi:10.35833/mpce.2020.000552 · Cited by 460
A Multi-Agent Reinforcement Learning-Based Data-Driven Method for Home Energy Management
Xu, X., Jia, Y., Xu, Y., Xu, Z., Chai, S., & Lai, C. S. (2020) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2020.2971427 · Cited by 420
Aguilar, J., Garces-Jimenez, A., R-Moreno, M. D., & García, R. (2021) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2021.111530 · Cited by 278
Grid integration impacts and control strategies for renewable based microgrid
Fazal, S., Enamul Haque, M., Taufiqul Arif, M., Gargoom, A., & Oo, A. M. T. (2023) · Sustainable Energy Technologies and Assessments · doi:10.1016/j.seta.2023.103069 · Cited by 74
A Review on Optimal Energy Management of Multimicrogrid System Considering Uncertainties
Ma, G., Li, J., & Zhang, X.-P. (2022) · IEEE Access · doi:10.1109/access.2022.3192638 · Cited by 65
Metadata
- Authors
- Du, Y., & Li, F.
- Venue
- IEEE Transactions on Smart Grid
- Published
- 2020-03
- Volume
- 11
- Issue
- 2
- Pages
- 1066-1076
- ISSN
- 1949-3053 (ISSN-L)
- 1949-3061
- OpenAlex
- W2963723202
- Cited by
- 393
- References
- 6
- FWCI
- 12.78
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
