journal article·2021-10-25Open access

A Multi-Layer Security Scheme for Mitigating Smart Grid Vulnerability against Faults and Cyber-Attacks

Jian Chen, Mohamed A. Mohamed, Udaya Dampage, Mostafa Rezaei, Saleh H. Salmen, Sami Al Obaid, Andres Annuk

Applied Sciences· Volume 11· Issue 21

Abstract

To comply with electric power grid automation strategies, new cyber-security protocols and protection are required. What we now experience is a new type of protection against new disturbances namely cyber-attacks. In the same vein, the impact of disturbances arising from faults or cyber-attacks should be surveyed by network vulnerability criteria alone. It is clear that the diagnosis of vulnerable points protects the power grid against disturbances that would inhibit outages such as blackouts. So, the first step is determining the network vulnerable points, and then proposing a support method to deal with these outages. This research proposes a comprehensive approach to deal with outages by determining network vulnerable points due to physical faults and cyber-attacks. The first point, the network vulnerable points against network faults are covered by microgrids. As the second one, a new cyber-security protocol named multi-layer security is proposed in order to prevent targeted cyber-attacks. The first layer is a cyber-security-based blockchain method that plays a general role. The second layer is a cyber-security-based reinforcement-learning method, which supports the vulnerable points by monitoring data. On the other hand, the trend of solving problems becomes routine when no ambiguity arises in different sections of the smart grid, while it is far from a big network’s realities. Hence, the impact of uncertainty parameters on the proposed framework needs to be considered. Accordingly, the unscented transform method is modeled in this research. The simulation results illustrate that applying such a comprehensive approach can greatly pull down the probability of blackouts.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Smart Grid Security and Resilience · Blockchain Technology Applications and Security · Electricity Theft Detection Techniques

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Robotics School of Fuzhou Polytechnic, Fuzhou 350108, China

Electrical Engineering Department, Faculty of Engineering, Minia University, Minia 61519, Egypt

Faculty of Engineering, Kotelawala Defence University, Kandawala Estate, Ratmalana 10390, Sri Lanka

Queensland Micro- and Nanotechnology Centre, Griffith University, Nathan, Brisbane 4111, Australia

Department of Botany and Microbiology, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia

Chair of Energy Application Engineering, Institute of Technology, Estonian University of Life Sciences, 51006 Tartu, Estonia

References (53)

Show source-provided references
1. A robust dispatch model for integrated electricity and heat networks considering price-based integrated demand response10.1016/j.energy.2021.121875 ↗
2. An Intelligent Secured Framework for Cyberattack Detection in Electric Vehicles’ CAN Bus Using Machine Learning10.1109/ACCESS.2019.2937576 ↗
3. Review of major approaches to analyze vulnerability in power system10.1016/j.ress.2018.11.019 ↗
4. Proactive Resilience of Power Systems against Natural Disasters: A Literature Review10.1109/ACCESS.2019.2952362 ↗
5. A Novel Three-Step Classification Approach Based on Time-Dependent Spectral Features for Complex Power Quality Disturbances10.1109/TIM.2021.3118090 ↗
6. Vulnerability Assessment in Power Systems: A Review
7. Vulnerability Analysis of the Nordic Power System10.1109/TPWRS.2005.857849 ↗
8. Combining Deep Learning and Optimization for Preventive Security-Constrained DC Optimal Power Flow10.1109/TPWRS.2021.3054341 ↗
9. A Reliability-Oriented Fuzzy Stochastic Framework in Automated Distribution Grids to Allocate m-PMUs10.1109/ACCESS.2019.2902465 ↗
10. Song, H. (2006, January 21–24). Static analysis of vulnerability and security margin of the power system. Proceedings of the 2005/2006 IEEE/PES Transmission and Distribution Conference and Exhibition, IEEE, Dallas, TX, USA.
11. A practical approach for integrated power system vulnerability analysis with protection failures10.1109/TPWRS.2004.835656 ↗
12. Mohseni-Bonab, S.M., Kamwa, I., Moeini, A., and Rabiee, A. (2020, January 2–6). Vulnerability Assessment in Power Systems: A Review and Representing Novel Perspectives. Proceedings of the 2020 IEEE Power & Energy Society General Meeting (PESGM), IEEE, Montreal, QC, Canada.10.1109/PESGM41954.2020.9281539 ↗
13. Ernster, T.A., and Srivastava, A.K. (2012, January 7–10). Power system vulnerability analysis-towards validation of centrality measures. Proceedings of the PES T&D 2012, IEEE, Orlando, FL, USA.10.1109/TDC.2012.6281483 ↗
14. Bulat, H., Franković, D., and Vlahinić, S. (2021). Enhanced Contingency Analysis—A Power System Operator Tool. Energies, 14.10.3390/en14040923 ↗
15. Simulation-optimization methods for designing and assessing resilient supply chain networks under uncertainty scenarios: A review10.1016/j.simpat.2020.102166 ↗
16. Severe Multiple Contingency Screening in Electric Power Systems10.1109/TPWRS.2008.919243 ↗
17. A stochastic machine learning based approach for observability enhancement of automated smart grids10.1016/j.scs.2021.103071 ↗
18. Adaptive robust vulnerability analysis of power systems under uncertainty: A multilevel OPF-based optimization approach10.1016/j.ijepes.2021.107432 ↗
19. Optimal reconfiguration for vulnerable radial smart grids under uncertain operating conditions10.1016/j.compeleceng.2021.107310 ↗
20. Athari, H., and Wang, Z. (2019, January 8). Enhanced AC quasi-steady state cascading failure model for grid vulnerability analysis under wind uncertainty. Proceedings of the 52nd Hawaii International Conference on System Sciences, Maui, HI, USA.10.24251/HICSS.2019.415 ↗
21. Kritikos, K., Papoutsakis, M., Ioannidis, S., and Magoutis, K. (2019, January 11–13). Towards Configurable Vulnerability Assessment in the Cloud. Proceedings of the 2019 IEEE 24th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), IEEE, Limassol, Cyprus.10.1109/CAMAD.2019.8858460 ↗
22. Power flow-based approaches to assess vulnerability, reliability, and contingency of the power systems: The benefits and limitations10.1016/j.ress.2020.106961 ↗
23. Chu, Z., Zhang, J., Kosut, O., and Sankar, L. (2020, January 11–13). Vulnerability assessment of large-scale power systems to false data injection attacks. Proceedings of the 2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), IEEE, Tempe, AZ, USA.10.1109/SmartGridComm47815.2020.9302956 ↗
24. Microgrid resilience: A holistic approach for assessing threats, identifying vulnerabilities, and designing corresponding mitigation strategies10.1016/j.apenergy.2020.114726 ↗
25. A Mixed-Integer LP Procedure for the Analysis of Electric Grid Security under Disruptive Threat10.1109/TPWRS.2005.851942 ↗
26. Veloza, O.P., and Cespedes, R.H. (2006, January 15–18). Regulatory mechanisms to mitigate the vulnerability of power systems to blackouts. Proceedings of the 2006 IEEE/PES Transmission & Distribution Conference and Exposition: Latin America, IEEE, Caracas, Venezuela.10.1109/TDCLA.2006.311558 ↗
27. A comprehensive approach for preventive and corrective control to mitigate voltage collapse10.1109/59.867175 ↗
28. A Distributed Stochastic Energy Management Framework Based-Fuzzy-PDMM for Smart Grids Considering Wind Park and Energy Storage Systems10.1109/ACCESS.2021.3067501 ↗
29. Evaluation of Reinforcement Learning-Based False Data Injection Attack to Automatic Voltage Control10.1109/TSG.2018.2790704 ↗
30. Q-Learning-Based Vulnerability Analysis of Smart Grid against Sequential Topology Attacks10.1109/TIFS.2016.2607701 ↗
31. Sayeed, S., and Hector, M. (2019). Assessing blockchain consensus and security mechanisms against the 51% attack. Appl. Sci., 9.10.3390/app9091788 ↗
32. DeepCoin: A Novel Deep Learning and Blockchain-Based Energy Exchange Framework for Smart Grids10.1109/TEM.2019.2922936 ↗
33. A Secured Advanced Management Architecture in Peer-to-Peer Energy Trading for Multi-Microgrid in the Stochastic Environment10.1109/ACCESS.2021.3092834 ↗
34. Designing blockchain-based access control protocol in iot-enabled smart-grid system10.1109/JIOT.2020.3030308 ↗
35. Moeini, A., Kamwa, I., de Montigny, M., and Lenoir, L. (2016, January 3–5). Application of Battery Energy Storage for network vulnerability mitigation. Proceedings of the 2016 IEEE/PES Transmission and Distribution Conference and Exposition (T&D), IEEE, Dallas, TX, USA.10.1109/TDC.2016.7519928 ↗
36. Optimal scheduling of reconfigurable grids considering dynamic line rating constraint10.1049/iet-gtd.2019.1570 ↗
37. Stochastic multi-carrier energy management in the smart islands using reinforcement learning and unscented transform10.1016/j.ijepes.2021.106988 ↗
38. On the assessment of the impact of a price-maker energy storage unit on the operation of power system: The ISO point of view10.1016/j.energy.2019.116224 ↗
39. A secured social-economic framework based on PEM-blockchain for optimal scheduling of reconfigurable interconnected microgrids10.1109/ACCESS.2021.3065400 ↗
40. Ding, S., Cao, Y., Vosoogh, M., Sheikh, M., and Almagrabi, A. (2020). A Directed Acyclic Graph Based Architecture for Optimal Operation and Management of Reconfigurable Distribution Systems with PEVs. IEEE Trans. Ind. Appl., 1.10.1109/TIA.2020.3009050 ↗
41. PHY-Layer Spoofing Detection With Reinforcement Learning in Wireless Networks10.1109/TVT.2016.2524258 ↗
42. Kiros, S., Khan, B., Padmanaban, S., Haes Alhelou, H., Leonowicz, Z., Mahela, O.P., and Holm-Nielsen, J.B. (2020). Development of Stand-Alone Green Hybrid System for Rural Areas. Sustainability, 12.10.3390/su12093808 ↗
43. Sheikh, M., Aghaei, J., Rajabdorri, M., Shafie-khah, M., Lotfi, M., Javadi, M.S., and Catalão, J.P. (2019, January 11–14). Multiobjective Congestion Management and Transmission Switching Ensuring System Reliability. Proceedings of the 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe), IEEE, Genova, Italy.10.1109/EEEIC.2019.8783489 ↗
44. Online Cyber-Attack Detection in Smart Grid: A Reinforcement Learning Approach10.1109/TSG.2018.2878570 ↗
45. Shetty, S., Kamhoua, C.A., and Njilla, L.L. (2019). Blockchain for Distributed Systems Security, John Wiley & Sons.10.1002/9781119519621 ↗
46. A two-stage optimal scheduling method for active distribution networks considering uncertainty risk10.1016/j.egyr.2021.07.023 ↗
47. Mohamed, M.A., Mirjalili, S., Dampage, U., Salmen, S.H., Obaid, S.A., and Annuk, A. (2021). A Cost-Efficient-Based Cooperative Allocation of Mining Devices and Renewable Resources Enhancing Blockchain Architecture. Sustainability, 13.10.3390/su131810382 ↗
48. A Wind Power Accommodation Capability Assessment Method for Multi-Energy Microgrids10.1109/TSTE.2021.3103910 ↗
49. Rezaei, M., Dampage, U., Das, B.K., Nasif, O., Borowski, P.F., and Mohamed, M.A. (2021). Investigating the Impact of Economic Uncertainty on Optimal Sizing of Grid-Independent Hybrid Renewable Energy Systems. Process, 9.10.3390/pr9081468 ↗
50. A novel fuzzy cloud stochastic framework for energy management of renewable microgrids based on maximum deployment of electric vehicles10.1016/j.ijepes.2021.106845 ↗
51. Markakis, E., Nikoloudakis, Y., Pallis, E., and Manso, M. (2019, January 15–18). Security assessment as a service cross-layered system for the adoption of digital, personalised and trusted healthcare. Proceedings of the 2019 IEEE 5th World Forum on Internet of Things (WF-IoT), IEEE, Limerick, Ireland.10.1109/WF-IoT.2019.8767249 ↗
52. Common Vulnerability Scoring System10.1109/MSP.2006.145 ↗
53. Vulnerability assessment as a service for fog-centric ICT ecosystems: A healthcare use case10.1007/s12083-019-0716-y ↗