Renewable energy optimization in isolated microgrids: A Python-based tool for cost-effective solutions using genetic algorithms

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.