AI-Driven Multi-Objective Optimization for Energy Transformation from Renewable Sources to Smart Grid Infrastructure

The world's movement towards sustainable power supply calls for smart systems for the joint optimisation of cost, environmental impact and reliability of the energy systems. In this work, we present an AI-based multiobjective optimization framework that combines Non-dominated Sorting Genetic Algorithm II (NSGA-II) with Deep Reinforcement Learning (DRL) in improving energy conversion process of renewable. Applicability of the framework was tested on real data from three different locations spanning Portugal, Germany, and India, and all combinations of solar, wind, hydro, and battery storage. The simulation results showed that the proposed method has remarkable advantages compared with some traditional approaches, such as the Weighted Sum Method and the Goal Programming. The AI model led to an average 21.4% LCOE, a 34.7% reduction in CO₂ emissions and increased system reliability that relaxed ULP down to 1.3%. Renewable integration also saw significant improvements; 70% penetration of renewables, battery utilization efficiencies of >85%, and reduction on curtailment by >65%. Comprehensive sensitivity analysis demonstrated the model's stability for practical uncertainties such as prediction error, market price fluctuation and demand variation. The solution was kept valid by the AI system over 95%, so the solution is quite robust and flexible. The insights also led to policy intimations, including targeted storage investments, dynamic pricing enablers, and flexible demand strategies customized for each region.