An open-source modeling tool for multi-objective optimization of renewable nano/micro-off-grid power supply system: Influence of temporal resolution, simulation period, and location

Fabian Schmid, Joscha Winzer, André Pasemann and Frank Behrendt

Energy, 2021, vol. 219, issue C

Abstract: A fundamental understanding of the sizing process is a key element for sizing affordable, reliable, and sustainable nano/micro-off-grid systems. Nevertheless, the openness and transparency of modeling approaches are still low and open-source tools are scarce in this field. In this study, an open-source modeling tool for the optimization of renewable nano/micro-off-grid power supply systems is developed. System component models based on datasheets consider dynamic and time-dependent influencing factors. The modeling tool uses a multi-objective optimization based on the Non-Sorting-Genetic-Algorithm-II aiming at minimizing costs and load outage. For a better understanding of the sizing process, the influence of temporal resolution, simulation period, and location on the Pareto-optimal fronts is analyzed. The system location and by that irradiance, ambient temperature, and wind speed shows to be the strongest influence factor, which leads up to 2–5 times higher costs for achieving the same security of energy supply. While a higher temporal resolution increases the costs and load outages due to a more realistic illustration of energy production and demand, a shorter simulation period shows an increase in the system costs but a reduction of load outages because of the non-observance of component replacement, its cost reduction, and degradation.

Keywords: Open-source energy system modeling tool; Renewable nano/micro-off-grid power supply system; Multi-objective optimization; High temporal resolution; Influencing factor analysis (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:219:y:2021:i:c:s0360544220326529

DOI: 10.1016/j.energy.2020.119545

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