Optimization of Power Flow in Smart Grids Using AI-Driven Algorithms

Abstract

The current advanced power grid infrastructure calls for optimized power flow because it enables stable operations together with reduced losses and improved system performance. Standard power flow optimization practices demonstrate insufficient ability when handling these present challenges. The research evaluates how Artificial Intelligence (AI)-based algorithms function for optimizing power flow within smart grid networks. The text evaluates AI technologies that include machine learning and reinforcement learning as well as evolutionary algorithms and hybrid models for their implementation for real-time load forecasting and voltage regulation and loss minimization and renewable energy integration. The research reviews numerous practical examples showing how AI-driven grid optimization works successfully in smart grid systems and achieves better efficiency alongside enhancement of reliability together with expense minimization results. The paper explores both the hurdles AI faced during integration alongside data quality problems alongside computational challenges and scalability aspects. This paper predicts future advancements through observations of AI system integration with IoT and blockchain and big data analytics because these technologies will probably transform the smart grid management field. The research findings demonstrate how artificial intelligence can produce significant changes to smart grid functioning and achieve sustainable energy system transition.The research examines Smart Grids and deals with Power Flow Optimization through Artificial Intelligence (AI) machines like Machine Learning and Reinforcement Learning with Evolutionary Algorithms together with Load Forecasting features for Voltage Regulation and Renewable Energy integration mechanisms and Loss Minimization strategies. It provides examples and discusses Data Quality criteria.