http://iet.metastore.ingenta.com
1887

access icon free GPU-based parallel real-time volt/var optimisation for distribution network considering distributed generators

  • HTML
    256.123046875Kb
  • PDF
    3.4142351150512695MB
  • XML
    214.412109375Kb
Loading full text...

Full text loading...

/deliver/fulltext/iet-gtd/12/20/IET-GTD.2017.1887.html;jsessionid=3252db57kg1nw.x-iet-live-01?itemId=%2fcontent%2fjournals%2f10.1049%2fiet-gtd.2017.1887&mimeType=html&fmt=ahah

References

    1. 1)
      • 1. Zhu, J.: ‘Optimization of power system operation’ (John Wiley & Sons, New Jersey, 2015, 2nd edn.).
    2. 2)
      • 2. Ahmadi, H., Marti, J. R., Dommel, H. W.: ‘A framework for volt-VAR optimization in distribution systems’, IEEE Trans. Smart Grid, 2015, 6, (3), pp. 14731483.
    3. 3)
      • 3. Saric, A. T., Stankovic, A. M.: ‘A robust algorithm for volt/Var control’. Proc. Power Syst. Conf. Expo., Seattle, WA, USA, Mar. 2009, pp. 18.
    4. 4)
      • 4. Zheng, W., Wu, W., Zhang, B., et al: ‘Robust reactive power optimisation and voltage control method for active distribution networks via dual time-scale coordination’, IET Gener. Transm. Distrib., 2017, 11, (6), pp. 14611471.
    5. 5)
      • 5. Sayadi, F., Esmaeili, S., Keynia, F.: ‘Two-layer volt/var/total harmonic distortion control in distribution network based on PVs output and load forecast errors’, IET Gener. Transm. Distrib., 2017, 11, (8), pp. 21302137.
    6. 6)
      • 6. Mohapatra, A., Bijwe, P. R., Panigrahi, B. K.: ‘An efficient hybrid approach for volt/Var control in distribution systems’, IEEE Trans. Power Deliv., 2014, 29, (4), pp. 17801788.
    7. 7)
      • 7. Rahimi, S., Zhu, K., Massucco, S., et al: ‘Stochastic volt-Var optimization function for planning of MV distribution networks’. Proc. IEEE Power Energy Soc. Gen. Meeting, Denver, CO, USA, Jul. 2015, pp. 15.
    8. 8)
      • 8. Borghetti, A.: ‘Using mixed integer programming for the volt/var optimization in distribution feeders’, Electr. Power Syst. Res., 2013, 98, pp. 3950.
    9. 9)
      • 9. Fang, X., Li, F., Wei, Y., et al: ‘Reactive power planning under high penetration of wind energy using benders decomposition’, IET Gener. Transm. Distrib., 2015, 9, (14), pp. 18351844.
    10. 10)
      • 10. Wang, Z., Wang, J., Chen, B., et al: ‘MPC-based voltage/var optimization for distribution circuits with distributed generators and exponential load models’, IEEE Trans. Smart Grid, 2014, 5, (5), pp. 24122420.
    11. 11)
      • 11. Malachi, Y., Singer, S.: ‘A genetic algorithm for the corrective control of voltage and reactive power’, IEEE Trans. Power Syst., 2006, 21, (1), pp. 295300.
    12. 12)
      • 12. Ulinuha, A., Masoum, M., Islam, S.: ‘Hybrid genetic-fuzzy algorithm for volt/var/total harmonic distortion control of distribution systems with high penetration of non-linear loads’, IET Gener. Transm. Distrib., 2011, 5, (4), pp. 425439.
    13. 13)
      • 13. Fukuyama, Y.: ‘Parallel particle swarm optimization for reactive power and voltage control verifying dependability’. Proc. IEEE Congr. Evol. Comput., Sendai, Japan, May 2015, pp. 304310.
    14. 14)
      • 14. Niknam, T., Firouzi, B. B., Ostadi, A.: ‘A new fuzzy adaptive particle swarm optimization for daily volt/Var control in distribution networks considering distributed generators’, Appl. Energy, 2010, 87, (6), pp. 19191928.
    15. 15)
      • 15. Chaudhary, D., Sun, W., Zhou, Q., et al: ‘Chance-constrained real-time volt/var optimization using simulated annealing’. Proc. IEEE Power Energy Soc. Gen. Meeting, Denver, CO, USA, Jul. 2015, pp. 15.
    16. 16)
      • 16. Zakariazadeh, A., Modaghegh, H., Jadid, S.: ‘Real time volt/Var control using advance metering infrastructure system in FAHAM project’. Proc. Int. Conf. and Exhibition on Electricity and Distribution, Stockholm, Sweden, Jun. 2013, pp. 14.
    17. 17)
      • 17. Manbachi, M., Sadu, A., Farhangi, H., et al: ‘Real-time co-simulation platform for smart grid volt-var optimization using IEC 61850’, IEEE Trans. Ind. Inf., 2016, 12, (4), pp. 13921402.
    18. 18)
      • 18. Manbachi, M., Sadu, A., Farhangi, H., et al: ‘Real-time co-simulated platform for novel volt-VAR optimization of smart distribution network using AMI data’. Proc. IEEE Int. Conf. Smart Energy Grid Eng., Oshawa, Canada, Aug. 2015, pp. 17.
    19. 19)
      • 19. Feng, X., Peterson, W., Yang, F., et al: ‘Smarter grids are more efficient’, ABB Rev., 2009, 3, pp. 3337.
    20. 20)
      • 20. Chandra, R., Dagum, L., Kohr, D., et al: ‘Parallel programming in OpenMP’ (Morgan Kaufmann, San Francisco, 2001).
    21. 21)
      • 21. NVIDIA: ‘CUDA c programming guide 8.0’ (NVIDIA Corporation, Santa Clara, CA, USA, 2017).
    22. 22)
      • 22. Jalili-Marandi, V., Zhou, Z., Dinavahi, V.: ‘Large-scale transient stability simulation of electrical power systems on parallel GPUs’, IEEE Trans. Parallel Distrib. Syst., 2012, 23, (7), pp. 12551266.
    23. 23)
      • 23. Zhou, Z., Dinavahi, V.: ‘Fine-grained network decomposition for massively parallel electromagnetic transient simulation of large power systems’, IEEE Power Energy Tech. Syst. J., 2017, 4, (3), pp. 5164.
    24. 24)
      • 24. Yan, S., Zhou, Z., Dinavahi, V.: ‘Large-scale nonlinear device-level power electronic circuit simulation on massively parallel graphics processing architectures’, IEEE Trans. Power Electron., 2017, PP, (99), pp. 119.
    25. 25)
      • 25. Zhou, G., Feng, Y., Bo, R., et al: ‘GPU-accelerated batch-ACPF solution for N-1 static security analysis’, IEEE Trans. Smart Grid, 2017, 8, (3), pp. 14061416.
    26. 26)
      • 26. Huang, S., Dinavahi, V.: ‘Fast batched solution for real-time optimal power flow with penetration of renewable energy’, IEEE Access, 2018, PP, (99), pp. 113.
    27. 27)
      • 27. Karimipour, H., Dinavahi, V.: ‘Parallel relaxation-based joint dynamic state estimation of large-scale power systems’, IET Gener. Transm. Distrib., 2016, 10, (2), pp. 452459.
    28. 28)
      • 28. Karimipour, H., Dinavahi, V.: ‘Extended Kalman filter-based parallel dynamic state estimation’, IEEE Trans. Smart Grid, 2015, 6, (3), pp. 15391549.
    29. 29)
      • 29. Teng, J.: ‘A direct approach for distribution system load flow solutions’, IEEE Trans. Power Deliv., 2003, 18, (3), pp. 882887.
    30. 30)
      • 30. Cano, J., Mojumdar, M. R., Norniella, J. G., et al: ‘Phase shifting transformer model for direct approach power flow studies’, Int. J. Electr. Power Energy Syst., 2017, 91, pp. 7179.
    31. 31)
      • 31. Zimmerman, R., Murillo-Sanchez, C., Thomas, R.: ‘MATPOWER: steady-state operations, planning, and analysis tools for power systems research and education’, IEEE Trans. Power Syst.., 2011, 26, (1), pp. 1219.
    32. 32)
      • 32. Teng, J.: ‘Modeling distributed generations in three-phase distribution load flow’, IET Gener. Transm. Distrib., 2008, 2, (3), pp. 330340.
    33. 33)
      • 33. Murugan, P: ‘Modified particle swarm optimisation with a novel initialisation for finding optimal solution to the transmission expansion planning problem’, IET Gener. Transm. Distrib., 2012, 6, (11), pp. 11321142.
    34. 34)
      • 34. Huang, S., Dinavahi, V.: ‘Multi-group particle swarm optimization for transmission expansion planning solution based on LU decomposition’, IET Gener. Transm. Distrib., 2017, 11, (6), pp. 14341442.
    35. 35)
      • 35. Davis, T.A.: ‘Direct methods for sparse linear systems’ (Society for Industrial and Applied Mathematics (SIAM), Philadelphia, 2006).
    36. 36)
      • 36. Roberge, V.: ‘Distribution feeder reconfiguration (DFR) test cases’, http://roberge.segfaults.net/joomla/index.php/dfr, (accessed July 2017).
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-gtd.2017.1887
Loading

Related content

content/journals/10.1049/iet-gtd.2017.1887
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address