Sensitivity-based relaxation and decomposition method to dynamic reactive power optimisation considering DGs in active distribution networks

Sensitivity-based relaxation and decomposition method to dynamic reactive power optimisation considering DGs in active distribution networks

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With the development of active distribution networks, new challenges such as overvoltage and power loss become critical. The reactive power optimisation serves as a voltage control measure to minimise the total transmission loss by coordinating the continuous and discrete reactive power compensators while guaranteeing the specific physical and operating constraints. To address the daily operating times of discrete control variables, the dynamic reactive power optimisation (DRPO) is set up to minimise total energy loss over several time periods when considering the inter-temporal constraints. However, DRPO is in fact a large-scale mixed integer non-linear non-convex programming that is difficult to solve. Therefore, second-order cones are employed to relax the non-convex power flow equations to obtain a mixed integer second order cone programming model. Furthermore, a sensitivity-based relaxation and decomposition method is proposed to further improve the computational performance. Solution quality and computational performance are compared with traditional methods on IEEE-33, 123 and 615-bus systems as well as two real-world distribution networks in China. The Results demonstrate that the fast performance and effectiveness of the proposed technique.


    1. 1)
      • 1. Bhattacharyya, B., Goswami, S.K.: ‘Sensitivity based evolutionary algorithms for reactive power dispatch’. Int. Conf. Power Systems (ICPS '09), Sharjah, United Arab Emirates, 10–12 November 2009, pp. 17.
    2. 2)
    3. 3)
    4. 4)
    5. 5)
    6. 6)
    7. 7)
    8. 8)
    9. 9)
    10. 10)
    11. 11)
      • 11. Yurong, W., Fangxing, L., Qiulan, W., et al: ‘Multi-objective reactive power planning based on fuzzy clustering and learning automata’. Int. Conf. on Power System Technology (POWERCON 2010), Hangzhou, China, 24–28 October2010, pp. 17.
    12. 12)
      • 12. Yurong, W., Fangxing, L., Qiulan, W.: ‘Reactive power planning based on fuzzy clustering and multivariate linear regression’. IEEE Power and Energy Society General Meeting, 25–29 July 2010, pp. 16.
    13. 13)
    14. 14)
      • 14. Iba, K.: ‘Reactive power optimization by genetic algorithm’. Proc. Int. Conf. Power Industry Computer Application, 1993, Phoinix, Arizona, USA, 4–7 May 1993, pp. 195201.
    15. 15)
    16. 16)
    17. 17)
      • 17. Yoshida, H., Kawata, K., Fukuyama, Y., et al: ‘A particle swarm optimization for reactive power and voltage control considering voltage security assessment’. Power Engineering Society Winter Meeting, 2001, Columbus, Ohio, USA, 28January–1 February 2001, pp. 492498.
    18. 18)
    19. 19)
    20. 20)
    21. 21)
    22. 22)
    23. 23)
    24. 24)
    25. 25)
    26. 26)
    27. 27)
    28. 28)
    29. 29)
      • 29. Ionescu, C.F., Bulac, C., Capitanescu, F., et al: ‘Multi-period power loss optimization with limited number of switching actions for enhanced continuous power supply’. IEEE 16th Int. Conf. Harmonics and Quality of Power (ICHQP 2014), Bucharest, Romania, 25–28 May 2014, pp. 3438.
    30. 30)
    31. 31)
    32. 32)
    33. 33)
    34. 34)
    35. 35)
    36. 36)
    37. 37)
    38. 38)
      • 38. Zhang, Z., Wang, J., Ding, T., et al: ‘A two-layer model for microgrid real-time dispatch based on energy storage system charging/discharging hidden costs’, IEEE Trans. Sustain. Energy, 2016, PP, (99), pp. 11.
    39. 39)
      • 39. ‘IBM ILOG CPLEX Optimization Studio CPLEX user's manual (V12.6)’. Available at, 2014.

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