Hybrid Deep Learning-Based Grid-Supportive Renewable Energy Systems for Maximizing Power Generation Using Optimum Sizing

The hybrid electricity production idea is a technology designed to produce and use electrical energy from many sources as part of an integrated setup. The combined size and power maximization technique for grid-connected renewable energy systems is presented in this research. This study optimizes three distinct geographic locations with various wind and sun renewable energy input potentials. Convolutional neural networks with long short-term memory are designed to maximize electricity production while taking consumer load, demand, and weather conditions into account. The established technique emphasizes the significance of taking into account the concurrent optimization of sizing and power management. To find the hybrid power system building option with the optimum cost-benefit ratio, the Shuffled Shepherd Optimization Technique is used. The optimization analysis uses annual demand data, solar irradiation, and wind turbine power output with a 10-min precision. Investigations have been done into how the system's various parts behave. The amount of PV panels, wind turbines, battery banks, and the capacity of the diesel generator, together with the error rate, are the best choice factors shown by simulation results for sizing and producing the electricity for hybrid energy system. The results support the proposed strategy's potential for producing hybrid renewable power.