Edge AI Energy Management for Solar Battery Microgrids
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Abstract Solar battery microgrids require energy management decisions that remain safe during forecast error, communication loss, changing electricity prices, and battery degradation. Cloud optimization can support planning, but exclusive dependence on remote computation creates latency, availability, privacy, and cybersecurity concerns. This technical review develops a practical architecture for edge artificial intelligence in microgrid energy management. It connects established hierarchical microgrid control with forecasting, constrained optimization, reinforcement learning, and battery health supervision. The proposed design separates millisecond protection and converter control from minute level scheduling, places a deterministic safety filter between learned policies and actuators, and reserves cloud services for model training, fleet analytics, and long horizon planning. A decision matrix compares rule based control, model predictive control, supervised learning assisted optimization, and reinforcement learning according to data requirements, interpretability, computational load, and commissioning risk. The review also proposes a staged implementation path for small facilities and community systems, with measurable gates for data quality, baseline performance, shadow testing, limited authority trials, and continuous monitoring. Unlike prior surveys that catalogue edge-AI techniques across the broad energy sector, this review is scoped specifically to the safety boundary between learned or optimized policies and deterministic converter control in solar battery microgrids, and it translates that boundary into auditable, staged implementation gates. The central conclusion is that edge AI is most defensible when it augments rather than replaces conventional control. Economic objectives must remain subordinate to electrical constraints, battery health, and graceful degradation. The resulting framework provides researchers and practitioners with a testable basis for designing resilient, auditable, and resource aware microgrid energy management systems.
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