Augmented Cyber-physical Model for Real-time Smart-grid Co-simulation
DOI:
https://doi.org/10.15837/ijccc.2025.1.6914Keywords:
augmented cyber-physical system, real-time simulation, co-simulation smart grid system advanced controlAbstract
Due to crucial importance of the electricity in almost every aspect of our life, power systems and their components continue to receive considerable attention, and important efforts are invested for development or improvement in the direction of smooth transition to full smart grid solutions. Even if operation and control of power system are well-known, new control solutions require careful and detailed investigations due to challenges emerged from high complexity, security or even the current operating conditions in high penetration of renewable energy sources or consummers with significant loads. In this context, the paper introduces a new concept and solution of augmented cyberphysical model to allow testing and simulation of the supervision, monitoring and control solutions in a mixed physical and virtual environment, facilitating complex investigation starting from common process level to complex interdependencies arise from communication infrastructure inherent failures to contingent issues such as software related or security attacks. The concept, architecture and an implementation on a real-time hardware-in-the-loop based platform are revealed and shown as an open and affordable research and development solution.
References
Abou Houran, M.; Bukhari, S. M. S.; Zafar, M. H.; Mansoor, M.; Chen, W. (2023). COACNN- LSTM: Coati optimization algorithm-based hybrid deep learning model for PV/wind power forecasting in smart grid applications. Applied Energy, 349, 121638, 2023. https://doi.org/10.1016/j.apenergy.2023.121638
Aksöz, A.; Oyucu, S.; Biçer, E.; Bayındır, R. (2024). Analysis of SARIMA Models for Forecasting Electricity Demand. In 2024 12th International Conference on Smart Grid (icSmartGrid), 767- 771, 2024. https://doi.org/10.1109/icSmartGrid61824.2024.10578181
Arumugham, V.; Ghanimi, H. M.; Pustokhin, D. A.; Pustokhina, I. V.; Ponnam, V. S.; Alharbi, M.; Sengan, S. (2023). An artificial-intelligence-based renewable energy prediction program for demand-side management in smart grids. Sustainability, 15(6), 5453, 2023. https://doi.org/10.3390/su15065453
Balachander, T;, Khot, S. A.; Huseyn, R.; Garg, S.; Vijay, S.; Pandey, V. (2024). An Innovative Method for Short Term Electrical Load Forecasting Based on Adaptive CNN-MRMR Model. In 2024 International Conference on Electronics, Computing, Communication and Control Technology (ICECCC), pp. 1-6, 2024. https://doi.org/10.1109/ICECCC61767.2024.10593853
Balakrishnan, R.; Geetha, V.; Kumar, M. R.; Leung, M. F. (2023). Reduction in residential electricity bill and carbon dioxide emission through renewable energy integration using an adaptive feed-forward neural network system and MPPT technique. Sustainability, 15(19), 14088, 2023. https://doi.org/10.3390/su151914088
Fernandes, S. V.; João, D. V.; Cardoso, B. B.; Martins, M. A.; Carvalho, E. G. (2022). Digital twin concept developing on an electrical distribution system-An application case. Energies, 15(8), 2836, 2022. https://doi.org/10.3390/en15082836
Galvin, R.; Yeager, K. (2009). Perfecting power [In My View]. IEEE Power and Energy Magazine, 8(1), 88-84, 2009. https://doi.org/10.1109/MPE.2009.934879
Galvin, R. (2024). Re-thinking energy justice to achieve a fair distribution of shared electricity from rooftop photovoltaics in a typical multi-apartment building in Germany: an interdisciplinary approach. Energy Research & Social Science, 112, 103531, 2024. https://doi.org/10.1016/j.erss.2024.103531
Goto, A.; Inoue, R.; Tezuka, T.; Yoshikawa, H. (1995). A research on tele-operation using virtual reality. In Proceedings 4th IEEE International Workshop on Robot and Human Communication, 147-152, 1995. https://doi.org/10.1109/ROMAN.1995.531951
Ju, C.; Son, H. I. (2022). Human-centered evaluation of shared teleoperation system for maintenance and repair tasks in nuclear power plants. International Journal of Control, Automation and Systems, 20(10), 3418-3432, 2022. https://doi.org/10.1007/s12555-021-0770-0
Kim, H.; Park, S.; Kim, S. (2023). Time-series clustering and forecasting household electricity demand using smart meter data. Energy Reports, 9, 4111-4121, 2023. https://doi.org/10.1016/j.egyr.2023.03.042
Mizuno, N.; Tazaki, Y.; Hashimoto, T.; Yokokohji, Y. (2023). A comparative study of manipulator teleoperation methods for debris retrieval phase in nuclear power plant decommissioning. Advanced Robotics, 37(9), 541-559, 2023. https://doi.org/10.1080/01691864.2023.2169588
Mounir, N.; Ouadi, H.; Jrhilifa, I. (2023). Short-term electric load forecasting using an EMD-BILSTM approach for smart grid energy management system. Energy and Buildings, 288, 113022, 2023. https://doi.org/10.1016/j.enbuild.2023.113022
Perez-Ramirez, M.; Arroyo-Figueroa, G.; Ayala, A. (2021). The use of a virtual reality training system to improve technical skill in the maintenance of live-line power distribution networks. Interactive Learning Environments, 29(4), 527-544. https://doi.org/10.1080/10494820.2019.1587636
Pham, T.; Boone, A. P.; Ngo, M. K. (2023). Heuristic Evaluation of Supervisory Control and Data Acquisition (SCADA) Displays. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 67(1), 1767-1774, 2023. https://doi.org/10.1177/21695067231192536
Pilo, F.; Pisano, G. I. U. D. I. T. T. A.; Soma, G. G. (2009). Advanced DMS to manage active distribution networks. In 2009 IEEE Bucharest PowerTech 1-8, 2009. https://doi.org/10.1109/PTC.2009.5281947
Quiñones, J. J.; Pineda, L. R.; Ostanek, J.; Castillo, L. (2023). Towards smart energy management for community microgrids: Leveraging deep learning in probabilistic forecasting of renewable energy sources. Energy Conversion and Management, 293, 117440, 2023. https://doi.org/10.1016/j.enconman.2023.117440
Stefan, H.; Mortimer, M.; Horan, B.; McMillan, S. (2024). How effective is virtual reality for electrical safety training? Evaluating trainees' reactions, learning, and training duration. Journal of safety research, 90, 48-61, 2024. https://doi.org/10.1016/j.jsr.2024.06.002
Šverko, M.; Grbac, T. G. (2024). Automated HMI design as a custom feature in industrial SCADA systems. Procedia Computer Science, 232, 1789-1798, 2024. https://doi.org/10.1016/j.procs.2024.02.001
Tarmanini, C.; Sarma, N.; Gezegin, C.; Ozgonenel, O. (2023). Short term load forecasting based on ARIMA and ANN approaches. Energy Reports. https://doi.org/10.1016/j.egyr.2023.01.060
Wang, Y.; Qu, F.; Wang, X.; Zeng, S.; Sun, Q.; Li, M.; Ge, J. (2024). Design of a teleoperated and mixed reality-based electric power live line working robot. In Journal of Physics: Conference Series, 2708(1), 012002,. IOP Publishing. https://doi.org/10.1088/1742-6596/2708/1/012002
Yuan, X.; Yan, J.; Sun, L.; Cheng, F.; Guo, Z.; Yu, H. (2023). The influence of presentation frames of visualization information for safety on situational awareness under a three-level userinterface design. International journal of environmental research and public health, 20(4), 3325, 2023. https://doi.org/10.3390/ijerph20043325
Zhang, D.; Jin, X.; Shi, P.; Chew, X. (2023). Real-time load forecasting model for the smart grid using bayesian optimized CNN-BiLSTM. Frontiers in Energy Research, 11, 1193662, 2023. https://doi.org/10.3389/fenrg.2023.1193662
Additional Files
Published
Issue
Section
License
Copyright (c) 2024 Gligor Adrian, Cristian-Dragoș Dumitru, Simona Dzitac, Attila Simo
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
ONLINE OPEN ACCES: Acces to full text of each article and each issue are allowed for free in respect of Attribution-NonCommercial 4.0 International (CC BY-NC 4.0.
You are free to:
-Share: copy and redistribute the material in any medium or format;
-Adapt: remix, transform, and build upon the material.
The licensor cannot revoke these freedoms as long as you follow the license terms.
DISCLAIMER: The author(s) of each article appearing in International Journal of Computers Communications & Control is/are solely responsible for the content thereof; the publication of an article shall not constitute or be deemed to constitute any representation by the Editors or Agora University Press that the data presented therein are original, correct or sufficient to support the conclusions reached or that the experiment design or methodology is adequate.