Artificial Intelligence-Enabled Techno-Economic Analysis and Optimization of Grid-Tied Solar PV-Fuel Cell Hybrid Power Systems for Enhanced Performance

¹ Electrical Engineering Department MPUAT, CTAE, UDAIPUR, INDIA
² Electrical and Electronics Engineering Department, CVR College of Engineering, Hyderabad, India
³ Electrical Engineering Department MPUAT, CTAE, UDAIPUR, Udaipur, India
⁴ Electrical Engineering Department, Institute of Engineering and Technology, MLSU, Udaipur, India

^* Corresponding author: poojaswarnakar93@gmail.com
^** Corresponding author: rn.bhargavi@cvr.ac.in

Abstract

The incorporation of energy from renewable sources into the power grid is crucial for achieving sustainable and environmentally friendly power generation. This study proposes an artificial intelligence (AI)-enabled methodology for the analysis & optimization of “grid-tied solar photovoltaic (PV)-fuel cell hybrid power systems.” The research aims to demonstrate how AI techniques can assist in decision-making, improve system performance, and achieve higher levels of energy efficiency and financial viability. The study presents the results of a project focusing on a renewable energy system that feeds into the grid and powers a university building. The hybrid power system’s performance and cost were evaluated using unified approaches to modeling, simulation, optimization, and control. The findings indicate that the AI-optimized “solar PV-fuel cell hybrid system connected to the grid” offers excellent performance, meeting 74% of the building’s energy needs through renewable sources. The system also achieved a low levelled price for energy and minimise CO2 emissions, further enhancing its environmental sustainability. The proposed AI-enabled approach proves to be a promising solution for creating grid-connected renewable energy systems with significant benefits for energy efficiency, cost-effectiveness, and environmental impact.