An enhanced energy management framework based on artificial gorilla troops for optimal operation of grid-connected multi-nanogrids

Abstract

The deployment of distributed energy resources (DERs) into power systems significantly improves their efficiency and reliability. Nanogrids (NGs), as small-scale systems that integrate DERs at the building level, require effective energy management to achieve optimal economic operation. This manuscript proposes an enhanced energy management system (EMS) for grid-connected NGs that combines day-ahead and real-time scheduling to minimize daily energy cost while maintaining the balance between power supply and demand. The day-ahead scheduling consists of two stages: first, applying demand-side management (DSM) using the load shifting approach with the day-ahead pricing curve; and second, determining the optimal powers of the DERs within the NGs. These resources are dynamically adjusted in real time to account for uncertainties in renewable generation, grid electricity prices, and load variations. Since energy scheduling is a complex, nonlinear optimization problem with multiple constraints, a recently developed metaheuristic technique, the Artificial Gorilla Troops Optimizer (AGTO), is proposed to obtain efficient solutions, and it is compared with different techniques such as the Honey Badger Algorithm (HBA), Aquila Optimizer (AO), and Particle Swarm Optimization (PSO). Simulation results show that the proposed AGTO-based EMS for grid-connected NGs achieves superior cost efficiency, saving approximately 15.83% compared to other approaches when determining the optimal setpoints of diesel generators and batteries, considering DSM in day-ahead scheduling.