Placement of minimum distributed generation units observing power losses and voltage stability with network constraints

Placement of minimum distributed generation units observing power losses and voltage stability with network constraints

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Distributed generations (DGs) are recently in growing attention as a solution to environmental and economical challenges caused by conventional power plants. In this study, a multi-objective framework as a nonlinear programming (NLP) is proposed for optimal placement and sizing of DG units. Objective functions include minimising the number of DGs and power losses as well as maximising voltage stability margin formulated as a function of decision variables. The objective functions are combined into one objective function. To avoid problems with choosing appropriate weighting factors, fuzzification is applied to objective functions to bring them into the same scale. DG units are placed at more efficient buses rather than end buses of radial links as usually determined by previous methods for improving voltage stability. Also, power system constraints including branch and voltage limits are observed in the problem. The proposed method not only is able to model all types of DG technologies but also it employs adaptive reactive limits for DGs rather than fixed limits. In addition, a three-stage procedure is proposed to gradually solve the multi-objective problem in order to prevent infeasible solutions. Also, a new technique is proposed to formulate the number of DGs without converting the NLP problem into mixed-integer NLP. Results of testing the proposed method show its efficiency.


    1. 1)
      • 1. Ackermanna, T., Anderssonb, G., Södera, L.: ‘Distributed generation: a definition’, Electr. Power Syst. Res., 2001, 57, (3), pp. 195204 (doi: 10.1016/S0378-7796(01)00101-8).
    2. 2)
      • 2. Rueda-Medina, A.C., Padilha-Feltrin, A.: ‘Distributed generators as providers of reactive power support—a market approach’, IEEE Trans. Power Syst., 2013, 28, (1), pp. 490502 (doi: 10.1109/TPWRS.2012.2202926).
    3. 3)
      • 3. Lee, S.H., Park, J.W.: ‘Selection of optimal location and size of multiple distributed generations by using Kalman filter algorithm’, IEEE Trans. Power Syst., 2009, 24, (3), pp. 13931400 (doi: 10.1109/TPWRS.2009.2016540).
    4. 4)
      • 4. El-Zonkoly, A.M.: ‘Optimal placement of multi-distributed generation units including different load models using particle swarm optimisation’, IET Gener. Transm. Distrib., 2011, 5, (7), pp. 760771 (doi: 10.1049/iet-gtd.2010.0676).
    5. 5)
      • 5. Juanuwattanakul, P., Masoum, M.A.S.: ‘Increasing distributed generation penetration in multiphase distribution networks considering grid losses, maximum loading factor and bus voltage limits’, IET Gener. Transm. Distrib., 2012, 6, (12), pp. 12621271 (doi: 10.1049/iet-gtd.2011.0841).
    6. 6)
      • 6. Rao, R.S., Ravindra, K., Satish, K., Narasimham, S.V.L.: ‘Power loss minimization in distribution system using network reconfiguration in the presence of distributed generation’, IEEE Trans. Power Syst., 2013, 28, (1), pp. 317325 (doi: 10.1109/TPWRS.2012.2197227).
    7. 7)
      • 7. Duong, Q.H., Mithulananthan, N.: ‘Multiple distributed generator placement in primary distribution networks for loss reduction’, IEEE Trans. Ind. Electron., 2013, 60, (4), pp. 17001708 (doi: 10.1109/TIE.2011.2112316).
    8. 8)
      • 8. Caisheng, W., Nehrir, M.H.: ‘Analytical approaches for optimal placement of distributed generation sources in power systems’, IEEE Trans. Power Syst., 2004, 19, (4), pp. 20682076 (doi: 10.1109/TPWRS.2004.836189).
    9. 9)
      • 9. Ochoa, L.F., Dent, C.J., Harrison, G.P.: ‘Distribution network capacity assessment: variable DG and active networks’, IEEE Trans. Power Syst., 2010, 25, (1), pp. 8795 (doi: 10.1109/TPWRS.2009.2031223).
    10. 10)
      • 10. Ochoa, L.F., Harrison, G.P.: ‘Minimizing energy losses: optimal accommodation and smart operation of renewable distributed generation’, IEEE Trans. Power Syst., 2011, 26, (1), pp. 198205 (doi: 10.1109/TPWRS.2010.2049036).
    11. 11)
      • 11. Atwa, Y.M., El-Saadany, E.F., Salama, M.M.A., Seethapathy, R.: ‘Optimal renewable resources mix for distribution system energy loss minimization’, IEEE Trans. Power Syst., 2010, 25, (1), pp. 360370 (doi: 10.1109/TPWRS.2009.2030276).
    12. 12)
      • 12. Al Abri, R.S., El-Saadany, E.F., Atwa, Y.M.: ‘Optimal placement and sizing method to improve the voltage stability margin in a distribution system using distributed generation’, IEEE Trans. Power Syst., 2013, 28, (1), pp. 326324 (doi: 10.1109/TPWRS.2012.2200049).
    13. 13)
      • 13. Hedayati, H., Nabaviniaki, S.A., Akbarimajd, A.: ‘A method for placement of DG units in distribution networks’, IEEE Trans. Power Deliv., 2008, 23, (3), pp. 16201628 (doi: 10.1109/TPWRD.2007.916106).
    14. 14)
      • 14. Canizares, C.A., Alvarado, F.L.: ‘Point of collapse and continuation methods for large AC/DC systems’, IEEE Trans. Power Syst., 1993, 8, (1), pp. 18 (doi: 10.1109/59.221241).
    15. 15)
      • 15. Akorede, M.F., Hizam, H., Aris, I., Ab Kadir, M.Z.A.: ‘Effective method for optimal allocation of distributed generation units in meshed electric power systems’, IET Gener. Transm. Distrib., 2011, 5, (2), pp. 276287 (doi: 10.1049/iet-gtd.2010.0199).
    16. 16)
      • 16. Ayres, H.M., Freitas, W., De Almeida, M.C., Da Silva, L.C.P.: ‘Method for determining the maximum allowable penetration level of distributed generation without steady-state voltage violations’, IET Gener. Transm. Distrib., 2010, 4, (4), pp. 495508 (doi: 10.1049/iet-gtd.2009.0317).
    17. 17)
      • 17. Zou, K., Agalgaonkar, A.P., Muttaqi, K.M., Perera, S.: ‘Distribution system planning with incorporating DG reactive capability and system uncertainties’, IEEE Trans. Sustain. Energy, 2012, 3, (1), pp. 112123 (doi: 10.1109/TSTE.2011.2166281).
    18. 18)
      • 18. Ajjarapu, V.: ‘Computational Techniques for Voltage Stability Assessment and Control’ (Iowa State University, Ames, Iowa, USA, Springer, 2006).
    19. 19)
      • 19. IEEE/CIGRE Joint Task Force on Stability Terms and Definitions: ‘Definition and classification of power system stability’, IEEE Trans. Power Syst., 2004, 19, (2), pp. 13871401.
    20. 20)
      • 20. Rafael, J.A.M.: ‘Analysis and application of optimization techniques to power system security and electricity markets’. PhD dissertation, University of Waterloo, Waterloo, ON, Canada, 2008.
    21. 21)
      • 21. Esmaili, M., Amjady, N., Shayanfar, H.A.: ‘Stochastic multi-objective congestion management in power markets improving voltage and transient stabilities’, Eur. Trans. Electr. Power, 2011, 21, (1), pp. 95115 (doi: 10.1002/etep.416).
    22. 22)
      • 22. Willis, H.L., Scott, W.G.: ‘Distributed power generation: planning and evaluation’ (Marcel Dekker Inc., New York, 2000).
    23. 23)
      • 23. Pochet, Y., Wolsey, L.A.: ‘Production planning by mixed integer programming’ (Springer, New York, 2006).
    24. 24)
      • 24. Esmaili, M., Amjady, N., Shayanfar, H.A.: ‘Multi-objective congestion management by modified augmented ɛ-constraint method’, Appl. Energy, 2011, 88, (3), pp. 755766 (doi: 10.1016/j.apenergy.2010.09.014).
    25. 25)
      • 25. Coello, C.A.C., Lamont, G.B., Veldhuisen, D.A.V.: ‘Evolutionary algorithms for solving multi-objective problems’ (Springer, USA, 2007).
    26. 26)
      • 26. Miettinen, K.: ‘Nonlinear multiobjective optimization’ (Springer, USA, 1999).
    27. 27)
      • 27. Tonkoski, R., Turcotte, D., EL-Fouly, T.H.M.: ‘Impact of high PV penetration on voltage profiles in residential neighborhoods’, IEEE Trans. Sustain. Energy, 2012, 3, (3), pp. 518527 (doi: 10.1109/TSTE.2012.2191425).
    28. 28)
      • 28. GAMS (General Algebraic Modeling System) software package,
    29. 29)
      • 29. Milano, F.: ‘Power System Analysis Toolbox (PSAT)’,

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