Hierarchical energy management system for coordinated operation of multiple grid-tied home microgrids

Abstract

A smart neighborhood (SN) comprising multiple home microgrids (HMGs) can provide cost-efficient electricity to end-users while supporting the main grid through ancillary services. The integration of renewable energy sources (RESs), energy storage systems (ESSs), and electric vehicles (EVs) introduces dynamic challenges, particularly under varying EV charging behaviors. To address these challenges, this study develops a hierarchical energy management system (HEMS) formulated as an optimization problem and solved using the Aquila optimizer (AO). The proposed HEMS enables the SN to operate as a cloud-based energy storage system (cloud-based ESS), minimizing energy imports from the main grid while maximizing local self-consumption and revenue. The performance of AO is benchmarked against the Particle Swarm Optimization (PSO) algorithm under two control architectures: (i) individual operation, where each local EMS (LEMS) optimizes its own HMG, and (ii) coordinated operation, where a central EMS (CEMS) synchronizes all HMGs, enabling the SN to function collectively as a cloud-based ESS. Simulation results highlight the superior performance of AO under the coordinated CEMS framework. For standard operation, AO reduces main grid imports to 30.62 kWh compared to 61.66 kWh, maintains higher SOC levels across ESSs and EVs (up to 90%), delivers greater total revenue (£44.662 vs. £22.907), and minimizes cumulative error (10.2% vs. 18.7%). Under different EV charging behaviors, AO demonstrates robust adaptability, achieving lower grid imports (40.43 kWh vs. 49.97 kWh), maintaining higher SOC across ESSs and EVs (up to 88.5%), delivering greater total revenue (£15.311 vs. £12.101, +26.5%), and reducing cumulative error from 158.19 to 146.25 (7.6% improvement). These results confirm that the AO-based HEMS efficiently coordinates distributed energy resources, enabling the SN to function as a reliable cloud-based ESS. It improves energy efficiency, economic returns, and grid support while maintaining resilience under dynamic EV charging conditions, providing a scalable and adaptive framework for future SN energy management.