Multilevel stacked deep learning assisted techno-economic assessment of hybrid renewable energy system - Kumar, Mantosh; Namrata, Kumari; Samadhiya, Akshit; Kumar, Nishant; Azar, Ahmad Taher; Kamal, Nashwa Ahmed; Hameed, Ibrahim A. (Frontiers in Energy Research, 2024)
The growing energy demand and target for net zero emission compelling the world to increase the percentage of clean energy sources which are freely available and abundant in nature. To fulfil this, a hyperparametric tuned multilevel deep learning stacked model assisted grid-connected hybrid renewable energy system (HRES) has been developed. The proposed system has been subjected to techno-economic assessment with a novel application of the rime-ice (RIME) optimization algorithm to determine the lowest possible cost of electricity (COE) corresponding to the best HRES system components. The analysis has been carried out for the residents of the eastern part of India. The results show that the prediction accuracy of the solar irradiance and wind speed are 95.92% and 95.80% respectively which have been used as inputs for the HRES. The proposed optimization used has shown the lowest COE of Rs. 4.65 per kWh and total net present cost (TNPC) of 7247 million INR with a renewable factor of 87.88% as compared to other optimizations like GWO, MFO and PSO. Further sensitivity analysis and power flow analysis for three consecutive days carried out have also been done to check the reliability of the HRES and its future perceptiveness.