INTELLIGENT ENERGY MANAGEMENT IN SOLAR-POWERED SMART GRIDS: A HEURISTIC–METAHEURISTIC ALGORITHMIC APPROACH FOR COST-EFFECTIVE, RELIABLE, AND SUSTAINABLE ENERGY OPTIMIZATION

Abstract

The rapid transition toward renewable energy has positioned solar power as a cornerstone of modern smart grids; however, the inherent intermittency of solar energy, combined with the increasing complexity of energy demand patterns, presents significant challenges for cost-effective, reliable, and sustainable grid operation. Traditional optimization methods often struggle to handle the nonlinear, dynamic, and multi-objective nature of energy management in solar-integrated smart grids. To address these limitations, this study introduces an intelligent energy management framework based on a heuristic–metaheuristic algorithmic approach for optimizing energy scheduling, power flow, and resource allocation in solar-powered smart grids. The proposed methodology leverages the adaptability and robustness of heuristic algorithms, such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO), while integrating them with metaheuristic enhancements to improve convergence speed, solution diversity, and scalability under large-scale grid scenarios. The optimization model incorporates solar photovoltaic (PV) generation, energy storage systems, and load demand forecasting, with an objective function that simultaneously minimizes operational costs, maximizes renewable energy utilization, and enhances system reliability. Constraints related to power balance, storage limits, and grid stability are embedded to ensure technical feasibility and real-world applicability. Simulation experiments were conducted using MATLAB/Simulink and Python on benchmark load and solar irradiance datasets, representing realistic smart grid scenarios. Results demonstrate that the heuristic–metaheuristic framework outperforms conventional optimization methods, achieving up to 18–25% reduction in energy costs, 12–20% improvement in renewable penetration, and significant gains in voltage stability and loss minimization. Comparative analysis reveals that hybrid heuristic–metaheuristic algorithms consistently deliver superior performance in balancing multiple objectives compared to standalone techniques. The findings confirm that the integration of heuristic and metaheuristic strategies provides a promising pathway for achieving intelligent, cost-effective, and sustainable energy management in solar-powered smart grids. Beyond cost savings and reliability improvements, this approach facilitates greater alignment with global sustainability goals by accelerating the transition toward renewable-based energy systems. Future work will extend the framework to incorporate demand-side response, electric vehicle integration, and real-time adaptive control using AI-enhanced predictive algorithms.