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

access icon free Optimal transmission access for generators in wind-integrated power systems: stochastic and robust programming approaches

Integrating wind generation in power systems has raised the issue of optimal transmission access for generators. The available transmission capacity for a generator is now subject to the uncertain wind generation. Therefore, the need for a transmission access mechanism has emerged. This study proposes three different optimisation models for calculating transmission access under uncertain wind generation. First, it develops a mathematical model to find the expected transmission access. A chance-constrained optimisation model is derived to find different levels of access to the transmission network with pre-specified reliability levels. The chance-constrained model provides detailed information regarding the available transmission access at different reliability levels. This gives options to a connecting generator regarding its choice of transmission access. Finally, a robust model for transmission access is proposed. The robust model provides the conservative transmission access which is assured against all future realisations of wind generation. The proposed expected, chance-constrained and robust approaches for optimal transmission access are numerically studied using an illustrative 2-bus example and the IEEE 30-bus and IEEE 300-bus case studies. The moment-matching technique is used to generate wind and demand scenarios. The numerical results show the utility of three derived models to calculate the optimal transmission access for generators.

References

    1. 1)
      • 31. ‘Power Systems Case Archive’, http://www.ee.washington.edu/research/pstca/pf30/pg_tca30bus.htm, accessed 14 October 2015.
    2. 2)
      • 34. ‘MATPOWER’, http://www.pserc.cornell.edu/matpower/, accessed 23 August 2016.
    3. 3)
      • 22. Rubasheuski, U., Oppen, J., Woodruff, D.L.: ‘Multi-stage scenario generation by the combined moment matching and scenario reduction method’, Oper. Res. Lett., 2014, 42, (5), pp. 374377.
    4. 4)
      • 21. Smith, J.E.: ‘Moment methods for decision analysis’, Manage. Sci., 1993, 39, (3), pp. 340358.
    5. 5)
      • 14. Bertsimas, D., Litvinov, E., Sun, X.A., et al: ‘Adaptive robust optimization for the security constrained unit commitment problem’, IEEE Trans. Power Syst., 2013, 28, (1), pp. 5263.
    6. 6)
      • 20. Saric, A.T., Stankovic, A.M.: ‘An application of interval analysis and optimization to electric energy markets’, IEEE Trans. Power Syst., 2006, 21, (2), pp. 515523.
    7. 7)
      • 6. SONI: ‘Generator connection process’ (SONI, 2011).
    8. 8)
      • 11. Thiam, F.B., DeMarco, C.L.: ‘Transmission expansion via maximization of the volume of feasible bus injections’, Electr. Power Syst. Res., 2014, 116, pp. 3644.
    9. 9)
      • 12. Zhang, H., Li, P.: ‘Chance constrained programming for optimal power flow under uncertainty’, IEEE Trans. Power Syst., 2011, 26, (4), pp. 24172424.
    10. 10)
      • 17. Jiang, R., Wang, J., Zhang, M., et al: ‘Two-stage minimax regret robust unit commitment’, IEEE Trans. Power Syst., 2013, 28, (3), pp. 22712282.
    11. 11)
      • 4. De Breucker, S., Driesen, J., Belmans, R.: ‘Power quality in transmission grids: Guaranteed standards for power plants in France and the United Kingdom’. 13th Int. Conf. on Harmonics and Quality of Power, Wollongong, NSW, September 2008, pp. 16.
    12. 12)
      • 3. Hill Michael: ‘Development of transmission reliability standards for generators’ (Hill Michael, 2011).
    13. 13)
      • 7. AEMC: ‘First interim report – optional firm access, design and testing’ (AEMC, 2014).
    14. 14)
      • 28. Boyd, S., Vandenberghe, L.: ‘Convex optimization’ (Cambridge University Press, 2004).
    15. 15)
      • 16. Xiong, P., Jirutitijaroen, P.: ‘Two-stage adjustable robust optimisation for unit commitment under uncertainty’, IET Gener. Transm. Distrib., 2014, 8, (3), pp. 573582.
    16. 16)
      • 33. Shaaban, M., Bell, K.: ‘Assessment of tradable short-term transmission access rights to integrate renewable generation’. Proc. 44th Int. of Universities Power Engineering Conf., Glasgow, Scotland, September 2009, pp. 15.
    17. 17)
      • 23. Hoyland, K., Wallace, S.W.: ‘Generating scenario trees for multi-stage decision problems’, Manage. Sci., 2001, 47, (2), pp. 295307.
    18. 18)
      • 10. Leite da Silva, A.M., de Carvalho Costa, J.G., da Fonseca Manso, L.A., et al: ‘Transmission capacity: availability, maximum transfer and reliability’, IEEE Trans. Power Syst., 2002, 17, (3), pp. 843849.
    19. 19)
      • 8. PSERC: ‘Electric power transfer capability: concepts, applications, sensitivity, uncertainty’ (PSERC, 2001).
    20. 20)
      • 19. Wang, Z., Alvarado, F.L.: ‘Interval arithmetic in power flow analysis’, IEEE Trans. Power Syst., 1992, 7, (3), pp. 13411349.
    21. 21)
      • 2. CIGRE: ‘Review of transmission planning access requirements’ (CIGRE, 2014).
    22. 22)
      • 35. ‘300 Bus Power Flow Test Case’, http://www2.ee.washington.edu/research/pstca/pf300/pg_tca300bus.htm, accessed 23 August 2016.
    23. 23)
      • 5. Uzuncan, E., Hesamzadeh, M.R.: ‘Optimal transmission entry capacity in wind-integrated power systems’. 2014 IEEE Int. Energy Conf. (ENERGYCON), Cavtat, Croatia, May 2014, pp. 14671473.
    24. 24)
      • 13. Uzuncan, E., Hesamzadeh, M.R.: ‘Optimal firm transmission access using chance-constrained optimisation for renewable integration’. 2015 IEEE Power & Energy Society General Meeting, Denver, CO, July 2015, pp. 15.
    25. 25)
      • 24. Zhen, L.: ‘Task assignment under uncertainty: stochastic programming and robust optimisation approaches’, Int. J. Prod. Res., 2015, 53, (5), pp. 14871502.
    26. 26)
      • 9. Ou, Y., Singh, C.: ‘Assessment of available transfer capability and margins’, IEEE Trans. Power Syst., 2002, 17, (2), pp. 463468.
    27. 27)
      • 30. Morris, M.D.: ‘Factorial sampling plans for preliminary computational experiments’, Technometrics, 1991, 33, (2), pp. 161174.
    28. 28)
      • 15. Wu, L., Shahidehpour, M., Li, Z.: ‘Comparison of scenario-based and interval optimization approaches to stochastic SCUC’, IEEE Trans. Power Syst., 2012, 27, (2), pp. 913921.
    29. 29)
      • 25. Assavapokee, T., Realff, M.J., Ammons, J.C.: ‘A min-max regret robust optimization approach for interval data uncertainty’, J. Optim. Theory Appl., 2008, 137, pp. 297316.
    30. 30)
      • 27. Morales, J.M., Conejo, A.J., Madsen, H., et al: ‘Integrating renewables in electricity markets: operational problems’ (Springer US, 2013).
    31. 31)
      • 26. Biggar, D.R., Hesamzadeh, M.R.: ‘The economics of electricity markets’ (Wiley, 2014).
    32. 32)
      • 1. Zhang, H., Li, P.: ‘Probabilistic analysis for optimal power flow under uncertainty’, IET Gener. Transm. Distrib., 2010, 4, (5), pp. 553661.
    33. 33)
      • 18. Hu, B., Wu, L., Marwali, M.: ‘On the robust solution to SCUC with load and wind uncertainty correlations’, IEEE Trans. Power Syst., 2014, 29, (6), pp. 29522964.
    34. 34)
      • 32. ‘Historical Market Data’, http://www.nordpoolspot.com/historical-market-data/, accessed 27 April 2015.
    35. 35)
      • 29. Pereira, M.V., Granville, S., Fampa, M.H.C., et al: ‘Strategic bidding under uncertainty: a binary expansion approach’, IEEE Trans. Power Syst., 2005, 20, (1), pp. 180188.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-gtd.2016.0041
Loading

Related content

content/journals/10.1049/iet-gtd.2016.0041
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address