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

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
R. Saravanan, S. Subramanian, V. Dharamalingam, S. Ganesan

R Saravanan, S Subramanian, V Dharamalingam and S Ganesan. Generation Scheduling of Electricity with Integrated Wind-Thermal Units using Grey Wolf Optimization Algorithm. International Journal of Computer Applications 156(3):37-44, December 2016. BibTeX

	author = {R. Saravanan and S. Subramanian and V. Dharamalingam and 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 = {December 2016},
	volume = {156},
	number = {3},
	month = {Dec},
	year = {2016},
	issn = {0975-8887},
	pages = {37-44},
	numpages = {8},
	url = {},
	doi = {10.5120/ijca2016912406},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


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.


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  33. FE(r) -Objective function valuation at trail r


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