An Optimized Binary Scheduling Controller for Microgrid Energy Management Considering Real Load Conditions

• Main Authors: Mannan M., Roslan M.F., Reza M.S., Mansor M., Jern K.P., Hossain M.J., Hannan M.A.
• Other Authors: 57224923024
• Format: Conference Paper
• Published: Institute of Electrical and Electronics Engineers Inc. 2024
• Subjects: Binary Particle Swarm Optimization; Distributed energy resources; Energy management; Microgrid; Optimization algorithm; Scheduling controller; Constrained optimization; Controllers; Cost reduction; Energy resources; Energy utilization; Operating costs; Particle swarm optimization (PSO); Power quality; Scheduling algorithms; Stochastic systems; Wind; Algorithms optimizations; Binary particle swarm; Binary particle swarm optimization; Distributed Energy Resources; Load condition; Optimization algorithms; Particle swarm algorithm; Power demands; Scheduling controllers

A dynamical power demand and stochastic nature of energy resources posses difficulties in controlling and managing output power. These challenges lead to instability and inconsistency of the entire operation which can cause unstable and power quality issues. This study presents an optimal schedule controller for microgrid energy management, utilizing the Binary Particle Swarm algorithm (BPSO) to minimize costs and ensure optimal power delivery to loads. The controller's aims include minimizing total operating costs for distributed energy resources and solving intricate constraint optimization issues with scheduling management operations. The proposed approach's effectiveness is evaluated within an IEEE 14-bus configuration with five microgrids (MGs) integrated with RESs using real load data from Perlis, Malaysia. The BPSO optimization technique offers an exceptional binary fitness function to find the optimal cell, utilizing real data such as solar radiation, wind speed, battery charging/discharging, fuel conditions, and demand. To confirm the efficiency of the developed controller, a comparison is conducted between the results achieved with and without microgrid (MG) integration. The results reveal the robustness of the BPSO algorithm in reducing energy consumption and cost by 199.6 MW to 316.53 MW and RM 87,250.35 to RM 138,327.5 respectively. As a result, an optimized scheduling controller-based BPSO optimization outperforms in terms of savings cost, reduced energy consumption, optimal DER use, and decreased CO2 emissions. � 2023 IEEE.