The development of smart grids has offered many technical solutions that can increase the reliability and resilience of distribution systems. Self-healing is an important characteristic of smart grids, as it pertains to the capability of the grid to isolate and restore the system, or part of it, to its normal operation following interruptions. This is achieved by adopting advanced monitoring and control systems and utilising all local available distributed sources. In this study, a smart self-healing optimisation strategy for smart grids is proposed. The proposed technique considers several factors, including the available power supply, system configuration, and load management. Moreover, a load prioritisation model is presented and incorporated into the proposed technique. The self-healing strategy is formulated as a mixed-integer linear programming problem, which is solved mathematically, ensuring global optimality of the solution. The strategy is tested by applying it to 16-bus and 33-bus smart grid systems. Further, the proposed formulation is utilised to solve the reconfiguration for the loss-minimisation problem for a 69-bus system. The simulation results indicate the capability of the proposed strategy in providing the optimal network configuration, optimal distributed generators output, and optimal load curtailment with remarkable accuracy and computational time.

References

1. 1)
  - 38. Baran, M.E., Wu, F.F.: ‘Network reconfiguration in distribution systems for loss reduction and load balancing’, IEEE Trans. Power Deliv., 1989, 4, (2), pp. 1401–1407.
2. 2)
  - 23. Liu, W., Sun, L., Lin, Z., et al: ‘Multi-objective restoration optimisation of power systems with battery energy storage systems’, IET Gener. Transm. Distrib., 2016, 10, (7), pp. 1749–1757.
3. 3)
  - 6. Oliveira, L.W., Oliveira, E.J., Silva, I.C., et al: ‘Optimal restoration of power distribution system through particle swarm optimization’. 2015 IEEE Eindhoven Power Tech, Eindhoven, Netherlands, July 2015.
4. 4)
  - 10. Lopez, J.C., Franco, J.F., Rider, M.J., et al: ‘Optimal restoration/maintenance switching sequence of unbalanced three-phase distribution systems’, IEEE Trans. Smart Grid, 2017, PP, (99), pp. 1–1, DOI: 10.1109/TSG.2017.2703152.
5. 5)
  - 43. Souza, S.S., Romero, R., Franco, J.F.: ‘Artificial immune networks copt-aiNet and Opt-aiNet applied to the reconfiguration problem of radial electrical distribution systems’, Electr. Power Syst. Res., 2015, 119, pp. 304–312.
6. 6)
  - 18. Kumar, G., Pindoriya, N.M.: ‘Outage management system for power distribution network’. 2014 Int. Conf. Smart Electric Grid (ISEG), Guntur, India, September 2014, pp. 1–8.
7. 7)
  - 2. McDermott, T.E., Drezga, I., Broadwater, R.: ‘A heuristic nonlinear constructive method for distribution system reconfiguration’, IEEE Trans. Power Syst., 1999, 14, (2), pp. 478–483.
8. 8)
  - 11. Romero, R., Franco, J.F., Leão, F.B., et al: ‘A new mathematical model for the restoration problem in balanced radial distribution systems’, IEEE Trans. Power Syst., 2016, 31, (2), pp. 1259–1268.
9. 9)
  - 17. John Dirkman, P.E.: ‘Enhancing utility outage management system (OMS) performance’ (Schneider Electric Company Report, 2014).
10. 10)
  - 16. Mahfouz, M.M., El-Sayed, M.A.: ‘Smart grid fault detection and classification with multi-distributed generation based on current signals approach’, IET Gener. Transm. Distrib., 2016, 8;10, (16), pp. 4040–4047.
11. 11)
  - 41. Rao, R.S., Ravindra, K., Satish, K., et al: ‘Power loss minimization in distribution system using network reconfiguration in the presence of distributed generation’, IEEE Trans. Power Syst., 2013, 28, (1), pp. 317–325.
12. 12)
  - 9. Cavalcante, P.L., López, J.C., Franco, J.F., et al: ‘Centralized self-healing scheme for electrical distribution systems’, IEEE Trans. Smart Grid, 2016, 7, (1), pp. 145–155.
13. 13)
  - 26. De Sá Ferreira, R.: ‘A mixed-integer linear programming approach to the ac optimal power flow in distribution systems’. PhD thesis, Universidade Federal do Rio de Janeiro, 2013.
14. 14)
  - 40. Nara, K., Shiose, A., Kitagawa, M., et al: ‘Implementation of genetic algorithm for distribution systems loss minimum econfiguration’, IEEE Trans. Power Syst., 1992, 7, (3), pp. 1044–1051.
15. 15)
  - 22. Miu, K.N., Chiang, H.D., McNulty, R.J.: ‘Multi-tier service restoration through network reconfiguration and capacitor control for large-scale radial distribution networks’, IEEE Trans. Power Syst., 2000, 15, (3), pp. 1001–1007.
16. 16)
  - 30. McCormick, G.P.: ‘Computability of global solutions to factorable nonconvex programs: part I – convex underestimating problems’, Math. Program., 1976, 10, (1), pp. 147–175.
17. 17)
  - 13. Sultana, B., Mustafa, M.W., Sultana, U., et al: ‘Review on reliability improvement and power loss reduction in distribution system via network reconfiguration’, Renew. Sust. Energy Rev., 2016, 66, pp. 297–310.
18. 18)
  - 24. Shahrin, M.A., Nosu, K., Aoki, H.: ‘Integrating distributed generator for restoration optimization’. 2012 IEEE International Conference on Power and Energy (PECon), Kota Kinabalu, Malaysia, December 2012, pp. 773–777.
19. 19)
  - 4. Kumar, Y., Das, B., Sharma, J.: ‘Genetic algorithm for supply restoration in distribution system with priority customers’, 2006 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), Stockholm, Sweden, June 2006.
20. 20)
  - 12. Oualmakran, Y., Meléndez, J., Herraiz, S., et al: ‘Survey on knowledge based methods to assist fault restoration in power distribution networks’. Int. Conf. Renewable Energies and Power Quality (ICREPQ'11), Las Palmas, Spain, April 2011.
21. 21)
  - 36. Civanlar, S., Grainger, J.J., Yin, H., et al: ‘Distribution feeder reconfiguration for loss reduction’, IEEE Trans. Power Deliv., 1988, 3, (3), pp. 1217–1223.
22. 22)
  - 32. Jabr, R.A., Singh, R., Pal, B.C.: ‘Minimum loss network reconfiguration using mixed-integer convex programming’, IEEE Trans. Power Syst., 2012, 27, (2), pp. 1106–1115.
23. 23)
  - 28. Salgado, R.D., Zeitune, A.F.: ‘Power flow solutions through tensor methods’, IET Gener. Transm. Distrib., 2009, 3, (5), pp. 413–424.
24. 24)
  - 1. Moslehi, K., Kumar, R.: ‘A reliability perspective of the smart grid’, IEEE Trans. Smart Grid, 2010, 1, (1), pp. 57–64.
25. 25)
  - 31. Borghetti, A.: ‘Using mixed integer programming for the volt/var optimization in distribution feeders’, Electr. Power Syst. Res., 2013, 98, pp. 39–50.
26. 26)
  - 15. Moghaddam, M.H., Moghaddassian, M., Leon-Garcia, A.: ‘Autonomous two-tier cloud based demand side management approach with microgrid’, IEEE Trans. Ind. Inf., 2016, 13, (3), pp. 1109–1120.
27. 27)
  - 5. Huang, C.M.: ‘Multiobjective service restoration of distribution systems using fuzzy cause-effect networks’, IEEE Trans. Power Syst., 2003, 18, (2), pp. 867–874.
28. 28)
  - 3. Gomes, F.V., Carneiro, S., Pereira, J.L.R., et al: ‘A new heuristic reconfiguration algorithm for large distribution systems’, IEEE Trans. Power Syst., 2005, 20, (3), pp. 1373–1378.
29. 29)
  - 27. Jiang, Q.Y., Chiang, H.D., Guo, C.X., et al: ‘Power–current hybrid rectangular formulation for interior-point optimal power flow’, IET Gener. Transm. Distrib., 2009, 3, (8), pp. 748–756.
30. 30)
  - 14. International Energy Agency.: ‘Technology Roadmap; Smart Grids’ (IEA, Paris, France, 2011).
31. 31)
  - 19. Jiao, Z., Wang, X., Gong, H.: ‘Wide area measurement/wide area information-based control strategy to fast relieve overloads in a self-healing power grid’, IET Gener. Transm. Distrib., 8, 2014, (6), pp. 1168–1176.
32. 32)
  - 33. Kumar, Y., Das, B., Sharma, J.: ‘Multiobjective multiconstraint service restoration of electric power distribution system with priority customers’, IEEE Trans. Power Deliv., 2008, 23, (1), pp. 261–270.
33. 33)
  - 20. Wang, F., Chen, C., Li, C., et al: ‘A multi-stage restoration method for medium-voltage distribution system with DGs’, IEEE Trans. Smart Grid, 2017, 8, (6), pp. 2627–2636.
34. 34)
  - 29. Nemhauser, G.L., Wolsey, L.A.: ‘Integer and combinatorial optimization’ (Wiley, New York, NY, USA, 1999).
35. 35)
  - 42. Kavousi-Fard, A., Niknam, T.: ‘Optimal distribution feeder reconfiguration for reliability improvement considering uncertainty’, IEEE Trans. Power Deliv., 2014, 29, (3), pp. 1344–1353.
36. 36)
  - 39. Savier, J.S., Das, D.: ‘Impact of network reconfiguration on loss allocation of radial distribution systems’, IEEE Trans. Power Deliv., 2007, 22, (4), pp. 2473–2480.
37. 37)
  - 25. Lei, S., Li, S., Yang, J., et al: ‘Distribution service restoration using chaotic optimization and immune algorithm’, 2011 International Conference on Information Science and Technology (ICIST), Nanjing, China, March 2011, pp. 1129–1133.
38. 38)
  - 8. Nagata, T., Hatakeyama, S., Yasouka, M., et al: ‘An efficient method for power distribution system restoration based on mathematical programming and operation strategy’, Power Syst. Technol., 2000, 3, pp. 1545–1550.
39. 39)
  - 37. Aman, M.M., Jasmon, G.B., Bakar, A.H., et al: ‘Optimum network reconfiguration based on maximization of system loadability using continuation power flow theorem’, Int. J. Electr. Power Energy Syst., 2014, 54, pp. 123–133.
40. 40)
  - 21. Gu, X., Zhong, H.: ‘Optimisation of network reconfiguration based on a two-layer unit-restarting framework for power system restoration’, IET Gener. Transm. Distrib., 2012, 6, (7), pp. 693–700.
41. 41)
  - 7. Souza, S.S., Romero, R., Pereira, J., et al: ‘Reconfiguration of radial distribution systems with variable demands using the clonal selection algorithm and the specialized genetic algorithm of Chu–Beasley’, J. Control Autom. Electr. Syst., 2016, 27, (6), pp. 689–701.
42. 42)
  - 34. Wei, W., Sun, M., Ren, R., et al: ‘Service restoration of distribution system with priority customers and distributed generation’, Innovative Smart Grid Technologies-Asia (ISGT Asia), May 2012, pp. 1–6.
43. 43)
  - 35. GAMS Development Corporation. General Algebraic Modeling System (GAMS) Release 24.8.5. Washington, DC, USA, 2017.

MILP-based technique for smart self-healing grids

References

Related content