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Application of a simulation tool based on a bio-inspired algorithm for optimisation of distributed power generation systems

2021-02-07

Abstract

The degradation of air quality, overheating and growing energy demand are closely related issues that indicate the impact of humankind on climate change. Consequently, governments and other multilateral agencies have shown interest in reducing air pollutant emissions from fossil fuel power generation sources. The most accepted options are related to clean energy sources, but, as we all know, we are far from meeting the world’s energy demand with clean generation sources. Other options are based on using fuels with a lower load of emissions and on the development of techniques to optimise power generation, reduce costs through efficient energy and reduce greenhouse gas emissions to the possible minimum. This study proposes a method to optimise the sensitivity factors as the operating point for a gas turbine power generator based on energy demands, electrical efficiency, fuel efficiency and the minimisation of greenhouse gas emissions. In order to address this, a multidisciplinary design/​assessment framework was developed. The results obtained from the simulation of a one-year energy consumption model for an average family in Colombia produced the point of operation for a gas turbine based on energy demands, efficient energy and the reduction of CO2 emissions to the atmosphere (i.e. the best trade-off). In this sense, the main contribution of this work is aimed at energy generation systems bio-inspired optimisation that reduce CO2 emissions into the atmosphere, especially in non-interconnected (off-grid) zones, but they rely on fossil fuel plant-based-power distributed generation.

Ramón Fernando Colmenares-Quintero, Germán David Góez-Sánchez, Juan Carlos Colmenares-Quintero, Luis Fernando Latorre-Noguera, and Damian Kasperczyk. Cogent Engineering8:1, (2021) 1909791, DOI: 10.1080/23311916.2021.1909791OPEN ACCESS!!!

https://​www​.tand​fon​li​ne​.com/​d​o​i​/​f​u​l​l​/​10​.​1080​/​23311916​.​2021​.​1909791

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