Abstract
This study evaluates the effects of time resolution on the optimization results of a renewable energy system for an off-grid island. The assessment uses a multi-objective genetic algorithm (MOGA) applied to Tilos Island in Greece. Three objective functions—levelized cost of electricity (LCOE), renewable ratio (RR), and profit—are considered across four distinct scenarios with six variables representing the number of renewable technologies. These cases are implemented using three time resolutions: minute-by-minute, 15-minute, and hourly. A significant difference in results is observed based on the time resolution used. With hourly data optimization, 100 % renewable energy coverage is achievable at Tilos’ current diesel generator cost (0.46 $/kWh). However, using minute-by-minute data, renewable energy coverage ranges from 85.87 % to 95.64 %, depending on the scenario. The primary reason for this discrepancy is the volatile nature of demand and power generation on Tilos Island. The analysis further indicates that the differences between minute-by-minute and hourly optimization diminish as the volatility of the input data to the algorithm decreases.
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