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Over the coming decades, Kenya is likely to see a large increase in electricity demand driven by economic growth and wider electrification of different sectors. At the same time, Kenya remains committed to maintain its high share of renewable generation. This study proposes a novel framework to soft link OSeMOSYS, a capacity expansion model (CEM), and FlexTool, a production cost model (PCM), to address the limitations of CEMs in the representation of variable renewable energy sources. Results show the effectiveness of the methodology in identifying critical grid issues that would have been missed by the capacity expansion model alone, especially in the case of a higher penetration of non-dispatchable sources. They also confirm that based on robust planning approaches, Kenya is well placed to maintain its very low carbon generation system under different demand growth projections, leveraging on firm generation from geothermal and high wind potential.

Integrated assessment models (IAMs) are a cornerstone of an effective approach to climate change mitigation. Despite the variety of methodologies for characterising the energy system, land use change, economics, and climate response, the modelling community has an open and urgent request for tools capable of more realistic interpretation of the energy transition, capturing human behaviour, and embodying the principles of transparency, reproducibility, and flexibility of use.This paper presents an open-source modelling framework designed to fill that gap. Named MUSE (ModUlar energy systems Simulation Environment), this new agent-based model supports flexible characterisation of agent decision-making, including individual goals, bounded-rationality, imperfect foresight, and limited knowledge during the decision process. MUSE integrates this agent-based approach in a partial-equilibrium framework and enables a technology-rich description of the energy systems with an unprecedented degree of flexibility for including technological, temporal, and geographical granularity. The structure of MUSE creates the ability to produce climate change mitigation assessments that are more grounded, and more tangible model outputs for conceiving effective approaches to mitigation. MUSE is available open source under a GNU General Public License v3.0 on GitHub at this link https://github.com/SGIModel/MUSE_OS.