Enhancing Smart Grid Systems: A Novel Mathematical Approach for Optimized Fault Recovery and Improved Energy Efficiency

Document Type : Research paper

Authors

1 Department of Electrical Engineering, Institut Technologi Padang, Indonesia.

2 Al-Hadi University College, 10011, Baghdad, Iraq.

3 Al-Manara College for Medical Sciences, Maysan, Iraq.

4 Department of Medical Laboratories Technology, AL-Nisour University College, Baghdad, Iraq.

5 College of Computer, National University of Science and Technology, Dhi Qar, Iraq.

6 College of Petroleum Engineering, Al-Ayen University, Thi-Qar, Iraq.

7 DSc in Economics, Professor, Department of Management Ferghana Polytechnic Institute, Ferghana, Uzbekistan.

8 Department of Informatics, Universitas Malikussaleh, Aceh, Indonesia.

9 Kazakh National Agrarian Research University, Department of Energy Saving and Automation, Republic of Kazakhstan.

Abstract

Sustainable and efficient energy solutions are needed in the fast-growing energy sector. Meeting these objectives requires smart distribution networks that maximize energy utilization, eliminate losses, and improve system reliability. However, these networks' usefulness and durability depend on their ability to quickly recover from faults. Intelligent distribution networks can self-heal, which speeds up restoration and ensures energy delivery. This paper proposes a comprehensive strategy for intelligent distribution network self-healing after flaws. Restoration involves identifying and isolating the damaged area using offline and online methods. Online approaches, notably islanding, have helped restore services in the affected region. This paper presents a novel linear mathematical approach to optimize online islanding. The model estimates the boundaries of islanded microgrids and the appropriate number of microgrids for faults, enabling quick restoration. This analysis also seeks to determine the fault-affected area's system layout. A mathematical model defines the ideal arrangement in the first layer of the two-layered approach. The next layer analyzes unit participation in the intelligent distribution system, focusing on rescheduling, allocation, and organization. Additionally, the study identifies the best energy storage solutions to aid restoration. The recommended strategy uses adaptive load reduction and demand response to maximize system recovery. The mathematical model benefits from various strategies, including faster execution and better outcomes. This research advances intelligent distribution networks by combining advanced mathematical modeling, self-healing, and smart load control. These upgrades boost distribution networks' effectiveness.

Keywords

Main Subjects

References

  1. K. H. M. Azmi, N. A. M. Radzi, N. A. Azhar, F. S. Samidi, I. T. Zulkifli, and A. M. Zainal, “Active electric distribution network: applications, challenges, and opportunities,” IEEE Access, vol. 10, pp. 134655–134689, 2022.
  2. J. Wei, A. Chammam, J. Feng, A. Alshammari, K. Tehranian, N. Innab, W. Deebani, and M. Shutaywi, “Power system monitoring for electrical disturbances in wide network using machine learning,” Sustainable Comput. Inf. Syst., vol. 42, p. 100959, 2024.
  3. V. H. de Souza, W. Satyro, J. C. Contador, L. F. Pinto, and M. C. Mitidiero, “The technology analysis model-tam 4.0 for implementation of industry 4.0,” Int. J. Ind. Eng. Manage., vol. 14, no. 4, pp. 271–281, 2023.
  4. E. J. Oughton, Z. Frias, S. van der Gaast, and R. van der Berg, “Assessing the capacity, coverage and cost of 5g infrastructure strategies: Analysis of the netherlands,” Telematics Inf., vol. 37, pp. 50–69, 2019.
  5. W. Altalabani and Y. Alaiwi, “Optimized adaptive pid controller design for trajectory tracking of a quadcopter,” 2022.
  6. J.-D. Hong, J. Mwakalonge, and K.-Y. Jeong, “Design of disaster relief logistics network system by combining three data envelopment analysis-based methods,” Int. J. Ind. Eng. Manage., vol. 13, no. 3, pp. 172–185, 2022.
  7. L. F. F. De Almeida, L. A. M. Pereira, A. C. Sodré, L. L. Mendes, J. J. Rodrigues, R. A. Rabelo, A. M. Alberti, et al., “Control networks and smart grid teleprotection: Key aspects, technologies, protocols, and case-studies,” IEEE Access, vol. 8, pp. 174049–174079, 2020.
  8. E. Shittu, A. Tibrewala, S. Kalla, and X. Wang, “Metaanalysis of the strategies for self-healing and resilience in power systems,” Adv. Appl. Energy, vol. 4, p. 100036, 2021.
  9. N. Nedjah, K. H. Cardoso, and L. de Macedo Mourelle, “An efficient distributed approach for a self-healing smart grid using minimal spanning tree,” Int. J. Energy Res., vol. 45, no. 10, pp. 15049–15084, 2021.
  10. S. S. Binyamin and S. Ben Slama, “Multi-agent systems for resource allocation and scheduling in a smart grid,” Sens., vol. 22, no. 21, p. 8099, 2022.
  11. L. Rafferty, Agent-based modeling framework for adaptive cyber defence of the Internet of Things. PhD thesis, 2022.
  12. K. Kotis, S. Stavrinos, and C. Kalloniatis, “Review on semantic modeling and simulation of cybersecurity and interoperability on the internet of underwater things,” Future Internet, vol. 15, no. 1, p. 11, 2022.
  13. A. A. Shobole and M. Wadi, “Multiagent systems application for the smart grid protection,” Renewable Sustainable Energy Rev., vol. 149, p. 111352, 2021.
  14. A. Sujil, J. Verma, and R. Kumar, “Multi agent system: concepts, platforms and applications in power systems,” Artif. Intell. Rev., vol. 49, pp. 153–182, 2018.
  15. M. Chen, “A multi-agent system design and implementation for flexible network management,” 2018.
  16. I. Loeser, M. Braun, C. Gruhl, J.-H. Menke, B. Sick, and S. Tomforde, “Towards organic distribution systems–the vision of self-configuring, self-organising, self-healing, and self-optimising power distribution management,” ArXiv Prepr. ArXiv:2112.07507, 2021.
  17. N. Dkhili, J. Eynard, S. Thil, and S. Grieu, “A survey of modelling and smart management tools for power grids with prolific distributed generation,” Sustainable Energy Grids Networks, vol. 21, p. 100284, 2020.
  18. D. Sarathkumar, M. Srinivasan, A. A. Stonier, R. Samikannu, N. R. Dasari, and R. A. Raj, “A technical review on self-healing control strategy for smart grid power systems,” in IOP Conf. Ser. Mater. Sci. Eng., vol. 1055, p. 012153, IOP Publishing, 2021.
  19. N. Shaukat, S. Ali, C. Mehmood, B. Khan, M. Jawad, U. Farid, Z. Ullah, S. Anwar, and M. Majid, “A survey on consumers empowerment, communication technologies, and renewable generation penetration within smart grid,” Renewable Sustainable Energy Rev., vol. 81, pp. 1453–1475, 2018.
  20. S. Haghifam, M. Dadashi, K. Zare, and H. Seyedi, “Optimal operation of smart distribution networks in the presence of demand response aggregators and microgrid owners: A multi follower bi-level approach,” Sustainable Cities Soc., vol. 55, p. 102033, 2020.
  21. H. Farzaneh, L. Malehmirchegini, A. Bejan, T. Afolabi, A. Mulumba, and P. P. Daka, “Artificial intelligence evolution in smart buildings for energy efficiency,” Appl. Sci., vol. 11, no. 2, p. 763, 2021.
  22. S. E. Ahmadi and N. Rezaei, “A new isolated renewable based multi microgrid optimal energy management system considering uncertainty and demand response,” Int. J. Electr. Power Energy Syst., vol. 118, p. 105760, 2020.
  23. S. Vuddanti and S. R. Salkuti, “Review of energy management system approaches in microgrids,” Energies, vol. 14, no. 17, p. 5459, 2021.
  24. S. Alrashed, “Key performance indicators for smart campus and microgrid,” Sustainable Cities Soc., vol. 60, p. 102264, 2020.
  25. C. Ma, “Smart city and cyber-security; technologies used, leading challenges and future recommendations,” Energy Rep., vol. 7, pp. 7999–8012, 2021.
  26. A. R. Jordehi, “Optimisation of demand response in electric power systems, a review,” Renewable Sustainable Energy Rev., vol. 103, pp. 308–319, 2019.
  27. K. Alshehri, J. Liu, X. Chen, and T. Bas¸ar, “A game-theoretic framework for multiperiod-multicompany demand response management in the smart grid,” IEEE Trans. Control Syst. Technol., vol. 29, no. 3, pp. 1019–1034, 2020.
  28. T. Nasir, S. S. H. Bukhari, S. Raza, H. M. Munir, M. Abrar, H. A. u. Muqeet, K. L. Bhatti, J.-S. Ro, and R. Masroor, “Recent challenges and methodologies in smart grid demand side management: State-of-the-art literature review,” Math. Probl. Eng., vol. 2021, pp. 1–16, 2021.
  29. U. Assad, M. A. S. Hassan, U. Farooq, A. Kabir, M. Z. Khan, S. S. H. Bukhari, Z. u. A. Jaffri, J. Olah, and J. Popp, “Smart grid, demand response and optimization: a critical review of computational methods,” Energies, vol. 15, no. 6, p. 2003, 2022.
  30. A. Alsharif, C. W. Tan, R. Ayop, A. Dobi, and K. Y. Lau, “A comprehensive review of energy management strategy in vehicle-to-grid technology integrated with renewable energy sources,” Sustainable Energy Technol. Assess., vol. 47, p. 101439, 2021.
  31. Y. Zhou, S. Cao, J. L. Hensen, and P. D. Lund, “Energy integration and interaction between buildings and vehicles: A state-of-the-art review,” Renewable Sustainable Energy Rev., vol. 114, p. 109337, 2019.
  32. S. K. Rathor and D. Saxena, “Energy management system for smart grid: An overview and key issues,” Int. J. Energy Res., vol. 44, no. 6, pp. 4067–4109, 2020.
  33. M. A. Gilani, A. Kazemi, and M. Ghasemi, “Distribution system resilience enhancement by microgrid formation considering distributed energy resources,” Energy, vol. 191, p. 116442, 2020.
  34. M. Stecca, L. R. Elizondo, T. B. Soeiro, P. Bauer, and P. Palensky, “A comprehensive review of the integration of battery energy storage systems into distribution networks,” IEEE Open J. Ind. Electron. Soc., vol. 1, pp. 46–65, 2020.
  35. M. W. Khan, J. Wang, M. Ma, L. Xiong, P. Li, and F. Wu, “Optimal energy management and control aspects of distributed microgrid using multi-agent systems,” Sustainable Cities Soc., vol. 44, pp. 855–870, 2019.
  36. P. Chetthamrongchai, O. G. Stepanenko, N. R. Saenko, S. Y. Bakhvalov, G. Aglyamova, and A. H. Iswanto, “A developed optimization model for mass production scheduling considering the role of waste materials,” Int. J. Ind. Eng. Manage., vol. 13, no. 2, pp. 135–144, 2022.
  37. R. C. Sabioni, J. Daaboul, and J. Le Duigou, “Joint optimization of product configuration and process planning in reconfigurable manufacturing systems,” Int. J. Ind. Eng. Manage., vol. 13, no. 1, pp. 58–75, 2022.
Journal of Operation and Automation in Power Engineering
Volume 11, Special Issue
Sustainable Power Systems, Energy Management, and Global Warming
December 2023
Pages 84-91

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History

  • Receive Date: 20 December 2023
  • Revise Date: 26 February 2024
  • Accept Date: 04 March 2024
  • First Publish Date: 04 March 2024

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