access icon free Multi-objective optimisation of step voltage regulator operation and optimal placement for distribution systems design using linkage combination update-non-dominated sorting genetic algorithm-II

© The Institution of Engineering and Technology
Received 30/08/2016, Accepted 22/08/2017, Revised 28/07/2017, Published 23/08/2017

This study proposes the application of combinatorial multi-objective optimisation (MOO) in an electrical power distribution system. Conventional electrical power systems do not consider reverse power flow, in which the power flows toward the feeder in the distribution system. However, reverse power flow toward the substation transformer is caused by voltage deviation with high penetration of distributed generators into a distribution system. Consequently, this causes faults in electric devices and may even lead to a massive blackout. To resolve voltage deviation problems, it is necessary to consider some trade-offs. With this background, this study reveals three points. The first and second contributions regard general engineering research issues such as the definition of a new optimisation problem framework. To solve the problems discussed in this study, a new method of MOO was required. This method of MOO is applied to the power system to minimise voltage deviation while simultaneously minimising the number of required voltage control devices and operation. In addition, a new MOO method to determine the optimal placement of control devices while retaining operation diversity is proposed. Finally, each optimisation method is compared with numerical simulation and the advantages are summarised from the simulation results.

Inspec keywords: power distribution control; genetic algorithms; voltage control

Other keywords: MOO; multiobjective optimisation; electrical power distribution system; voltage deviation problems; combinatorial multiobjective optimisation; electric devices; optimal placement; distribution systems design; substation transformer; voltage control devices; linkage combination update-non-dominated sorting genetic algorithm-II; step voltage regulator operation; distributed generators; operation diversity

Subjects: Voltage control; Distribution networks; Control of electric power systems; Optimisation techniques; Optimisation techniques; Power system control

References

    1. 1)
      • 1. Ling, A.P.A., Kokichi, S., Masao, M.: ‘The Japanese smart grid initiatives, investments, and collaborations’, Int. J. Adv. Sci. Appl., 2012, 3, (7), pp. 44–54.
    2. 2)
      • 7. Foote, C., Burt, G., Wasiak, I., et al: ‘A power-quality management algorithm for low-voltage grids with distributed resources’, IEEE Trans. Power Deliv., 2008, 23, (2), pp. 1055–1062.
    3. 3)
      • 34. Shukla, A.: ‘Multi-objective unit commitment with renewable energy using hybrid approach’, IET Renew. Power Gener., 2016, 10, (11), pp. 327–338.
    4. 4)
      • 9. Santos-Martin, D., Lemon, S.: ‘Simplified modeling of low voltage distribution networks for PV voltage impact studies’, IEEE Trans. Smart Grid, 2015, PP, (99), pp. 1–8.
    5. 5)
      • 16. Han, X., Sandels, C., Zhu, K., et al: ‘Modelling framework and the quantitative analysis of distributed energy resources in future distribution networks’, Int. J. Emerging Electr. Power Syst., 2013, 14, (5), pp. 421–431.
    6. 6)
      • 21. Almeida, L. M., Ludermir, T. B.: ‘A multi-objective memetic and hybrid methodology for optimizing the parameters and performance of artificial neural networks’, Neurocomputing, 2010, 73, (7–9), pp. 1438–1450.
    7. 7)
      • 30. Aparecido Ferreira, C., and Prada, R.: ‘Improved model for tap-changing transformer’, IET Gener. Transm. Distrib., 2013, 7, (11), pp. 1289–1295.
    8. 8)
      • 3. Ziadi, Z., Taira, S., Oshiro, M., et al: ‘Optimal power scheduling for smart grids considering controllable loads and high penetration of photovoltaic generation’, IEEE Trans. Smart Grid, 2014, 5, (5), pp. 2350–2359.
    9. 9)
      • 15. Roselyn, J.P., Devaraj, D., Dash, S.S.: ‘Multi-objective differential evolution for voltage security constrained optimal power flow in deregulated power systems’, Int. J. Emerging Electr. Power Syst., 2013, 14, pp. 591–607.
    10. 10)
      • 36. Mavrotas, G.: ‘Effective implementation of the ε-constraint method in multi-objective mathematical programming problems’, Appl. Math. Comput., 2009, 213, (2), pp. 455–465.
    11. 11)
      • 11. Senjyu, T., Miyazato, Y., Yona, A., et al: ‘Optimal distribution voltage control and coordination with distributed generation’, IEEE Trans. Power Deliv., 2008, 23, (2), pp. 1236–1242.
    12. 12)
      • 27. Liang, R.-H., Wang, Y.-S.: ‘Fuzzy-based reactive power and voltage control in a distribution system’, IEEE Trans. Power Deliv., 2003, 18, (2), pp. 610–618.
    13. 13)
      • 22. Tang, Y., He, H., Ni, Z., et al‘Advances in Computational Intelligence and Learning’. 17th European Symp. Artificial Neural Networks, 2014, 125, pp. 125–133.
    14. 14)
      • 19. Deb, K.: ‘Multi-objective optimization using evolutionary algorithms’ (Wiley, 2001).
    15. 15)
      • 5. Lee, S., Kwon, B., Lee, S.: ‘Joint energy management system of electric supply and demand in houses and buildings’, IEEE Trans. Power Syst., 2014, 29, (6), pp. 2804–2812.
    16. 16)
      • 10. Woyte, A., Van Thong, V., Belmans, R., et al: ‘Voltage fluctuations on distribution level introduced by photovoltaic systems’, IEEE Trans. Energy Convers., 2006, 21, (1), pp. 202–209.
    17. 17)
      • 29. Mazhari, S., Monsef, H., Romero, R.: ‘A multi-Objective distribution system expansion planning incorporating customer choices on reliability’, IEEE Trans. Power Syst., 2015, PP, (99), pp. 1–11.
    18. 18)
      • 28. Munoz-Delgado, G., Contreras, J., Arroyo, J.: ‘Joint expansion planning of distributed generation and distribution networks’, IEEE Trans. Power Syst., 2015, 30, (5), pp. 2579–2590.
    19. 19)
      • 14. Ram Prabhakar, J., Ragavan, K.: ‘STATCOM-based wind-solar-hydro electric power system with modified real and reactive power controls’, Int. J. Emerging Electr. Power Syst., 2014, 15, pp. 45–58.
    20. 20)
      • 13. Tanaka, K., Oshiro, M., Toma, S., et al: ‘Decentralised control of voltage in distribution systems by distributed generators’, IET Gener. Transm. Distrib., 2010, 4, (11), pp. 1251–1260.
    21. 21)
      • 24. Wang, J., Fu, C., Zhang, Y.: ‘SVC control system based on instantaneous reactive power theory and fuzzy PID’, IEEE Trans. Ind. Electron., 2008, 55, (4), pp. 1658–1665.
    22. 22)
      • 8. Mazumder, S., Ghosh, A., Zare, F.: ‘Improving power quality in low-voltage networks containing distributed energy resources’, Int. J. Emerging Electr. Power Syst., 2013, 14, (1), pp. 67–78.
    23. 23)
      • 26. Cezar Rabelo, B., Hofmann, W., d Silva, J., et al: ‘Reactive power control design in doubly fed induction generators for wind turbines’, IEEE Trans. Ind. Electron., 2009, 56, (10), pp. 4154–4162.
    24. 24)
      • 23. Shigenobu, R., Adewuyi, O.B., Yona, A., et al: ‘Demand response strategy management with active and reactive power incentive in the smart grid: a two-level optimization approach’, AIMS Energy, 5(energy-05-00482, pages =).
    25. 25)
      • 4. Han, J., Choi, C.-S., Park, W.-K., et al: ‘Smart home energy management system including renewable energy based on zigBee and PLC’, IEEE Trans. Consum. Electron., 2014, 60, (2), pp. 198–202.
    26. 26)
      • 18. Sheng, W., Liu, K.-Y., Liu, Y., et al: ‘Optimal placement and sizing of distributed generation via an improved nondominated sorting genetic algorithm II’, IEEE Trans. Power Deliv., 2015, 30, (2), pp. 569–578.
    27. 27)
      • 35. Shukla, A.: ‘Multi-objective unit commitment using search space-based crazy particle swarm optimisation and normal boundary intersection technique’, IET. Gener. Transm. Distrib., 2016, 10, (9), pp. 1222–1231.
    28. 28)
      • 17. Ganguly, S.: ‘Multi-objective planning for reactive power compensation of radial distribution networks with unified power quality conditioner allocation using particle swarm optimization’, IEEE Trans. Power Syst., 2014, 29, (4), pp. 1801–1810.
    29. 29)
      • 31. Lust, T.: ‘New metaheuristics for solving MOCO problems: application to the knapsack problem, the traveling salesman problem and IMRT optimization’, PhD thesis, Université de Mons, 2010.
    30. 30)
      • 20. Deb, K., Pratap, A., Agarwal, S., et al: ‘A fast and elitist multiobjective genetic algorithm: NSGA-II’, IEEE Trans. Evol. Comput., 2002, 6, (2), pp. 182–197.
    31. 31)
      • 25. Viawan, F., Karlsson, D.: ‘Voltage and reactive power control in systems with synchronous machine-Based distributed generation’, IEEE Trans. Power Deliv., 2008, 23, (2), pp. 1079–1087.
    32. 32)
      • 32. Coello Coello, C.A., Dhaenens, C., and Jourdan, L.: ‘Multi-objective combinatorial optimization: problematic and context’, Adv. Multi-Object. Nat. Inspired Comput., 2010, 272, pp. 1–21.
    33. 33)
      • 33. Balasubramanian, K.P., Santhi, R.K.: ‘Best compromised schedule for multi-objective unit commitment problems’, Indian J. Sci. Technol., 2016, 9, (2).
    34. 34)
      • 6. Song, I.-K., Jung, W.-W., Kim, J.-Y., et al: ‘Operation schemes of smart distribution networks with distributed energy resources for loss reduction and service restoration’, IEEE Trans. Smart Grid, 2013, 4, (1), pp. 367–374.
    35. 35)
      • 12. Borghetti, A., Bosetti, M., Grillo, S., et al: ‘Short-term scheduling and control of active distribution systems with high penetration of renewable resources’, IEEE Syst. J., 2010, 4, (3), pp. 313–322.
    36. 36)
      • 2. Xin, H., Qu, Z., Seuss, J., et al: ‘A self-organizing strategy for power flow control of photovoltaic generators in a distribution network’, IEEE Trans. Power Syst., 2011, 26, (3), pp. 1462–1473.
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