Enhanced smart microgrid scheduling: An empirical study on simultaneous energy supply and demand optimization using binary sparrow search algorithm

This paper introduces a comprehensive energy management and demand-side scheduling approach for microgrids, incorporating diverse distributed energy resources, namely Wind Turbine System (WTS), Photovoltaic System (PVS), Battery Storage System (BSS), and the utility grid on the supply side, alongside both shiftable and non-shiftable devices on the demand side. At the core of this approach is the Binary Sparrow Search Algorithm (SSA), which optimizes the Energy Management System (EMS) to minimize total operating costs while balancing user comfort and system efficiency. The EMS schedules energy supply and demand across a 24-hour horizon with 15-minute intervals, enhancing the microgrid's responsiveness to environmental changes and refining operational control for peak performance. The proposed SSA-EMS system undergoes rigorous benchmarking against established strategies, including Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), as well as an unoptimized baseline, demonstrating superior effectiveness. Empirical validation with real-world datasets confirms the system's resilience and efficiency, achieving a Peak-to-Average Ratio (PAR) of 9.62 outperforming PSO's 10.50, GA's 11.21, and the unoptimized system's 16.23. Additionally, the SSA-EMS achieves a 33.34% cost reduction, presenting significant financial advantages over competing approaches. This study also addresses battery health, integrating constraints to prolong battery life and ensure sustainable microgrid operations. These results underscore the SSA-EMS's capability to support reliable, cost-effective, and environmentally responsive microgrid management, showcasing its potential to drive advancements in sustainable energy systems.