Increasing spatial and temporal resolution in energy system optimisation model : the case of Kenya

At the time of writing, 759 million people (2019) still lack access to electricity globally. It is important for energy planning to describe plausible pathways to achieve national goals, using tools such as energy systems models to explore scenarios and provide insight. Until recently, modelling energy access in countries with a low electrification rate was conducted at low spatial (e.g., national) and/or temporal resolutions (e.g., annual time slices or 'overnight' electrification). In this paper, we develop methods in an open-source computational workflow with high spatial resolution in an open-source energy systems optimisation model. We use Kenya as our case application where approx. 16 million people still lack access to electricity (2019). One reference scenario and two diagnostic hypothetical scenarios are developed to assess the model. The spatial resolution of approximately 40 by 40km cells leads to 591 demand cells split between electrified and un-electrified population. The results show that in the reference scenario, the optimal supply option for the unelectrified population is PV panels and batteries. At the same, an oversupply of the planned power plants is observed. The model can capture dynamics between spatially explicit supply options and central power plants in one model.