Real-Time Embedded Control for Microgrid Energy Management and Load Optimization

Abstract

ABSTRACT: Microgrids require highly responsive energy management systems to balance distributed generation, storage, and varying consumer loads. This study introduces a real-time embedded controller that coordinates these resources using adaptive scheduling, multi-criteria optimization, and on-board learning. The controller continuously monitors microgrid frequency, state-of-charge of storage units, and renewable output to determine optimal dispatch strategies. A hybrid optimization approach—combining rule-based logic, reinforcement learning, and dynamic priority allocation—enables the microgrid to maintain stability during islanded and grid-connected modes. Field-based evaluation reveals significant improvements in load sharing accuracy, peak shaving efficiency, and battery lifecycle enhancement.

KEYWORDS: Microgrids, Energy management, Embedded control, Load optimization, Reinforcement learning