access icon free Probabilistic methodology for estimating the optimal photovoltaic capacity in distribution systems to avoid power flow reversals

The large-scale integration of photovoltaic generation (PVG) on distribution systems (DSs) preserving their technical constraints related to voltage fluctuations and active power (AP) flow is a challenging problem. Solar resources are accompanied by uncertainty regarding their estimation and intrinsically variable nature. This study presents a new probabilistic methodology based on quasi-static time-series analysis combined with the golden section search algorithm to integrate low and high levels of PVG into DSs to prevent AP flow in reverse direction. Based on the analysis of two illustrative case studies, it was concluded that the successful integration of PVG is not only related to the photovoltaic-cell manufacturing prices and conversion efficiency but also with the manufacturing prices of power electronic devices required for reactive power control.

Inspec keywords: search problems; distribution networks; power generation economics; load flow; photovoltaic power systems; time series; probability

Other keywords: voltage fluctuations; technical constraints; power electronic devices manufacturing prices; probabilistic methodology; active power flow; large-scale photovoltaic generation integration; optimal photovoltaic capacity estimation; photovoltaic-cell manufacturing prices; conversion efficiency; golden section search algorithm; reactive power control; solar resources; quasistatic time-series analysis; distribution systems

Subjects: Solar power stations and photovoltaic power systems; Power system management, operation and economics; Optimisation techniques; Other topics in statistics; Distribution networks; Combinatorial mathematics

References

    1. 1)
      • 23. Baghaee, H.R., Mirsalim, M., Gharehpetian, G.B., et al: ‘Fuzzy unscented transform for uncertainty quantification of correlated wind/PV microgrids: possibilistic–probabilistic power flow based RBFNNs’, IET Gener. Transm. Distrib., 2017, 11, (6), pp. 867877.
    2. 2)
      • 46. Kheldoun, A., Bradai, R., Boukenoui, R., et al: ‘A new golden section method-based maximum power point tracking algorithm for photovoltaic systems’, Energy Convers. Manage., 2016, 111, pp. 125136.
    3. 3)
      • 11. Fan, M., Vittal, V., Heydt, G.T., et al: ‘Probabilistic power flow analysis with generation dispatch including photovoltaic resources’, IEEE Trans. Power Syst., 2013, 28, (2), pp. 17971805.
    4. 4)
      • 49. Das, D., Kothari, D.P., Kalam, A.: ‘Simple and efficient method for load flow solution of radial distribution networks’, Int. J. Electr. Power Energy Syst., 1995, 17, (5), pp. 335346.
    5. 5)
      • 6. Martinez, J.A., Dinavahi, V., Nehrir, M.H., et al: ‘Tools for analysis and design of distributed resources-part IV: future trends’, IEEE Trans. Power Deliv., 2011, 26, (3), pp. 16711680.
    6. 6)
      • 48. Khodr, H.M., Ocque, L., Yusta, J.M., et al: ‘New load flow method S-E oriented for large radial distribution networks’. Transmission & Distribution Conf. and Exposition: Latin America, Caracas, Venezuela, August 2006, pp. 16.
    7. 7)
      • 18. Xie, Z.Q., Ji, T.Y., Li, M.S., et al: ‘Quasi-Monte Carlo based probabilistic optimal power flow considering the correlation of wind speeds using copula function’, IEEE Trans. Power Syst., 2017.
    8. 8)
      • 39. Erbs, D.G., Klein, S.A., Beckman, W.A.: ‘Estimation of degree-days and ambient temperature bin data from monthly-average’, ASHRAE J., 1983, 25, pp. 6065.
    9. 9)
      • 42. Rampinelli, G.A., Krenzinger, A., Romero, F.C.: ‘Mathematical models for efficiency of inverters used in grid connected photovoltaic systems’, Renew. Sustain. Energy Rev., 2014, 34, pp. 578587.
    10. 10)
      • 28. Rouhani, M., Mohammadi, M., Kargarian, A.: ‘Parzen window density estimator-based probabilistic power flow with correlated uncertainties’, IEEE Trans. Sustain. Energy, 2016, 7, (3), pp. 11701181.
    11. 11)
      • 9. Xiao, Q., He, Y., Chen, K., et al: ‘Point estimate method based on univariate dimension reduction model for probabilistic power flow computation’, IET Gener. Transm. Distrib., 2017, 11, (14), pp. 35223531.
    12. 12)
      • 21. Martinez-Velasco, J.A., Guerra, G.: ‘Reliability analysis of distribution systems with photovoltaic generation using a power flow simulator and a parallel Monte Carlo approach’, Energies, 2016, 9, (7), pp. 121.
    13. 13)
      • 44. Teng, J.-H.: ‘Modelling distributed generations in three-phase distribution load flow’, IET Gener. Transm. Distrib., 2008, 2, (3), pp. 330340.
    14. 14)
      • 14. Hajian, M., Rosehart, W.D., Zreipour, H.: ‘Probabilistic power flow by Monte Carlo simulation with Latin supercube sampling’, IEEE Trans. Power Syst., 2013, 28, (2), pp. 15501559.
    15. 15)
      • 10. Fan, M., Vittal, V., Heydt, G.T., et al: ‘Probabilistic power flow studies for transmission systems with photovoltaic generation using cumulants’, IEEE Trans. Power Syst., 2012, 27, (4), pp. 22512261.
    16. 16)
      • 33. Hung, D.Q., Mithulananthan, N., Lee, K.Y.: ‘Determining PV penetration for distribution systems with time-varying load models’, IEEE Trans. Power Syst., 2014, 29, (6), pp. 30483057.
    17. 17)
      • 12. Hong, Y.-Y., Lin, F.-J., Lin, Y.-C., et al: ‘Chaotic PSO-based VAR control considering renewables using fast probabilistic power flow’, IEEE Trans. Power Deliv., 2014, 29, (4), pp. 16661674.
    18. 18)
      • 17. Zhang, L., Cheng, H., Zhang, S., et al: ‘Probabilistic power flow calculation using the Johnson system and Sobol's quasi-random numbers’, IET Gener. Transm. Distrib., 2016, 10, (12), pp. 30503059.
    19. 19)
      • 16. Navarro-Espinosa, A., Ochoa, L.F.: ‘Probabilistic impact assessment of low carbon technologies in LV distribution systems’, IEEE Trans. Power Syst., 2016, 31, (3), pp. 21922203.
    20. 20)
      • 29. Ni, F., Nguyen, P.H., Cobben, J.F.G.: ‘Basis-adaptive sparse polynomial chaos expansion for probabilistic power flow’, IEEE Trans. Power Syst., 2017, 32, (1), pp. 694704.
    21. 21)
      • 26. Tang, J., Ni, F., Ponci, F., et al: ‘Dimension-adaptive sparse grid interpolation for uncertainty quantification in modern power systems: probabilistic power flow’, IEEE Trans. Power Syst., 2016, 31, (2), pp. 907919.
    22. 22)
      • 19. Abdelaziz, M.M.A.: ‘OpenCL-accelerated probabilistic power for active distribution networks’, IEEE Trans. Sustain. Energy, 2017.
    23. 23)
      • 47. Green, M.A., Emery, K., Hishikawa, Y., et al: ‘Solar cell efficiency tables (version 47)’, Prog. Photovolt., Res. Appl., 2015, 24, (1), pp. 311.
    24. 24)
      • 15. Peng, X., Lin, L., Zheng, W., et al: ‘Crisscross optimization algorithm and Monte Carlo simulation for solving optimal distributed generation allocation problem’, Energies, 2015, 8, (12), pp. 1364113659.
    25. 25)
      • 31. Yin, H., Zivanovic, R.: ‘Using probabilistic collocation method for neighbouring wind farms modeling and power flow computation of South Australia’, IET Gener. Transm. Distrib., 2017, 11, (14), pp. 35683575.
    26. 26)
      • 37. Deboever, J., Grijalva, S., Reno, M.J., et al: ‘Fast quasi-static time series (QSTS) for yearlong PV impact studies using vector quantization’, Sol. Energy, 2018, 159, pp. 538547.
    27. 27)
      • 1. Petinrin, J.O., Shaaban, M.: ‘Impact of renewable generation on voltage control in distribution systems’, Renew. Sustain. Energy Rev., 2016, 65, pp. 770783.
    28. 28)
      • 8. Aien, M., Khajeh, M.G., Rashidinejad, M., et al: ‘Probabilistic power flow of correlated hybrid wind-photovoltaic power systems’, IET Renew. Power Gener., 2014, 8, (6), pp. 649658.
    29. 29)
      • 27. Ren, Z., Li, W., Billinton, R., et al: ‘Probabilistic power flow analysis based on the stochastic response surface method’, IEEE Trans. Power Syst., 2016, 31, (3), pp. 23072315.
    30. 30)
      • 32. Lin, C.-H., Hsieh, W.-L., Chen, C.-S., et al: ‘Optimization of photovoltaic penetration in distribution systems considering annual duration curve of solar irradiation’, IEEE Trans. Power Syst., 2012, 27, (2), pp. 10901097.
    31. 31)
      • 36. Mather, B.: ‘Fast determination of distribution-connected PV impacts using a variable time-step quasi-static time-series approach’. Tech. Rep. NREL/CP-5D00-67769, National Renewable Energy Laboratory, Golden, CO, August 2017.
    32. 32)
      • 7. Li, G., Zhang, X.-P.: ‘Modeling of plug-in hybrid electric vehicle charging demand in probabilistic power flow calculations’, IEEE Trans. Smart Grid, 2012, 3, (1), pp. 492499.
    33. 33)
      • 45. Seguin, R., Woyak, J., Costyk, D., et al: ‘High-penetration PV integration handbook for distribution engineers’. Tech. Rep. NREL/TP-5D00-63114, National Renewable Energy Laboratory, Golden, CO, January 2016.
    34. 34)
      • 22. Hariri, A., Faruque, M.O.: ‘A hybrid simulation tool for the study of PV integration impacts on distribution networks’, IEEE Trans. Sustain. Energy, 2017, 8, (2), pp. 648657.
    35. 35)
      • 34. Montoya-Bueno, S., Muñoz, J.I., Contreras, J.: ‘A stochastic investment model for renewable generation in distribution systems’, IEEE Trans. Sustain. Energy, 2015, 6, (4), pp. 14661474.
    36. 36)
      • 43. Teng, J.-H.: ‘A direct approach for distribution system load flow solutions’, IEEE Trans. Power Deliv., 2003, 18, (3), pp. 882887.
    37. 37)
      • 41. Lambert, T., Gilman, P., Lilienthal, P.: ‘Micropower system modeling with HOMER’, in Farret, F.A., Simões, M.G. (Eds.): ‘Integration of alternative sources of energy’ (John Wiley & Sons, Hoboken, NJ, USA, 2006), pp. 379418.
    38. 38)
      • 2. Yang, Y., Enjeti, P., Blaabjerg, F., et al: ‘Wide-scale adoption of photovoltaic energy: grid code modifications are explored in the distribution grid’, IEEE Ind. Appl. Mag., 2015, 21, (5), pp. 2131.
    39. 39)
      • 30. Ren, Z., Wang, K., Li, W., et al: ‘Probabilistic power flow analysis of power systems incorporating tidal current generation’, IEEE Trans. Sustain. Energy, 2017, 8, (3), pp. 11951203.
    40. 40)
      • 4. Sinha, S., Chandel, S.S.: ‘Review of software tools for hybrid renewable energy systems’, Renew. Sustain. Energy Rev., 2014, 32, pp. 192205.
    41. 41)
      • 38. Graham, V.A., Hollands, K.G.T.: ‘A method to generate synthetic hourly solar radiation globally’, Sol. Energy, 1990, 44, (6), pp. 333341.
    42. 42)
      • 40. Short, T.A.: ‘Electric power distribution handbook’ (CRC Press LLC, Boca Raton, Florida, 2004).
    43. 43)
      • 5. Dugan, R.C.: ‘The open distribution system simulator (OpenDSS)’ (Electric Power Research Institute, Palo Alto, CA, 2016).
    44. 44)
      • 3. Liu, E., Bebic, J.: ‘Distribution system voltage performance analysis for high-penetration photovoltaics’. Tech. Rep. NREL/SR-581-42298, National Renewable Energy Laboratory, Golden, CO, February 2008.
    45. 45)
      • 20. Zhou, G., Bo, R., Chien, L., et al: ‘GPU-accelerated algorithm for online probabilistic power flow’, IEEE Trans. Power Syst., 2018, 33, (1), pp. 11321135.
    46. 46)
      • 24. Peng, S., Tang, J., Li, W.: ‘Probabilistic power flow for AC/VSC-MTDC hybrid grids considering rank correlation among diverse uncertainty sources’, IEEE Trans. Power Syst., 2017, 32, (5), pp. 40354044.
    47. 47)
      • 35. Ding, F., Nagarajan, A., Chakraborty, S., et al: ‘Photovoltaic impact assessment of smart inverter volt-var control on distribution system conservation voltage reduction and power quality’. Tech. Rep. NREL/TP-5D00-67296, National Renewable Energy Laboratory, Golden, CO, December 2016.
    48. 48)
      • 13. Wang, X., Gong, Y., Jiang, C.: ‘Regional carbon emission management based on probabilistic power flow with correlated stochastic variables’, IEEE Trans. Power Syst., 2015, 30, (2), pp. 10941103.
    49. 49)
      • 25. Xiao, Q.: ‘Dimension reduction method for probabilistic power flow calculation’, IET Gener. Transm. Distrib., 2015, 9, (6), pp. 540549.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-rpg.2017.0777
Loading

Related content

content/journals/10.1049/iet-rpg.2017.0777
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading