MILP-based technique for smart self-healing grids

Access Full Text

MILP-based technique for smart self-healing grids

For access to this article, please select a purchase option:

Buy article PDF
$19.95
(plus tax if applicable)
Add to cart
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)
Add to cart

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Generation, Transmission & Distribution — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

© The Institution of Engineering and Technology
Received 28/11/2017, Accepted 13/02/2018, Revised 26/01/2018, Published 19/02/2018

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.

Inspec keywords: linear programming; mathematical analysis; integer programming; minimisation; load management; smart power grids

Other keywords: 69-bus system; smart self-healing optimisation strategy; power supply; 16-bus smart grid system; mixed-integer linear programming problem; optimal distributed generator output; smart self-healing grid; loss-minimisation problem; reliability; mathematical solution; load prioritisation model; power system monitoring; distribution system; optimal network configuration; load management; MILP-based technique; power system restoration; optimal load curtailment; 33-bus smart grid system

Subjects: Optimisation techniques; Power system management, operation and economics; Mathematical analysis

References

    1. 1)
      • 1. Moslehi, K., Kumar, R.: ‘A reliability perspective of the smart grid’, IEEE Trans. Smart Grid, 2010, 1, (1), pp. 57–64.
    2. 2)
      • 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.
    3. 3)
      • 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.
    4. 4)
      • 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.
    5. 5)
      • 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.
    6. 6)
      • 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.
    7. 7)
      • 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.
    8. 8)
      • 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.
    9. 9)
      • 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.
    10. 10)
      • 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.
    11. 11)
      • 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.
    12. 12)
      • 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.
    13. 13)
      • 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.
    14. 14)
      • 14. International Energy Agency.: ‘Technology Roadmap; Smart Grids’ (IEA, Paris, France, 2011).
    15. 15)
      • 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.
    16. 16)
      • 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.
    17. 17)
      • 17. John Dirkman, P.E.: ‘Enhancing utility outage management system (OMS) performance’ (Schneider Electric Company Report, 2014).
    18. 18)
      • 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.
    19. 19)
      • 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.
    20. 20)
      • 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.
    21. 21)
      • 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.
    22. 22)
      • 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.
    23. 23)
      • 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.
    24. 24)
      • 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.
    25. 25)
      • 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.
    26. 26)
      • 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.
    27. 27)
      • 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.
    28. 28)
      • 28. Salgado, R.D., Zeitune, A.F.: ‘Power flow solutions through tensor methods’, IET Gener. Transm. Distrib., 2009, 3, (5), pp. 413–424.
    29. 29)
      • 29. Nemhauser, G.L., Wolsey, L.A.: ‘Integer and combinatorial optimization’ (Wiley, New York, NY, USA, 1999).
    30. 30)
      • 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.
    31. 31)
      • 31. Borghetti, A.: ‘Using mixed integer programming for the volt/var optimization in distribution feeders’, Electr. Power Syst. Res., 2013, 98, pp. 39–50.
    32. 32)
      • 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.
    33. 33)
      • 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.
    34. 34)
      • 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.
    35. 35)
      • 35. GAMS Development Corporation. General Algebraic Modeling System (GAMS) Release 24.8.5. Washington, DC, USA, 2017.
    36. 36)
      • 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.
    37. 37)
      • 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.
    38. 38)
      • 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.
    39. 39)
      • 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.
    40. 40)
      • 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.
    41. 41)
      • 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.
    42. 42)
      • 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.
    43. 43)
      • 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.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-gtd.2017.1844
Loading

Related content

content/journals/10.1049/iet-gtd.2017.1844
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading