When sizing distributed hybrid renewable energy systems (HRESs) it has been found that capital costs can significantly be reduced when socio-demographic factors are considered. Unique electricity usage patterns have previously been classified using users’ socio-demographic factors and used to optimize the size of individual stand-alone HRESs. An optimization model is formulated where an individual HRES is assigned to each of the six socio-demographic classifications and power sharing is implemented with neighboring sites in a specific configuration. Solving the optimization problem with a hybrid approach using Matlab’s genetic algorithm and fmincon results in an 82,10% reduction in capital costs compared to the system without power sharing.
Applied Energy Symposium and Forum, Renewable Energy Integration with Mini/Microgrids, REM 2018, 29–30 September 2018, Rhodes, Greece