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Smart meters generate high-resolution data that can feed forecasting models for a range of distribution network applications. However, the literature remains fragmented across forecasting methods, data structures, and operational use cases. No comprehensive taxonomy links forecasting model characteristics to the distinct requirements of grid flexibility, microgrid management, and congestion control. This limits the ability of researchers and system operators to choose models according to data availability, forecast horizon, and grid function. This article develops a taxonomy of smart-meter forecasting models for distribution-level operation. The taxonomy maps model categories, data inputs, forecast horizons, and application domains into a structured classification framework. A systematic literature review of 27 peer-reviewed journal articles published from 2017 to 2024 was conducted. Models were classified by mathematical structure, data requirements, forecast horizon, and relevance to grid flexibility, microgrid energy management, and congestion control. The taxonomy organises forecasting approaches into classical time series, conventional machine learning, deep learning, hybrid and ensemble, and probabilistic forecasting categories. Six tables support the review method, model classification, smart-meter data mapping, application suitability analysis, full taxonomy, and research gap assessment. The taxonomy provides a structured decision-support tool for selecting and designing forecasting models for specific distribution grid tasks. It highlights gaps in cross-domain transferability, real-time deployment, benchmarking, data integration, and operational validation.

Transactive energy and peer-to-peer trading are key enablers of distributed energy markets. Optimization models drive these systems, but a systematic taxonomy linking models to prosumer flexibility, EV coordination, and grid constraints is lacking. This article develops a taxonomy of transactive energy and P2P trading optimization models. It classifies models according to their treatment of prosumer flexibility, EV integration, and local network constraints. A structured literature review of 27 articles published between 2017 and 2025 was conducted. Models were categorized across market, optimization, flexibility, EV, and network-integration dimensions. The taxonomy organizes models by market mechanism, optimization method, and depth of grid and flexibility integration. It reveals significant gaps in holistic multi-vector coordination. The taxonomy provides a systematic framework for comparing optimization models in local energy markets. It also identifies research needs for integrated, grid-aware, flexibility-oriented transactive systems.