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access icon free Networked microgrids for service restoration in resilient distribution systems

  • Author(s): Anmar Arif 1, 2  and  Zhaoyu Wang 1
    • View affiliations
  • Source: Volume 11, Issue 14, 28 September 2017, p. 3612 – 3619
    DOI:  10.1049/iet-gtd.2017.0380 , Print ISSN 1751-8687, Online ISSN 1751-8695
© The Institution of Engineering and Technology
Received 14/03/2017, Accepted 07/06/2017, Published 09/06/2017

This study presents a novel networked microgrid (MG)-aided approach for service restoration in power distribution systems. This study considers both dispatchable and non-dispatchable distributed generators (DGs), and energy storage systems. The uncertainty of the customer load demands and DG outputs are modelled in a scenario-based form. A stochastic mixed-integer linear program is formulated with the objective to maximise the served load, while satisfying the operation constraints of the distribution system and MGs. The interaction among MGs is modelled using the type 1 special ordered set. Two approaches are developed and compared: (i) a centralised approach where all MGs are controlled by a distribution system operator, and (ii) a decentralised approach where the distribution system and MGs are managed by different entities. The proposed restoration models are tested on a modified IEEE 123-bus distribution system. The results demonstrate the advantages of leveraging networked MGs to facilitate service restoration.

Inspec keywords: integer programming; distributed power generation; centralised control; decentralised control; linear programming; power generation control; energy storage; distribution networks; power system restoration; stochastic programming; demand side management; uncertain systems

Other keywords: served load maximisation; operation constraints; service restoration; customer load demand uncertainty; power distribution systems; modified IEEE 123-bus distribution system; networked MG-aided approach; dispatchable distributed generators; distribution system operator; nondispatchable distributed generators; resilient distribution systems; decentralised approach; energy storage systems; networked microgrids; centralised approach; stochastic mixed-integer linear program

Subjects: Control of electric power systems; Optimisation techniques; Distribution networks; Optimisation techniques; Multivariable control systems; Power system management, operation and economics; Distributed power generation

References

    1. 1)
      • 11. Wang, Z., Wang, J.: ‘Self-healing resilient distribution systems based on sectionlization into microgrids’, IEEE Trans. Power Syst., 2015, 30, (6), pp. 3139–3149.
    2. 2)
      • 13. Li, J., Ma, X.Y., Liu, C.C., et al: ‘Distribution system restoration with microgrids using spanning tree search’, IEEE Trans. Power Syst., 2014, 29, (6), pp. 3021–3029.
    3. 3)
      • 20. 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. 1106–1116.
    4. 4)
      • 10. Xu, Y., Liu, C.C., Schneider, K., et al: ‘Microgrids for service restoration to critical load in a resilient distribution system’, IEEE Trans. Smart Grid, 2016, PP, (99), pp. 1–12.
    5. 5)
      • 12. Shahnia, F., Bourbour, S., Ghosh, A.: ‘Coupling neighboring microgrids for overload management based on dynamic multicriteria decision making’, IEEE Trans. Smart Grid, 2015, 8, (2), pp. 969–983.
    6. 6)
      • 14. Siddiqui, S., Gabriel, S.A.: ‘An SOS1-based approach for solving MPECs with a natural gas market application’, Netw. Spat. Econ., 2012, 13, (2), pp. 205–227.
    7. 7)
      • 9. Wang, Y., Chen, C., Wang, J., et al: ‘Research on resilience of power systems under natural disasters - a review’, IEEE Trans. Power Syst., 2016, 31, (2), pp. 1604–1613.
    8. 8)
      • 17. Baran, M., Wu, F.: ‘Optimal capacitor placement on radial distribution systems’, IEEE Trans. Power Deliv., 1989, 4, (1), pp. 725–734.
    9. 9)
      • 19. Moshi, G.G., Bovo, C., Berizzi, A., et al: ‘Optimal operational planning for PV-wind-diesel-battery microgrid’. Proc. IEEE PowerTech, 2015, pp. 1–6.
    10. 10)
      • 15. Torquato, R., Shi, Q., Xu, W., et al: ‘A Monte Carlo simulation platform for studying low voltage residential networks’, IEEE Trans. Smart Grid, 2014, 5, (6), pp. 2766–2776.
    11. 11)
      • 1. Campbell, R.J.: ‘Weather-related power outages and electric system resiliency’ (Library of Congress, 2012).
    12. 12)
      • 16. Lu, N., Diao, R., Hafen, R., et al: ‘A comparison of forecast error generators for modeling wind and load uncertainty’. IEEE PES General Meeting, 2013, pp. 1–5.
    13. 13)
      • 5. Wang, Z., Chen, B., Wang, J., et al: ‘Robust optimization based optimal DG placement in microgrids’, IEEE Trans. Smart Grid, 2014, 5, (5), pp. 2173–2182.
    14. 14)
      • 3. Wei, Y., Ji, C., Galvan, F., et al: ‘Dynamic modeling and resilience for power distribution’. Proc. Int. Conf. Smart Grid Communications, 2013, pp. 85–90.
    15. 15)
      • 25. Kersting, W.: ‘Radial distribution test feeders’, IEEE Trans. Power Syst., 1991, 6, (3), pp. 975–985.
    16. 16)
      • 23. Shi, W., Xie, X., Chu, C.C., et al: ‘Distributed optimal energy management in microgrids’, IEEE Trans. Smart Grid, 2015, 6, (3), pp. 1137–1146.
    17. 17)
      • 2. Executive office of the President: ‘Economic benefits of increasing electric grid resilience to weather outages’ (White House, 2013).
    18. 18)
      • 4. Che, L., Khodayar, M., Shahidehpour, M.: ‘Only connect: microgrids for distribution system restoration’, IEEE Power Energy Mag., 2014, 12, (1), pp. 70–81.
    19. 19)
      • 18. Arif, A., Wang, Z., Wang, J., et al: ‘Power distribution system outage management with co-optimization of repairs, reconfiguration, and DG dispatch’, IEEE Trans. Smart Grid, 2017, PP, (99), pp. 1–10.
    20. 20)
      • 6. Moreira, C.L., Resende, F.O., Lopes, J.A.P.: ‘Using low voltage microgrids for service restoration’, IEEE Trans. Power Syst., 2007, 22, (1), pp. 395–403.
    21. 21)
      • 7. Wang, Z., Chen, B., Wang, J., et al: ‘Decentralized energy management system for networked microgrids in grid-connected and islanded modes’, IEEE Trans. Smart Grid, 2015, 7, (2), pp. 1097–1105.
    22. 22)
      • 8. Wang, Z., Chen, B., Wang, J., et al: ‘Networked microgrids for self-healing power systems’, IEEE Trans. Smart Grid, 2016, 7, (1), pp. 310–319.
    23. 23)
      • 21. Bizuayehu, A.W., Sánchez de la Nieta, A.A., Contreras, J., et al: ‘Impacts of stochastic wind power and storage participation on economic dispatch in distribution systems’, IEEE Trans. Sustain. Energy, 2016, 7, (3), pp. 1336–1345.
    24. 24)
      • 24. Kim, H.M., Kinoshita, T., Shin, M.C.: ‘A multiagent system for autonomous operation of islanded microgrids based on a power market environment’, Energies, 2010, 3, (12), pp. 1972–1990.
    25. 25)
      • 22. Hatziargyriou, N., Dimeas, A., Tsikalakis, A.: ‘Centralized and decentralized control of microgrids’, Int. J. Distrib. Energy Resour., 2005, 1, (3), pp. 197–212.
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