Africa is a resource rich continent in both fossil fuels and renewable energy sources. Nevertheless, its significantly underdeveloped electricity infrastructure and un-deployed power generation potential lead to more than 620 million people to lack electricity access and the rest to face shortages and high prices. Africa’s rapidly increasing electricity demand, from 621 TWh in 2012 to about 1870 TWh in 2040, as well as rapid population growth, make urgent the necessity for national and regional investments which will lead on reliable and affordable energy widely available and achieve social and economic development. This thesis examines the national associated costs and investments which are needed to electrify the current un-electrified population of the continent by 2030 and provide them to have an electricity consumption of 696kWh/household (TIER 3) that year and 2195kWh/household (TIER 5) in 2050, as well as the continent to cover its electricity demands for the period 2010-2050. To conduct this study, The Electricity Model Base for Africa (TEMBA) [1] developed in the Open Source energy Modelling SYstem (OSeMOSYS) [2] is used, with the initial modeling platform to remain the same but several parameters to be updated to the latest up-to date source and the objective of the TEMBA study to differentiate. The parameters which have been updated in this study for each country include: electricity demands (2010-2050), existing and planned power plant capacities, technology costs, fuel availability and prices, national fuel reserves, local transmission and distribution losses, and renewable resources potential. Moreover, emission factors have been added into the model to estimate greenhouse gas emissions. Different trade scenarios have been implemented to investigate the power generation potential and the financial requirements which are needed by the continent to cover its electricity demands. An expanded electricity trading scheme in Africa, as is being indicated in the Enhanced scenario, exploits its country’s energy resource potential and lead to a decrease of the investment and fuel costs as well as of the carbon dioxide emissions. The open-source nature of the model allows the data and the model under which this study is being conducted to be publicly available for future research.