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IGDT-based robust optimal utilisation of wind power generation using coordinated flexibility resources

IGDT-based robust optimal utilisation of wind power generation using coordinated flexibility resources

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This study investigates the application of a robust method to solve the problem of security constrained unit commitment (SCUC) with flexible resources for managing the uncertainty of significant wind power generation (WPG) to sustain the load-generation balance. The flexible resources include up/down ramping capability of thermal units, hourly demand response, energy storage system and transmission switching action through an integrated scheme. The application of mixed-integer linear programming to deal with the SCUC problem with flexibility resources has been discussed in this study using information-gap decision theory (IGDT) to realise a robust strategy for power system decision maker. Besides, this study proposes an effective solution strategy based on Benders' decomposition to solve the proposed problem. Numerical simulation results on the modified six-bus system and IEEE 118-bus system clearly demonstrate the benefits of applying flexibility resources for managing the WPG uncertainty and validate the applicability of the proposed IGDT-based SCUC model.

References

    1. 1)
      • 1. Lin, W., Wen, J., Cheng, S., et al: ‘An investigation on the active-power variations of wind farms’, IEEE Trans. Ind. Appl., 2012, 48, pp. 10871094.
    2. 2)
      • 2. Wright, L.F.: ‘Information gap decision theory: decisions under severe uncertainty’, J. R. Stat. Soc. A (Stat. Soc.), 2004, 167, pp. 185186.
    3. 3)
      • 3. Aien, M., Hajebrahimi, A., Fotuhi-Firuzabad, M.: ‘A comprehensive review on uncertainty modeling techniques in power system studies’, Renew. Sustain. Energy Rev., 2016, 57, pp. 10771089.
    4. 4)
      • 4. Geoffrion, A.M.: ‘Generalized benders decomposition’, J. Optim. Theory Appl., 1972, 10, pp. 237260.
    5. 5)
      • 5. Wu, L., Shahidehpour, M., Li, Z.: ‘Comparison of scenario-based and interval optimization approaches to stochastic SCUC’, IEEE Trans. Power Syst., 2012, 27, pp. 913921.
    6. 6)
      • 6. Wu, H., Shahidehpour, M., Li, Z., et al: ‘Chance-constrained day-ahead scheduling in stochastic power system operation’, IEEE Trans. Power Syst., 2014, 29, pp. 15831591.
    7. 7)
      • 7. Siahkali, H., Vakilian, M.: ‘Fuzzy generation scheduling for a generation company (GenCo) with large scale wind farms’, Energy Convers. Manage., 2010, 51, pp. 19471957.
    8. 8)
      • 8. Martinez-Mares, A., Fuerte-Esquivel, C.R.: ‘A robust optimization approach for the interdependency analysis of integrated energy systems considering wind power uncertainty’, IEEE Trans. Power Syst., 2013, 28, pp. 39643976.
    9. 9)
      • 9. Soroudi, A., Ehsan, M.: ‘IGDT based robust decision making tool for DNOs in load procurement under severe uncertainty’, IEEE Trans. Smart Grid, 2013, 4, pp. 886895.
    10. 10)
      • 10. Aghaei, J., Agelidis, V.G., Charwand, M., et al: ‘Optimal robust unit commitment of CHP plants in electricity markets using information gap decision theory’, IEEE Trans Smart Grid, (Article in press), DOI: 10.1109/TSG.2016.2521685, Available Online: http://ieeexplore.ieee.org/abstract/document/7401127/.
    11. 11)
      • 11. Mohammadi-Ivatloo, B., Zareipour, H., Amjady, N., et al: ‘Application of information-gap decision theory to risk-constrained self-scheduling of GenCos’, IEEE Trans. Power Syst., 2013, 28, pp. 10931102.
    12. 12)
      • 12. Yamin, H.Y., Shahidehpour, S.M.: ‘Risk and profit in self-scheduling for GenCos’, IEEE Trans. Power Syst., 2004, 4, pp. 21042106.
    13. 13)
      • 13. García-González, J., Moraga, R., Mateo, A.: ‘Risk-constrained strategic bidding of a hydro producer under price uncertainty’. Power Engineering Society General Meeting, 2007, IEEE, 2007, pp. 14.
    14. 14)
      • 14. Jabr, R.A.: ‘Robust self-scheduling under price uncertainty using conditional value-at-risk’, IEEE Trans. Power Syst., 2005, 20, pp. 18521858.
    15. 15)
      • 15. Rabiee, A., Soroudi, A., Keane, A.: ‘Information gap decision theory based OPF with HVDC connected wind farms’, IEEE Trans. Power Syst., 2015, 30, pp. 33963406.
    16. 16)
      • 16. Moradi-Dalvand, M., Mohammadi-Ivatloo, B., Amjady, N., et al: ‘Self-scheduling of a wind producer based on information gap decision theory’, Energy, 2015, 81, pp. 588600.
    17. 17)
      • 17. Kazemi, M., Mohammadi-Ivatloo, B., Ehsan, M.: ‘Risk-based bidding of large electric utilities using information gap decision theory considering demand response’, Electr. Power Syst. Res. , 2014, 114, pp. 8692.
    18. 18)
      • 18. Murphy, C., Soroudi, A., Keane, A.: ‘Information gap decision theory-based congestion and voltage management in the presence of uncertain wind power’, IEEE Trans. Sustain. Energy, 2016, 7, pp. 841849.
    19. 19)
      • 19. Generalized Algebraic Modeling Systems (GAMS). http://www.gams.com.
    20. 20)
      • 20. Paudyal, S., Canizares, C.A., Bhattacharya, K.: ‘Optimal operation of distribution feeders in smart grids’, IEEE Trans. Ind. Electron., 2011, 58, pp. 44954503.
    21. 21)
      • 21. Nasrolahpour, E., Ghasemi, H.: ‘A stochastic security constrained unit commitment model for reconfigurable networks with high wind power penetration’, Electr. Power Syst. Res. , 2015, 121, pp. 341350.
    22. 22)
      • 22. Qiu, F., Wang, J.: ‘Chance-constrained transmission switching with guaranteed wind power utilization’, IEEE Trans. Power Syst., 2015, 30, pp. 12701278.
    23. 23)
      • 23. Khodaei, A., Shahidehpour, M.: ‘Transmission switching in security-constrained unit commitment’, IEEE Trans. Power Syst., 2010, 25, pp. 19371945.
    24. 24)
      • 24. Khodaei, A., Shahidehpour, M.: ‘Security-constrained transmission switching with voltage constraints’, Int. J. Electr. Power Energy Syst., 2012, 35, pp. 7482.
    25. 25)
      • 25. Hedman, K.W., Ferris, M.C., O'Neill, R.P., et al: ‘Co-optimization of generation unit commitment and transmission switching with N − 1 reliability’, IEEE Trans. Power Syst., 2010, 25, pp. 10521063.
    26. 26)
      • 26. Murillo-Sanchez, C.E., Zimmerman, R.D., Lindsay Anderson, C., et al: ‘Secure planning and operations of systems with stochastic sources, energy storage, and active demand’, IEEE Trans. Smart Grid, 2013, 4, pp. 22202229.
    27. 27)
      • 27. Wu, H., Shahidehpour, M., Alabdulwahab, A., et al: ‘Thermal generation flexibility with ramping costs and hourly demand response in stochastic security-constrained scheduling of variable energy sources’, IEEE Trans. Power Syst., 2015, 30, pp. 29552964.
    28. 28)
      • 28. Wu, H., Shahidehpour, M., Al-Abdulwahab, A.: ‘Hourly demand response in day-ahead scheduling for managing the variability of renewable energy’, IET Gener. Transm. Distrib., 2013, 7, pp. 226234.
    29. 29)
      • 29. Khanabadi, M., Ghasemi, H., Doostizadeh, M.: ‘Optimal transmission switching considering voltage security and N − 1 contingency analysis’, IEEE Trans. Power Syst., 2013, 28, pp. 542550.
    30. 30)
      • 30. Charwand, M., Ahmadi, A., Sharaf, A.M., et al: ‘Robust hydrothermal scheduling under load uncertainty using information gap decision theory’, Int. Trans. Electr. Energy Syst., 26(3), 2015.
    31. 31)
      • 31. Ostrowski, J., Anjos, M.F., Vannelli, A.: ‘Tight mixed integer linear programming formulations for the unit commitment problem’, IEEE Trans. Power Syst., 2012, 1, pp. 3946.
    32. 32)
      • 32. Hazarika, D., Sinha, A.: ‘An algorithm for standing phase angle reduction for power system restoration’, IEEE Trans. Power Syst., 1999, 14, pp. 12131218.
    33. 33)
      • 33. Liu, C., Wang, J., Ostrowski, J.: ‘Heuristic prescreening switchable branches in optimal transmission switching’, IEEE Trans. Power Syst., 2012, 27, pp. 22892290.
    34. 34)
      • 34. CPLEX, GAMS. The Solver Manuals. GAME/CPLEX, 1996. http://www.gams.com/.
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