Optimal grid expansion under future electricity demand for groundwater irrigation in Ethiopia

Abstract

Ethiopia is a lower-income country with an agrarian based, fast-growing economy that is susceptible to climate change impacts. As the nation continues to develop, domestic electricity consumption and electricity export opportunities will increase alongside a growing electricity demand for groundwater irrigation to mitigate the effects of drought. There are upcoming mega-dams– the Grand Ethiopian Renaissance Dam, Genale Dawa III, and Koysha – that will triple the electricity generation capacity. However, the central electricity grid is not sufficient to efficiently utilize this new capacity. We model future grid expansion scenarios by building a regional-scale linear optimal power flow model based on a transshipment representation of power flow that guides cost-optimal generation operations and transmission expansion, while satisfying competing electricity demand for domestic electricity consumption, groundwater irrigation, and international electricity export.We first evaluate cost-optimal generation and transmission expansion for a baseline case, with an estimate of current Ethiopian electricity demand. We then consider five potential future demand scenarios. These demand scenarios include estimates of both present and future domestic electricity demand, multiple levels of productive electricity demand for groundwater irrigation, and demand for international exports. Compared to the baseline, seasonal capacity factors for generators in the five model scenarios increased when collocated with an increase in electricity demand except for the generators that already operated at maximum capacity. All scenarios with higher domestic regional demand than the baseline surpass the transmission expansion planned by the Ethiopian Electric Power Corporation, independent of changes in irrigation and export demand. Our analysis suggests that the current expansion plan is not sufficient to meet future demand. Our research contributes to the literature by creating an optimal power flow model that can be applied in data-poor environments to model endogenous electricity infrastructure decisions under different scenarios of internal and external demand.