Energy Management in Integrated Microgrids: An Optimal Schedule Controller Utilizing Gradient Descent Algorithm

• Main Authors: Abdolrasol M.G.M., Ker P.J., Hannan M.A., Ayob A., Tiong S.K.
• Other Authors: 35796848700
• Format: Conference Paper
• Published: Institute of Electrical and Electronics Engineers Inc. 2024
• Subjects: Efficient Energy Management; Energy Scheduling; Gradient Descent Algorithm; Integrated Microgrids; Renewable Energy Integration; Renewable Resources; Distributed power generation; Electric loads; Energy efficiency; Energy utilization; Gradient methods; Microgrids; Optimization; Smart power grids; Binary gradients; Efficient energy management; Energy; Energy scheduling; Gradient descent algorithms; Integrated microgrid; Microgrid; Optimal schedule; Renewable energy integrations; Renewable resource; Energy management

This paper presents a new optimal controller using the Binary Gradient Descent (BGD) algorithm to manage distributed generations effectively in a grid network. The algorithm aims to minimize power consumption from the main grid and prioritize sustainable resource utilization over buying electricity from the local network grid. The proposed approach is evaluated on the IEEE fourteen bus test system with integrated Microgrids (MGs) and distributed generations, using real load demand data from Perlis, Malaysia, for 24-hour test case studies. Weather data, including wind, solar, fuel, and battery status, is integrated into the BGD algorithm for optimizing ON and OFF schedules. The results demonstrate a significant 46.3% reduction in energy consumption achieved by the BGD algorithm, contributing to the advancement of optimization algorithms for sustainable energy management. The developed BGD algorithm's effectiveness is further validated through a comparative analysis with conventional methods. � 2023 IEEE.