Affiliation:
1. Melentiev Energy Systems Institute SB RAS
2. Central South University
Abstract
The article analyzes the development of methods for modeling and control of multi-energy microgrids through cyber-physical systems. We used the methods of literature review and meta-analysis based on publications from international databases Scopus and Web of Science, Russian database eLibrary, digital platform IEEEXplore et al. According to the analysis, Smart Grid implementation drives the development of cyber-physical systems. As summarized in this study, control interfaces, data transmission channels, and remote debugging ports are vulnerable parts of IoT devices that can possibly be attacked by intruders. A review of the recent publications in this field finds multi-agent technologies to be an effective approach not only for the operational control of multi-energy microgrid modes, but also for the construction of its reliable information network at the level of medium and low voltage systems. In the field of distributed energy systems, literature review of information technology indicates that the more capabilities are added to receive and process various kinds of information (transaction data, mode parameters, status of controllers, etc.) from external sources, the more vulnerable a multi-energy microgrid is to any cyber threats. Modern mathematical methods such as artificial intelligence, dynamic optimization, and multi-agent approaches should be used to effectively solve the problem of load distribution between different energy sources with cost minimization.
Publisher
Irkutsk National Research Technical University
Reference81 articles.
1. Bamberger Y., Baptista J., Belmans R., Buchholz B.M., Chebbo M., Del Valle J.L., et al. Vision and strategy for Europe’s electricity networks of the future. In: European technology platform Smart Grids. 2006, р. 4-35.
2. Buhholz B.M., Styczynski Z.A. Smart Grids. Fundamentals and technologies in electric power systems of the future, 2017, 461 р. (Russ. ed.: Smart grids – osnovy i tekhnologii energosistem budushchego. Moscow: Moscow Power Engineering Institute: 2017, 461 р.)
3. Zuo Hongyan, Zhang Bin, Huang Zhonghua, Wei Kexiang, Zhu Hong, Tan Jiqiu. Effect analysis on SOC values of the power lithium manganate battery during discharging process and its intelligent estimation. Energy. 2022;238(B):121854. https://doi.org/10.1016/j.energy.2021.121854.
4. Zhao Xiaohuan, E Jiaqiang, Wu Gang, Deng Yuanwang, Han Dandan, Zhang Bin, et al. A review of studies using graphenes in energy conversion, energy storage and heat transfer development. Energy Conversion and Management. 2019;184:581-599. https://doi.org/10.1016/j.enconman.2019.01.092.
5. Mirzaei M.A., Sadeghi-Yazdankhah A., Mohammadi-Ivatloo B., Marzband M., Shafie-khah M., Catalão J.P.S. Integration of emerging resources in IGDT-based robust scheduling of combined power and natural gas systems considering flexible ramping products. Energy. 2019;189:116195. https://doi.org/10.1016/j.energy.2019.116195.