Tunde Aderinto, Texas A&M University – Kingsville

Using Genetic Algorithm optimization to Estimate the amount of energy harvestable by a Wind-Solar PV Hybrid Energy Farm

Abstract: The renewable energy potential of a particular location is usually estimated through independent resource assessment of different renewable energy sources. It may overestimate its potential of the particular location if simply adding up different energy sources’ potentials. A new approach is proposed to estimate the renewable energy potential of a particular location by considering the influences among different renewable energy sources during their harvesting processes using genetic algorithm optimization method. This work considers only the combined energy harvestable by a Wind-Solar PV hybrid using the rated performance of a typical wind turbine and solar PV panels in specified layout. The optimization process considers the wake effect on the wind turbines. Constraints include the minimum distance between individual wind turbines, working area of wind turbine and ground cover area of the solar panels using specifications from National Renewable Energy Laboratory (NREL). Solar radiation data from two different regions in the US were used for the solar PV.

Results showed that, the net power output was not the sum of individual systems. In a wind-solar hybrid energy farm, using smaller wind turbine may increase hybrid energy farm’s total power output compared with larger wind turbines. Meanwhile, Power output of a hybrid energy farm may be lower than using single resource.

Presentation Author(s):
Tunde Aderinto*

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