A MILP Model Incorporated With the Risk Management Tool for Self-Healing Oriented Service Restoration

Document Type : Research paper

Authors

Department of Technical Engineering, University of Mohaghegh Ardabili, Ardabil, Iran

Abstract

The inevitable emergence of intelligent distribution networks has introduced new features in these networks. According to most experts, self-healing is one of the main abilities of smart distribution networks. This feature increases the reliability and resiliency of networks by reacting fast and restoring the critical loads (CLs) during a fault. Nevertheless, the stochastic nature of the components in a power system imposes significant computational risk in enabling the system to self-heal. In this paper, a mathematical model is introduced for the self-healing operation of networked Microgrids (MGs) to assess the risk in the optimal service restoration (SR) problem. Electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs) and their stochastic nature besides the distributed generation units (DGs), the ability to reconfiguration, and demand response program are considered simultaneously. The objective function is designed to maximize the restored loads and minimize the risk. The Conditional Value-at-Risk (CVaR) is used to calculate the risk of the SR as one of the most efficient and famous risk indices. In the general case study and considering $\beta $ equal to the 0, 1, 2, 3, and 4, expected values of SR for the risk-averse problem is 21.2, 20, 19.3, 19.1, and 19\% less than the risk-neutral problem, respectively. The formulation of the problem is mixed-integer linear programming (MILP), and the model is tested in the modified Civanlar test system. The analysis of several case studies has proved the performance of the proposed model and the importance of risk management in the problem.

Keywords

References

  1. Wang, T. Huang, X. Li, J. Tang, Z. Wu, Y. Mo, L. Xue, Y Zhoe, T. Niu, S. Sun, "A Resilience Assessment Framework for Distribution Systems Under Typhoon Disasters," IEEE Access, Vol. 9, Nov 2021.
  2. Li, Y. Deng, M. Zhang, J. Li, Sh. Chen, S. Zhang, "Integrated Multistage Self-Healing in Smart Distribution Grids Using Decentralized Multiagent," IEEE Access, Vol. 9, Nov 2021.
  3. M Mohammadi-Hosseininejad, A. Fereidunian, A. Shahsavari, H. Lesani, "A Healer Reinforcement Approach to Self-Healing in Smart Grid by PHEVs Parking Lot Allocation," IEEE Trans. Ind. Inf., vol. 12, Dec 2016.
  4. R. Aguero, "Applying self-healing schemes to modern power distribution systems," in Proc. IEEE Power Energy Soc. Gen. Meeting, San Diego, CA, USA, pp. 1–4. 2012.
  5. Shahryari, H. Shayeghi, B. Mohammadi-Ivatloo, M. Moradzadeh, "Optimal energy management of microgrid in day-ahead and intra-day markets using a copula-based uncertainty modeling method.," J. Oper. Autom. Power Eng., vol. 2, pp. 86-96, 2019.
  6. Shayeghi, M. Alilou, "Multi-Objective Demand Side Management to Improve Economic and Environmental Issues of a Smart Microgrid," J. Oper. Autom. Power Eng., Vol. 9, No. 3, pp. 182-192, Dec. 2021.
  7. National Energy Technology Laboratory, "A systems view of the modern grid," 2007 [Online]. Available: https://www.netl.doe.gov/ , Accessed: 2018.
  8. AfsariArdabili, S.J. Seyedshenava, H. shayeghi, "SelfHealing Policy in Distribution Network Considering Technical Indices," Smart Grid Conf. (SGC), 2019.
  9. Hsu, and H. Huang, "Distribution system service restoration using the artificial neural network approach and pattern recognition method," IEEE Proc. Gener. Transmiss. Distrib, vol. 142, no. 3, pp. 251–256, May 1995.
  10. Fukuyama, and H. Endo, "A hybrid system for service restoration using expert system and genetic algorithm," in Proc. IEEE Int. Conf. Intell. Syst. Appl. Power Syst., Orlando, FL, USA, 1996, pp. 394–398.
  11. Farzin, M. Fotuhi-Firuzabad, and M. Moeini-Aghtaie, "Enhancing power system resilience through hierarchical outage management in multi-microgrids," IEEE Trans. Smart Grid, vol. 7, no. 6, pp. 2869-2879, Nov. 2016.
  12. Shahryari, H. Shayeghi, B. Mohammadi-ivatloo, M. Moradzadeh, "A copula-based method to consider uncertainties for multi-objective energy management of microgrid in presence of demand response," Energy, Vol. 175, pp. 879-890, 2019.
  13. Bie, P. Zhang, G. Li, B. Hua, M. Meehan, Xi. Wang, "Reliability Evaluation of Active Distribution Systems Including Microgrids," IEEE Trans. Power Syst., Vol. 12, 2012.
  14. Xiang, J. Liu, Y. Liu, "Robust Energy Management of Microgrid With Uncertain Renewable Generation and Load," IEEE Trans. Smart Grid, Vol. 7, 2016.
  15. Hussain, A. Rousis, I. Konstantelos, G. Strbac, J. Jeon, H. Kim, "Impact of Uncertainties on Resilient Operation of Microgrids: A Data-Driven Approach," IEEE Access, Vol. 7, 2019.
  16. Liu, H. Chen, W. Zhang, B. Yurkovich, G. Rizzoni, "Energy Management Problems Under Uncertainties for GridConnected Microgrids: a Chance Constrained Programming Approach," IEEE Trans. Smart Grid, Vol. 8, 2017.
  17. Vesperman, T. Hamacher, J. Kazempour "Risk Trading in Energy Communities," IEEE Trans. Smart Grid, Vol. 12, 2021.
  18. Mieth, M. Roveto, Y. Dvorkin, "Risk Trading in a Chance-Constrained Stochastic Electricity Market," IEEE Control Syst. Lett., Vol. 5, 2021.
  19. Bao, Y. Ding, X. Zhou, C. Guo, C. Shao, "Risk assessment and management of electricity markets: A review with suggestions," CSEE J. Power Energy Syst., Vol. 7, 2021.
  20. Negnevitsky, D. Nguyen, M. Piekutowski, "Risk Assessment for Power System Operation Planning With High Wind Power Penetration," EEE Trans. Power Syst., Vol. 30, 2015.
  21. Costa, F. Thome, J. Garcia, M. Pereira, "ReliabilityConstrained Power System Expansion Planning: A Stochastic Risk-Averse Optimization Approach," IEEE Trans. Power Syst., Vol. 36, 2021.
  22. Izadi, A. Safdarian, "Financial Risk Evaluation of RCS Deployment in Distribution Systems," IEEE Syst. J., Vol. 13, 2019.
  23. Dolatabadi, B. Mohammadi-Ivatloo, "Stochastic RiskConstrained Optimal Sizing for Hybrid Power System of Merchant Marine Vessels," IEEE Trans. Ind. Inf., Vol. 14, 2018.
  24. Conejo, L, Baringo, S.J Kazempour, A. Siddiqui, "Investment in Electricity Generation and Transmission Decision Making under Uncertainty," springer, 2016.
  25. Conejo, M. Carrion, J. Morales, "Decision Making Under Uncertainty in Electricity Markets," Springer, New York, 2010.
  26. Civanlar, J. J. Grainger, H. Yin and S. S. H. Lee, "Distribution feeder reconfiguration for loss reduction" IEEE Trans. Power Deliv., vol. 3, no.3, pp. 1217–1223, 1988.
  27. Liu, D. Wang, H. Jia, N. Djilali, W. Zhang, "Aggregation and bidirectional charging power control of plug-in hybrid electric vehicles: generation system adequacy Analysis," IEEE Trans. Sustain. Energy,Vol. 6, pp. 325–35, 2015.
  28. Guille and G. Gross, "The integration of PHEV aggregations into a power system with wind resources," in Proc. Bulk Power Syst. Dyn. Control Symp., Buzios, Brazil, pp. 1–9, Aug. 2010.
  29. Momen, A. Abessi, Sh. Jadid, M. Shafie-khah, J. Catalao, "Load restoration and energy management of a microgrid with distributed energy resources and electric vehicles participation under a two-stage stochastic framework," Int. J. Electr. Power Energy Syst., Vol. 133, Dec 2021.
  30. R. Bussieck, A. Pruessner, "Mixed-integer nonlinear programming," SIAG/OPT Newsl. Views News, Vol. 14, pp. 19–22, 2003.
  31. Shin, R. Baldick, "Plug-in electric vehicle to home (V2H) operation under a grid Outage," IEEE Trans. Smart Grid, Vol. 8(4), pp.2032–41, 2017.
  32. R. Birge and F. Louveaux. "Introduction to stochastic programming," Springer- Verlag, New York, 1997.
  33. Zadsar, M.R Haghifam, S.M Miri, "Approach for selfhealing resilient operation of active distribution network with microgrid," IET Gener. Transm. Distrib., Vol. 11, Iss. 18, pp. 4633-4643, 2017.
  34. Zhao, X. Dong, J. Bornemann, "Service Restoration for a Renewable-Powered Microgrid in Unscheduled Island Mode," IEEE Trans. Smart Grid, Vol. 6, no. 3, MAY 2015.
Journal of Operation and Automation in Power Engineering
Volume 12, Issue 1
January 2024
Pages 1-13

Files

History

  • Receive Date: 26 February 2022
  • Revise Date: 04 July 2022
  • Accept Date: 26 July 2022
  • First Publish Date: 31 August 2022

Share

How to cite

Statistics