The DOI matches its Crossref record; 50 of the 50 references it cites were found in a registry or an index.
Incentive-based demand response for smart grid with reinforcement learning and deep neural network
Abstract
Abstract Balancing electricity generation and consumption is essential for smoothing the power grids. Any mismatch between energy supply and demand would increase costs to both the service provider and customers and may even cripple the entire grid. This paper proposes a novel real-time incentive-based demand response algorithm for smart grid systems with reinforcement learning and deep neural network, aiming to help the service provider to purchase energy resources from its subscribed customers to balance energy fluctuations and enhance grid reliability. In particular, to overcome the future uncertainties, deep neural network is used to predict the unknown prices and energy demands. After that, reinforcement learning is adopted to obtain the optimal incentive rates for different customers considering the profits of both service provider and customers. Simulation results show that this proposed incentive-based demand response algorithm induces demand side participation, promotes service provider and customers profitabilities, and improves system reliability by balancing energy resources, which can be regarded as a win-win strategy for both service provider and customers.
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Cites
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Applications of artificial intelligence and machine learning in smart cities
Ullah, Z., Al-Turjman, F., Mostarda, L., & Gagliardi, R. (2020) · Computer Communications · doi:10.1016/j.comcom.2020.02.069 · Cited by 756
Antonopoulos, I., Robu, V., Couraud, B., Kirli, D., Norbu, S., Kiprakis, A., Flynn, D., Elizondo-Gonzalez, S., & Wattam, S. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.109899 · Cited by 656
Artificial Intelligence Enabled Demand Response: Prospects and Challenges in Smart Grid Environment
Khan, M. A., Saleh, A. M., Waseem, M., & Sajjad, I. A. (2023) · IEEE Access · doi:10.1109/access.2022.3231444 · Cited by 179
Coordinated energy management for a cluster of buildings through deep reinforcement learning
Pinto, G., Piscitelli, M. S., Vázquez-Canteli, J. R., Nagy, Z., & Capozzoli, A. (2021) · Energy · doi:10.1016/j.energy.2021.120725 · Cited by 108
Xie, J., Ajagekar, A., & You, F. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.121162 · Cited by 109
Metadata
- Authors
- Lu, R., & Hong, S. H.
- Venue
- Applied Energy
- Publisher
- Elsevier BV
- Published
- 2019-02
- Volume
- 236
- Pages
- 937-949
- ISSN
- 0306-2619 (ISSN-L)
- OpenAlex
- W2904635416
- Cited by
- 394
- References
- 50
- FWCI
- 12.32
- 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).
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