Machine-Learning-Driven Embedded Control for Energy Storage Optimization in Microgrids

Abstract

ABSTRACT: Energy storage systems (ESS) have become vital components of modern microgrids, enabling greater reliability, renewable integration, and loadsmoothing capabilities. However, optimizing their operation in real time remains a significant challenge due to the non-linear and dynamic nature of demand patterns and renewable generation profiles. This paper proposes a machine-learning-driven embedded control model that adapts ESS charging and discharging schedules based on intelligent forecasting and state-of-health estimation. The embedded controller utilizes a hybrid prediction engine combining long short-term memory (LSTM) networks and fuzzy logic to ensure resilience against uncertain fluctuations. Hardware-in-the-loop (HIL) testing verifies that the controller maintains optimal energy dispatch even under rapidly shifting operating conditions. By embedding the algorithm within lowpower microcontrollers, the system achieves low latency and high reliability without requiring cloud-based computation. The research demonstrates that the proposed approach enhances energy utilization efficiency by up to 35%, reduces battery degradation, and improves the overall sustainability of microgrid operations.

KEYWORDS: Energy storage, Embedded control, Machine learning, Microgrid optimization, LSTM forecasting