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ARTÍCULO
TITULO

The impact of political objectives on optimal electricity generation and transmission in the Southern African Power Pool

Philipp A. Trotter    
Roy Maconachie    
Marcelle C. McManus    

Resumen

Southern Africa?s electrification is at odds with United Nations goals to provide modern energy for all by 2030. Electricity planning, a crucial tool to optimally match future supply and demand, has largely focused on minimising costs in southern Africa, sometimes complemented by a discussion of a discrete set of environmental scenarios. Political objectives, although their significance is well documented, had been overlooked in Southern African Power Pool (SAPP) quantitative electrification planning research. This study created a novel geo?referenced, multi?objective linear programming (MOLP) model that combines continuous cost and carbon dioxide (CO2) emission minimisation objectives with the political goal of achieving national electricity self?sufficiency, yielding the optimal trade?off between these three objectives. It solved the MOLP for three different political risk scenarios in order to examine a further political objective. The results revealed a sharp monetary trade?off between CO2 emission reductions and national electricity sovereignty objectives in the SAPP. Furthermore, curtailing international political risks had significant consequences for trade and the optimal generation mix. While the optimal size of the frequently recommended Grand Inga dam in the Democratic Republic of Congo was considerably reduced when either national electricity sovereignty or political risk constraints were imposed on the model, solar energy shares increased significantly in both cases. This was the case especially when CO2 emissions were simultaneously curtailed. Increased technological, institutional and policy?making efforts to implement solar PV at scale would therefore imply immediate political and environmental merits for national governments in the SAPP, and present a sustainable development opportunity for the region.

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