Optimal sizing of standalone hybrid wind/PV power systems using genetic algorithms

Proper design of standalone renewable energy power systems is a challenging task, as the coordination among renewable energy resources, generators, energy storages and loads is very complicated. The types and sizes of wind turbine generators (WTGs), the tilt angles and sizes of photovoltaic (PV) panels and the capacity of batteries must be optimized when sizing a standalone hybrid wind/PV power system, which may be defined as a mixed multiple-criteria integer programming problem. In our research, we investigated the genetic algorithm (GA) with elitist strategy for optimally sizing a standalone hybrid wind/PV power system. Our objective is selected as minimizing the total capital cost, subject to the constraint of the loss of power supply probability (LPSP). The LPSP of every individual of the GA´s population is calculated by simulation of 8760 hours in a year. Studies have proved that the genetic algorithm converges very well and the methodology proposed is feasible for optimally sizing standalone hybrid wind/PV power systems