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Distributed energy systems—microgrids, virtual power plants, DER-heavy distribution grids, and utility DERMS programs—require two structurally distinct kinds of intelligence: fast, numerically grounded control operating at milliseconds to seconds, and human-facing decision support operating at seconds to minutes. These are not variations on the same problem. They require different architectures, different evaluation metrics, and different failure-mode frameworks. This review examines the current literature to determine where large language models (LLMs) and where specialized neural networks are defensible choices for each. The review synthesizes evidence across five dimensions: per-inference cost and fixed cost structure, output accuracy and reliability, training demands, user perception of cost and efficacy, and actual experienced outcomes in control loops and deployments. Sources span national laboratory reports from NREL and INL, peer-reviewed applied ML and energy systems literature, vendor performance benchmarks, and governance frameworks from NIST and OWASP. The synthesis does not treat any single source as definitive—it triangulates across these categories to surface where consensus holds and where gaps remain. The central finding is that the architecture question has largely converged: deterministic solvers handle numeric authority, task-specific neural networks handle tight control loops and calibrated probabilistic inference, and LLMs function as a governed orchestration and interface layer—not as actuators. The deployment question remains open. Validation infrastructure, governance frameworks for LLM-connected critical systems, and standardized benchmarks for agentic grid tools are each early-stage relative to the pace of deployment interest documented by Gartner, BCG, and McKinsey. A secondary finding is that the performance gap between LLM and specialized neural approaches is most acute precisely where energy operations are most sensitive: calibrated uncertainty quantification and deterministic latency. This review is intended as a working reference for practitioners and researchers evaluating AI architecture decisions for distributed energy systems, and as a foundation for ongoing benchmark and governance work in the sector.