Power-system time-series forecasting increasingly involves diverse forecasting entities

heterogeneous data sources

and complex operational constraints. Large language models (LLMs) and time-series foundation models (TSFMs) offer new approaches to cross-entity reuse

zero- and few-shot adaptation

multimodal fusion

and forecasting workflow coordination

yet the sources of their gains and their applicability remain unclear. This paper classifies existing methods by their knowledge sources and roles in the forecasting pipeline into text-pre-trained LLMs

LLM-based time-series adaptation methods

native TSFMs

multimodal forecasting models

and tool-enabled agents. These methods are reviewed in terms of capability requirements

key techniques

application scenarios

and evidence of effectiveness. Current evidence suggests that their main value lies in cross-entity reuse

rapid adaptation

the use of external information

and probabilistic forecasting. The independent contribution of language pre-training to purely numerical tasks still requires controlled validation; zero-shot TSFM performance depends on pre-training data and target-domain shifts; and multimodal gains depend on whether external information is genuinely available at forecasting time. Future work should strengthen cross-region and cross-entity validation and jointly evaluate probabilistic calibration

physical and hierarchical consistency

resource costs

and operational value under fixed downstream decision models.