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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.

Generative AI, including generative adversarial networks, diffusion models, variational approaches, and large language models, is increasingly being explored for smart-grid operation. Its appeal lies in its ability to create synthetic data, expand scenario sets, and support human operators facing complex and uncertain grid conditions. This review critically examines the state of generative AI in four key smart-grid applications: synthetic load profiles, contingency simulation, human-in-the-loop dispatch, and associated governance risks. It evaluates not only technical capability but also reliability, accountability, privacy, and operational readiness. A critical review of 28 peer-reviewed articles published between 2017 and 2026 is conducted. The selected literature is synthesized across methodological contributions, application domains, evaluation practices, and governance concerns. Generative AI shows promise in creating realistic load data and expanding contingency sets, but reliability, interpretability, and governance frameworks remain underdeveloped. Human-in-the-loop dispatch integration is still nascent, and most studies remain closer to proof-of-concept than operational deployment. While generative AI can enhance grid flexibility, simulation richness, and planning capability, its current maturity is uneven. Substantial research is needed to address technical, ethical, and regulatory challenges before these systems can be responsibly embedded in smart-grid operation.

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.

Smart-grid optimization increasingly uses artificial intelligence, automated control, and data-driven market coordination to manage low-carbon electricity systems. These developments create new opportunities for flexibility, but they also raise unresolved questions about equity, accountability, and justice. Existing reviews often examine energy justice, smart-grid optimization, algorithmic governance, and flexibility markets as separate domains. This fragmentation limits understanding of how optimization models and market rules may reproduce or reduce social inequalities. This article critically reviews the literature on justice-aware smart-grid optimization and develops a structured future research agenda. It focuses on how energy equity, algorithmic governance, and low-carbon flexibility markets can be integrated into future smart-grid research and policy. A targeted review of 26 peer-reviewed articles published between 2017 and 2025 integrates perspectives from energy justice, smart-grid studies, AI governance, and electricity market design. The review identifies conceptual tensions, methodological gaps, and governance challenges rather than presenting new empirical data. The review shows that justice is often operationalized narrowly in optimization, while broader concerns about recognition, participation, and accountability remain underdeveloped. It also finds that flexibility markets can distribute benefits unevenly unless market design explicitly addresses barriers to access and agency. A more interdisciplinary and justice-centered approach is needed to guide smart-grid optimization. The proposed agenda calls for stronger integration between technical modeling, energy justice theory, participatory governance, and regulatory innovation.

Smart-grid forecasting requires models that can reason across heterogeneous temporal, environmental, and structural information. Existing forecasting architectures often treat load sequences, weather context, and grid topology as separate modeling inputs rather than as jointly learnable forecasting evidence. This article proposes a foundation-model architecture for multi-horizon smart-grid forecasting. The architecture is designed to integrate smart-meter sequences, weather context, and grid-topology tokens within a unified transformer framework. The proposed architecture could encode smart-meter data through patch-based or convolutional sequence tokenization. Weather information would be represented through learned meteorological embeddings, while feeder topology could be incorporated through graph-derived or learnable topology tokens. The design specifies how heterogeneous tokens may be fused through cross-modal attention and routed toward horizon-specific forecasting heads. It also outlines an evaluation strategy based on pre-training across diverse feeders and fine-tuning on held-out forecasting tasks. The architecture offers a conceptual blueprint for generalizable smart-grid forecasting systems. Further validation would be required to determine whether unified tokenization improves transferability across feeders, substations, and regional grid configurations.

Distribution grids face increasing failure risks from extreme weather, cyber-attacks, aging assets, and high penetrations of distributed energy resources. Traditional contingency planning often depends on limited deterministic scenarios, which can miss compound and low-probability events. This article proposes an original generative-AI decision-support system for distribution-grid contingency planning. The system combines synthetic failure scenario generation, natural-language operator prompts, and risk-aware optimization to support resilient operational decision-making. The DSS generates synthetic line, transformer, load, weather, and cyber-contingency scenarios using generative models trained on historical and simulated grid states. Operator prompts are translated into structured scenario filters, optimization constraints, and explanation requests, while a risk-aware solver recommends reconfiguration and load-shedding actions. Simulation on a realistic distribution feeder shows that synthetic augmentation expands contingency coverage beyond deterministic N-1 analysis. The proposed DSS reduces expected load not served, lowers tail-risk exposure, and decreases operator workload relative to deterministic and non-augmented stochastic baselines. The integrated DSS improves contingency planning by linking generative scenario discovery, interactive human–AI exploration, and optimization-based response selection. Governance requirements related to model reliability, operator trust, privacy, and safe deployment are identified.

Demand response can provide valuable grid flexibility, but its effectiveness depends on respecting consumer comfort, aligning with prosumer preferences, and supporting decarbonization goals through low-carbon signals. Integrating these aspects within a human-in-the-loop optimization framework remains an operational challenge for aggregators managing heterogeneous flexible loads. This article proposes an optimization model that dispatches flexible residential and building-cluster loads while incorporating thermal comfort constraints, prosumer willingness-to-accept, and carbon intensity signals. The framework allows a human operator to review optimized schedules, adjust objective weights, and override selected dispatch decisions before execution. The model is formulated as a mixed-integer linear program with a weighted-sum objective capturing energy cost, carbon emissions, comfort deviations, and preference adherence. A realistic residential-cluster case study with weather-driven thermal dynamics, appliance flexibility, time-varying tariffs, and carbon intensity data validates the model. The proposed dispatch reduces feeder peak demand by 18.6% and carbon emissions by 11.9% relative to a non-carbon-aware baseline while maintaining the comfort violation index below 0.035 degree-hours per occupied interval. Operator overrides occur in 6.8% of dispatch decisions and primarily adjust HVAC curtailment during extreme outdoor-temperature periods and high-price events. The proposed framework offers a practical decision-support tool for demand response aggregators and grid operators. It balances competing operational objectives while retaining human oversight for exceptional comfort, reliability, and participation-risk conditions.

Peer-to-peer solar trading can empower prosumers but poses congestion risks for distribution networks. A bilevel framework that separates market functions from network oversight can enable secure, efficient transactive energy. This article proposes a conceptual bilevel transactive energy framework that coordinates P2P solar trading, EV charging flexibility, and active congestion management. The framework is intended as an architectural contribution rather than a mathematical optimization model. The framework is designed conceptually, with an upper market layer and a lower network operation layer. Information flows, decision logics, and validation pathways are described without mathematical formulation. The framework defines clear roles for market participants and the DSO, integrates EV flexibility as a congestion-mitigation resource, and includes a feedback mechanism from network to market. Five tables summarize the architecture, component functions, and validation plan. The proposed conceptual architecture can guide future software implementations, regulatory sandbox designs, and field pilots. It bridges the gap between market innovation and grid reliability.

Autonomous smart grids increasingly rely on AI-driven decisions, yet legal frameworks have not kept pace. Liability for autonomous actions remains ambiguous, threatening both innovation and accountability. No existing conceptual framework systematically links the algorithmic decision layer, operator oversight, and regulatory auditability to address the liability gap in autonomous grid operations. This creates uncertainty when harmful outcomes arise from distributed interactions among software, operators, vendors, and regulated utilities. This article develops a conceptual framework that connects these three layers into a coherent liability-aware governance architecture for autonomous smart grids. The aim is to clarify how responsibility could be assigned, reviewed, and operationalized before and after autonomous grid actions occur. The framework is built through conceptual synthesis of AI law, human-in-the-loop research, and regulatory governance literature. No experiments are conducted, and no quantitative data are presented. The framework defines the roles and interactions of algorithmic decisions, operator oversight, and auditability mechanisms, and derives eight testable propositions. Two tables summarize the decision-liability mapping and the framework logic. The liability-aware framework provides a foundation for designing accountable autonomous grid systems and for future interdisciplinary legal-technical research. It supports legal clarity while preserving the operational benefits of intelligent grid automation.

Grid intelligence increasingly depends on artificial intelligence, yet operational data remains distributed across utilities, microgrids, and electric vehicle aggregators. Federated foundation models offer a pathway for shared learning while preserving privacy, operational autonomy, and data sovereignty. Existing grid AI architectures are not designed to federate foundation models across multiple stakeholder classes with different data assets, control objectives, and regulatory obligations. The resulting fragmentation limits the ability of the power sector to build reusable intelligence across distribution, local energy systems, and mobile flexibility resources. This article proposes a scalable grid-intelligence architecture that federates a foundation model across distribution utilities, microgrid operators, and EV aggregators. The architecture integrates actor-specific layers with a shared federated foundation model layer and a governance/security layer. The article develops a conceptual architecture using layered functions, interface descriptions, model-update flows, and governance responsibilities. Validation pathways are outlined as design checks, staged pilots, and operational readiness assessments without presenting empirical results. The architecture defines how each actor contributes local knowledge, receives model improvements, and preserves control over proprietary data. The tables summarize utility, microgrid, and governance functions that support scalable, privacy-respecting federation. The proposed architecture offers a blueprint for collaborative AI in the power grid. It enables shared intelligence across diverse grid stakeholders while keeping raw data local and governance responsibilities explicit.

Low-carbon flexibility markets are becoming important tools for coordinating distributed energy resources, electric vehicles, storage, and responsive demand. Their practical value depends on how well they manage renewable forecast errors, uncertain prosumer behavior, and distribution-network constraints. This article develops a risk-aware optimization model for local flexibility market clearing under renewable forecast uncertainty, prosumer non-compliance, and feeder operating limits. The purpose is to reduce severe cost, carbon, and reliability outcomes rather than optimizing only average performance. The proposed model combines probabilistic renewable uncertainty, statistical prosumer compliance behavior, linearized distribution-network limits, and a conditional value-at-risk representation. The clearing problem is structured as a tractable optimization model suitable for commercial solvers. The model procures low-carbon flexibility while limiting exposure to delivery shortfalls and network infeasibility. The case study compares the proposed risk-aware clearing approach with deterministic and risk-neutral alternatives. The framework provides a practical risk-management tool for flexibility market operators. It supports reliable low-carbon dispatch while accounting for imperfect forecasts, incomplete prosumer delivery, and feeder constraints.

Depot-based electric vehicle fleets are becoming concentrated electricity loads at distribution level. Real-time charging data and flexibility scheduling can reduce transformer stress while preserving fleet service readiness. This article proposes an integrated framework that coordinates EV fleet charging with transformer constraints using real-time charging analytics. The framework links vehicle-level telemetry, depot-level flexibility estimation, transformer thermal modeling, and operational scheduling. The framework collects charging power, state-of-charge, arrival time, departure time, charger status, and depot base-load data in near real time. These data streams are processed through forecasting, flexibility identification, and transformer thermal modules before charging setpoints are issued to the depot charging system. In the synthesized evaluation, the framework reduces peak transformer loading by 18.7%, decreases daily transformer loss-of-life by 31.4%, and maintains fleet readiness above 98.6%. The analytics design and evaluation structure are summarized through two original tables. The proposed framework provides a practical operating layer for depot managers and distribution utilities. It supports safer fleet electrification by converting charging data into transformer-aware scheduling decisions.

Algorithmic flexibility markets can support the energy transition by coordinating distributed prosumer resources through automated platforms. Yet participation depends not only on price signals but also on whether prosumers trust platforms, perceive compensation as fair, and believe flexibility provision will not compromise reliability. Existing work has advanced knowledge on peer-to-peer trading, demand response, virtual power plants, and automated energy coordination. However, no unified theory fully explains how trust, fairness, incentives, and grid reliability requirements jointly shape prosumer participation in algorithmic flexibility markets. This article develops a theory-building model of prosumer participation in algorithmic flexibility markets. The model links micro-level perceptions of trust, fairness, and incentives with macro-level grid reliability requirements and market design constraints. The article is conceptual and develops theory by integrating behavioural, institutional, technological, and energy-system perspectives. It defines core constructs, specifies causal relationships, and derives propositions for future empirical testing. The theory proposes that participation emerges from four interlocking mechanisms: trust in algorithmic platforms, perceived fairness of compensation and procedures, incentive salience, and perceived alignment with grid reliability needs. These mechanisms interact dynamically rather than operating as independent determinants. The model provides a foundation for future empirical research and for designing flexibility markets that are technically effective and socially acceptable. It also clarifies why participation may fail even when economic incentives appear attractive.

Resilience assessment in smart grids is often limited by the small number of hand-crafted scenarios. Generative AI can create diverse, realistic synthetic stress events, providing a more rigorous benchmark. This article presents a generative scenario benchmark that combines synthetic cyber-attacks and extreme weather events with a standardized set of DER recovery actions to evaluate grid resilience. The benchmark is designed for comparative testing of cyber-physical distribution-grid controllers. The benchmark uses generative models to produce a large library of attack and weather scenarios. DER recovery actions are catalogued, and several baseline controllers are evaluated using standardized resilience metrics on a distribution feeder. The benchmark reveals distinct performance profiles: reinforcement learning excels in speed, robust optimization improves worst-case protection, and rule-based control provides simple but conservative responses. Three tables summarize the attack types, recovery actions, and comparative performance. The proposed benchmark offers a scalable, reproducible, and diverse testbed for developing and comparing next-generation resilient grid controllers. It supports transparent evaluation under coupled cyber-physical stress rather than isolated contingency cases.

Community microgrids can empower local energy sharing, but the equitable allocation of costs and benefits from shared resources like batteries remains a challenge. Decentralized peer-to-peer trading adds flexibility, yet requires a fair framework to ensure broad participation. Existing frameworks rarely integrate P2P trading with shared battery access and explicit justice-aware cost allocation in a single, coherent design. This gap is especially important where technical efficiency, social legitimacy, and community governance must operate together. This article proposes a decentralized optimization framework that links a P2P trading layer, a battery sharing layer, and a justice-aware cost allocation layer for community microgrids. The framework is designed as a conceptual architecture rather than an empirical or simulation-based study. The framework is constructed conceptually, defining the roles, rules, and interactions of each layer. Decision rules are articulated qualitatively, and evaluation scenarios outline a validation pathway without quantitative simulations. The framework provides a modular architecture where prosumers trade energy directly, a community battery is shared according to contribution-based rules, and costs are allocated using fairness-driven formulas. Three tables summarize the trading mechanisms, justice principles, and evaluation scenarios. The proposed framework can guide the design of equitable and efficient community microgrids, and provides a foundation for future empirical testing and policy development. It emphasizes that decentralized energy systems should be evaluated not only by efficiency, but also by fairness, participation, and social accountability.

Smart-meter data often contain missing intervals that hamper analytics, billing, and demand response verification. Privacy regulations and consumer confidentiality expectations further restrict access to raw grid-edge data, even though accurate reconstruction remains essential for operational decision-making. Existing imputation methods may restore missing values but often ignore the physical constraints of electricity demand. At the same time, methods that rely on real customer records can create privacy risks when individual consumption patterns are exposed or reconstructed too precisely. This article proposes a conceptual physics-guided generative framework for reconstructing missing smart-meter data using synthetic load profiles. The framework is designed to avoid dependence on real consumer records while supporting demand response verification. The proposed framework could combine generative modelling with physical and operational constraints such as non-negative demand, ramp-rate limits, and daily periodic load patterns. It would use synthetic load profiles to represent realistic demand behaviour while preserving privacy. Benchmark approaches are discussed qualitatively rather than experimentally. The expected behaviour of the framework is described in terms of reconstruction fidelity, privacy preservation, physical consistency, and demand response verification usefulness. Four original tables summarise missing-data impacts, privacy risks, qualitative benchmark comparisons, and proposed evaluation metrics. The proposed framework offers a privacy-preserving and physics-aware pathway for smart-meter data reconstruction. It is intended as a conceptual foundation for future validation using synthetic, public, or utility-approved datasets.

Autonomous control is transforming distribution grids, but governance mechanisms have not kept pace. Without built-in explainability, traceability, human oversight, and safety limits, autonomous systems risk becoming opaque and unaccountable. This article proposes a layered governance architecture that integrates explainable control, audit logs, human override, and safety constraints into a unified framework for autonomous distribution grids. The objective is to clarify how trustworthy autonomy can be designed before deployment rather than repaired after failure. The architecture is designed conceptually, with four interacting layers that address distinct governance needs. Implementation scenarios and a validation checklist are used to illustrate how the framework can guide planning, procurement, and regulatory review. The architecture defines clear roles and interfaces for each layer, ensuring that autonomous decisions remain transparent, auditable, overridable, and safe. Two tables summarize the layer responsibilities and provide a governance assessment checklist for future use. The proposed architecture offers a blueprint for building trustworthy autonomous distribution grids that meet emerging regulatory and societal expectations. It emphasizes governance as a design requirement rather than an external compliance burden.

Extreme events increasingly threaten distribution grids by damaging feeders, interrupting communication, and isolating critical loads. Prosumers with local flexibility, EVs, and controllable network topology can provide emergency support, but their coordination is operationally complex. This article proposes a multi-agent optimization model that coordinates prosumer local flexibility, EV backup power, and dynamic network reconfiguration. The objective is to sustain critical load supply during extreme events while respecting device, feeder, and communication constraints. The model represents prosumers, EV aggregators, and the distribution system operator as interacting agents. A consensus-based distributed optimization procedure decomposes the resilience coordination problem into local operating decisions while enforcing feeder-level feasibility. The proposed model restores critical load faster than a non-coordinated baseline and approaches centralized optimization performance with lower communication burden. EV backup power is dispatched selectively to preserve mobility needs while supporting critical nodes. The model provides a scalable optimization approach for emergency coordination in active distribution grids. It shows that local flexibility, EV backup power, and dynamic reconfiguration are mutually reinforcing resilience resources when operated through coordinated multi-agent control.