Optimization of Wind Farm Layout using Genetic Algorithms

Abstract

In order to increase the economic feasibility, sustainability, and efficiency of energy production, this research proposes an improved optimization framework for hybrid wind-solar energy systems that use an augmented Genetic Algorithm (GA). Wind turbine size and photovoltaic (PV) panel orientation were optimized using historical data on wind and solar resources, system load profiles, and component specifications. There was an 18% increase in energy production, a 14% improvement in wind turbine efficiency, and a 16% increase in solar panel output because to the GA's outstanding performance. An 18% reduction in the payback time and a 12% reduction in the Levelized Cost of Energy (LCOE) were achieved. Results from the evaluation of the project's social and environmental consequences showed that community acceptability increased by 9 percentage points and land-use efficiency by 12 percentage points. A sensitivity study verified that the system could withstand several economic and environmental scenarios. The results demonstrate the promise of GA-based optimization in improving the efficiency of renewable energy hybrid systems.