A Real-Time Energy Management Strategy for Multi-Microgrids With Multiple Energy Resources Using Quantum Inspired Balancing

Abstract

Energy management systems (EMSs) play a pivotal role in modern power systems by orchestrating resource optimization, cost reduction, and resilience enhancement amid increasing renewable penetration and decentralized energy paradigms. This paper introduces a novel three-tiered hierarchical EMS framework for multi-microgrid (MMG) networks, designed to address the dual challenges of dynamic energy coordination and operational efficiency in heterogeneous grid environments. The architecture consists of a distributed control layer that autonomously regulates distributed energy resources and energy storage systems within individual MGs through adaptive droop control. The optimization planning layer incorporates advanced day-ahead stochastic scheduling algorithms and introduces a novel quantum-inspired adaptive balancing strategy, which dynamically adjusts the obtained day-ahead schedules to mitigate forecasting errors and enhance operational robustness under uncertainty within an individual MG. The integrated grid management layer features a decentralized, privacy-preserving peer-to-peer energy exchange mechanism that coordinates surplus and deficit energy profiles across MMGs without compromising data privacy. The layer only required the energy access/deficit information along with the buy/sell price to optimally manage the energy transfer within MGs. Comprehensive simulations under heterogeneous forecast uncertainty scenarios show the effectiveness of the proposed EMS to optimize energy flows and cost efficiency while maintaining grid stability under various type of forecast errors. Moreover, comparisons with existing EMSs validate the optimal performance of the proposed method.