Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon free Multi-objective stochastic optimal power flow considering voltage stability and demand response with significant wind penetration

In this study, a multi-objective stochastic optimal power flow (SOPF) problem with the presence of uncertain wind power generations is introduced. In particular, this study has two main contributions. First, it proposes a multi-objective SOPF which consists of the operating cost, voltage stability and emission effects as the objective functions. The wind uncertainty is formulated as a scenario-based technique. Demand response program is considered in this study, which is one of the most efficient control ways to reduce the risk of voltage instability after a contingency occurrence or a stressed loading condition. In addition, the proposed approach uses the technique of fuzzification to normalise all objective functions and to find the optimal solution. The second contribution proposes a line voltage stability index (LVSI). The proposed LVSI can detect precisely the voltage collapse in comparison with other LVSIs, especially after the occurrence of a given contingency due to the dynamic elements of the system. The proposed multi-objective SOPF is also carried out with different existing LVSIs as the objective functions. These approaches are tested and validated by the modified WECC test system, the IEEE 39-bus.

References

    1. 1)
      • 14. Xie, L., Chiang, H.D., Li, S.H.: ‘Optimal power flow calculation of power system with wind farms’. IEEE Power and Energy Society General Meeting, 2011, pp. 16.
    2. 2)
      • 5. Avalos, R.J., Canizares, C.A., Anjos, M.F.: ‘A practical voltage-stability-constrained optimal power flow’. Power and Energy Society General Meeting – Conversion and Delivery of Electrical Energy in the 21st Century, 2008, 2008, pp. 16.
    3. 3)
      • 33. Mohamed, A., Jasmon, G., Yusoff, S.: ‘A static vo4ltage collapse indicator using line stability factors’, J. Ind. Technol., 1989, 7, pp. 7385.
    4. 4)
      • 37. Milano, F.: ‘An open source power system analysis toolbox’, IEEE Trans. Power Syst., 2005, 20, pp. 11991206.
    5. 5)
      • 8. Rosehart, W.D., Canizares, C.A., Quintana, V.H.: ‘Multiobjective optimal power flows to evaluate voltage security costs in power networks’, IEEE Trans. Power Syst., 2003, 18, pp. 578587.
    6. 6)
      • 31. Moghavvemi, M., Omar, F.: ‘Technique for contingency monitoring and voltage collapse prediction’, IEE Proc.- Gener. Transm. Distrib., 1998, 145, pp. 634640.
    7. 7)
      • 40. Potter, C., Lew, D., McCaa, J., et al: ‘Creating the dataset for the western wind and solar integration study (USA)’, Wind Eng., 2008, 32, pp. 325338.
    8. 8)
      • 7. Canizares, C., Rosehart, W., Berizzi, A., et al: ‘Comparison of voltage security constrained optimal power flow techniques’. Power Engineering Society Summer Meeting, 2001, vol. 3, pp. 16801685.
    9. 9)
      • 30. Moghavvemi, M., Faruque, O.: ‘Real-time contingency evaluation and ranking technique’, IEE Proc. - Gener. Transm. Distrib., 1998, 145, pp. 517524.
    10. 10)
      • 13. Jabr, R.A., Pal, B.C.: ‘Intermittent wind generation in optimal power flow dispatching’, IET. Gener. Transm. Distrib., 2009, 3, pp. 6674.
    11. 11)
      • 21. ‘Assessment of demand response and advanced metering’. FERC Report, 2012.
    12. 12)
      • 6. Milano, F., Canizares, C.A., Invernizzi, M.: ‘Voltage stability constrained OPF market models considering N–1 contingency criteria’, Electr. Power Syst. Res., 2005, 74, pp. 2736.
    13. 13)
      • 25. Niknam, T., Narimani, M.R., Aghaei, J., et al: ‘Improved particle swarm optimisation for multi-objective optimal power flow considering the cost, loss, emission and voltage stability index’, IET. Gener. Transm. Distrib., 2012, 6, pp. 515527.
    14. 14)
      • 18. Bienstock, D., Chertkov, M., Harnett, S.: ‘Chance-constrained optimal power flow: risk-aware network control under uncertainty’, SIAM Rev., 2014, 56, pp. 461495.
    15. 15)
      • 3. Duman, S., Güvenç, U., Sönmez, Y., et al: ‘Optimal power flow using gravitational search algorithm’, Energy Convers. Manage., 2012, 59, pp. 8695.
    16. 16)
      • 9. Lage, G.G., da Costa, G.R.M., Canizares, C.A.: ‘Limitations of assigning general critical values to voltage stability indices in voltage-stability-constrained optimal power flows’. IEEE Int. Conf. on Power System Technology (POWERCON), 2012, 2012, pp. 16.
    17. 17)
      • 26. Lu, S., Lou, S., Wu, Y., et al: ‘Power system economic dispatch under low-carbon economy with carbon capture plants considered’, IET. Gener. Transm. Distrib., 2013, 7, pp. 9911001.
    18. 18)
      • 15. Youssef, E., Azab, R.M.E., Amin, A.M.: ‘Comparative study of voltage stability analysis for renewable energy grid-connected systems using PSS/E’. SoutheastCon, 2015, pp. 16.
    19. 19)
      • 11. Rosehart, W.D., Canizares, C.A., Quintana, V.H.: ‘Effect of detailed power system models in traditional and voltage-stability-constrained optimal power-flow problems’, IEEE Trans. Power Syst., 2003, 18, pp. 2735.
    20. 20)
      • 35. Cohon, J.L.: ‘Multiobjective programming and planning’ (Dover Publications, 2013).
    21. 21)
      • 36. Chilvers, I., Jenkins, N., Crossley, P.: ‘Distance relaying of 11 kV circuits to increase the installed capacity of distributed generation’, IEE Proc. – Gener. Transm. Distrib., 2005, 152, pp. 4046.
    22. 22)
      • 16. Roald, L., Misra, S., Chertkov, M., et al: ‘Chance constrained optimal power flow with curtailment and reserves from wind power plants’, arXiv preprint arXiv:1601.04321, 2016.
    23. 23)
      • 34. Eremia, M., Shahidehpour, M.: ‘Handbook of electrical power system dynamics: modeling, stability, and control’ (Wiley, 2013).
    24. 24)
      • 20. Pandzic, H., Dvorkin, Y., Qiu, T., et al: ‘Toward cost-efficient and reliable unit commitment under uncertainty’, IEEE Trans. Power Syst., 2016, 31, pp. 970982.
    25. 25)
      • 2. Andersson, G.: ‘Modelling and analysis of electric power systems’ (EEH-Power Systems Laboratory, Swiss Federal Institute of Technology (ETH), Zürich, Switzerland, 2004).
    26. 26)
      • 10. Venkatesh, B., Arunagiri, A., Gooi, H.B.: ‘Unified OPF method for maximizing voltage stability margin using successive fuzzy LP’, Electr. Power Syst. Res., 2003, 64, pp. 119128.
    27. 27)
      • 29. Musirin, I., Rahman, T.A.: ‘On-line voltage stability based contingency ranking using fast voltage stability index (FVSI)’. IEEE/PES Transmission and Distribution Conf. and Exhibition 2002: Asia Pacific, 2002, pp. 11181123.
    28. 28)
      • 23. Kyoto Protocol to the United Nations Framework Convention on Climate Change, 1992. Available at: http://www.unfccc.int.
    29. 29)
      • 4. Adaryani, M.R., Karami, A.: ‘Artificial bee colony algorithm for solving multi-objective optimal power flow problem’, Int. J. Electr. Power Energy Syst., 2013, 53, pp. 219230.
    30. 30)
      • 22. Rabiee, A., Soroudi, A., Mohammadi-ivatloo, B., et al: ‘Corrective voltage control scheme considering demand response and stochastic wind power’, IEEE Trans. Power Syst., 2014, 29, pp. 29652973.
    31. 31)
      • 28. Chandrasekaran, K., Simon, S.P.: ‘Multi-objective unit commitment problem with reliability function using fuzzified binary real coded artificial bee colony algorithm’, IET. Gener. Transm. Distrib., 2012, 6, pp. 10601073.
    32. 32)
      • 24. National Emission Ceilings for Certain Atmospheric Pollutants, Directive 2001/81/EC of the European Parliament and of the Council, 2001. Available at: http://www.europa.int.eu.
    33. 33)
      • 19. Bouffard, F., Galiana, F.D., Conejo, A.J.: ‘Market-clearing with stochastic security – part II: case studies’, IEEE Trans. Power Syst., 2005, 20, pp. 18271835.
    34. 34)
      • 12. Zabaiou, T., Dessaint, L.A., Kamwa, I.: ‘Preventive control approach for voltage stability improvement using voltage stability constrained optimal power flow based on static line voltage stability indices’, IET Gener. Transm. Distrib., 2014, 8, pp. 924934.
    35. 35)
      • 27. Esmaili, M.: ‘Placement of minimum distributed generation units observing power losses and voltage stability with network constraints’, IET. Gener. Transm. Distrib., 2013, 7, pp. 813821.
    36. 36)
      • 32. Jalboub, M.K., Rajamani, H.S., Liang, D.T.W., et al: ‘Investigation of voltage stability indices to identify weakest bus (TBC)’. Mobile Multimedia Communications: 6th Int. ICST Conf., MOBIMEDIA 2010, Lisbon, Portugal, 6–8 September 2010. Revised Selected Papers, J. Rodriguez, R. Tafazolli, and C. Verikoukis, Eds., edn Berlin, Heidelberg: Springer Berlin Heidelberg, 2012, pp. 682687.
    37. 37)
      • 1. Cain, M.B., O'Neill, R.P., Castillo, A.: ‘History of optimal power flow and formulations’(Federal Energy Regulatory Commission (FERC), 2012).
    38. 38)
      • 17. Summers, T., Warrington, J., Morari, M., et al: ‘Stochastic optimal power flow based on convex approximations of chance constraints’. Power Systems Computation Conf. (PSCC), 2014, pp. 17.
    39. 39)
      • 39. Zimmerman, R.D., Murillo-Sánchez, C.: ‘MATPOWER’. Available at: http://www.pserc.cornell.edu//matpower/.
    40. 40)
      • 38. Athay, T., Podmore, R., Virmani, S.: ‘A practical method for the direct analysis of transient stability’, IEEE Trans. Power Appar. Syst., 1979, PAS-98, pp. 573584.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-gtd.2016.1994
Loading

Related content

content/journals/10.1049/iet-gtd.2016.1994
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address