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State-of-the-Art Artificial Intelligence Techniques for Distributed Smart Grids: A Review

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Ali, Syed Saqib, Choi, Bong Jun·2020·Electronics
DOI 10.3390/electronics9061030
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Abstract

The power system worldwide is going through a revolutionary transformation due to the integration with various distributed components, including advanced metering infrastructure, communication infrastructure, distributed energy resources, and electric vehicles, to improve the reliability, energy efficiency, management, and security of the future power system. These components are becoming more tightly integrated with IoT. They are expected to generate a vast amount of data to support various applications in the smart grid, such as distributed energy management, generation forecasting, grid health monitoring, fault detection, home energy management, etc. With these new components and information, artificial intelligence techniques can be applied to automate and further improve the performance of the smart grid. In this paper, we provide a comprehensive review of the state-of-the-art artificial intelligence techniques to support various applications in a distributed smart grid. In particular, we discuss how artificial techniques are applied to support the integration of renewable energy resources, the integration of energy storage systems, demand response, management of the grid and home energy, and security. As the smart grid involves various actors, such as energy produces, markets, and consumers, we also discuss how artificial intelligence and market liberalization can potentially help to increase the overall social welfare of the grid. Finally, we provide further research challenges for large-scale integration and orchestration of automated distributed devices to realize a truly smart grid.

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Cited by

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Metadata

Authors
Ali, S. S., & Choi, B. J.
Venue
Electronics
Publisher
MDPI AG
Published
2020-06-22
Volume
9
Issue
6
Pages
1030
ISSN
2079-9292 (ISSN-L)
DOI
10.3390/electronics9061030
OpenAlex
W3036785826
Cited by
339
References
3
FWCI
11.56
Type
journal article
Language
en
Open access
gold
Licence
creativecommons.org/licenses/by/4.0/ (vor)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: State-of-the-Art Artificial Intelligence Techniques for Distributed Smart Grids: A Review. https://app.cochat.ai/papers/10.3390/electronics9061030
The paper
Ali, S. S., & Choi, B. J. (2020). State-of-the-Art Artificial Intelligence Techniques for Distributed Smart Grids: A Review. Electronics. https://doi.org/10.3390/electronics9061030

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

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.