Search

Smart-meter data is sensitive, and its centralization raises privacy concerns. Federated learning and privacy-preserving techniques promise to enable analytics without compromising user confidentiality. The practical viability of these methods in real-world smart-grid applications remains uncertain. A critical synthesis of their limitations, privacy assumptions, and deployment gaps is still needed. This review critically evaluates the state-of-the-art in federated and privacy-preserving learning for smart-meter analytics, grid-edge control, and DER coordination. It focuses on both methodological promise and deployment realism. A critical review of 27 peer-reviewed articles from 2017 to 2025 is conducted. The assessment focuses on technical performance, privacy guarantees, scalability, security, and deployment readiness. The review identifies key strengths in theoretical privacy and decentralized learning. It also finds substantial limitations in communication efficiency, non-IID robustness, adversarial resilience, and energy-sector-specific feasibility. Federated and privacy-preserving learning holds great promise, but critical technical and regulatory barriers must be overcome. The review provides a roadmap for future interdisciplinary research.

High penetration of rooftop photovoltaic generation introduces rapid voltage fluctuations in low-voltage distribution feeders. These fluctuations challenge conventional voltage regulation because inverter actions must respond locally while remaining coordinated across electrically coupled nodes. This article proposes a multi-agent reinforcement learning model for grid-edge voltage regulation under distributed PV variability and communication latency. The objective is to maintain feeder voltages within operational limits while reducing unnecessary reactive power support, curtailment, and communication burden. The voltage control task is formulated as a decentralized partially observable Markov decision process. Smart inverter and battery agents use local observations during execution, while centralized training uses feeder-wide voltage, PV, and control information to train coordinated policies. Simulation results show that the proposed MARL controller improves voltage compliance relative to local Volt/VAr control and approaches centralized optimal power flow performance. The controller remains stable under bounded latency and asynchronous agent updates. The proposed framework offers a robust decentralized control strategy for high-PV distribution feeders. It combines grid-edge autonomy with coordinated learning, making it suitable for communication-limited voltage regulation.

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.