Analysis and Comparison of Novel Energy Management and Load Control Methods in Smart Grids

چکیده

With the expansion of smart grids and the growing integration of renewable energy sources into power systems, optimal energy management and load control have emerged as critical challenges for ensuring the stability and efficiency of modern grids. This study investigates and analyzes five state-of-the-art energy management and load control methods in smart grids: the hybrid PSO–GA algorithm for intelligent load shedding, a Home Energy Management System (HEMS) for smart appliance control based on real-time electricity pricing, residential load management in future smart energy environments through inter-home coordination, short-term load forecasting using Artificial Neural Networks combined with Multiple Linear Regression (ANN–MLR), and a Mixed-Integer Linear Programming (MILP) model for economic decision-making at the network level. The analysis highlights that integrating artificial intelligence, precise forecasting, and economic energy management is crucial for optimizing energy consumption, reducing operational costs, and enhancing the stability of smart grids. Simulation results demonstrate a reduction of 15–18% in total energy costs and an improvement of up to 2.5 times in load forecasting accuracy compared to conventional models. These findings indicate that combining algorithmic strategies, practical implementations, and economic optimization models offers an effective and comprehensive approach for the design and operation of future smart grids, enabling more sustainable, reliable, and cost-efficient energy systems.