access icon free SR-based chance-constrained economic dispatch for power systems with wind power

In this study, the authors present a chance-constrained economic dispatch model for power systems with the integration of wind power. In the proposed model, the automatic generation control (AGC) regulation is adopted to compensate for the power variations caused by the forecast errors of wind power and load. Moreover, the power flow limits and the transient stability constraints under contingencies are formulated as chance constraints through the security region (SR) approach. On the basis of the hyper-plane characteristics of the boundaries of SR, the chance constraints of power flow limits and transient stability limits are converted to equivalent deterministic linear inequalities. The proposed method can assure that there is enough reserve capacity in the dispatch solution to accommodate the power variations caused by wind power and load, and the branch power flow limits and the transient stability constraints under uncertain power injections and contingencies are satisfied with a high probability. Numerical tests show that the proposed method has well-convergence property and computational efficiency and it serves as a useful tool for power dispatchers to identify a balance between economics and robustness of power system operation.

Inspec keywords: power generation dispatch; power system transient stability; wind power plants; load flow; power generation economics; probability

Other keywords: power dispatchers; security region approach; forecast errors; chance-constrained economic dispatch model; chance constraints; hyper-plane characteristics; transient stability limits; branch power flow limits; power systems; wind power; power flow limits; SR approach; power variations; SR-based chance-constrained economic dispatch; transient stability constraints; dispatch solution; equivalent deterministic linear inequalities; AGC regulation; uncertain power injections; power system operation; reserve capacity

Subjects: Power system management, operation and economics; Wind power plants; Other topics in statistics; Power system control

References

    1. 1)
      • 31. Clark, K., Miller, N., Sanchez-Gasca, J.: ‘Modeling of GE wind turbine-generators for grid studies’ (General Electric International Inc., USA, 2009).
    2. 2)
      • 40. Yu, Y., Qin, C.: ‘Security region based security-constrained unit commitment’, Sci. China Ser. E, 2013, 56, (7), pp. 27322744.
    3. 3)
      • 29. Bruninx, K., Delarue, E.: ‘A statistical description of the error on wind power forecasts for probabilistic reserve sizing’, IEEE Trans. Power Syst., 2014, 5, (3), pp. 9951002.
    4. 4)
      • 36. Yu, Y.: ‘Security region of bulk power system’. Proc. Int. Conf. Power System Technology, Kunming, People's Republic of China, October 2002, vol. 1, pp. 1317.
    5. 5)
      • 8. De, J., Hobbs, B., Belmans, R.: ‘Value of price responsive load for wind integration in unit commitment’, IEEE Trans. Power Syst., 2014, 29, (2), pp. 675685.
    6. 6)
      • 11. Ding, T., Bie, Z., Bai, L., et al: ‘Adjustable robust optimal power flow with the price of robustness for large-scale power systems’, IET Gener. Transm. Distrib., 2016, 10, (1), pp. 164174.
    7. 7)
      • 47. Subcommittee, P.M.: ‘IEEE reliability test system’, IEEE Trans. Power Appl. Syst., 1979, 98, (6), pp. 20472054.
    8. 8)
      • 26. van Ackooij, W.,, Malick, J.: ‘Eventual convexity of probability constraints with elliptical distributions’, Math. Program., 2019, 175, pp. 127.
    9. 9)
      • 17. Liu, C., Wang, X., Guo, J., et al: ‘Chance-constrained scheduling model of grid-connected microgrid based on probabilistic and robust optimisation’, IET Gener. Transm. Distrib., 2018, 12, (11), pp. 24992509.
    10. 10)
      • 34. Nguyen, T., Pai, M.: ‘Dynamic security-constrained rescheduling of power systems using trajectory sensitivities’, IEEE Trans. Power Syst., 2003, 18, (2), pp. 848854.
    11. 11)
      • 28. Bludszuweit, H., Dominguez-Navarro, J., Llombart, A.: ‘Statistical analysis of wind power forecast error’, IEEE Trans. Power Syst., 2008, 23, (3), pp. 983991.
    12. 12)
      • 25. Henrion, R., Strugarek, C.: ‘Convexity of chance constraints with independent random variables’, Comput. Optim. Appl., 2008, 41, pp. 263276.
    13. 13)
      • 13. Zhang, H., Li, P.: ‘Application of sparse-grid technique to chance constrained optimal power flow’, IET Gener. Transm. Distrib., 2013, 7, (5), pp. 491499.
    14. 14)
      • 44. Qin, C., Liu, Y., Yu, Y., et al: ‘Dynamic security region of power systems with double fed induction generator’, Trans. China Electrotech. Soc., 2015, 30, (18), pp. 157163.
    15. 15)
      • 2. Foley, A., Leahy, P., Marvuglia, A., et al: ‘Current methods and advances in forecasting of wind power generation’, Renew. Energy, 2012, 37, pp. 18.
    16. 16)
      • 4. Rajagopal, R., Bitar, E., Varaiya, P., et al: ‘Risk-limiting dispatch for integrating renewable power’, Int. J. Electr. Power, 2013, 44, pp. 615628.
    17. 17)
      • 38. Makarov, Y., Lu, S., Guo, X., et al: ‘Wide area security region final report’, PNNL-19331, Washington, D.C., USA: U.S. Department of Energy, 2010.
    18. 18)
      • 21. Wang, Z., Shen, C., Liu, F., et al: ‘Chance-constrained economic dispatch with non-Gaussian correlated wind power uncertainty’, IEEE Trans. Power Syst., 2017, 32, (6), pp. 48804893.
    19. 19)
      • 9. Tong, X., Luo, X., Yang, H., et al: ‘A distributionally robust optimization-based risk-limiting dispatch in power system under moment uncertainty’, Int. Trans. Electr. Energy Syst., 2017, 27, p. e2343.
    20. 20)
      • 41. Yu, Y.: ‘Review of study on methodology of security region of power system’, J. Tianjin Univ., 2008, 41, (6), pp. 635646.
    21. 21)
      • 33. Vittal, E., O'Malley, M., Keane, A.: ‘Rotor angle stability with high penetrations of wind generation’, IEEE Trans. Power Syst., 2012, 27, (1), pp. 353362.
    22. 22)
      • 20. Wu, Z., Zeng, P., Zhang, X., et al: ‘A solution to the chance-constrained two-stage stochastic program for unit commitment with wind energy integration’, IEEE Trans. Power Syst., 2016, 31, (6), pp. 41854196.
    23. 23)
      • 37. EPRI.: ‘Direct methods for security regions of bulk power system’, 1008608. Palo Alto, CA, USA: EPRI, 2004.
    24. 24)
      • 35. Xia, S., Luo, X., Chan, K., et al: ‘Probabilistic transient stability constrained optimal power flow for power systems with multiple correlated uncertain wind generations’, IEEE Trans. Sustain. Energy, 2016, 7, (3), pp. 11331144.
    25. 25)
      • 18. Wu, H., Shahidehpour, M., Li, Z., et al: ‘Chance-constrained day-ahead scheduling in stochastic power system operation’, IEEE Trans. Power Syst., 2014, 29, (4), pp. 15831591.
    26. 26)
      • 12. Chen, R., Sun, H., Guo, Q., et al: ‘Reducing generation uncertainty by integrating CSP with wind power: an adaptive robust optimization-based analysis’, IEEE Trans. Sustain. Energy, 2015, 6, (2), pp. 583594.
    27. 27)
      • 23. Henrion, R.: ‘Introduction to chance constraint programming’, Tutorial paper for the Stochastic Programming Community HomePage, http://www.wiasberlin.de/people/henrion/publikat.html, 2004.
    28. 28)
      • 39. Chen, S., Chen, Q., Xia, Q., et al: ‘Steady-state security assessment method based on distance to security region boundaries’, IET Gener. Transm. Distrib., 2013, 7, (3), pp. 288297.
    29. 29)
      • 14. Lu, M., Dvorkin, Y., Backhaus, S.: ‘A robust approach to chance constrained optimal power flow with renewable generation’, IEEE Trans. Power Syst., 2016, 31, (5), pp. 38403849.
    30. 30)
      • 42. Yu, Y., Feng, F.: ‘Active power steady-state security regions of power system’, Sci. China Ser. A, 1990, 33, (12), pp. 14881500.
    31. 31)
      • 27. Pagnoncelli, B., Ahmed, S., Shapiro, A.: ‘Sample average approximation method for chance constrained programming: theory and applications’, J. Opt. Theory Appl., 2009, 142, (2), pp. 399416.
    32. 32)
      • 22. Charnes, A., Cooper, W.: ‘Chance-constrained programming’, Manage. Sci., 1959, 6, (1), pp. 7379.
    33. 33)
      • 5. van Ackooij, W., Danti Lopez, I., Frangioni, A., et al: ‘Large scale unit commitment under uncertainty: an updated literature survey’, Ann. Oper. Res., 2018, 271, (1), pp. 1185.
    34. 34)
      • 43. Zeng, Y., Yu, Y.: ‘A practical direct method for determining dynamic security regions of electric power systems’, Proc. CSEE, 2003, 23, (5), pp. 2428.
    35. 35)
      • 10. Xiong, P., Jirutitijaroen, P., Singh, C.: ‘A distributionally robust optimization model for unit commitment considering uncertain wind power generation’, Electr. Power Syst. Res., 2016, 32, pp. 111.
    36. 36)
      • 15. Roald, L., Misra, S., Krause, T., et al: ‘Corrective control to handle forecast uncertainty: a chance constrained optimal power flow’, IEEE Trans. Power Syst., 2017, 32, (2), pp. 16261637.
    37. 37)
      • 1. Bouffard, F., Galiana, F.: ‘Stochastic security for operations planning with significant wind power generation’, IEEE Trans. Power Syst., 2008, 23, (2), pp. 306316.
    38. 38)
      • 32. Kayikci, M., Milanovic, J. ‘Assessing transient response of DFIG-based wind plants-the influence of model simplifications and parameters’, IEEE Trans. Power Syst., 2008, 23, (2), pp. 545554.
    39. 39)
      • 24. Pr'ekopa, A.: ‘Stochastic programming’ (Kluwer, Dordrecht, Netherlands, 1995).
    40. 40)
      • 3. Allen, J., Bruce, F.: ‘Power generation operation and control’ (John Wiley Press, Hoboken, NJ, USA, 1984).
    41. 41)
      • 7. Che, P., Tang, L., Wang, J.: ‘Two-stage minimax stochastic unit commitment’, IET Gener. Transm. Distrib., 2018, 12, (4), pp. 947956.
    42. 42)
      • 30. Pena, R., Clare, J., Asher, G.: ‘Doubly fed induction generator using back-to-back PWM converters and its application to variable-speed wind-energy generation’, IEEE Proc. Electr. Power Appl., 1996, 143, (3), pp. 231241.
    43. 43)
      • 16. Wang, Q., Guan, Y., Wang, J.: ‘A chance-constrained two-stage stochastic program for unit commitment with uncertain wind power output’, IEEE Trans. Power Syst., 2012, 27, (1), pp. 206215.
    44. 44)
      • 6. Wang, J., Shahidehpour, M., Li, Z.: ‘Security-constrained unit commitment with volatile wind power generation’, IEEE Trans. Power Syst., 2008, 23, (3), pp. 13191327.
    45. 45)
      • 46. Miao, F., Vittal, V., Heydt, G., et al: ‘Probabilistic power flow analysis with generation dispatch including photovoltaic resources’, IEEE Trans. Power Syst., 2013, 28, (2), pp. 17971805.
    46. 46)
      • 19. Zhao, C., Wang, Q., Wang, J., et al: ‘Expected value and chance constrained stochastic unit commitment ensuring wind power utilization’, IEEE Trans. Power Syst., 2014, 29, (6), pp. 26962705.
    47. 47)
      • 45. Kendall, M., Stuart, A.: ‘The advanced theory of statistics’ (Macmillan, New York, NY, USA, 1977).
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