Isolated areas often rely on diesel generators for electricity production, which is associated with high costs and environmental impacts. Microgrids (MG) that integrate renewable energy and storage offer a more sustainable alternative. To support the techno-economic planning of such systems, this paper presents a modular Python-based tool for evaluating renewable energy penetration in isolated hybrid microgrids through single- or bi-objective optimization using genetic algorithms (GA). The tool combines a rule-based dispatch simulator with a GA optimizer and supports both hourly and minute-resolution data. It enables users to assess and optimize key performance indicators such as diesel consumption and Levelized Cost of Energy (LCOE). Applied to a real case study in Nunavik, Quebec, the tool evaluates five scenarios including wind integration and storage. Results indicate that optimized scenarios can reduce diesel consumption by up to 87% and the LCOE by up to 58% relative to diesel-only configurations. The proposed tool provides a flexible and practical framework for assessing and optimizing renewable integration in isolated MGs. • Developed a modular Python code for fast techno-economic optimization in microgrids. • Implemented an automated dispatch strategy for diesel generators using efficiency criteria. • Enabled reading input data with varying time steps and operating accordingly. • Executed bi-objective optimization using a genetic algorithm. • Analyze multiple diesel generators and other sources simultaneously.
Cadena-Zarate et al. (Sun,) studied this question.
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