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

Fast technique for unit commitment by genetic algorithm based on unit clustering

Fast technique for unit commitment by genetic algorithm based on unit clustering

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

Buy article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(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:
 
 
 
 
 
IEE Proceedings - Generation, Transmission and Distribution — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

The paper presents a new approach to the large-scale unit-commitment problem. To reduce computation time and to satisfy the minimum up/down-time constraint easily, a group of units having analogous characteristics is clustered. Then, this ‘clustered compress’ problem is solved by means of a genetic algorithm. Besides, problem-oriented powerful tools such as relaxed-pruned ELD, intelligent mutation, shift operator etc. make the proposed approach more effective with respect to both cost and execution time. The proposed algorithm is tested using the reported problem data set. Simulation results for systems of up to 100-unit are compared with previous reported results. Numerical results show an improvement in the solution cost compared with the results obtained from a genetic algorithm with standard operations.

References

    1. 1)
      • C.W. Richter , G.B. Sheble . A profit-based unit commitment GA for the competitive environment. IEEE Trans. , 2 , 715 - 721
    2. 2)
      • T.T. Maifed , G.B. Sheble . Genetic-based unit commitment problem. IEEE Trans. , 3 , 1359 - 1365
    3. 3)
      • C. Li , R.B. Johnson , A.J. Svoboda . A new unit commitment method. IEEE Trans. , 1 , 113 - 118
    4. 4)
      • I.G. Damousis , A.G. Bakirtzis , P.S. Dokopoulos . A solution to the unit-commitment problem using integer-coded genetic algorithm. IEEE Trans. , 2 , 1165 - 1172
    5. 5)
      • A.G. Bakirtzis , V. Petridis . A genetic algorithm solution to the unit commitment problem. IEEE Trans. , 1 , 83 - 92
    6. 6)
      • S.O. Orero , M.R. Irving . Large scale unit commitment using a hybrid genetic algorithm. Electr. Power Energy Syst. , 1 , 45 - 55
    7. 7)
      • A.J. Svoboda , C.-L. Tseng , C. Li , R.B. Johnson . Short-term resource scheduling with ramp constraints. IEEE Trans. , 1 , 77 - 83
    8. 8)
      • T. Senjyu , H. Yamashiro , K. Shimabukuro , K. Uezato , T. Funabashi . Fast solution technique for large-scale unit commitment problem using genetic algorithm. IEE Proc.-Gener. Transm. Distrib. , 6 , 753 - 760
    9. 9)
      • G.K. Purushothama , L. Jenkins . Simulated annealing with local search-a hybrid algorithm for unit commitment. IEEE Trans. , 1 , 273 - 278
    10. 10)
      • A.H. Mantawy , Y.L. Abdel-Magid , S.Z. Selim . A simulated annealing algorithm for unit commitment. IEEE Trans. , 1 , 197 - 204
    11. 11)
      • J. Valenzuela , A.E. Amith . A seeded memetic algorithm for large unit commitment problems. J. Heuristics , 173 - 195
    12. 12)
      • K.A. Juste , H. Kita , E. Tanaka , J. Hasegawa . An evolutionary programming to the unit commitment problem. IEEE Trans. , 4 , 1452 - 1459
    13. 13)
      • C.-P. Cheng , C.-W. Liu , C.-C. Liu . Unit commitment by Lagrangian relaxation and genetic algorithm. IEEE Trans. , 2 , 707 - 714
    14. 14)
      • G.S. Lauer , D.P. Bertsekas , N.R. Sandell , T.A. Posbergh . Solution of large-scale optimal unit commitment problems. IEEE Trans. , 79 - 86
http://iet.metastore.ingenta.com/content/journals/10.1049/ip-gtd_20045299
Loading

Related content

content/journals/10.1049/ip-gtd_20045299
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address