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Active distribution networks increasingly rely on stochastic optimization to manage uncertainties arising from renewable generation, flexible demand, distributed storage, electric vehicles, and local energy markets. These uncertainties affect voltage quality, congestion, operational cost, reliability, and the value of flexibility. This systematic review categorizes and evaluates uncertainty-aware stochastic optimization techniques for demand response, DER coordination, and flexibility provision in active distribution networks. It focuses on methodological transparency, thematic synthesis, and identification of research gaps. A PRISMA-guided systematic review of 31 peer-reviewed journal articles published from 2017 to 2023 was conducted. A structured search, screening, eligibility assessment, and data extraction process was used to organize the evidence. The review maps dominant stochastic frameworks, including two-stage stochastic programming, chance-constrained optimization, robust-stochastic formulations, and distributionally robust optimization. Five synthesis tables are used across the full manuscript to summarize study selection, article characteristics, method taxonomies, application mappings, and research gaps. Stochastic optimization supports secure and efficient operation of active distribution grids, but important gaps remain in scalability, real-world validation, interoperability, and regulatory integration. The review outlines an agenda for more adaptive, transparent, and implementation-ready optimization methods.

Foundation models are transforming artificial intelligence by enabling pre-training, adaptation, and reuse across heterogeneous tasks. Their application in energy systems is emerging rapidly, creating a need for systematic synthesis across forecasting, control, and decision-support settings. This systematic review identifies, categorizes, and evaluates the use of foundation models in energy-system forecasting, grid-edge control, and smart-grid decision support. It focuses on how large language models, pre-trained transformers, multi-modal models, and emerging time-series foundation models are adapted for energy tasks. A PRISMA-guided search of peer-reviewed literature from 2017 to 2025 resulted in 30 included studies. Models were classified by architecture, fine-tuning method, data modality, and application domain. The review finds that large language models dominate decision-support applications, while pre-trained transformers and time-series foundation models are most developed in forecasting. Grid-edge control applications remain nascent, with most studies addressing planning, diagnostics, or dispatch rather than closed-loop physical deployment. Foundation models hold significant promise for smart grids, but critical gaps remain in real-time control, safety assurance, privacy, and evaluation. A research agenda is proposed to guide rigorous, trustworthy, and operationally relevant deployment.