Abstract

The previous study developed a life-cycle carbon emissions (LCCE) algorithm in MS Excel. Despite improvements, a comprehensive approach is needed to conduct life-cycle carbon emissions inventory (LCCEI) analysis using current methods. This study diverges from existing research by assessing LCCEI data of power generation and transmission systems on studied grids, considering component lifespans, recycling pollutants, and retirement rates. The life-cycle carbon emissions inventory analysis results improve understanding of power system environmental performance, aligning with stakeholders’ objectives. This study aims to enhance the environmental performance of electric power systems in Kenya, Rwanda, and Tanzania by evaluating the LCCE of power generation and transmission within their national grids. The selected grids are the right participants for the study, of non-renewable and renewable grid electricity generation mixes, due to their different environmental features, potential power trade, upcoming grid interconnection, and power transmission practices at various scales. The study applied a life cycle assessment method and simulated the learning patterns using RStudio. The data (emission factors and activity) has been collected from the reports (scientific and technical) and national utility actors. The presented results show that only Kenyan generation and transmission systems have a lifetime decarbonization performance relationship between renewable energy sources dominated power systems and non-renewable energy sources dominated power systems. A major challenge of this study has been the scarcity of primary data, leading to reliance on some secondary and external sources. Therefore, future research should consider the use of more internal and primary data sources, and the use of the most current data, including new technologies adopted from cradle-to-grave of the systems. This study’s findings inform better system designs, policies, and plans for improved environmental performance in electrical power systems.

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