Advancements in continental power system planning for Africa

This report provides details regarding the design and setup of the System Planning Test–Continental Masterplan (SPLAT-CMP) model, the underlying model of the Africa Continental Master Plan (CMP) for electricity generation and transmission. CMP is an initiative tasked to the African Union Development Agency (AUDA-NEPAD) as per the recommendations adopted by the African heads of States at the 34th AU-Summit in 2021. Subsequently, in the 37th AU-Summit in 2024, the African heads of States officially endorsed the first CMP plan as an AU Agenda 2063 Flagship Project. IRENA supports the CMP initiative as an officially endorsed modelling partner, providing capacity building to AUDA-NEPAD on power systems modelling, resulting in the use of SPLAT-CMP as one of the key tools to quantify the long-term goals of the CMP.

Built from IRENA’s long standing SPLAT-MESSAGE modelling methodology, the SPLAT-CMP model features:

• updated datasets on cost and performance parameters of existing and planned electricity generation and cross-border interconnection assets across 48 countries of the African continent;
• geo-referenced supply options marking excellent renewable power supply regions across the continent and their representative generation patterns, identified with the open source Model Supply Regions (MSR) Toolset based on a peer reviewed scientific methodology;
• site-specific generation profiles for hydropower generation assets, obtained from IRENA’s hydropower atlas; and
• a comprehensive set of cross-border electricity interconnection expansion options beyond planned projects, entailing differing capital cost assumptions reflecting the underlying terrain and the distances between nearest grid connection points of the neighbouring countries.

By providing a detailed description of the methodology behind the SPLAT-CMP model, this report aims to enhance the transparency and the reproducibility of the modelling results. It will also enable the CMP stakeholders to better understand the model results and serve as basis for future use and upgrades in the next CMP phase.