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

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.

Keywords

• artificial intelligence
• solar pv
• renewable energy
• grid-tied energy system
• modelling
• hybrid power system
• hydrogen fuel cell
• simulation