Hybrid Renewable Energy Systems and Their Optimization for Remote Community Applications

There is a growing interest in implementing hybrid renewable energy systems (HRES) in remote communities where people are using diesel power. In the HRES, two or more renewable energy sources are combined, allowing the communities to counteract the weaknesses of one renewable energy source with the strengths of another. This study aims to design, simulate and optimize the HRES consisting of photovoltaic panels, wind turbines, and a bio-based generator for applications in remote and northern communities in Canada. A methodology is developed to optimize HRESs so that net present cost (NPC) and levelized cost of electricity (LCOE) of the energy systems can be minimized. The optimization is performed using a genetic algorithm-based model. A sample remote northern community in Canada is selected for a case study. The HRES being investigated targets to supply an average load demand of 2205 kWh/day and the peak load of 236.06kW for the sample community. Results show that the LCOE for the top optimal HRES configuration is $0.308/kWh, and its NPC is $4.29M. Through the present study, the HRES is shown to be an effective approach to replacing diesel power in remote communities.