An Analysis of Advanced Algorithms for Energy Management and Load Control in Modern Smart Grids

Abstract

The rapid expansion of smart grids and the increasing penetration of renewable energy resources into modern power systems have introduced significant challenges in energy management and load control, particularly with respect to maintaining grid reliability, operational efficiency, and system stability. This study presents a comprehensive investigation of five advanced approaches for energy management and load control within smart grid infrastructures. The examined methods include a hybrid Particle Swarm Optimization–Genetic Algorithm (PSO-GA) technique for intelligent load shedding, a Home Energy Management System (HEMS) designed for smart appliance scheduling based on real-time electricity pricing, a coordinated residential load management framework for future smart energy communities, a short-term load forecasting model integrating Artificial Neural Networks with Multiple Linear Regression (ANN-MLR), and a Mixed-Integer Linear Programming (MILP) model developed for network-level economic optimization and decision-making. The results of the analysis emphasize the importance of integrating artificial intelligence techniques, accurate forecasting mechanisms, and economically driven energy management strategies to improve energy utilization, minimize operational expenditures, and reinforce the overall stability of smart grids. Simulation outcomes reveal that the proposed approaches can reduce total energy costs by approximately 15–18% while enhancing load forecasting accuracy by nearly 2.5 times in comparison with traditional methods. These findings demonstrate that the combination of intelligent optimization algorithms, practical energy management frameworks, and economic decision-making models provides a robust and efficient solution for the development and operation of next-generation smart grids, ultimately contributing to more sustainable, resilient, and cost-effective power systems.