Optimal wind turbine sizing to minimize energy loss

• Published in: International journal of electrical power & energy systems Vol. 53; pp. 656 - 663
• Main Authors: Ugranlı, Faruk, Karatepe, Engin
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.12.2013; Elsevier
• Subjects: Applied sciences; Buses (vehicles); Direct energy conversion and energy accumulation; Electric power generation; Electric power plants; Electrical engineering. Electrical power engineering; Electrical power engineering; Energy conservation; Energy loss minimization; Exact sciences and technology; Fuzzy-c means clustering; Genetic algorithm; Genetic algorithms; Methodology; Miscellaneous; Operation. Load control. Reliability; Optimization; Power networks and lines; Sizing; Weighting factors; Wind turbines; Wind-based DG; Fuzzy-c means clustering; Genetic algorithm; Weighting factors; Energy loss minimization; Wind-based DG; Automatic classification; Output power; Wind generator; Distributed power generation; Time variation; Optimization; Load flow; Energy loss; Power flow; Time curve; IEEE standards; Execution time; Flow field; Long term; Dimensioning; Renewable energy sources; Weighting; Electrical network; Objective function; Planning; Electric power production
• ISSN: 0142-0615; 1879-3517
• DOI: 10.1016/j.ijepes.2013.05.035

•A weighting-based methodology is developed to find optimum wind-turbine size.•The time-varying characteristics of load and wind-generation profiles are considered.•The harmony of correspondence between load and generation profiles is preserved.•The optimization is carried out by using genetic algorithm with utilizing power flow analysis.•The fuzzy-c means clustering is utilized to reduce execution time and allow long term planning. The integration of renewable distributed generation (DG) in power systems has been increasing day by day. One of the most promising DG technologies is wind turbine among the renewable sources. Therefore, the optimization of DG whose the output power is varying with time is very crucial for the future power systems. However, it is difficult to establish a suitable objective function by taking into account of time varying characteristics. In this paper, a methodology based on weighting factors is proposed in order to minimize energy loss by finding the optimal sizes of wind turbines. The optimization is carried out by using the genetic algorithm with utilizing power flow analysis. The contribution of this paper is to allow considering the time varying characteristics of both load and wind-generation profile in a pairwise manner without violating the harmony of correspondence between load and generation profile. In addition, the proposed methodology is merged with the fuzzy-c means clustering to reduce execution time and allow long term planning due to the fact that the computational burden of the genetic algorithm is substantially high. The proposed methodology is applied to the IEEE-30 bus test system for 4days and annual energy loss minimization scenarios. The results show that energy loss can be reduced significantly by using the proposed methodology.