Open Access Open Access  Restricted Access Subscription or Fee Access

Cost Improvement of Clustering based Unit Commitment Employing Combined Genetic Algorithm-Simulated Annealing

V.C. Jagan Mohan, Dr.M. Damodar Reddy, K. Subbaramaiah

Abstract


Fuel cost savings can be obtained by proper commitment of available generating units. This paper describes a new approach to the unit commitment problem through classification of units into various clusters based on hybrid technique of genetic algorithm and simulated annealing. This classification is carried out in order to reduce the overall operating cost and to satisfy the minimum up/down constraints easily. Unit commitment problem is an important optimizing task in daily operational planning of power systems which can be mathematically formulated as a large scale nonlinear mixed-integer minimization problem. A new methodology employing the concept of cluster algorithm called as additive and divisive hierarchical clustering has been employed based on hybrid technique of genetic algorithm and simulated annealing in order to carry out the technique of unit commitment. Proposed methodology involves two individual algorithms. While the load is increasing, additive cluster algorithm has been employed while divisive cluster algorithm is used when the load is decreasing. The proposed technique is tested on a 10 unit system and the simulation results show the performance of the proposed technique.


Keywords


Unit Commitment, Additive Clustering, Divisive Clustering, Genetic Algorithm, Simulated Annealing

Full Text:

PDF

References


Happ. H. H., R. C .Johnson, W. J .Wright, “Large Scale hydro-thermal unit commitment-method and results”, IEEE Transactions on Power Apparatus and Systems, Vol. PAS-90, 1971, 1373-1383.

K.S.Swarup and S.Yamashiro, “Unit commitment solution methodology using genetic algorithm”, IEEE Tr. on PS,Vol.17,No.1,PP. 87-91, Feb 2002.

Dimitris N.Simopoulos, Stavroula D.Kavatza and Costas D.Kavatza and Costas D.Vournas, “Unit Commitment by Enhanced Simulated Annealing Algorithm”, IEEE trans.on Power Systems, Vol.21, No.1, Feb 2006.

T.Senjyu, H.Yamashiro, K.Uezato and T.Funabasni, “A Unit commitment problem by using genetic Algorithm based on unit characteristic classification”, proceedings of IEEE power Engineering Society winter meeting, Vol. 1,pp 58 -63,2002.

T. Senjyu, K. Shimabukuro, A Fast technique for unit commitment problem by extended priority list, IEEE Tr.on PS, Vol.18, No.2, May 2003.

G. B. Sheble and T. T. Maifeld, Unit commitment by genetic algorithm and expert systems, ESPR, Vol.30, No.2, 1994, 115-121.

S.A.Kazarlis, A.G.Bakirtzis and V.Petridis, “A genetic algorithm solution to the unit commitment problem”, IEEE Transactions on Power Systems, Vol 11, No 1, Feb 1996, pp 83-91.

Fred N Lee and Qibei Feng, “Multi area unit commitment”, IEEE Transactions on Power Systems, Vol 7, No 2, May 1992, pp 591-599.

Sun-Nien Yu, “A Triple-Stage Algorithm for Optimal Unit Scheduling of Thermal Units”, Power Systems Conference and Exposition, (PSCE '09), March 2009, pp 1-6.

K. A. Juste, H. Kitu, E. Tunaka and J. Hasegawa, “An Evolutionary Programming Solution to the Unit Commitment Problem”, IEEE Transactions on Power Systems, Vol. 14, No. 4, Nov 1999, pp 1452-1459.

A Rudolf and R Baryrleithner, “A genetic algorithm for solving unit commitment problem of a hydro-thermal power system”, IEEE Transactions on Power Systems, Vol 14, No 4, Nov 1999, pp 1460-1468.

Narayana Prasad Padhy, “Unit Commitment-A Bibliographical Survey”, IEEE Transactions on Power Systems, Vol 19, No 2, May 2004, pp 1196-1205.

Toshiyuki Sawa, Yasuo Sato, Mitsuo Tsurugai, and Tsukasa Onishi, “Security Constrained Integrated Unit Commitment Using Quadratic Programming”, POWERTECH 2007, pp 1858-1863.

M. Kurban. and U. Basaran Filik, “Unit Commitment Scheduling by Using the Autoregressive and Artificial Neural Network Models Based Short-Term Load Forecasting”, Proc.,International Conference on Probabilistic Methods Applied to Power Systems, May 2008, pp 1-5.

Lei Wu, Mohammad Shahidehpour and Tao Li, “Cost of Reliability Analysis Based on Stochastic Unit Commitment”, IEEE Transactions On Power Systems, Vol. 23, No. 3, Aug 2008, pp 1364-1374.

Wenping Chang and Xianjue Luo, “A solution to the unit commitment using hybrid genetic algorithm”, TENCON-2008, IEEE Region 10 Conference, Nov 2008, pp 1-6.

A Sima Uyar and Belgin Turkay, “Evolutionary algorithms for the unit commitment problem”, Turkish Journal of Electrical Engineering, Vol 16, No 3, 2008, pp 1-16.

Chariklia A.Georgopoulou and Kyriakos C.Giannakoglou, “Two level two objective evolutionary algorithms for solving unit commitment problems”, Applied Energy, 86, 2009, pp 1229-1239.

Taher Niknam,Amin Khodaei and Farhad Fallahi, “A new decomposition approach for the thermal unit commitment problem” Applied Energy, 86, 2009, pp 1667-1674.

V.C.Jagan Mohan, M.Damodar Reddy, K.Subbaramaiah, “Implementation of clustering based unit commitment employing genetic algorithm”, International Journal of Coimbatore Institute of Information Technology, Dec 2012

V.C.Jagan Mohan, M.Damodar Reddy, K.Subbaramaiah, “Implementation of fast technique for unit commitment based on unit clustering”, International Journal of Energy Technologies and Policy, Vol 2, No 7, 2012, pp 54-63.


Refbacks

  • There are currently no refbacks.