Hybrid renewable energy resources incorporated optimal power flow using single phase multi-group teaching learning-based optimiser. (21st October 2022)

• Record Type: Journal Article
• Authors: Pandya, Sundaram B.; Jariwala, Hitesh R.
• Is Part Of: International journal of computer aided engineering and technology. Volume 17:Number 4(2022)
• Page Start: 361
• Page End: 387
• Publication Date: 2022-10-21
• Subjects: wind power units -- solar PV energy -- small hydro power -- probability density function -- PDF; Computer-aided engineering -- Periodicals; 620.00285
• Languages: English
• ISSNs: 1757-2657

Abstract

The latest scenario of electrical system consists of conventional generating units along with the renewable energy resources. The proposed article recommends a method for the solution of optimal power flow, integrating with wind generating units, solar photovoltaic system and hybrid solar with small hydro power that is run-of-river with traditional coal-based generating stations. The irregularity of renewable source's output intensifies the complications of the optimal power flow issue. In the proposed work, lognormal, Weibull and Gumble probability density functions are also utilised for predicting power outputs of the renewables, respectively. The modified IEEE-30 bus test system is used to validate the results, which is incorporated with wind-solar-small hydro generating plants. The single phase multi-group teaching learning-based optimiser is used as the optimisation tool and the simulation results are compared with the newly developed algorithm.