Abstract
This working paper describes the use of existing open-source energy models explicitly developed for Kenya. The first model presents the whole energy system, encompassing power generation, industrial, and transportation sectors, among others. The second model is a Climate, Land, Energy, and Water system (CLEWs) model, focusing on intricate interdependencies between the energy and land systems within the Kenyan context. The CLEWs model presents the interlinkages evident in sectors such as cooking (representing energy) and agriculture (representing land). While both models address aspects of the energy system, the CLEWs model uniquely integrates additional dimensions such as land and water systems. Merging these models has the potential to capture additional interactions between various systems. For instance, the utilization of fossil fuels extends beyond energy generation to activities in the land and water domains, including mechanization and pumping, respectively. Similarly, electricity finds application in residential settings for irrigation and public water distribution, showcasing interdependencies across sectors. Furthermore, each model is characterized by individual data input parameters, which carry uncertainties. Consequently, a proposed future step post-merger involves conducting a global sensitivity analysis (GSA). A GSA will enable finding influential versus non-influential parameters within the model. Influential parameters capable of substantially altering model outcomes are particularly interesting, notably the objective function (OF) in the context of energy system models (ESMs). Central to the model's optimization problem, the OF seeks to minimize total system costs encompassing infrastructure, operational, maintenance, and fuel-related expenditures. Identifying influential input parameters is a crucial component of factor prioritization. It offers insights into model behavior and facilitates informed discussions on sectoral interactions within ESMs. The expected findings will have significant implications for integrated land, energy, and water systems, shedding light on the pivotal role of specific parameters within the CLEWs modeling framework.
