We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 November 2024
Reseach Article

Generation Scheduling of Electricity with Integrated Wind-Thermal Units using Grey Wolf Optimization Algorithm

by R. Saravanan, S. Subramanian, V. Dharamalingam, S. Ganesan
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 156 - Number 3
Year of Publication: 2016
Authors: R. Saravanan, S. Subramanian, V. Dharamalingam, S. Ganesan
10.5120/ijca2016912406

R. Saravanan, S. Subramanian, V. Dharamalingam, S. Ganesan . Generation Scheduling of Electricity with Integrated Wind-Thermal Units using Grey Wolf Optimization Algorithm. International Journal of Computer Applications. 156, 3 ( Dec 2016), 37-44. DOI=10.5120/ijca2016912406

@article{ 10.5120/ijca2016912406,
author = { R. Saravanan, S. Subramanian, V. Dharamalingam, S. Ganesan },
title = { Generation Scheduling of Electricity with Integrated Wind-Thermal Units using Grey Wolf Optimization Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2016 },
volume = { 156 },
number = { 3 },
month = { Dec },
year = { 2016 },
issn = { 0975-8887 },
pages = { 37-44 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume156/number3/26692-2016912406/ },
doi = { 10.5120/ijca2016912406 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:01:38.005509+05:30
%A R. Saravanan
%A S. Subramanian
%A V. Dharamalingam
%A S. Ganesan
%T Generation Scheduling of Electricity with Integrated Wind-Thermal Units using Grey Wolf Optimization Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 156
%N 3
%P 37-44
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Integrating wind power with any other energy source in power system has many operational and scheduling complications because of its inconsistent nature in the process ofwind forecasting. In this paper, a new meta-heuristic optimization method named Grey Wolf Optimization algorithm is involved for solving the problem of generation scheduling (GS) to obtain best possible solution in power systems taking into account the load balance, reserve requirement, wind power availability constraints, inequality and equality constraints. The proposed GWO method is applied to a test system involves 40 conventional units and 2 wind farms. The system performance of GWO algorithm is establishedbyevaluating the results obtained for different number of trails and various iterationsfor five different populations. Calculation of the solution for different populations in the systemdiscloses that the best optimal scheduleachieved by Grey Wolf Optimization algorithm.

References
  1. Ouyang, Z, Shahidehpour S.M.1991.An intelligent dynamic programming for unit commitment application,IEEE Transaction on power systems. 6(3) 1203.
  2. Sasaki, H, et.a..l002E. 2005. A solution method of unit commitment by artificial neuraltabu-search-based hybrid-optimisation technique’, IEEProc.,Gener. Transm. Distrib., 152, (4), . 563–574
  3. Annakkage,U.D,et al.. 1995. Unit commitment by parallel simulated annealing, proc. Inst. Elect. Eng., Gen.Transm.Dist .142-595.
  4. Purushothama, G.K., and Jenkins, L. 2003: ‘Simulated annealing with local search. A hybrid algorithm for unit commitment’, IEEE Trans., PWRS-18, (1), 273–278.
  5. Simopoulos, D.N., Kavatza, S.D., and Vournas, C.D.2006: ‘Unit commitment by an enhanced simulated annealing algorithm’, IEEETrans. , PWRS-21, (1), . 68–76.
  6. Lau, T.W., Chung, C.Y., Wong, K.P. 2009, et al.:` Quantum inspired evolutionary algorithm approach for unit commitment’,IEEETrans. Power Syst.,24(3) .1503-1512.
  7. Patra,S.Goswami,S.K., Goswami, B.2008: Differential evolution algorithm for solving unit commitment with ramp constraints, Elect. Power compon.syst.,36.(8), 771-787.
  8. Dilip, D., Sapatarshi, D.2012:` A binary –real -coded differential evolution for unit commitment problem’, Elect Power Syst.,,42,517-524.
  9. Huang.K.Y, yang.H.T, Yang.C.L. 1988, A new thermal unit commitment approach using constraint logic programming, IEEE Trans. Power syst. 13 (3).
  10. S.A. Kazarlis, A.G. Bakirtzis, V.Petridid,(1996) A genetic algorithm solution to the unit commitment problem,IEEE Trans .Power syst.11(1)83.
  11. WaltersD.C and Sheble.G.B .1993, “Genetic algorithm solution of economic dispatch with the valve-point loading”, IEEE Trans.on Power Systems, Vol. 8, No. 3, 1325-1332
  12. Bakirtzis, A.G., and Petridis, V.1996 ‘A genetic algorithm solution to the unit commitment problem’, IEEE Trans., , PWRS-11, (1), 83–92
  13. Virmani. S, et.al. 1989, Implementation of a Lagrangian relaxation based unit commitment problem, IEEE Trans. Power syst. 4(4). 1373
  14. Cheng.C. P,.Liu C. W, Liu.C.C. 2000, Unit commitment by lagrangian relaxation and genetic algorithms, IEEE Trans. Power Syst.15(2)707.
  15. Thillainathan,Logenthiran,WaiLokWoo, Van Tung Phan,Lagrangian relaxation hybrid with evolutionary algorithm with short term scheduling, Electrical power and Energy Systems., 64356-364.
  16. Cohen. A . I, Yoshimura. M, A 1983 branch – and a bound algorithm for unit commitment, IEEE Trans.PowerApp .Syst., PAS-102(2) 444
  17. Mori. H, Matsuzaki. O. 2001, Application of priority –list-embedded tabu search to unit commitment in power systems,Inst.Elect.Eng.Jpn.121-B(4)535.
  18. Juste, K.A., Kiat, H., Tanaka, E., and Hasegawa. 2005, J.: ‘Unit commitment by a tabu-search-based hybrid-optimisation technique’, IEEProc.,Gener. Transm. Distrib., 152, (4), pp. 563–574
  19. Karki. R. 2007, “Renewable Energy Credit Driven Wind Power Growth for System Reliability”, Electric Power System Research, vol. 77, 797– 803.
  20. Hetzer J, Yu DC. 2008 An economic dispatch model incorporating wind power. IEEE Transactions on Energy Conversion; 23:603–611.
  21. ChenC.-L., Mar. 2008, “Optimal wind-thermal generating unit commitment, ”IEEE Trans. Energy Convers., vol. 23, no. 1, pp. 273–280.
  22. Siahkali.H,Vakilian.M(2009),“Electricity generation scheduling with large-scale wind farms using particle swarm optimization”, Electric Power Systems Research 79 826–836.
  23. Jeong, Y-W., Park, J-B., Jang,S-H.2010, et al.: `A new quantum inspired binary PSO application to unit commitment problems for power systems’, IEEE Trans Power syst., ,25,(3),pp.1486-1495.
  24. Xiang Yu,Xueqing,(2014)`Unit commitment using lagrangian and particle swarm optimization’, , Electrical power and Energy Systems., 61510-522.
  25. AnupShukla,.SinghS.N(2016),`Advanced three stage pseudo-inspired weight improved crazy particle swarm optimization for unit commitment’, Electrical power and Energy Systems., 9623-36.
  26. Marian Marcoveccio, G., Augusto Novals, Q2014., Ignacio Grossmann, E `Deterministic optimization of the thermal unit commitment problem: a branch and cut search’. ComputChem , Eng.,,67,pp53-68.
  27. . GaingZ.L,2003 Discrete particle swarm optimization algorithm for unit commitment, in: IEEE PES General Meeting, , p.418
  28. GaingZ.L.,(2003) Particle swarm optimization to solving the economic dispatch considering the generator constraints, IEEE Trans. Power Syst. 18 (3) 1187-1195.
  29. Eberhart.R, Shi.R,(2000), Comparing inertia weights and constriction factors in particle swarm optimization, in:proc. Congr. Evolu. Comp. p. 84.
  30. ChenP.H, ChangH.C.(1995), Large-scale economic dispatch by genetic algorithm, IEEE Trans. Power Syst. 10 (4) 1919-1926.
  31. EngelEngelbrech.A.P,2005Fundamentalsof Computational swarm intelligence, John wiley& Sons, England
  32. Nomeclature
  33. FE(r) -Objective function valuation at trail r
Index Terms

Computer Science
Information Sciences

Keywords

Generation scheduling Grey wolf optimization Total generation cost reduction Wind power availability.