Load service restoration in active distribution network based on stochastic approach

Load service restoration in active distribution network based on stochastic approach

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

Buy article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

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
Your details
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.

A new stochastic framework is proposed for distribution system restoration (DSR) problem. In this framework, dynamic programming (DP) is used to solve DSR problem for a distribution system that accommodates various technologies of distributed generation units and storage systems. Time and sequence of distribution feeders which should be energised after a blackout are chosen as stages of DP algorithm. In addition, uncertainties associated with the power received from transmission network in the period of system restoration are modelled as some probabilistic scenarios. The complexity of attained optimisation problem is reduced using some state reduction techniques. These techniques enable DP algorithm to reach near-optimal solution as well as computationally efficient calculation procedure. The proposed restoration framework is applied on a real-world test system and the results prove its effectiveness and practicality.


    1. 1)
      • 1. Moeini-Aghtaie, M., Farzin, H., Fotuhi-Firuzabad, M., et al: ‘Generalized analytical approach to assess reliability of renewable-based energy hubs’, IEEE Trans. Power Syst., 2017, 32, (1), pp. 368377.
    2. 2)
      • 2. Pérez-Guerrero, R., Heydt, G.T., Jack, N.J., et al: ‘Optimal restoration of distribution systems using dynamic programming’, IEEE Trans. Power Deliv., 2008, 23, (3), pp. 15891596.
    3. 3)
      • 3. Perez-Guerrero, R.E., Heydt, G.T.: ‘Distribution system restoration via subgradient-based Lagrangian relaxation’, IEEE Trans. Power Syst., 2008, 23, (3), pp. 11621169.
    4. 4)
      • 4. Fink, L.H., Liou, K.-L., Liu, C.-C.: ‘From generic restoration actions to specific restoration strategies’, IEEE Trans. Power Syst., 1995, 10, (2), pp. 745752.
    5. 5)
      • 5. López, J.C., Franco, J.F., Rider, M.J.: ‘Optimisation-based switch allocation to improve energy losses and service restoration in radial electrical distribution systems’, IET Gener. Transm. Distrib., 2016, 10, (11), pp. 27922801.
    6. 6)
      • 6. Pham, T.T.H., Bésanger, Y., Hadjsaid, N.: ‘New challenges in power system restoration with large scale of dispersed generation insertion’, IEEE Trans. Power Syst., 2009, 24, (1), pp. 398406.
    7. 7)
      • 7. Wang, Z., Wang, J.: ‘Service restoration based on AMI and networked MGs under extreme weather events’, IET Gener. Transm. Distrib., 2017, 11, (2), pp. 401408.
    8. 8)
      • 8. Liu, C.-C., Lee, S.J., Venkata, S.: ‘An expert system operational aid for restoration and loss reduction of distribution systems’, IEEE Trans. Power Syst., 1988, 3, (2), pp. 619626.
    9. 9)
      • 9. Ucak, C., Pahwa, A.: ‘An analytical approach for step-by-step restoration of distribution systems following extended outages’, IEEE Trans. Power Deliv., 1994, 9, (3), pp. 17171723.
    10. 10)
      • 10. Toune, S., Fudo, H., Genji, T., et al: ‘Comparative study of modern heuristic algorithms to service restoration in distribution systems’, IEEE Trans. Power Deliv., 2002, 17, (1), pp. 173181.
    11. 11)
      • 11. Chen, C.-S., Lin, C.-H., Tsai, H.-Y.: ‘A rule-based expert system with colored petri net models for distribution system service restoration’, IEEE Trans. Power Syst., 2002, 17, (4), pp. 10731080.
    12. 12)
      • 12. Hsu, Y.-Y., Huang, H.-M.: ‘Distribution system service restoration using the artificial neural network approach and pattern recognition method’, IEE Proc. Gener. Transm. Distrib., 1995, 142, (3), pp. 251256.
    13. 13)
      • 13. Hsu, Y.-Y., Kuo, H.-C.: ‘A heuristic based fuzzy reasoning approach for distribution system service restoration’, IEEE Trans. Power Deliv., 1994, 9, (2), pp. 948953.
    14. 14)
      • 14. Ciric, R.M., Popovic, D.: ‘Distribution network restoration using fuzzy set approach and mix integer programming’. p. 177.
    15. 15)
      • 15. Lim, S.-I., Lee, S.-J., Choi, M.-S., et al: ‘Restoration index in distribution systems and its application to system operation’, IEEE Trans. Power Syst., 2006, 21, (4), pp. 19661971.
    16. 16)
      • 16. Momoh, J., Caven, A.C.: ‘Distribution system reconfiguration scheme using integer interior point programming technique’. pp. 234241.
    17. 17)
      • 17. Sheng, W., Liu, K.-Y., Cheng, S.: ‘Optimal power flow algorithm and analysis in distribution system considering distributed generation’, IET Gener. Transm. Distrib., 2014, 8, (2), pp. 261272.
    18. 18)
      • 18. Miu, K.N., Chiang, H.-D., Yuan, B., et al: ‘Fast service restoration for large-scale distribution systems with priority customers and constraints’, IEEE Trans. Power Syst., 1998, 13, (3), pp. 789795.
    19. 19)
      • 19. Amini, A.A., Weymouth, T.E., Jain, R.C.: ‘Using dynamic programming for solving variational problems in vision’, IEEE Trans. Pattern Anal. Mach. Intell., 1990, 12, (9), pp. 855867.
    20. 20)
      • 20. Bellman, R.E., Dreyfus, S.E.: ‘Applied dynamic programming’ (Princeton University Press, London, UK, 2015).
    21. 21)
      • 21. Hosseini, S.A., Madahi, S.S.K., Razavi, F., et al: ‘Optimal sizing and siting distributed generation resources using a multiobjective algorithm’, Turkish J. Electr. Eng. Comput. Sci., 2013, 21, (3), pp. 825850.

Related content

This is a required field
Please enter a valid email address