Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 339

The DOI matches its Crossref record; 83 of the 83 references it cites were found in a registry or an index.

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

Open access
Ali, Syed Saqib, Choi, Bong Jun·2020·Electronics

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.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3390/electronics9061030

Cites

83 of 83 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Research works printed but not linked: 50

  1. International Energy Agency (2020, May 12). CO2 Emissions from Fuel Consumption Report—2019 Edition. Available online: http://wds.iea.org/wds/pdf/WorldCo2_Documentation.pdf.
  2. Neves, 2018, Comparison of Different Demand Response Optimization Goals on an Isolated Microgrid, Sustain. Energy Technol. Assess., 30, 209
  3. Specht, M., Rohjans, S., Trefke, J., Uslar, M., and Vázquez, J.M.G. (2013). International Smart Grid Roadmaps and Their Assessment. EAI Endorsed Trans. Energy Web, 13.
  4. Basso, T., and DeBlasio, R. (2012). IEEE Smart Grid Series of Standards IEEE 2030 (Interoperability) and IEEE 1547 (Interconnection) Status. · doi:10.1109/isgt.2012.6175748
  5. Saleem, 2019, Internet of Things-Aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions, IEEE Access, 7, 62962, 10.1109/ACCESS.2019.2913984 · DOI
  6. Jacobson, 2019, Distributed Intelligence: A Critical Piece of the Microgrid Puzzle, Electr. J., 32, 10, 10.1016/j.tej.2019.05.001 · doi:10.1016/j.tej.2019.05.001
  7. Qiao, J., Zhu, B., Wang, X., and Luo, K. (2019, January 12–15). Application Research of Artificial Intelligence Technology in Error Diagnosis of Electric Energy Meter. Proceedings of the IEEE International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), Chengdu, China. · doi:10.1109/icccbda.2019.8725622
  8. Vaccaro, 2010, An Integrated Framework for Smart Microgrids Modeling, Monitoring, Control, Communication, and Verification, Proc. IEEE, 99, 119, 10.1109/JPROC.2010.2081651 · doi:10.1109/jproc.2010.2081651
  9. Collier, 2010, Ten Steps to A Smarter Grid, IEEE Ind. Appl. Mag., 16, 62, 10.1109/MIAS.2009.935500 · doi:10.1109/mias.2009.935500
  10. Lasseter, R.H. (2002, January 27–31). MicroGrids. Proceedings of the IEEE Power Engineering Society Winter Meeting, Conference Proceedings (Cat. No. 02CH37309), New York, NY, USA.
  11. Nichols, D.K., Stevens, J., Lasseter, R., Eto, J., and Vollkommer, H. (2006, January 18–22). Validation of the CERTS Microgrid Concept—The CEC/CERTS Microgrid Testbed. Proceedings of the IEEE Power Engineering Society General Meeting, Montreal, QC, Canada. · doi:10.1109/pes.2006.1709248
  12. McMillin, B. (2015, January 1–5). Distributed Intelligence in the Electric Smart Grid. Proceedings of the IEEE Annual Computer Software and Applications Conference, Taichung, Taiwan. · doi:10.1109/compsac.2015.338
  13. Ma, 2013, An Intelligent Control Scheme to Support Voltage of Smart Power Systems, IEEE Trans. Ind. Inform., 9, 1405, 10.1109/TII.2013.2243741 · doi:10.1109/tii.2013.2243741
  14. Duan, Q. (2016, January 17–21). A Price based Demand Response Scheduling Model in Day-ahead Electricity Market. Proceedings of the IEEE Power and Energy Society General Meeting (PESGM), Boston, MA, USA.
  15. 2019, Review of SCADA System for Distribution Power System Automation, Eng. Res. J., 42, 93
  16. Meng, F., Akella, R., Crow, M.L., and McMillin, B. (2010, January 26–28). Distributed Grid Intelligence for Future Microgrid with Renewable Sources and Storage. Proceedings of the North American Power Symposium, Arlington, TX, USA. · doi:10.1109/naps.2010.5618963
  17. Eck, B., Fusco, F., Gormally, R., Purcell, M., and Tirupathi, S. (2019, January 25–28). AI Modelling and Time-series Forecasting Systems for Trading Energy Flexibility in Distribution Grids. Proceedings of the ACM International Conference on Future Energy Systems, Phoenix, AZ, USA. · doi:10.1145/3307772.3330158
  18. Elkazaz, 2016, Operation Optimization of Distributed Generation using Artificial Intelligence Techniques, Ain Shams Eng. J., 7, 855, 10.1016/j.asej.2016.01.008 · doi:10.1016/j.asej.2016.01.008
  19. Navigant Research (2020, May 12). Press Rel. Available online: https://www.navigantresearch.com/news-and-views/global-microgrid-capacity-is-expected-to-grow-from-14-gw-in-2015-to-76-gw-in-2024.
  20. Mohn, T. (2012, January 22–26). Campus microgrids: Opportunities and Challenges. Proceedings of the IEEE Power and Energy Society General Meeting, San Diego, CA, USA. · doi:10.1109/pesgm.2012.6344610
  21. Kim, 2013, Advanced Power Distribution System Configuration for Smart Grid, IEEE Trans. Smart Grid, 4, 353, 10.1109/TSG.2012.2233771 · doi:10.1109/tsg.2012.2233771
  22. Satrape, J.V. (1987). Potential Impacts of Artificial Intelligence Expert Systems on Geothermal Well Drilling Costs, Meridian Corp. No. DOE/SF/16299-T4, MCD-033-87-TA. · doi:10.2172/5292949
  23. Kishor, 2007, A Review on Hydropower Plant Models and Control, Renew. Sustain. Energy Rev., 11, 776, 10.1016/j.rser.2005.06.003 · doi:10.1016/j.rser.2005.06.003
  24. Chen, C., Duan, S., Cai, T., Liu, B., and Yin, J. (2009, January 17–20). Energy Trading Model for Optimal Microgrid Scheduling based on Genetic Algorithm. Proceedings of the IEEE International Power Electronics and Motion Control Conference, Wuhan, China.
  25. Alsafasfeh, 2019, Robust Decentralized Power Flow Optimization for Dynamic PV System, IEEE Access, 7, 63789, 10.1109/ACCESS.2019.2916974 · doi:10.1109/access.2019.2916974
  26. Fathima, A.H., and Palanisamy, K. (2016). Energy Storage Systems for Energy Management of Renewables in Distributed Generation Systems. Energy Manag. Distrib. Gener. Syst., 157. · doi:10.5772/62766
  27. Rahbar, K., Moghadam, M.R.V., Panda, S.K., and Reindl, T. (2016, January 6–9). Shared Energy Storage Management for Renewable Energy Integration in Smart Grid. Proceedings of the IEEE Power & Energy Society Innovative Smart Grid, Minneapolis, MN, USA. · doi:10.1109/isgt.2016.7781230
  28. Sun, 2014, Real-Time Power Balancing in Electric Grids with Distributed Storage, IEEE J. Sel. Top. Signal Process., 8, 1167, 10.1109/JSTSP.2014.2333499 · doi:10.1109/jstsp.2014.2333499
  29. Park, S., Hussain, A., and Kim, H. (2019). Impact Analysis of Survivability-Oriented Demand Response on Islanded Operation of Networked Microgrids with High Penetration of Renewables. Energies, 12. · doi:10.3390/en12030452
  30. Kim, H., Kim, Y., Yang, K., and Thottan, M. (2011, January 17–20). Cloud based Demand Response for Smart Grid: Architecture and Distributed Algorithms. Proceedings of the IEEE International Conference on Smart Grid Communications (SmartGridComm), Brussels, Belgium.
  31. Ali, H., Hussain, A., Bui, V., Jeon, J., and Kim, H. (2019). Welfare Maximization-Based Distributed Demand Response for Islanded Multi-Microgrid Networks using Diffusion Strategy. Energies, 12. · doi:10.3390/en12193701
  32. Ahmad, 2019, Effective Bulk Energy Consumption Control and Management for Power Utilities using Artificial Intelligence Techniques Under Conventional and Renewable Energy Resources, Int. J. Electr. Power Energy Syst., 109, 242, 10.1016/j.ijepes.2019.02.023 · doi:10.1016/j.ijepes.2019.02.023
  33. Sangpetch, T., and Lo, K.L. (2002, January 17–19). Stochastic Modeling and AI Techniques for Power System Reinforcements in a Competitive Energy Market. Proceedings of the IET International Conference on Power System Management and Control, London, UK. · doi:10.1049/cp:20020065
  34. Marmiroli, M. (2014). Developing and Testing a Next Generation Energy Management System. IEEE Smart Grid Resour. Cent., Available online: https://resourcecenter.smartgrid.ieee.org/publications/newsletters/SGNL0177.html.
  35. Lodder, 2016, Artificial Intelligence Techniques and the Smart Grid: Towards Smart Meter Convenience while Maintaining Privacy, J. Internet Law, 19, 20
  36. Squartini, S., Boaro, M., Angelis, F., Fuselli, D., and Piazza, F. (2013, January 9–11). Optimization Algorithms for Home Energy Resource Scheduling in Presence of Data Uncertainty. Proceedings of the IEEE International Conference on Intelligent Control and Information Processing (ICICIP), Beijing, China. · doi:10.1109/icicip.2013.6568091
  37. Nowak, 2011, Towards a Convergent Digital Home Network Infrastructure, IEEE Trans. Consum. Electron., 57, 1695, 10.1109/TCE.2011.6131143 · doi:10.1109/tce.2011.6131143
  38. Moon, 2010, ANN based Thermal Control Models for Residential Buildings, Build. Environ., 45, 1612, 10.1016/j.buildenv.2010.01.009 · doi:10.1016/j.buildenv.2010.01.009
  39. Gharghan, 2015, Accurate Wireless Sensor Localization Technique based on Hybrid PSO-ANN Algorithm for Indoor and Outdoor Track Cycling, IEEE Sens. J., 16, 529, 10.1109/JSEN.2015.2483745 · doi:10.1109/jsen.2015.2483745
  40. Veldman, 2010, Smart Grids Put into Practice: Technological and Regulatory Aspects, Compet. Regul. Netw. Ind., 11, 287
  41. Ogasawara, J. (2005). Current Status and Evaluation of Electricity Market Liberalization in Japan, USA and Europe, IEEE. IEEE 391st Regular Research Session.
  42. Vaccaro, A., ElFouly, T., Canizares, C., and Bhattacharya, K. (2015, January 29). Local Learning-ARIMA Adaptive Hybrid Architecture for Hourly Electricity Price Forecasting. Proceedings of the IEEE Eindhoven PowerTech, Eindhoven, The Netherlands. · doi:10.1109/ptc.2015.7232253
  43. Dogaru, 2019, Cyber Attacks of a Power Grid Analysis using a Deep Neural Network Approach, J. Contr. Eng. Appl. Inform., 21, 42
  44. Lu, R. (2016). Privacy-Enhancing Aggregation Techniques for Smart Grid Communications, Springer. · doi:10.1007/978-3-319-32899-7
  45. Shi, E., Chan, H., Rieffel, E., Chow, R., and Song, D. (2011, January 7). Privacy-Preserving Aggregation of Time-Series Data. Proceedings of the Annual Network & Distributed System Security Symposium (NDSS), San Diego, CA, USA.
  46. Lai, C., and Loi, L. (2015, January 9–12). Application of Big Data in Smart Grid. Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, Kowloon, China. · doi:10.1109/smc.2015.126
  47. Boumkheld, N., Ghogho, M., and Koutbi, M. (2016, January 5–7). Intrusion Detection System for the Detection of Blackhole Attacks in a Smart Grid. Proceedings of the IEEE International Symposium on Computational and Business Intelligence (ISCBI), Olten, Switzerland. · doi:10.1109/iscbi.2016.7743267
  48. Khanna, 2017, AI based Approach to Identify Compromised Meters in Data Integrity Attacks on Smart Grid, IET Gener. Transm. Distrib., 12, 1052, 10.1049/iet-gtd.2017.0455 · doi:10.1049/iet-gtd.2017.0455
  49. Telang, A.S., Bedekar, P., and Wakde, S.D. (2020). Towards Smart Energy Technology by Integrating Smart Communication Techniques. Techno-Societal, Springer. · doi:10.1007/978-3-030-16848-3_62
  50. Wang, Y., Gu, D., Wen, M., Xu, J., and Li, H. (2010). Denial of Service Detection with Hybrid Fuzzy Set based Feed Forward Neural Network. International Symposium on Neural Networks, Springer. · doi:10.1007/978-3-642-13318-3_71

Cited by

See all 339

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)
OpenAlex
W3036785826
Cited by
339
References
83
FWCI
11.56
Type
journal article
Language
en
Open access
gold

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 ›

Are you an author, or do you see a problem with this page? Request a review

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.