NDLI: Optimal power scheduling of an off-grid renewable hybrid system used for heating and lighting in a typical residential house

Content Provider	IEEE Xplore Digital Library
Author	Tutkun, N. San, E.S.
Copyright Year	2013
Description	Author affiliation: Dept. of Electr. & Electron. Eng, Duzce Univ., Duzce, Turkey (Tutkun, N.; San, E.S.)
Abstract	In Turkey, there is a significant increase in demand for electricity due to population and economic growth taken place in recent years. This is a major economic factor affecting electricity price on market as well as other factors such as an increase in oil and natural gas prices. To be more specific around 50% of electricity is generated by natural gas generators and 95% of natural gas is imported from other countries. However, Turkey has considerable amount of wind and solar energy potentials to generate electricity through wind turbines and photo-voltaic arrays either on-grid or off-grid. An off-grid hybrid system is more attractive in rural areas where access to grid is limited or unavailable. This study focuses on a power scheduling in a simple renewable hybrid system in order to minimize the operational unit cost using the binary-coded genetic algorithm instead of using mixed integer linear programming. The preliminary results indicated that the binary-coded genetic algorithm produced encouraging and meaningful outcomes to minimize operational unit cost in a typical renewable microgrid photo-voltaic/wind hybrid system.
Starting Page	352
Ending Page	355
File Size	459357
Page Count	4
File Format	PDF
e-ISBN	9781479928033
DOI	10.1109/EEEIC-2.2013.6737935
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-11-01
Publisher Place	Poland
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Power demand Electricity Sociology Cost reduction Microgrid renewable systems Hybrid power systems Optimal power schedulin Batteries Wind turbines Statistics Genetic algorithms
Content Type	Text
Resource Type	Article

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