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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

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

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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.

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. 4454.
    2. 2)
      • 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. 14621473.
    3. 3)
      • 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. 23502359.
    4. 4)
      • 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. 198202.
    5. 5)
      • 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. 28042812.
    6. 6)
      • 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. 367374.
    7. 7)
      • 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. 10551062.
    8. 8)
      • 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. 6778.
    9. 9)
      • 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. 18.
    10. 10)
      • 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. 202209.
    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. 12361242.
    12. 12)
      • 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. 313322.
    13. 13)
      • 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. 12511260.
    14. 14)
      • 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. 4558.
    15. 15)
      • 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. 591607.
    16. 16)
      • 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. 421431.
    17. 17)
      • 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. 18011810.
    18. 18)
      • 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. 569578.
    19. 19)
      • 19. Deb, K.: ‘Multi-objective optimization using evolutionary algorithms’ (Wiley, 2001).
    20. 20)
      • 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. 182197.
    21. 21)
      • 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. 14381450.
    22. 22)
      • 22. Tang, Y., He, H., Ni, Z., et alAdvances in Computational Intelligence and Learning’. 17th European Symp. Artificial Neural Networks, 2014, 125, pp. 125133.
    23. 23)
      • 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 =).
    24. 24)
      • 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. 16581665.
    25. 25)
      • 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. 10791087.
    26. 26)
      • 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. 41544162.
    27. 27)
      • 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. 610618.
    28. 28)
      • 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. 25792590.
    29. 29)
      • 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. 111.
    30. 30)
      • 30. Aparecido Ferreira, C., and Prada, R.: ‘Improved model for tap-changing transformer’, IET Gener. Transm. Distrib., 2013, 7, (11), pp. 12891295.
    31. 31)
      • 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.
    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. 121.
    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)
      • 34. Shukla, A.: ‘Multi-objective unit commitment with renewable energy using hybrid approach’, IET Renew. Power Gener., 2016, 10, (11), pp. 327338.
    35. 35)
      • 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. 12221231.
    36. 36)
      • 36. Mavrotas, G.: ‘Effective implementation of the ε-constraint method in multi-objective mathematical programming problems’, Appl. Math. Comput., 2009, 213, (2), pp. 455465.
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