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Strategies to improve the voltage quality in active low-voltage distribution networks using DSO's assets

Strategies to improve the voltage quality in active low-voltage distribution networks using DSO's assets

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This study addresses the problem of voltage variations in active low-voltage distribution networks caused by distributed photovoltaic (PV) generation. Three strategies based on model predictive control (MPC) are introduced to flatten the voltage profile in a cost-optimal way. The compared strategies are the business as usual approach that manipulates a controllable on-load tap changer at the primary substation, the problematic feeder control strategy (CS) that adds an additional degree of freedom by controlling the critical secondary substations (SSs), and finally the compensation strategy, which controls the primary substation and compensates the non-critical SSs. A sensitivity analysis on the CSs has been conducted comparing the voltage variation reduction and the asset utilization with regard to the accuracy of the prediction models and the forecasted disturbance data. The results show that better (and more costly) characterisation of these parameters only provide a marginal improvement in the reduction of the voltage variations due to the restriction caused by the heavy tap change penalisation. Moreover, the tested case-study shows that the problematic feeder CS outperforms the compensation strategy in terms of larger voltage variation reduction for similar asset utilisation.

References

    1. 1)
      • 1. International Energy Agency: ‘Technology roadmap: solar photovoltaic energy’ (IEA, 2014) (PDF). Available at http://www.iea.org.
    2. 2)
    3. 3)
    4. 4)
    5. 5)
    6. 6)
    7. 7)
    8. 8)
    9. 9)
      • 9. Liu, B., et al: ‘Probabilistic load forecasting via quantile regression averaging on sister forecasts’, IEEE Trans. Smart Grid, 2015, pp. 19493053.
    10. 10)
    11. 11)
      • 11. Pudjianto, D., Djapic, P., Dragovic, J., et al: ‘Grid integration cost of photovoltaic power generation’ (Energy Futures Lab, Imperial College, London, UK, 2013).
    12. 12)
    13. 13)
    14. 14)
    15. 15)
      • 15. Lasseter, R.H., Paigi, P.: ‘Microgrid: a conceptual solution’. Power Electronics Specialists Conf., 2004. PESC 04. 2004 IEEE 35th Annual, 2004, vol. 6.
    16. 16)
    17. 17)
    18. 18)
    19. 19)
    20. 20)
    21. 21)
    22. 22)
      • 22. EN 50160: ‘Voltage characteristics of electricity supplied by public distribution systems’, 1999.
    23. 23)
    24. 24)
      • 24. Dede, A., Della Giustina, D., Rinaldi, S., et al: ‘Smart meters as part of a sensor network for monitoring the low voltage grid’. 2015 IEEE Sensors Applications Symp. (SAS), 2015.
    25. 25)
      • 25. Zong, Y., Mihet-Popa, L., Kullmann, D., et al: ‘Model predictive controller for active demand side management with PV self-consumption in an intelligent building’. 2012 Third IEEE PES Int. Conf. and Exhibition on Innovative Smart Grid Technologies (ISGT Europe), 2012.
    26. 26)
    27. 27)
      • 27. Camacho, E.F., Alba, C.B.: ‘Model predictive control’ (Springer Science & Business Media, London, UK, 2013).
    28. 28)
    29. 29)
    30. 30)
      • 30. IEEE PES distribution systems analysis subcommittee radial test feeders. Available at http://www.ewh.ieee.org/soc/pes/dsacom/testfeeders.
    31. 31)
      • 31. Lenzi, V., Ulbig, A., Andersson, G.: ‘Impacts of forecast accuracy on grid integration of renewable energy sources’. IEEE PES PowerTech 2013, June 2013.
    32. 32)
    33. 33)
      • 33. EPRI: ‘Open distribution system simulator’, 2013. Available at http://www.sourceforge.net/projects/electricdss/.
    34. 34)
      • 34. Reno, M.J., Coogan, K.: ‘Grid integrated distributed PV (GridPV)’. SAND2013-6733, Sandia National Laboratories, 2013.
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