International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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Volume 8 Issue 2
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AI-Driven Multi-Objective Optimization Framework for Enhancing Smart Grid Efficiency and Reliability

Author(s) Mr. Lalit chouhan, Prof. Burla Sridhar
Country India
Abstract The modernization of electrical power networks has increased the need for intelligent, data-driven optimization strategies capable of improving efficiency, reliability, and renewable integration. Traditional optimization approaches often fail to handle the non-linearity and uncertainty present in smart grid environments. This paper proposes a novel AI-driven multi-objective optimization framework that integrates machine learning–based forecasting, intelligent control, and metaheuristic algorithms to simultaneously minimize power losses, enhance voltage stability, and maximize renewable utilization. A hybrid Artificial Neural Network and Particle Swarm Optimization (ANN-PSO) model is implemented to achieve coordinated multi-objective decision-making. Simulation results demonstrate significant performance improvements over classical optimization methods, highlighting the applicability of AI techniques for real-time smart grid operation and long-term planning. The transition from conventional power systems to smart grids has introduced advanced digital control, bidirectional communication, and distributed energy resources (DERs). While this evolution supports improved energy efficiency and sustainability, it also introduces operational challenges due to fluctuating loads, intermittent renewable energy sources, and increased system complexity. As smart grids move toward automation and self-healing capabilities, optimization becomes essential for ensuring reliable and efficient energy delivery.
Keywords Smart Grid, Multi-Objective Optimization, Artificial Intelligence, ANN, PSO, Renewable Integration, Voltage Stability, Power Loss Minimization
Field Engineering
Published In Volume 8, Issue 1, January-February 2026
Published On 2026-02-20
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E-ISSN 2582-2160
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