Modeling the uncertainties and active power generation of wind-solar energy with data acquisition from telemetry weather measurement

This research enhances the estimation methods for renewable energy generation, particularly wind and solar power, by addressing uncertainties due to environmental factors such as wind speed and solar irradiation levels, which vary with weather, climate, and seasonal changes. Key contributions include the use of real-time, online automatic weather stations for efficient data collection, capturing weather parameters at 5-minute intervals over a year, resulting in a comprehensive dataset of 37,374 data points for wind speed and 18,993 for solar irradiation levels. The research innovatively models these uncertainties using Weibull and lognormal probability density functions (PDFs) for wind and solar energy, respectively. Results indicated a potential conversion of 69 % of wind energy into electricity using an optimally configured wind farm system comprising 200 units of 0.5 kW turbines. Similarly, a 100 kW solar PV power plant could convert up to 35 % of solar irradiation into electricity. The combined power contribution of both wind and solar PV systems to the grid was estimated at 37 kW. The research also introduced the use of 3D photogrammetry for land analysis, using aerial drones to map potential sites for wind farm and PV power plant installations, which could serve as a reference for future renewable energy projects aiming for high efficiency and reliability in electrical energy production. This approach not only contributes to better planning and installation strategies but also enhances the predictability and management of renewable energy resources.