Abstract
This report illustrates the effectiveness of utilizing MicroGridsPy for modeling and optimizing rural off-grid electrification. The analysis includes a variety of energy sources, such as solar panels, wind turbines, and energy storage systems, to ensure a reliable and sustainable energy solution. Multiple scenarios were analyzed on both the demand and supply sides of the microgrid model. The primary objectives of this study include: emphasizing the significance of identifying the energy needs and usage patterns of rural communities; assessing the available renewable energy sources; determining the optimal scenario that minimizes the Net Present Cost (NPC) and the Levelized Cost of Energy (LCOE) while reducing greenhouse gas (GHG) emissions; and formulating policy recommendations.The key finding of this study is that for evolving energy demand, the most cost-effective approach to microgrid installation involves capacity expansion. Another key observation is that the LCOE is directly related to the NPC hence any policy intervention that affects the NPC is likely to have a direct effect on the LCOE, which is one of the major factors considered when determining electricity tariff. In summary, MicroGridsPy serves as an excellent platform for managing and monitoring microgrids, particularly in the context of implementing rural electrification.