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Microgrids are a promising solution for providing renewable electricity access to rural populations in the Global South. To ensure such renewable microgrids are affordable, careful planning and dimensioning are required. High-resolution data on electricity generation and consumption is necessary for optimal design. Unfortunately, real-world electricity data for microgrids in the Global South is scarce, and the little data that is available has a low temporal resolution. Therefore, in this paper, we introduce a unique highresolution real-world electricity data set from three microgrids in the Democratic Republic of the Congo, Rwanda, and Haiti. The data has a temporal resolution of up to five seconds and focuses on microgrids with renewable generation from either hydropower or photovoltaic systems. Furthermore, we include data from both residential and industrial microgrids. We describe the recorded data and highlight the advantages of the high resolution. We demonstrate how this resolution offers insight into consumption patterns and enables the analysis of grid voltage and frequency, which is highly relevant for the planning and dimensioning of affordable renewable microgrids in the Global South.

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Microgrids using renewable energy sources play an important role in providing universal electricity access in rural areas in the Global South. Current methods of system dimensioning rely on stochastic load profile modeling, which has limitations in microgrids with industrial consumers due to high demand side uncertainties. In this paper, we propose an alternative approach considering demand side management during system design which we implemented using a genetic scheduling algorithm. The developed method is applied to a test case system on Idjwi Island, Democratic Republic of the Congo (DRC), which is to be powered by a micro hydropower plant (MHP) in combination with a photovoltaic (PV) system and a battery energy storage system (BESS). The results show that the increased flexibility of industrial consumers can significantly reduce the cost of electricity. Most importantly, the presented method quantifies the trade-off between electricity cost and consumer flexibility. This gives local stakeholders the ability to make an informed compromise and design an off-grid system that covers their electricity needs in the most cost-efficient way.

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