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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.

Residential prosumers with rooftop photovoltaic systems and behind-the-meter batteries are changing the operating conditions of low-voltage and medium-voltage distribution feeders. When these resources respond independently to retail tariffs, their aggregate behavior can intensify reverse power flows, transformer loading, and voltage excursions. This paper proposes a bilevel optimization model that coordinates residential demand response, rooftop photovoltaic generation, and battery storage under distribution-feeder congestion constraints. The objective is to align prosumer economic decisions with distribution system operator requirements for congestion relief and voltage compliance. The upper-level problem represents the distribution system operator and minimizes feeder congestion, voltage deviations, and coordination costs. The lower-level problem represents residential prosumers that minimize their individual energy costs through load shifting, photovoltaic self-consumption, and battery dispatch. The proposed formulation produces coordinated schedules that reduce peak feeder loading and voltage violations relative to uncoordinated operation. Battery arbitrage and flexible demand are redirected from purely price-driven operation toward feeder-aware congestion mitigation. The bilevel coordination framework provides an operational method for managing residential distributed energy resources without replacing prosumer autonomy. It enables distribution system operators to procure flexibility while maintaining economically rational prosumer behavior.

Microgrids need real-time energy management that integrates accurate load and solar forecasts with efficient battery dispatch while preserving battery lifetime. Forecast uncertainty can increase unnecessary grid imports, battery cycling, and curtailment when operational decisions rely on single-source prediction. This paper proposes a real-time forecasting and dispatch framework combining a hybrid load–solar prediction model with a degradation-aware battery scheduling strategy. The objective is to improve short-horizon operational decisions while reducing avoidable battery ageing. A hybrid forecasting model uses distinct methods for load and solar components. The dispatch strategy minimises operational cost while incorporating a degradation penalty derived from a cycle-life model. The framework is tested on representative microgrid data against persistence, statistical, and non-degradation dispatch benchmarks. The hybrid forecaster reduces prediction error compared to single-model approaches, and the degradation-aware dispatch extends estimated battery life by 8.6% while maintaining near-optimal economic performance. The framework also reduces unnecessary charge–discharge reversals during periods of volatile photovoltaic output. The integrated real-time system offers a practical, computationally light solution for microgrid operators, balancing cost, accuracy, and battery health. Its design supports deployment in campus, community, and commercial microgrids with limited computational infrastructure.

Distribution networks increasingly host distributed energy resources whose flexibility, if properly harnessed, can support grid operation. Smart-meter data provides a basis for estimating this flexibility by revealing consumption, generation, and behind-the-meter behavioural patterns. The lack of an integrated framework linking smart-meter analytics, DER availability, and operational decision-making hinders the realization of flexibility-aware smart grids. Existing approaches often treat data analytics, flexibility modelling, and distribution-level operation as separate domains. This article develops a conceptual framework that links smart-meter analytics, DER availability estimation, flexibility estimation, and decision support into a coherent system for distribution-level operation. The aim is to clarify how information can flow from metering data to actionable DSO decisions. A conceptual synthesis of literature on smart-meter analytics, DER modelling, flexibility estimation, aggregation, and DSO decision-support architectures is performed. No empirical data, simulations, experiments, or quantitative analyses are used. The framework comprises three interrelated layers: smart-meter analytics, DER availability estimation, and decision support, connected through a flexibility estimation layer. Propositions regarding data granularity, flexibility accuracy, uncertainty handling, and operational benefit are articulated. Two tables summarize DER availability indicators and framework architecture. The framework provides a foundation for future empirical testing and can guide DSOs in building flexibility-aware operational capabilities. It also clarifies how data, flexibility, and decision processes should be conceptualized as linked components of smart-grid operation.

Electric vehicles are increasingly connected to distribution feeders whose transformers, cables, and voltage-control devices were not originally designed for coincident residential charging. Smart charging coordination can convert this challenge into an operational flexibility resource when user needs and network limits are treated jointly. This case study applies stochastic EV charging coordination to a realistic distribution-network testbed to reduce evening peaks, improve voltage profiles, and support local flexibility procurement. The objective is operational rather than purely algorithmic: the study evaluates what a distribution system operator and an aggregator can learn from coordinated charging under uncertain conditions. The case uses empirical-style charging-session statistics, a radial low-voltage feeder model, and stochastic scenarios for arrival time, departure time, energy demand, and feeder loading. Uncontrolled charging, deterministic coordination, and stochastic coordination are compared using the same network and mobility assumptions. The stochastic formulation is evaluated through network-level indicators for feeder peak demand, transformer loading, voltage deviation, flexibility availability, and procurement cost. The comparison is structured to show whether uncertainty-aware coordination is more robust than deterministic scheduling when charging behavior and grid conditions deviate from forecasts. Stochastic EV charging coordination is a practical planning and operational tool for distribution networks with rising residential and workplace charging demand. The case study provides implementation lessons for DSOs, aggregators, and flexibility-market designers.

Renewable-rich microgrids face operational complexity from variable generation, storage dynamics, flexible loads, and the need for human oversight. A layered architecture can manage this complexity by separating functions while preserving coordination. Existing microgrid control architectures often entangle forecasting, scheduling, demand response, and supervisory control. This can make energy management systems brittle, difficult to maintain, and insufficiently accommodating of operator override. This article proposes a layered architecture that cleanly separates forecasting, battery scheduling, demand response, and operator override functions. The aim is to support modular design, resilient operation, and transparent human supervisory control. The architecture is developed conceptually using established ideas from microgrid energy management, hierarchical control, renewable integration, storage scheduling, demand response, and human-in-the-loop supervision. No experiments are conducted, and no quantitative results are presented. The architecture comprises four layers with defined functions, inputs, outputs, decision horizons, and interfaces. A validation plan is reserved for the second part of the article, while this part establishes the architecture logic and the forecasting layer. The proposed architecture offers a blueprint for building modular, resilient, and human-centred microgrid energy management systems. It is intended as a conceptual reference model for future implementation, testing, and refinement.

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.

Day-ahead distribution-grid operation requires anticipating customer demand, renewable generation, network loading, and available demand-side flexibility. Smart-meter data provide a practical basis for identifying recurring consumption patterns and embedding those patterns into operational scheduling. This article presents an original forecasting-optimization decision-support system for day-ahead distribution-grid operation. The system integrates smart-meter clustering, renewable prediction, and flexible load scheduling to support distribution system operators. The proposed system processes smart-meter profiles into consumer clusters, predicts day-ahead photovoltaic and wind generation from weather-driven features, and schedules flexible loads through constrained optimization. The decision-support workflow is designed to produce feeder-level schedules, expected voltage conditions, congestion indicators, and operator-facing recommendations. The clustering module identifies consumer groups with different peak timing, ramping behavior, and flexibility availability. The integrated DSS is structured to reduce peak loading, improve voltage profiles, and lower operational cost compared with a baseline day-ahead plan without forecast-informed scheduling. The proposed DSS offers a practical framework for data-driven distribution-grid operation. It supports renewable integration by translating forecasts and customer flexibility into actionable schedules for day-ahead planning.

Real-time coordination of distributed energy resources is increasingly complex because distribution grids now operate with high variability, bidirectional power flows, and rapidly changing local constraints. Digital twins, graph neural networks, state estimation, and uncertainty-aware dispatch provide complementary capabilities for managing this complexity. This article proposes a layered digital-twin architecture that combines state estimation, graph neural network analytics, and uncertainty-aware dispatch for real-time DER coordination. The objective is to describe an architecture that is conceptual, modular, and suitable for active distribution network operation. The architecture is designed conceptually, with layers for physical grid mirroring, measurement fusion, topology-aware learning, and robust dispatch. A validation strategy is positioned as a future pathway rather than as an experimental contribution. The proposed architecture modularizes the DER coordination task into interoperable layers with distinct responsibilities and interfaces. Three tables are used to define the architecture layers, applicable GNN functions, and uncertainty-handling mechanisms. The architecture provides a blueprint for next-generation distribution management systems that require real-time observability, learning-based analytics, and robust decision support. It is intended to guide future implementation, simulation, and field validation.

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.

Smart-meter load forecasting is essential for distribution-grid operation, flexibility scheduling, congestion management, and renewable integration. However, centralizing household-level smart-meter records can expose private behavioral patterns and reduce consumer trust. This article proposes a federated forecasting framework for accurate smart-meter load prediction across heterogeneous distribution feeders. The framework enables multiple feeders to collaboratively train forecasting models without transferring raw household consumption data. The proposed framework combines feeder-local temporal neural networks, federated aggregation, feeder-aware personalization, and optional differential privacy. It is validated through simulation on multi-feeder smart-meter datasets with different customer mixes, photovoltaic penetration levels, missing-data rates, and load variability. The framework achieves forecasting accuracy close to a centralized privacy-free benchmark while outperforming local-only and non-personalized federated baselines under strong feeder heterogeneity. Differential privacy introduces a controlled accuracy penalty that remains acceptable for day-ahead and intra-day feeder operation. The proposed federated forecasting framework provides a practical privacy-preserving solution for distribution system operators that need accurate load forecasts across diverse feeders. The results indicate that collaborative learning can preserve household privacy while improving feeder-level prediction accuracy.

Distribution-grid congestion is becoming more difficult to anticipate as photovoltaic generation, batteries, electric vehicles, and flexible demand reshape feeder power flows. Graph neural networks provide a natural way to represent feeders, but a conceptual framework is needed to connect topology, physics, and flexibility signals coherently. This conceptual research article proposes a physics-informed graph neural network framework for forecasting distribution-line congestion and voltage violations. The framework is designed to produce hourly congestion probabilities over a 24-hour horizon. The proposed model represents buses as graph nodes and lines or transformers as graph edges, while DER flexibility signals are encoded as forward-looking node attributes. Physical consistency is introduced through power-flow residuals, voltage-bound penalties, and thermal-limit penalties in the learning objective. Rather than reporting experimental results, the article specifies the architecture, input requirements, loss formulation, benchmarking logic, and explainability design required for future empirical validation. The framework is intended to guide implementation on realistic feeder datasets. The proposed PINN-GNN is expected to improve congestion awareness by combining electrical topology, flexibility availability, and physics-informed regularization. Its explainability layer is designed to help operators identify which lines, buses, or DER resources most influence a forecasted congestion event.

Cyber-physical attacks on smart grids can compromise the measurements, forecasts, and control signals used in dispatch. A resilient decision-support system must detect anomalies, explain their operational meaning, and support secure re-dispatch before manipulated data causes unsafe operation. This article proposes a cyber-resilient decision-support system that integrates anomaly detection, explainable alerts, and robust optimization for smart-grid dispatch. The system is designed as an operator-facing architecture rather than a standalone detection model. The system combines machine learning-based anomaly detection, explainable artificial intelligence-driven alert generation, and robust dispatch re-optimization. Validation is structured around false data injection, voltage measurement manipulation, and load manipulation attacks on an IEEE test feeder. The proposed decision-support system identifies cyber-physical anomalies, converts model outputs into actionable explanations, and updates dispatch decisions under attack-induced uncertainty. Compared with non-resilient dispatch and detection-only benchmarks, it reduces unsafe voltage excursions and avoidable line-flow stress. The integrated architecture improves cyber-resilience by connecting detection, explanation, and mitigation in one operational workflow. It supports faster operator interpretation while maintaining dispatch feasibility under uncertain measurements.

Autonomous control is increasingly relevant to smart-grid operation, where control systems may support voltage regulation, frequency control, dispatch, and anomaly response. Yet their trustworthiness remains uncertain when decisions are opaque, intervention options are unclear, and cyber-physical conditions are unstable. Existing research often treats explainability, operator intervention, and cyber-physical uncertainty as separate design or evaluation issues. This separation limits theory development because trust in autonomous grid control depends on how these mechanisms operate together. This article develops a conceptual theory of trustworthy autonomous smart-grid control. The theory links explainability, operator intervention, and cyber-physical uncertainty to both perceived and operational trustworthiness. The article uses theory-building logic rather than empirical testing. It synthesizes concepts from trustworthy AI, human-automation interaction, explainable AI, and cyber-physical security to construct mechanisms and derive propositions. The proposed model specifies causal pathways through which explanation quality, intervention capability, and uncertainty handling shape trustworthiness. It also identifies interaction effects, boundary conditions, and testable propositions for future research. The theory provides a foundation for designing, evaluating, and empirically testing trustworthy autonomous control systems in smart-grid environments. It emphasizes that trustworthiness is not produced by transparency, human oversight, or robustness alone, but by their coordinated operation.

Microgrids are increasingly relied upon to maintain supply during disruptions, but their resilience depends on timely islanding, accurate renewable forecasts, and efficient battery dispatch. Digital twins offer a platform to integrate these functions into a coordinated control environment. Existing microgrid control architectures often treat islanding, forecast uncertainty, and battery recovery as separate problems. An integrated conceptual framework that unifies them within a digital-twin environment is therefore needed. This article proposes a conceptual framework for digital-twin-assisted microgrid control that coordinates resilience-oriented islanding, renewable forecast error handling, and battery dispatch recovery. The framework is intended as a design-oriented contribution rather than an experimental validation. The framework is developed through conceptual synthesis of digital twin architectures, microgrid resilience theory, forecasting uncertainty modeling, and battery storage control. No experiments, simulations, field measurements, or empirical results are reported. The framework describes a multi-layered control architecture that continuously assesses resilience, triggers islanding under forecast uncertainty, and manages battery dispatch for post-islanding recovery. Four tables summarize digital twin functions, forecast error types, control architecture, and resilience indicators. The framework provides a design blueprint for resilient microgrid controllers that leverage digital twins. It can guide future simulation studies, hardware-in-the-loop testing, and field validations.

Electric vehicle charging imposes new operational stress on distribution grids, especially where local transformer capacity and feeder voltage margins are limited. Grid-edge intelligence could enable charging decisions to be made closer to the physical constraints that shape those impacts. Existing EV charging control architectures often depend on centralized optimization, cloud-side visibility, or stable communication paths. Such designs can create privacy, latency, and resilience gaps when local operating conditions change rapidly. This article proposes a layered grid-edge intelligence architecture for distributed EV charging control. The architecture integrates local forecasting, federated model updates, and resilience-aware coordination into a unified system design. The architecture is developed conceptually through functional layers, interfaces, data flows, and operational workflows. Implementation scenarios and validation stages are outlined to guide future simulation, hardware-in-the-loop testing, and field deployment. The proposed architecture defines local forecasting nodes, a federated update layer, and resilience coordination functions that interact through structured protocols. Five tables are specified across the full manuscript to summarize model roles, coordination strategies, resilience mechanisms, deployment scenarios, and validation procedures. The architecture offers a blueprint for grid-edge EV charging management that balances intelligence, privacy, and resilience. It is intended to support future practical implementation rather than report experimental results.

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.