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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.

The value of distribution-level flexibility is increasingly important as electricity systems become more decentralized, variable, and prosumer-oriented. Prosumers, aggregators, and network constraints jointly shape whether flexible capacity becomes technically useful and economically valuable. Existing research lacks a unified theory that explains how flexibility value emerges from the interplay of prosumer decision-making, DER aggregation, and local congestion management. As a result, valuation is often treated as either a market-pricing problem, a network-operations problem, or a behavioural-participation problem rather than as a co-determined process. This article builds a theory model linking prosumer demand response, DER aggregation, and local congestion management to the valuation of grid flexibility. The model explains how distributed flexibility becomes valuable when behavioural willingness, aggregation capability, and network scarcity align. The model is developed conceptually, drawing on microeconomics, mechanism design, contract theory, and grid operation principles. It is intended to generate causal explanations and testable propositions rather than empirical estimates or optimization results. The model identifies causal pathways from prosumer behaviour and aggregator strategies to flexibility value, moderated by congestion intensity. Seven propositions articulate testable relationships, while the full model distinguishes flexibility creation, coordination, scarcity revelation, and value allocation. The theory provides a foundation for future empirical testing and can inform market design and regulatory decisions for flexibility services. It clarifies why flexibility value cannot be attributed to DER assets alone but must be understood as an institutional and network-contingent outcome.