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Modelling of environmental emission in Kenyan, Rwandan, and Tanzanian electrical power systems


There is limited information on environmental emissions in African grid electricity generation and transmission systems, especially from the sub-Saharan African countries. The developed parameters are useful for evaluating the extent to which grid electricity generation and transmission system drivers are designed and operated in the context of environmental governance (EG) factors. The environmental pressure caused by the studied power systems was evaluated in terms of carbon emission levels. To simplify the study, variable parameters were sampled from the national power grids of three sub-Saharan countries, namely: Kenya, Rwanda, and Tanzania. The developed inventory workbooks accounted for the residual carbon emissions related to the grid generation and transmission capacity survival lifetime, retired system capacity, and recycling rate, aiming to reduce the uncertainty in grid emissions in the study area. The obtained area of the curve for the business as usual and EG models reveals that Rwanda has the potential to contribute more emissions per unit power, followed by Tanzania and Kenya. The higher carbon emission uncertainty levels (65%–75%) obtained from the EG simuland and life cycle carbon emissions revealed that only limited EG factors were considered during the design and operation of the studied grid electricity generation and transmission systems. However, the possibility of significant lifetime decarbonisation performances from generation and transmission systems was also shown in the EG-modelled output, owing to its lower carbon emission uncertainty levels (15%–25%). The logarithmic regression trend lines presented by this research show a higher R² value for the EG modelled life cycle carbon emission (LCCE) output (R² = 0.8689) and EG simuland LCCE output (R² = 0.9209), compared to EG modelled LCCE output (R² = 0.7526) and EG simuland LCCE output (R² = 0.8223) obtained from the linear regression trend lines, implying a very good relationship between the structural assumptions and simplifications constituting the model itself for case studied by the year 2049. The study suggests monitoring of a wide range of environmental parameters (apart from carbon) and associated energy storage technologies, considering both cumulative data and expanded systems.