Clusters partition and zonal voltage regulation for distribution networks with high penetration of PVs

Clusters partition and zonal voltage regulation for distribution networks with high penetration of PVs

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.

Integration of distributed generation (DG) at large scale with high penetration challenges the radial structure of the traditional distribution networks and the effectiveness of the conventional voltage regulation methods. In this study, the clusters partitioning and voltage regulation are researched. The modified electrical distance is introduced. An effective method, based on spectral clustering algorithm, is proposed for the partitioning of the DG network via the judgement of critical load buses. Two-stage voltage regulation optimisation is realised in each sub-community. The optimal objects are the minimal voltage fluctuation and the network loss of the distributed network. The independent variables are reactive-power absorption and active-power curtailment for each controllable photovoltaic node. An advanced particle swarm optimisation algorithm is applied to the voltage regulation for the sub-communities. After a case study of the IEEE 33-bus system, a regional distribution network in Anhui province of China is analysed. Simulation results indicate that the node voltages are stabilised with the improvement of power quality employing the proposed clusters partitioning method and zonal power control scheme.


    1. 1)
      • 1. Mahmud, N., Zahedi, A.: ‘Review of control strategies for voltage regulation of the smart distribution network with high penetration of renewable distributed generation’, Renew. Sustain. Energy Rev., 2016, 64, pp. 582595.
    2. 2)
      • 2. Haque, M.M., Wolfs, P.: ‘A review of high PV penetrations in LV distribution networks: present status, impacts and mitigation measures’, Renew. Sustain. Energy Rev., 2016, 62, pp. 11951208.
    3. 3)
      • 3. Ebad, M., Grady, W.M.: ‘An approach for assessing high-penetration PV impact on distribution feeders’, Electr. Power Syst. Res., 2016, 133, pp. 347354.
    4. 4)
      • 4. Muttaqi, K.M., Le, A.D.T., Negnevitsky, M., et al: ‘A coordinated voltage control approach for coordination of OLTC, voltage regulator, and DG to regulate voltage in a distribution feeder’, IEEE Trans. Ind. Appl., 2015, 51, (2), pp. 12391248.
    5. 5)
      • 5. Gaunt, C.T., Namanya, E., Herman, R.: ‘Voltage modelling of LV feeders with dispersed generation: limits of penetration of randomly connected photovoltaic generation’, Electr. Power Syst. Res., 2017, 143, pp. 16.
    6. 6)
      • 6. Tayal, D.: ‘Achieving high renewable energy penetration in western Australia using data digitization and machine learning’, Renew. Sustain. Energy Rev., 2017, 80, pp. 15371543.
    7. 7)
      • 7. Magnusson, S., Fischione, C., Li, N.: ‘Voltage control using limited communication’, Int. Fed. Autom. Control, IFAC PapersOnLine, 2017, 50, (1), pp. 16.
    8. 8)
      • 8. González, S.L., Frías, P., Mateo, C.: ‘Techno-economic assessment of forecasting and communication on centralized voltage control with high PV penetration’, Electr. Power Syst. Res., 2017, 151, pp. 338347.
    9. 9)
      • 9. Leitea, L., Boaventura, W., Errico, L., et al: ‘Integrated voltage regulation in distribution grids with photovoltaic distribution generation assisted by telecommunication infrastructure’, Electr. Power Syst. Res., 2016, 136, pp. 110124.
    10. 10)
      • 10. Yang, Y., Li, H., Aichhorn, A., et al: ‘Sizing strategy of distributed battery storage system with high penetration of photovoltaic for voltage regulation and peak load shaving’, IEEE Trans. Smart Grid, 2014, 5, (2), pp. 982991.
    11. 11)
      • 11. Wang, X.X., Wang, C.S., Tao, X., et al: ‘Optimal voltage regulation for distribution networks with multi-microgrids’, Appl. Energy, 2017, 110, pp. 10271036.
    12. 12)
      • 12. Mehmood, K.K., Khan, S.U., Lee, S.J., et al: ‘A real-time optimal coordination scheme for the voltage regulation of a distribution network including an OLTC, capacitor banks, and multiple distributed energy resources’, Int. J. Electr. Power, 2018, 94, pp. 114.
    13. 13)
      • 13. Rafi, F.H.M., Hossain, M.J., Lu, J.: ‘Hierarchical controls selection based on PV penetrations for voltage rise mitigation in a LV distribution network’, Int. J. Electr. Power, 2016, 81, pp. 123139.
    14. 14)
      • 14. Darwish, E.M., Hasanien, H.M., Atallah, A., et al: ‘Reactive power control of three-phase low voltage system based on voltage to increase PV penetration levels’, Ain Shams Eng. J., 2017.
    15. 15)
      • 15. Kryonidis, G.C., Demoulias, C.S., Papagiannis, G.K.: ‘A nearly decentralized voltage regulation algorithm for loss minimization in radial MV networks with high DG penetration’, IEEE Trans. Sustain. Energy, 2016, 7, (4), pp. 14301439.
    16. 16)
      • 16. Tian, M., Lau, W.H., Chong, S., et al: ‘A schedule-control aided strategy for charging large number of EVs under normal and line failure scenarios’, IEEE Access, 2017, PP, (99), p. 1.
    17. 17)
      • 17. Zhou, Y., Li, H., Liu, L.: ‘Integrated autonomous voltage regulation and islanding detection for high penetration PV applications’, IEEE Trans. Power Electr., 2013, 28, (6), pp. 28262841.
    18. 18)
      • 18. Li, P., Ji, H., Wang, C., et al: ‘A coordinated control method of voltage and reactive power for active distribution networks based on soft open point’, IEEE Trans. Sustain. Energy, 2017, PP, (99), p. 1.
    19. 19)
      • 19. Azzouz, M.A., Shaaban, M.F., ElSaadany, E.F: ‘Real-time optimal voltage regulation for distribution networks incorporating high penetration of PEVs’, IEEE Syst. J., 2015, 30, (6), pp. 32343245.
    20. 20)
      • 20. Azzouz, M.A., Shaaban, M.F., ElSaadany, E.F.: ‘Real-time fuzzy voltage regulation for distribution networks incorporating high penetration of renewable sources’, IEEE Syst. J., 2017, 11, (3), pp. 17021711.
    21. 21)
      • 21. Bahramipanah, M., Cherkaoui, R., Paolone, M.: ‘Decentralized voltage control of clustered active distribution network by means of energy storage systems’, Electr. Power Syst. Res., 2016, 136, pp. 370382.
    22. 22)
      • 22. Ezhilarasi, G.A., Swarup, K.S.: ‘Network partitioning using harmony search and equivalencing for distributed computing’, J. Parallel Distrib. Comput., 2012, 72, (8), pp. 936943.
    23. 23)
      • 23. Vallee, F., Brunieau, G., Pirlot, M., et al: ‘Optimal wind clustering methodology for adequacy evaluation in system generation studies using non-sequential Monte Carlo simulation’, IEEE Trans. Power Electr., 2011, 26, (4), pp. 21732184.
    24. 24)
      • 24. Smith, J.W., Dugan, R., Sunderman, W.: ‘Distribution modeling and analysis of high penetration PV’. Power Energy Society General Meeting, Detroit, MI, USA, October 2011, pp. 17.
    25. 25)
      • 25. Bahramipanah, M.: ‘Primary voltage control in active distribution networks via broadcast signals – the case of distributed storage’, IEEE Smart Grid, 2014, 5, (5), pp. 23142325.
    26. 26)
      • 26. Zhang, M., Chen, J.: ‘Islanding and scheduling of power distribution systems with distributed generation’, IEEE Trans. Power Syst., 2015, 30, (6), pp. 31203129.
    27. 27)
      • 27. Zhao, B., Xu, Z., Xu, C., et al: ‘Network partition based zonal voltage control for distribution networks with distributed PV systems’, IEEE Trans. Smart Grid, 2017, PP, (99), p. 1.
    28. 28)
      • 28. Arefifar, S.A., Mohamed, A.R.I., ElFouly, T.: ‘Optimized multiple microgrid-based clustering of active distribution systems considering communication and control requirements’, IEEE Trans. Ind. Electr., 2015, 62, (2), pp. 711723.
    29. 29)
      • 29. Jia, Y., Xu, Z.: ‘A direct solution to bi-objective partitioning problem in electric power networks’, IEEE Trans. Power Syst., 2016, PP, (99), p. 1.
    30. 30)
      • 30. Cotilla, S.E., Hines, P.D.H., Barrows, C., et al: ‘Multi-attribute partitioning of power networks based on electrical distance’, IEEE Trans. Power Syst., 2013, 28, (4), pp. 49794987.
    31. 31)
      • 31. Christakou, K., Leboudec, J.Y., Paolone, M., et al: ‘Efficient computation of sensitivity coefficients of node voltages and line currents in unbalanced radial electrical distribution networks’, IEEE Trans. Smart Grid, 2012, 4, (2), pp. 741750.
    32. 32)
      • 32. Munshi, A.A., Mohamed, Y.A.R.I.: ‘Photovoltaic power pattern clustering based on conventional and swarm clustering methods’, Sol. Energy, 2016, 124, pp. 3956.
    33. 33)
      • 33. Davies, D.L., Bouldin, D.W.: ‘A cluster separation measure’, IEEE Trans. Pattern Anal. Mach., 1979, PAMI-1, (2), pp. 224227.
    34. 34)
      • 34. Jain, R., Koronios, A.: ‘Innovation in the cluster validating techniques’, Fuzzy Optim. Decis. Mak., 2008, 7, (3), pp. 233241.

Related content

This is a required field
Please enter a valid email address