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access icon free Approach of hybrid PBIL control in distributed generation parameters for IEEE and real time Indian utility system

© The Institution of Engineering and Technology
Received 14/06/2016, Accepted 22/09/2016, Revised 15/09/2016, Published 26/09/2016

This study presents a method for regulation parameters of a distributed generation (DG) system by means of a hybrid optimisation algorithm. This aims in increasing the stability and reducing the losses and the cost of generation. The hybrid algorithm which includes probability based incremental learning and micro genetic algorithm are tested among other computational intelligence techniques to validate the efficiency of the method by maximising the total social welfare and minimising the network congestion. Simultaneous optimisation of DG parameters which includes DG size, location and type is explored using generation rescheduling and with load curtailment which is vindicated on a modified IEEE distribution system and in a real time Indian utility system. Results show us that the proposed method presents advantages of low computational complexity.

Inspec keywords: distributed power generation; power generation scheduling; power generation control; computational complexity; power distribution control; probability; power engineering computing; learning (artificial intelligence); genetic algorithms; power system stability

Other keywords: distributed generation parameters; generation cost; generation rescheduling; regulation parameters; hybrid optimisation; loss reduction; computational complexity; computational intelligence; microgenetic algorithm; social welfare; real time Indian utility system; modified IEEE distribution system; probability based incremental learning; hybrid PBIL control; load curtailment; network congestion

Subjects: Neural computing techniques; Control of electric power systems; Other topics in statistics; Optimisation techniques; Power engineering computing; Distributed power generation; Power system control; Stability in control theory; Other topics in statistics; Optimisation techniques; Power system management, operation and economics

References

    1. 1)
      • 30. Elgerd, I.O.: ‘Energy system theory-an introduction’ (McGraw-Hill Inc., New York, 1971).
    2. 2)
      • 6. Kaplan, M.: ‘Optimization of number, location, size, control type and control setting of shunt capacitors on radial distribution feeders’, IEEE Trans. Power Appar. Syst., 1984, 103, (9), pp. 2659–2665.
    3. 3)
      • 13. Gozel, T., Hocaoglu, M.H.: ‘Än analytical method for the sizing and sitting of distributed generators in radial system’, Int. J. Electr. Power Syst. Res., 2009, 79, pp. 912–918.
    4. 4)
      • 18. Nayanatara, C., Baskaran, J., Kothari, D.P.: ‘Simulated annealing approach for congestion minimization using distributed powerr generation’, 2015, 1 pp. 0276–0281, 978-1-4673-6524-6.
    5. 5)
      • 15. Ziari, I., Ledwich, G., Ghosh, A., et al: ‘Integrated distribution systems planning to improve reliability under load growth’, IEEE Trans. Power Deliv., 2012, 27, (2), p. 757.
    6. 6)
      • 24. Alagoz, B.B., Kygusuz, A., Akcin, M., et al: ‘A closed loop energy price controlling method for real time energy balancing in a smart grid energy market’, Energy, 2013, 59, pp. 95–104.
    7. 7)
      • 2. Akorde, M.F., Nizam, H., Aris, I., et al: ‘A review of strategies for optimal placement of distributed generation in power distribution systems’, Res. J. Appl. Sci., 2011, 5, (2), pp. 137–145.
    8. 8)
      • 12. Jiang, W., Zhang, L., zhao, H.: ‘Research on power sharing strategy of hybrid energy storage system in photovoltaic power station based on multi objective optimization’, IET Renew. Power Gener., 2016, 10, (5), pp. 575–583.
    9. 9)
      • 21. Chowdhury, A.A., Agarwal, S.K., Koval, D.O.: ‘Reliability modeling of distributed generation in conventional distribution systems planning and analysis’, IEEE Trans. Ind. Appl., 2003, 39, (5), pp. 1493–1498.
    10. 10)
      • 11. Zareipour, H., Bhattacharya, K., Canizares, C.A.: ‘Distributed generation: current status and challenges’. IEEE Proc. of NAPS, 2004.
    11. 11)
      • 32. Goldberg, D.E.: ‘Genetic algorithms in search, optimization and machine learning’ (Addison-Wesley, 1989).
    12. 12)
      • 20. Yammani, C., Maheswarapu, S., kumari, s.: ‘Optimal placement of Multi DGs in distribution system with considering the DG bus available limits’, Energy Power, 2012, 2, (1), pp. 18–23.
    13. 13)
      • 3. Xu, Q., He, D., Zhang, N., et al: ‘A short-term wind power forecasting approach with adjustment of numerical weather prediction input by data mining’, IEEE Trans. Sustain. Energy, 2015, 6, (4), pp. 1283–1291.
    14. 14)
      • 10. Moghaddas-Tafreshi, S.M., Mashhour, E.: ‘Distributed generation modeling for power flow studies and a three phase unbalanced power flow solution for radial distribution systems considering distributed generation’, Electr. Power Syst. Res., 2009, 79, (4) pp. 680–686.
    15. 15)
      • 22. Teng, J.-H., Liu, Y.-H., Chen, C.-Y., et al: ‘Value- based distributed generator placements for service quality improvements’, Int. J. Electr. Power Energy Syst., 2007, 29, (3), pp. 268–274.
    16. 16)
      • 27. Ackermann, T., Andersson, G., Soder, L.: ‘Distributed generation: A definition’, Electr. Power Syst. Res., 2001, 57, (3), pp. 195–204.
    17. 17)
      • 28. Khattam, W.E., Bhattacharya, K., Hegazy, Y., et al: ‘Optimal investment planning for distributed generation in a competitive electricity market’, IEEE Trans. Power Syst., 2004, 19, (4), pp. 206–212.
    18. 18)
      • 9. Celli, G., Pilo, F.: ‘Penetration level assessment of distributed allocation in MV distribution networks’. 22nd IEEE PES Int. Conf. on Power Industry Computer Applications, PICA 2001, Sydney, 2001, pp. 81–86.
    19. 19)
      • 17. Nayanatara, C., Baskaran, J., Kothari, D.P.: ‘Hybrid optimization implemented for distributed generation parameters in a power system network’, Int. J. Electr. Power Energy Syst.2016, 78, pp. 690–699.
    20. 20)
      • 4. Wang, C., Nehrir, M.H.: ‘Analytical approaches for optimal placement of distributed generation sources in power systems’, IEEE Trans. Power Syst., 2004, 19, (4), pp. 2068–2076.
    21. 21)
      • 26. Singh, R.K., Goswami, S.K.: ‘Optimum allocation of distributed generations based on nodal pricing for profit, loss reduction and voltage improvement including voltage rise issue’, Int. J. Electr. Power Energy Syst., 2010, 32, (6), pp. 637–644.
    22. 22)
      • 19. Baranand, M.E., Wu, F.F.: ‘Optimum sizing of capacitor placed on radial distribution systems’, IEEE Trans. Power Deliv., 1989, 4, (1), pp. 735–743.
    23. 23)
      • 29. Singh, A.K., Parid, S.K.: ‘Evaluation of current status and future directions of wind energy in India’, Clean Technol. Environ. Policy, 2013, 15, (4), pp. 643–655.
    24. 24)
      • 14. Silvestri, A., Berizzi, A., Buonanno, S.: ‘Distributed generation planning using genetic algorithms’. Int. Conf. Electric Power Engineering, Power Tech Budapest 99, 1999, p. 257.
    25. 25)
      • 5. Alagoz, B.B., Kaygusuz, A.: ‘Dynamic energy pricing by closed loop fractional- order PI control system and energy balancing in smart grid energy markets’, Trans. Inst. Meas. Control, 2016, 38, (5), pp. 565–578.
    26. 26)
      • 7. Acharya, N., Mahat, P., Mithulananthan, N.: ‘An analytical approach for DG allocation in primary distribution network’, Int. J. Electr. Power Energy Syst., 2006, 28, (10), pp. 669–678.
    27. 27)
      • 16. Nayanatara, C., Baskaran, J., Kothari, D.P.: ‘GA implemented for distribution generation parameters in IEEE and indian utility system’, Middle-East J. Sci. Res., 2015, 23, (6), pp. 1127–1136.
    28. 28)
      • 23. Hosseizadeh, M., salmasi, F.R.: ‘Power management of an isolated hybrid AC/DCmicro-grid with fuzzy control of battery banks’, IET Renew. Power Gener., 2015, 9, (5), pp. 484–493.
    29. 29)
      • 1. EI-Zonkoly, A.M.: ‘Optimal placement of multi-distributed generation units including different load models using particle swarm optimization’, IET Gener. Transm. Distrib., 2011, 5, (7), pp. 760–771.
    30. 30)
      • 31. Kothari, D.P., Dhillon, J.S.: ‘Power system optimization’ (Prentice-Hall, New Delhi, 2006).
    31. 31)
      • 8. Zhi-Yu, X., Hai-Nai, Q., Wei-Hui, S., et al: ‘Virtual power plant –based pricing control for wind/thermal cooperated generation in China’, IEEE Trans. Syst. Man Cybern. Syst., 2016, 46, (5), pp. 706–712.
    32. 32)
      • 25. Rau, N.S., Wan, Y.H.: ‘Optimum location of resources in distributed planning’, IEEE Trans. Power Syst., 1994, 9, (4), pp. 2014–2020.
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