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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.

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.

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.

Artificial intelligence is increasingly used for real-time decision support and control in cyber-physical smart grids. However, the growing reliance on data-driven control creates a need for methods that are both explainable to human operators and consistent with electrical-network physics. This review proposes a two-dimensional classification scheme for AI methods in smart-grid control. The scheme combines an explainability dimension with a physics-informed dimension and applies the resulting framework to voltage regulation, congestion management, and microgrid control. A systematic literature search identified 25 peer-reviewed journal articles published between 2017 and 2025. The selected studies were classified according to their explainability mechanism, degree of physical-knowledge integration, application domain, and relevance to cyber-physical grid operation. The review shows that most studies emphasize either explainability or physical consistency, while methods combining both remain limited. Voltage regulation is dominated by reinforcement-learning and safety-constrained control studies, whereas microgrid control contains a broader variety of AI methods. The proposed classification provides a structured basis for analysing trustworthy AI in smart-grid control. It highlights methodological gaps and identifies under-developed combinations of explainability, physics-informed learning, and operational deployment.

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.

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.

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.

This study explores the consequences of synchronized false data injection attacks (FDIAs) on voltage behavior in smart microgrids that rely on renewable-based distributed energy resources (DERs)—a cornerstone of modern smart city energy infrastructure. Employing both simulation-based and real-world experimental techniques, the work introduces a unified framework for analyzing and defending against coordinated threats, thereby safeguarding the reliable operation of renewable-integrated DERs, which are central to today’s urban power ecosystems. In the proposed scenario, multiple attackers independently select the most effective moments to execute two complementary cyberattacks, one producing undervoltage and the other causing overvoltage within the same smart microgrid. Each attack model is driven by an objective function that maximizes the severity of voltage deviations—either excessive undervoltage or harmful overvoltage—induced by its corresponding FDIA. Under this coordinated scheme, the adversaries construct a multi-objective optimization problem (MOOP) that simultaneously provokes significant voltage violations across the microgrid’s most exposed zones. Due to the competing objectives in this MOOP, a Pareto-front approach is applied to generate a set of non-dominated optimal solutions, along with a single best compromise solution (BCS). The potency of this combined FDIA strategy is substantiated through detailed simulation studies and laboratory-based experiments. Specifically, the well-known IEEE 13-node test feeder was reconfigured to represent a microgrid environment for the numerical simulations, while the physical validation utilized a small-scale hybrid photovoltaic/wind microgrid testbed featuring actual renewable generation sources. 

Recent advances in residential automation technologies and the widespread adoption of distributed energy resources (DERs) have dramatically amplified the need for sophisticated energy management strategies that harmonize operational reliability with cost-effectiveness in smart residential microgrids (SRMG). When smart homes fail to coordinate effectively or exchange energy, irregularities in the SRMG load profile emerge, potentially breaching critical technical thresholds. This work presents a fully decentralized architecture for SRMG that accommodates heterogeneous smart homes that actively participate in peer-to-peer (P2P) energy trading. This architecture targets the simultaneous minimization of load profile variability across the microgrid, reduction of household electricity bills, and preservation of occupant comfort levels enabled by P2P exchanges. At the household level, the home energy management (HEM) system is engineered to curtail overall energy expenditure by leveraging available DER capacity to enable P2P transactions and maintain continuous bidirectional links with the SRMG operator (SRMGO). Real-time, ongoing data interchange between SRMGO and HEM units proves indispensable for achieving superior load-profile smoothing in a decentralized setting. The proposed mechanism achieves roughly a 4.25% decrease in load-profile deviations while keeping energy costs unchanged, thereby confirming that decentralized P2P energy exchanges markedly improve both microgrid load balancing and household expenditure stability. Simulations conducted with the general algebraic modeling system (GAMS) software confirm that incorporating P2P energy mechanisms within a decentralized structure satisfactorily meets the SRMG’s technical constraints and the monetary priorities of participating smart homes.

Microgrids provide an effective strategy for delivering energy while keeping expenses to a minimum. Within residential microgrids, individuals with diverse personality traits can collaborate in multiple ways. Additionally, the distinct decision styles arising from these personality differences can have a substantial impact on the success of their joint activities. The present study explores patterns of collaboration among neighbors with a range of personality traits within residential microgrids that form part of a smart-city ecosystem, thereby supporting energy exchange and reducing the total cost of energy provision. The methodology introduced in this work accounts for uncertainties arising from social conduct and organizes joint action through a game-theoretic structure. That structure works toward achieving an efficient and fair division of energy across participating neighbors, simultaneously driving down expenditures, encouraging cooperative behavior, and applying disincentives for non-cooperative conduct in line with each person’s behavioral profile. The suggested technique was tested on a residential benchmark microgrid using the GAMS CPLEX optimizer; two contrasting cases were examined to evaluate how energy storage affects trading decisions. By establishing mean values for discount and penalty rates, the method becomes better suited to diverse microgrid topologies. The analysis further addresses the tension between the utility company’s prices and the most advantageous rates from the neighbors’ perspective. Simulation results indicate that collaborative behavior enables neighbors to meet surplus requirements and can reduce the microgrid’s aggregate operating costs by up to 80%.

This study introduces a coordinated control approach to address the challenge of volt-var optimization (VVO), incorporating load tap-changing transformers, step-voltage regulators, and switched-capacitor banks, along with a photovoltaic (PV)-integrated microgrid. The harmony search (HS) metaheuristic algorithm was applied to efficiently compute the globally optimal operating parameters for these devices, ensuring robust performance across fluctuating operating conditions. The key aims of the volt-var control strategy were to achieve substantial reductions in active power losses, peak demand, and voltage deviations throughout the distribution system, while simultaneously maintaining bus voltages within acceptable regulatory limits under all possible load scenarios. The optimization problem is a mixed-integer nonlinear program with discrete decision variables, binary variables representing capacitor bank switching states (on/off), integer variables for voltage regulator tap positions, continuous variables for the microgrid’s injected current, and nonlinear network power-flow constraints. The proposed methodology was tested and validated on the standard IEEE 13-node distribution test feeder. Time-series simulations used realistic daily load profiles from the main grid, along with the microgrid’s PV output, both resolved at 15-minute intervals. All power flow computations were carried out with OpenDSS (version 9.5.1.1, released in 2022), an established open-source distribution system analysis tool. The developed control framework supports both real-time operation and long-term planning studies. Simulation results showed that the integrated volt-var coordination significantly reduced total active power losses, lowered peak demand, and effectively mitigated voltage variations across the network.

Advanced control strategies have been applied to manage variable power output in standalone microgrids, reducing the risk of frequency instability and power-quality deterioration. Nevertheless, a key difficulty arises: these computationally intensive techniques, when used without stability checks, may fail to cope with the fast dynamics of real-world conditions, particularly under communication delays. This research proposes a versatile real-time solution for frequency regulation that relies on an artificial neural network (ANN) confined to stable operating regions. The proposed method combines stabilizing PID controllers—derived via small-signal analysis—with an automated optimization of ANN weights and reinforcement learning (RL)-guided constraint selection. Initially, stabilizing PID controllers are developed using the stability boundary locus approach and the Mikhailov criterion, with explicit consideration of communication delays. Subsequently, controller parameters are fine-tuned in real time via an automated search for optimal coefficient sets, using a constrained ANN to keep frequency deviations within a predefined parameter space. The framework is further strengthened by an RL-based tuning mechanism trained on an interpolated stability boundary curve, guaranteeing both stability and enhanced performance. This unique integration of ANN, RL, and sophisticated PID tuning represents a significant advancement in addressing frequency control challenges in isolated AC microgrids. Experimental results demonstrate that the proposed approach surpasses conventional techniques thanks to its reduced parameter search domain. Specifically, it achieves greater reductions in both transient and steady-state frequency deviations compared to semi-constrained and unconstrained alternatives. The enhanced performance metrics and stability assessments confirm that the method delivers superior system behavior and robustness under varying operating conditions.

The mitigation of negative environmental impacts and the socioeconomic benefits associated with renewable energy systems encourage greater incorporation of distributed energy systems into conventional power grids. A novel form of the sharing economy is emerging, involving the shared use of energy resources to lower transaction costs via cloud-based platform services. In light of challenges arising from outdated infrastructure and diverse approaches to renewable energy integration, Distributed Energy and Micro Grids (DE-MG) represent an effective solution to enhance the quality of energy supply services. However, constraints related to microgrid scalability, budget adherence, and security can complicate implementation. Consumers achieve more favorable pricing for renewable energy allocation via cloud-based Peer-to-Peer (P2P) networks. The core goal is to minimize the daily energy expenditure for microgrid power in commercial buildings. In the proposed approach, cloud-based P2P combined with peer-Multi-Agent System (p-MAS) optimization methods is employed to reduce system peak loads and to integrate Demand Response (DR) with an Energy Management System (EMS) within a commercial MG. To address gaps in understanding how different power market structures and individual decision-making behaviors influence local interactions and overall market results, cloud-based P2P for Modeling Leveraging Agents (MLA) is applied to perform bill calculations. Finally, a performance metric is developed to assess cost and reliability, enabling evaluation of the societal benefits of cloud-based P2P energy exchange models. Comparisons are presented between the proposed cloud-based P2P model and an existing P2P energy exchange model across various pricing scenarios and resource categories. The key application of a distributed P2P model for power trading in a microgrid is to reduce electricity costs while improving the grid’s environmental reliability.

This study introduces an improved bidirectional multi-input DC–DC converter (MIPC) tailored for hybrid energy storage systems (HESS) in DC microgrid environments. Traditional batteries exhibit limited charge and discharge rates, which can impose high current stresses, thereby reducing their operational lifespan. Supercapacitors (SCs), due to their high power density, respond rapidly to sudden load variations, mitigating these limitations; however, they are unsuitable for long-term energy storage. Combining batteries with SCs in a HESS leverages their complementary properties, creating an efficient energy storage solution. The proposed MIPC links the HESS to the DC microgrid, enabling independent regulation of battery and SC power as well as efficient energy transfer among storage units. A unified control strategy was developed to manage both charging and discharging of the HESS. A model predictive control (MPC) scheme demonstrated superior DC bus voltage restoration during step changes in photovoltaic (PV) generation and load conditions, compared with conventional proportional-integral (PI) controllers. The MPC reduces current stress, prolongs battery lifespan, and improves overall system performance, ensuring rapid DC grid voltage stabilization. Simulation and experimental tests under varying PV output and load profiles confirmed enhanced DC-link voltage regulation.