A large number of distributed generations (DG) integrated into a distribution network challenge the planning for traditional power systems making traditional deterministic power flow calculations not applicable. Based on the interval and credibility of the blind number theory, this study is about the uncertainty of DG and the correlation between DGs. The interval models for wind power and solar power are established based on the characteristics of the wind power generator output and the photovoltaic cell output. The improved analytic hierarchy process is applied to determine the credibility of the intervals. The blind number theory and Cholesky decomposition are applied to study the correlation between wind speed and light intensity. According to the result of the power flow calculation with uncertainty, the voltage operational quality index is proposed to measure the impact of the DG. Through a modified IEEE 33-bus system, the feasibility and effectiveness of the blind number model for the DG and the necessity of considering the correlation between wind speed and light intensity are verified.

References

1. 1)
  - 10. Meng, X., Gu, W., Song, X., et al: ‘Improved affine arithmetic based optimization model for interval power flow analysis’, IET Gener. Transm. Distrib., 2016, 10, (15), pp. 3910–3918.
2. 2)
  - 12. Soroudi, A., Ehsan, M., Caire, R., et al: ‘Possibilistic evaluation of distributed generations impacts on distribution networks’, IEEE Trans. Power Syst., 2011, 26, (4), pp. 2293–2301.
3. 3)
  - 4. 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. 1797–1805.
4. 4)
  - 13. Pourahmadi-Nakhli, M., Seifi, A.R., Taghavi, R.: ‘A nonlinear-hybrid fuzzy/probabilistic load flow for radial distribution systems’, Electr. Power Energy Syst., 2013, 47, (6), pp. 69–77.
5. 5)
  - 16. He, Y.X., Wang, W., Wu, L.Q., et al: ‘Assessment the connecting style of power distribution network based on fuzzy and blind number theory’. Proc. Int. Conf. Machine Learning and Cybernetics, Kunming, China, July 2008, pp. 681–686.
6. 6)
  - 5. 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. 649–658.
7. 7)
  - 23. Hajian, M., Rosehart, W.D., Zareipour, H.: ‘Probabilistic power flow by Monte Carlo simulation with latin supercube sampling’, IEEE Trans. Power Syst., 2013, 28, (2), pp. 1550–1559.
8. 8)
  - 7. Hong, Y.Y., Lin, F.J., Yu, T.H.: ‘Taguchi method-based probabilistic load flow studies considering uncertain renewables and loads’, IET Renew. Power Gener., 2015, 10, (2), pp. 221–227.
9. 9)
  - 2. Makarov, Y.V., Etingov, P.V., Ma, J., et al: ‘Incorporating wind generation forecast uncertainty into power system operation, dispatch, and unit commitment procedures’, IEEE Trans. Sustain. Energy, 2011, 2, (4), pp. 433–442.
10. 10)
  - 19. Ohnishi, S., Yamanoi, T., Imai, H.: ‘A fuzzy representation for non-additive weights of AHP’. Proc. Int. Conf. Fuzzy Systems, Taiwan, Taipei, June 2011, pp. 672–675.
11. 11)
  - 1. Zou, K., Agalgaonkar, A.P., Muttaqi, K.M., et al: ‘Distribution system planning with incorporating DG reactive capability and system uncertainties’, IEEE Trans. Sustain. Energy, 2012, 3, (3), pp. 112–123.
12. 12)
  - 22. Shu, Z., Jirutitijaroen, P.: ‘Latin hypercube sampling techniques for power systems reliability analysis with renewable energy sources’, IEEE Trans. Power Syst., 2011, 26, (4), pp. 2066–2073.
13. 13)
  - 24. Huntington, D.E., Lyrintzis, C.S.: ‘Improvements to and limitations of Latin hypercube sampling’, Probabilistic Eng. Mech., 1998, 13, (4), pp. 245–253.
14. 14)
  - 8. Pereira, L.E.S., Costa, V.M.D., Rosa, A.L.S.: ‘Interval arithmetic in current injection power flow analysis’, Electr. Power Energy Syst., 2012, 43, (1), pp. 1106–1113.
15. 15)
  - 15. Šošić, D., Žarković, M., Dobrić, G.: ‘Fuzzy-based Monte Carlo simulation for harmonic load flow in distribution networks’, IET Gener. Transm. Distrib., 2015, 9, (3), pp. 267–275.
16. 16)
  - 20. Albayrak, E., Erensal, Y.C.: ‘Using analytic hierarchy process (AHP) to improve human performance: an application of multiple criteria decision making problem’, J. Intell. Manuf., 2004, 15, (4), pp. 491–503.
17. 17)
  - 11. Pirnia, M., Cañizares, C.A., Bhattacharya, K., et al: ‘A novel affine arithmetic method to solve optimal power flow problems with uncertainties’, IEEE Trans. Power Syst., 2014, 29, (6), pp. 2775–2783.
18. 18)
  - 21. Kuthanazhi, V., Rao, A.B.: ‘Selection of photovoltaic modules for off-grid rural application based on analytical hierarchy process (AHP)’. Proc. Int. Conf. Photovoltaic Specialists Conf., Austin, Texas, June 2012, pp. 002888–002893.
19. 19)
  - 14. Miranda, V., Saraiva, J.T.: ‘Fuzzy modelling of power system optimal load flow’, IEEE Trans. Power Syst., 1992, 7, (2), pp. 843–849.
20. 20)
  - 9. Vaccaro, A., Caizares, C.A., Bhattacharya, K.: ‘A range arithmetic-based optimization model for power flow analysis under interval uncertainty’, IEEE Trans. Power Syst., 2013, 28, (2), pp. 1179–1186.
21. 21)
  - 18. Jannat, M., Savić, A.: ‘Optimal capacitor placement in distribution networks regarding uncertainty in active power load and DG units production’, IET Gener. Transm. Distrib., 2016, 10, (12), pp. 3060–3067.
22. 22)
  - 3. Liu, K., Wu, H., Pang, Y., et al: ‘Mathematic express and its application of unascertained information’ (Science Press, 1999, 1st edn.).
23. 23)
  - 17. Aien, M., Ramezani, R., Ghavami, S.M.: ‘Probabilistic load flow considering wind generation uncertainty’, Eng. Technol. Appl. Sci. Res., 2011, 1, (5), pp. 126–132.
24. 24)
  - 6. Aien, M., Fotuhi-Firuzabad, M., Rashidinejad, M.: ‘Probabilistic optimal power flow in correlated hybrid wind–photovoltaic power systems’, IEEE Trans. Smart Grid, 2014, 5, (1), pp. 130–138.

Uncertain flow calculations of a distribution network containing DG based on blind number theory

References

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