access icon free Random fuzzy power flow of distribution network with uncertain wind turbine, PV generation, and load based on random fuzzy theory

No study in the literature considers both randomness and fuzziness simultaneously, which actually coexist as the penetration of renewable energy in power system increases. In order to handle these two kinds of uncertain features simultaneously, a novel random fuzzy power flow (RFPF) calculation method for a distribution network based on random fuzzy theory is presented here. Firstly, the random fuzzy models of wind and photovoltaic (PV) generation, and loads are set up for the first time according to their features of randomness and fuzziness. Then, a two-fold random fuzzy simulation is conducted to obtain the results of the RFPF calculations; the random simulation stage is based on the 2m + 1 scheme of the point estimate method. Finally, the proposed method is applied to two test systems. The results show that the proposed method is feasible and effective in identifying important areas in the power system affected by distribution generation and loads with these two uncertainties.

Inspec keywords: random processes; load flow; fuzzy set theory; photovoltaic power systems; wind turbines; wind power plants

Other keywords: 2m + 1 scheme; random fuzzy power flow calculation method; two-fold random fuzzy simulation; random fuzzy theory; renewable energy penetration; uncertain wind turbine; wind generation; point estimate method; random simulation stage; PV generation; distribution network; test systems; power system; RFPF calculation method; distribution generation

Subjects: Solar power stations and photovoltaic power systems; Wind power plants; Combinatorial mathematics; Other topics in statistics

References

    1. 1)
      • 21. Zheng, Y, Yang, J, Hu, Z, et al: ‘Credibility theory-based available transfer capability assessment’, Energies, 2015, 8, (6), pp. 60596078.
    2. 2)
      • 18. Wang, B, Wang, S, Zhou, X, et al: ‘Multi-objective unit commitment with wind penetration and emission concerns under stochastic and fuzzy uncertainties’, Energy, 2016, 111, pp. 1831.
    3. 3)
      • 20. Liu, Y.K.,, Liu, B.: ‘Expected value operator of random fuzzy variable and random fuzzy expected value models’, Int. J. Uncertain. Fuzziness Knowl.-Based Syst., 2011, 11, (2), pp. 195215.
    4. 4)
      • 24. Angelini, E, Grassini, S, Parvis, M, et al: ‘An application of the random-fuzzy variables for the safeguard of underwater cultural heritage’. Instrumentation and Measurement Technology Conf., Montevideo, 2014, pp. 342347.
    5. 5)
      • 22. Ferrero, A., Prioli, M., Salicone, S.: ‘Joint random-fuzzy variables: a tool for propagating uncertainty through nonlinear measurement functions’, IEEE Trans. Instrum. Meas., 2016, 65, (5), pp. 10151021.
    6. 6)
      • 23. Liu, R., Bai, X.: ‘Random fuzzy production and distribution plan of agricultural products and its PSO algorithm’. Int. Conf. on Progress in Informatics and Computing, Shanghai, 2014, pp. 3236.
    7. 7)
      • 1. Borkowska, B.: ‘Probabilistic load flow’, IEEE Trans. Power Appar. Syst., 1974, PAS-93, (3), pp. 752759.
    8. 8)
      • 34. Altunkaynak, A., Erdik, T., Dabanlı, İ., et al: ‘Theoretical derivation of wind power probability distribution function and applications’, Appl. Energy, 2012, 92, (4), pp. 809814.
    9. 9)
      • 5. Ren, Z., Wang, K., Li, W., et al: ‘Probabilistic power flow analysis of power systems incorporating tidal current generation’, IEEE Trans. Sustain. Energy, 2017, pp, (99), pp. 11.
    10. 10)
      • 33. Baran, M.E., Wu, F.F.: ‘Optimal capacitor placement on radial distribution systems’, IEEE Trans. Power Deliv., 2002, 4, (1), pp. 725734.
    11. 11)
      • 26. Jorgensen, P., Christensen, J.S, Tande, J.O.: ‘Probabilistic load flow calculation using Monte Carlo techniques for distribution network with wind turbines’. Int. Conf. on Harmonics and Quality of Power Proc., 1998. Proc., Athens, 2002, vol. 2, pp. 11461151.
    12. 12)
      • 25. Bie, Z, Zhang, P, Li, G, et al: ‘Reliability evaluation of active distribution systems including microgrids’, IEEE Trans. Power Syst., 2012, 27, (4), pp. 23422350.
    13. 13)
      • 4. Kabir, M.N., Mishra, Y., Bansal, R.C.: ‘Probabilistic load flow for distribution systems with uncertain PV generation’, Appl. Energy, 2016, 163, pp. 343351.
    14. 14)
      • 17. Tang, D, Wang, P.: ‘Nodal impact assessment and alleviation of moving electric vehicle loads: from traffic flow to power flow’, IEEE Trans. Power Syst., 2016, 31, (6), pp. 42314242.
    15. 15)
      • 30. Caramia, P., Carpinelli, G., Varilone, P.: ‘Point estimate schemes for probabilistic three-phase load flow’, Electr. Power Syst. Res., 2010, 80, (2), pp. 168175.
    16. 16)
      • 2. Allan, R.N., Borkowska, B., Grigg, C.H.: ‘Probabilistic analysis of power flows’, Proc. Inst. Electr. Eng., 2010, 121, (12), pp. 15511556.
    17. 17)
      • 31. Alavi, S.A., Ahmadian, A., Aliakbar-Golkar, M.: ‘Optimal probabilistic energy management in a typical micro-grid based-on robust optimization and point estimate method’, Energy Convers. Manage., 2015, 95, pp. 314325.
    18. 18)
      • 12. Hua, C., Tong, Y., Han, F.: ‘A fuzzy power flow calculation based on forward-backward sweep method’. Control and Decision Conf., Yinchuan, 2016, pp. 31183123.
    19. 19)
      • 9. Kalesar, B.M., Seifi, A.R.: ‘Fuzzy load flow in balanced and unbalanced radial distribution systems incorporating composite load model’, Int. J. Electr. Power Energy Syst., 2010, 32, (1), pp. 1723.
    20. 20)
      • 19. Liu, B, Liu, B.: ‘Theory and practice of uncertain programming’ (Physica-Verlag, Heidelberg, 2009).
    21. 21)
      • 32. Kumar, S.V.D.A., Reddy, K.R.: ‘Computation of the power flow solution of a radial distribution system for harmonic components’, Int. J. Energy Inf. Commun., 2013, 4, (1), pp. 37.
    22. 22)
      • 11. Matos, M.A., Gouveia, E.: ‘The fuzzy power flow revisited’, IEEE Trans. Power Syst., 2005, 23, (1), pp. 213218.
    23. 23)
      • 6. 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.
    24. 24)
      • 3. Villanueva, D., Pazos, J.L., Feijoo, A.: ‘Probabilistic load flow including wind power generation’, IEEE Trans. Power Syst., 2011, 26, (3), pp. 16591667.
    25. 25)
      • 7. Miranda, V., Matos, M.A.C.C.: ‘Distribution system planning with fuzzy models and techniques’. Int. Conf. on Electricity Distribution, Brighton, 1989, vol. 6, pp. 472476.
    26. 26)
      • 16. Azizipanah-Abarghooee, R, Terzija, V, Golestaneh, F, et al: ‘Multiobjective dynamic optimal power flow considering fuzzy-based smart utilization of mobile electric vehicles’, IEEE Trans. Ind. Inf., 2016, 12, (2), pp. 503514.
    27. 27)
      • 14. Nikmehr, N., Najafi-Ravadanegh, S.: ‘Optimal operation of distributed generations in micro-grids under uncertainties in load and renewable power generation using heuristic algorithm’, IET Renew. Power Gener., 2015, 9, (8), pp. 982990.
    28. 28)
      • 8. Miranda, V., Matos, M.A., Saraiva, J.T.: ‘Fuzzy load flow-new algorithms incorporating uncertain generation and load representation’. Proc. of PSCC – Power Systems Computation Conf., Graz, Austria, 1990.
    29. 29)
      • 28. Su, C.L.: ‘Probabilistic load-flow computation using point estimate method’, IEEE Trans. Power Syst., 2005, 20, (4), pp. 18431851.
    30. 30)
      • 29. Morales, J.M., Perez-Ruiz, J.: ‘Point estimate schemes to solve the probabilistic power flow’, IEEE Trans. Power Syst., 2007, 22, (4), pp. 15941601.
    31. 31)
      • 13. Bazrafshan, M., Gatsis, N.: ‘Decentralized stochastic optimal power flow in radial networks with distributed generation’, IEEE Trans. Smart Grid, 2017, 8, (2), pp. 787801.
    32. 32)
      • 10. Bijwe, P.R., Raju, G.K.V.: ‘Fuzzy distribution power flow for weakly meshed systems’, IEEE Trans. Power Syst., 2006, 21, (4), pp. 16451652.
    33. 33)
      • 27. Hong, H.P.: ‘An efficient point estimate method for probabilistic analysis’, Reliab. Eng. Syst. Safety, 1998, 59, (3), pp. 261267.
    34. 34)
      • 15. Kim, S.S., Kim, M.K., Park, J.K.: ‘Consideration of multiple uncertainties for evaluation of available transfer capability using fuzzy continuation power flow’, Int. J. Electr. Power Energy Syst., 2008, 30, (10), pp. 581593.
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