http://iet.metastore.ingenta.com
1887

Two-stage glowworm swarm optimisation for economical operation of hydropower station

Two-stage glowworm swarm optimisation for economical operation of hydropower station

For access to this article, please select a purchase option:

Buy article PDF
$19.95
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Renewable Power Generation — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

This study proposes a two-stage glowworm swarm optimisation (GSO) algorithm for the economical operation of the inner plant of a hydropower station. Binary GSO and real-coded GSO (RCGSO) algorithms are applied with different types of encodings to solve the unit commitment sub-problem and the economic load distribution (ELD) sub-problem, respectively. Moreover, an improved dynamic patching mechanism is developed to avoid invalid calculations and enrich the diversity of the solutions. A luciferin transfer mechanism helps the algorithm escape the local optimum and a local research mechanism enhances the diversity of the solution space by selecting from among the derived solutions. The RCGSO algorithm uses a variable-step mechanism to avoid missing the optimal solution. In comparison with the genetic algorithm and particle swarm optimisation, the RCGSO is significantly robust and provides better solutions to ELD sub-problems. Numerical simulations exhibited the superiority of the two-stage GSO algorithm in terms of stably and quickly solving the economical operation problem of hydropower stations.

References

    1. 1)
      • 1. Bard, J.F.: ‘Short-term scheduling of thermal electric generators using Lagrangian relaxation’, Oper. Res., 1988, 36, (5), pp. 756766.
    2. 2)
      • 2. Oliveira, P., Blair-Fish, J., McKee, S., et al: ‘Parallel Lagrangian relaxation in power scheduling’, Comput. Syst. Eng., 1992, 3, (5), pp. 609612.
    3. 3)
      • 3. Baldick, R.: ‘The generalized unit commitment problem’, IEEE Trans. Power Syst., 1995, 10, (1), pp. 465475.
    4. 4)
      • 4. Peterson, W.L., Brammer, S.R.: ‘A capacity based Lagrangian relaxation unit commitment with ramp rate constraints’, IEEE Trans. power Syst., 1995, 10, (2), pp. 10771084.
    5. 5)
      • 5. Allen, R.B., Bridgeman, S.G.: ‘Dynamic programming in hydropower scheduling’, J. Water Resour. Plan. Manage., 1986, 112, (3), pp. 339353.
    6. 6)
      • 6. Snyder, W.L., Powell, H.D., Rayburn, J.C.: ‘Dynamic programming approach to unit commitment’, IEEE Trans. Power Syst., 1987, 2, (2), pp. 339348.
    7. 7)
      • 7. Hobbs, W.J., Hermon, G., Warner, S., et al: ‘An enhanced dynamic programming approach for unit commitment’, IEEE Trans. Power Syst., 1988, 3, (3), pp. 12011205.
    8. 8)
      • 8. Turgeon, A.: ‘Optimal scheduling of thermal generating units’, IEEE Trans. Autom. Control, 1978, 23, (6), pp. 10001005.
    9. 9)
      • 9. Dillon, T.S., Edwin, K.W., Kochs, H.D., et al: ‘Integer programming approach to the problem of optimal unit commitment with probabilistic reserve determination’, IEEE Trans. Power Syst., 1978, 6, (97), pp. 21542166.
    10. 10)
      • 10. Finardi, E.C., Da Silva, E.L.: ‘Unit commitment of single hydroelectric plant’, Elect. Power Syst. Res., 2005, 75, (2), pp. 116123.
    11. 11)
      • 11. Cohen, A.I., Yoshimura, M.: ‘A branch-and-bound algorithm for unit commitment’, IEEE Trans. Power Syst., 1983, 102, (2), pp. 444451.
    12. 12)
      • 12. Huang, K.Y., Yang, H.T., Huang, C.L.: ‘A new thermal unit commitment approach using constraint logic programming’, IEEE Trans. Power Syst., 1998, 13, (3), pp. 936945.
    13. 13)
      • 13. Juste, K.A., Kita, H., Tanaka, E., et al: ‘An evolutionary programming solution on the unit commitment problem’, IEEE Trans. Power Syst., 1999, 14, (4), pp. 14521459.
    14. 14)
      • 14. Sinha, N., Chakrabarti, R., Chattopadhyay, P.K.: ‘Evolutionary programming techniques for economic load dispatch’, IEEE Trans. Evol. Comput., 2003, 7, (1), pp. 8394.
    15. 15)
      • 15. Rajan, C., Christober, A.: ‘Hydro-thermal unit commitment problem using simulated annealing embedded evolutionary programming approach’, Electr. Power Energy Syst., 2011, 33, (4), pp. 939946.
    16. 16)
      • 16. Kazarlis, S.A., Bakirtzis, A.G., Petridis, V.A.: ‘A genetic algorithm solution to the unit commitment problem’, IEEE Trans. Power Syst., 1996, 11, (1), pp. 8392.
    17. 17)
      • 17. Abido, M.A.: ‘A niched pareto genetic algorithm for multiobjective environmental/ economic dispatch’, Electr. Power Energy Syst., 2003, 25, (2), pp. 97105.
    18. 18)
      • 18. Mantawy, A.H., Abdel-Magid, Y.L., Selim, S.Z.: ‘Unit commitment by tabu search’, IEEE Proc. Gener. Transm. Distrib., 1998, 145, (1), pp. 5664.
    19. 19)
      • 19. Lin, W.-M., Cheng, F.-S., Tsay, M.-T.: ‘An improved tabu search for economic dispatch with multiple minima’, IEEE Trans. Power Syst., 2002, 17, (1), pp. 108112.
    20. 20)
      • 20. Mantawy, A.H., Abdel-Magid, Y.L., Selim, S.Z.: ‘A simulated annealing algorithm for unit commitment’, IEEE Trans. Power Syst., 1998, 13, (1), pp. 197204.
    21. 21)
      • 21. Simopoulos, D.N., Kavatza, S.D., Vournas, C.D.: ‘Unit commitment by an enhanced simulated annealing algorithm’, IEEE Trans. Power Syst., 2006, 21, (1), pp. 6876.
    22. 22)
      • 22. Ting, T.O., Rao, M.V. C., Loo, C.K.: ‘A novel approach for unit commitment problem via an effective hybrid particle swarm optimisation’, IEEE Trans. Power Syst., 2006, 21, (1), pp. 411418.
    23. 23)
      • 23. Zhao, B., Guo, C.X., Bai, B.R., et al: ‘An improved particle swarm optimisation algorithm for unit commitment’, Elect. Power Energy Syst., 2006, 28, (7), pp. 482490.
    24. 24)
      • 24. Valenzuela, J., Smith, A.E.: ‘A seeded memetic algorithm for large unit commitment problems’, J. Heuristics, 2002, 8, (2), pp. 173195.
    25. 25)
      • 25. Simon, S.P., Padhy, N.P., Anand, R.S.: ‘An ant colony system approach for unit commitment problem’, Electr. Power Energy Syst., 2006, 28, (5), pp. 315323.
    26. 26)
      • 26. Vaisakh, K., Srinivas, L.R.: ‘Evolving ant colony optimisation based unit commitment’, Appl. Soft Comput., 2011, 11, (2), pp. 28632870.
    27. 27)
      • 27. Krishnanand, K.N., Ghose, D.: ‘Detection of multiple source locations using a glowworm metaphor with applications to collective robotics’. Proc. IEEE Swarm Intelligence Symp., 2005 (SIS 2005). 2005, 2005, pp. 8491.
    28. 28)
      • 28. Krishnanand, K.N., Ghose, D.: ‘Glowworm swarm optimisation for simultaneous capture of multiple local optima of multimodal functions’, Swarm Intell., 2009, 3, (2), pp. 87124.
    29. 29)
      • 29. Gong, Q.Q., Zhou, Y.Q., Yang, Y.: ‘Artificial glowworm swarm optimisation algorithm for solving 0-1 knapsack problem’, Adv. Mater.Res., 2011, 143, pp. 166171.
    30. 30)
      • 30. Yang, Y., Zhou, Y.Q.: ‘Glowworm swarm optimisation algorithm for solving numerical integral’, Intell. Comput. Inf. Sci., 2011, 134, pp. 389394.
    31. 31)
      • 31. Li, M.W., Wang, X., Gong, Y., et al: ‘Binary glowworm swarm optimisation for unit commitment’, J. Mod. Power Syst. Cle., 2014, 2, (4), pp. 357365.
    32. 32)
      • 32. Chen, R.Z.: ‘Improved self-adaptive glowworm swarm optimisation algorithm’, Appl. Mech. Mater., 2014, 519, pp. 798801.
    33. 33)
      • 33. Huang, K., Zhou, Y.Q.: ‘A novel chaos glowworm swarm optimisation algorithm for optimisation functions’. 7th Int. Conf. on Intelligent Computing (ICIC 2011), 2012, vol. 6840, pp. 426434.
    34. 34)
      • 34. Yepes, V., Martí, J.V., García-Segura, T.: ‘Cost and CO2 emission optimisation of precast–prestressed concrete U-beam road bridges by a hybrid glowworm swarm algorithm’, Autom. Constr., 2015, 49, (part A), pp. 123134.
    35. 35)
      • 35. Jayakumar, D.N., Venkatesh, P.: ‘Glowworm swarm optimisation algorithm with topsis for solving multiple objective environmental economic dispatch problem’, Appl. Soft Comput., 2014, 23, pp. 375386.
    36. 36)
      • 36. Hamming, R.W.: ‘Error detecting and error correcting codes’, Bell Labs Tech. J., 1950, 29, (2), pp. 147160.
    37. 37)
      • 37. Steane, A.M.: ‘Error correcting codes in quantum theory’, Phys. Rev. Lett., 1996, 77, (5), pp. 793797.
    38. 38)
      • 38. Buras, N.: ‘Scientific allocation of water resources’ (American Elsevier Publishing Company Inc., New York, USA, 1972).
    39. 39)
      • 39. Saeed, M.O.B., Zerguine, A., Zummo, S.A.: ‘A variable step-size strategy for distributed estimation over adaptive networks’, EURASIP J. Adv. Signal Process., 2013, 2013, (1), p. 135.
    40. 40)
      • 40. Li, Y.H., Zhan, Z.H., Lin, S.J., et al: ‘Competitive and cooperative particle swarm optimisation with information sharing mechanism for global optimisation problems’, Inf. Sci., 2015, 293, pp. 370382.
    41. 41)
      • 41. Krishnanand, K.N., Ghose, D.: ‘Chasing multiple mobile signal sources: a glowworm swarm optimization approach’. Third Indian Int. Conf. on Artificial Intelligence (IICAI 07), Pune, India, 2007.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-rpg.2017.0466
Loading

Related content

content/journals/10.1049/iet-rpg.2017.0466
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address