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

Active distribution networks face growing renewable curtailment as distributed photovoltaic and wind generation increasingly encounter voltage, congestion, and flexibility constraints. Battery storage scheduling under uncertainty has become a central operational strategy for absorbing otherwise curtailed energy and improving distribution-level renewable integration. Despite substantial growth in optimization-based storage studies, the literature remains fragmented across stochastic programming, chance-constrained optimization, robust optimization, and distributionally robust formulations. No critical review has systematically compared these paradigms for storage scheduling and curtailment reduction specifically in active distribution networks. This critical review assesses and contrasts stochastic and robust optimization models for battery storage scheduling aimed at reducing renewable curtailment in active distribution networks. It evaluates how these approaches represent uncertainty, network constraints, operational objectives, and implementation readiness. A structured critical review of 27 peer-reviewed journal articles published between 2017 and 2024 was conducted. The analysis focuses on mathematical formulations, uncertainty modeling, storage representation, curtailment objectives, network constraints, and operational applicability. Stochastic models offer strong expected-performance reasoning but often depend on scenario quality and can become computationally demanding. Robust models provide stronger protection against adverse uncertainty realizations but may become conservative when uncertainty sets are poorly calibrated, while gaps remain in real-time scheduling, multi-service operation, degradation modeling, and field validation. The choice between stochastic and robust optimization depends on operator risk preference, data availability, computational resources, and distribution network complexity. Hybrid, data-driven, and implementation-oriented methods offer promising future pathways for reducing renewable curtailment through battery storage scheduling.

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.

This research proposes a novel, low-power routing protocol tailored for IoT networks that merges intelligent data-aggregation clustering with optimized path selection to overcome critical data-handling challenges in smart urban environments. The protocol relies on a layered architecture that includes Data Fusion Heads (DFH), intermediate relay DFHs, and the marine predators algorithm—an effective nature-inspired optimizer equipped with a purpose-built fitness function. This function simultaneously refines multiple criteria: the geographic clustering density of Sensor Nodes (SNs) inside each data fusion group (DFG), the total hop distance toward the sink, intra-group node proximity, residual battery level (RE), Average Scale of Building Occlusions (ASBO), and the rotation interval of the Primary DFH (PDFH). One of the protocol’s core advancements is the systematic application of in-network data fusion, which drastically reduces duplicate packet forwarding and markedly improves the accuracy and usefulness of collected information across all DFGs. By aggregating readings from multiple sensors, the volume of outbound traffic is greatly reduced, yielding substantial improvements in energy efficiency. Two complementary forwarding strategies are supported: single-hop forwarding, where already fused payloads travel directly to the sink, and multi-hop forwarding, in which an appropriate PDF relay is dynamically elected using a weighted relay selection metric that incorporates remaining energy (RE), sink distance, and ASBO. By integrating robust fault-tolerance procedures, intelligent redundancy elimination, and efficient fusion mechanisms, the protocol achieves markedly greater dependability and sustains strong operation under adverse or unpredictable network conditions. Detailed simulation experiments, along with side-by-side benchmarking against contemporary methods, confirm the protocol’s clear advantage across essential performance indicators—namely, network longevity, total energy expenditure, data throughput, and mean end-to-end latency. Compared with leading recent approaches such as the Particle Swarm Optimization-based energy-efficient clustering protocol (PSO-EEC), linearly decreasing inertia weight PSO (LDIWPSO), Optimized Fuzzy Clustering Algorithm (OFCA), and Novel PSO-based Protocol (NPSOP), the presented solution delivers outstanding gains. Concretely, network lifetime is prolonged by 299% versus PSO-EEC, 264% versus LDIWPSO, 306% versus OFCA, and 249% versus NPSOP. Energy usage drops by 254% relative to PSO-EEC, 264% relative to LDIWPSO, 247% relative to OFCA, and 253% relative to NPSOP. Throughput rises by 67% over PSO-EEC, 59% over LDIWPSO, 53% over OFCA, and 50% over NPSOP. By skillfully combining data aggregation with carefully tuned routing decisions, this protocol establishes a new benchmark for power-efficient organization and communication within IoT-based data fusion groups, delivering a dependable and adaptable framework suitable for diverse smart-city and IoT scenarios.

This research addresses the optimization of scheduling strategies for virtual power plants (VPPs), as demonstrated in Portugal’s national New Generation Storage (NGS) initiative, aiming to maximize societal benefits and boost the effectiveness of energy transactions in today’s advanced power networks. VPPs play a crucial role in sustainable urban energy ecosystems by combining various distributed energy resources (DERs), such as renewable generation units and battery storage installations. The underlying optimization challenge is expressed as a Mixed-Integer Linear Programming (MILP) model and tackled via a parallelized simulated annealing (SA) heuristic running on high-performance computing (HPC) platforms. The parallel design greatly enhances the speed of searching the solution landscape, thereby allowing the framework to cope with the growing intricacy associated with expanded DER fleets and increasingly complex dispatch requirements. The developed technique aligns closely with smart city aspirations by enabling flexible, high-efficiency energy management, supporting real-time pricing schemes, and extending the durability of essential components, such as energy storage units. Comprehensive simulation experiments reveal the method’s strong performance in reducing computation time, maintaining high-quality solutions, and scaling effectively to larger problem sizes, thereby supporting instantaneous operational decisions in competitive energy markets. In addition, the improved synchronization among DERs strengthens overall grid reliability, increases market agility, and fosters the emergence of robust, decarbonized urban energy landscapes. This investigation emphasizes the decisive impact of powerful computing resources in overcoming the complexities of contemporary energy infrastructures, illustrating the potential of cutting-edge algorithmic approaches and HPC systems to deliver scalable, responsive, and environmentally sound energy management solutions tailored to smart urban settings. Ultimately, the outcomes pave the way for energy systems that are both socially inclusive and ecologically sustainable, fully consistent with the essential aims of urban resilience and long-term sustainable progress.

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.

Amid the ongoing acceleration of urban development, tertiary sector consumption and household electricity demand have emerged as the dominant components of overall municipal power consumption. Such loads are highly sensitive to unpredictable factors such as occupant behavior and meteorological conditions, which often lead to irregular deviations from expected trends and, consequently, considerable prediction inaccuracies. The present study introduces an integrated prediction approach to improve forecast precision for city-wide electricity demand, particularly across intervals characterized by anomalous consumption shifts. Initially, a systematic metric is developed to detect and quantify these irregular load excursions by analyzing the residual series obtained through decomposition of the raw load sequence. Next, a data-expansion strategy grounded in Generative Adversarial Networks is developed to synthetically augment the otherwise sparse set of anomalous instances, thereby enriching the dataset available for model training. Lastly, the sophisticated TimesNet architecture is adopted to effectively model the intricate, nonlinear dynamics observed in load behavior throughout periods of unusual variation. Validation experiments performed on real-world consumption records from Chongqing, China, substantiate the performance gains achieved by the suggested framework.

Energy efficiency in industrial systems remains a major challenge, as conventional data-driven models are often constrained by limited precision and insufficient data. Integrating the physical laws that govern energy systems can enhance performance and ensure greater physical plausibility; however, such models often struggle with loss computation and often overlook dynamic interactions among subsystems, leading to excessive simplification and reduced real-world utility. Accordingly, this study explores a theoretical framework for constructing a conservation-law-guided neural network to improve energy-efficiency forecasting in industrial processes. The framework embeds physical principles directly into floating nodes built upon a long short-term memory (LSTM) architecture, enabling the model to capture relationships among process variables more effectively. At the same time, gradient aggregation enhances both fluidity and interpretability. Performance was assessed through two large-scale industrial case studies—vinyl chloride monomer production and detergent powder manufacturing—where the proposed model achieved notable gains in prediction accuracy and overall reliability, delivering test prediction improvements of 12.2% and 5.87% compared to previously published approaches. Compared with methods that rely on network architecture modifications, the proposed model demonstrated superior reliability and reproducibility in energy-efficiency forecasts. Furthermore, the model effectively identified energy inefficiencies, resulting in a 4.21% decrease in energy use and a corresponding reduction of 377.35 tons in carbon emissions.

Effectively integrating renewable energy sources into electricity markets is essential to aligning real-time supply with demand and reducing revenue losses from curtailment. Storage technologies offer an effective means to overcome this issue by boosting grid reliability and operational efficiency, while also enhancing market liquidity and dampening price fluctuations. In short-term electricity markets, participants face a wide range of choices, each with distinct risks and benefits, and well-designed trading strategies are crucial for profit maximization. This research investigates the optimization of trading activities in the day-ahead and balancing markets of the Irish electricity system over the period 2019–2022, using quantile-based forecasting techniques. By applying three trading strategies subject to realistic operational constraints, the study assesses different approaches, raises trading frequency, and incorporates flexible timestamp-based orders. The results highlight substantial profit opportunities from concurrent participation in both day-ahead and balancing markets, particularly with larger battery storage capacities. Even though higher trading volumes entail greater costs and compressed profit margins, the adoption of adaptive dual-market strategies proves instrumental in maximizing returns and overcoming market-specific obstacles. Lastly, a scenario-based analysis assesses the economic feasibility of four commercial battery storage systems, demonstrating that larger battery units deliver shorter payback periods. 

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.

A new method for generating time series simulations of electricity price scenarios is introduced. The “Time Series Simulation Conditional Generative Adversarial Network” (TSS-CGAN) produces short-term scenarios of electricity prices. Specifically, the network can output a 24-dimensional vector that reflects the typical dynamics of electricity markets. This approach can serve as an alternative to conventional financial-mathematical techniques, such as statistical factor models, for modeling the price distribution around a given forecast. The dataset spans 3 years from 2020 to 2023. The empirical analysis focuses on the EPEX SPOT market in Europe. An electricity price scenario consists of the prices for the hourly contracts from a day-ahead auction on the EPEX SPOT power exchange. The model takes multivariate time series as input features, including point forecasts of electricity prices along with fundamental data on generation and load profiles. The TSS-CGAN architecture integrates the principles of Conditional Generative Adversarial Networks with 1D Convolutional Neural Networks and Bidirectional Long Short-Term Memory units. Model performance is assessed using both qualitative and quantitative measures. For evaluation purposes, 10,000 simulations are generated for a test period. Qualitative assessment examines whether the model correctly captures and reproduces key electricity market-specific patterns. The quantitative evaluation involves comparing standard error metrics against benchmark models such as DeepAR, Prophet, and Temporal Fusion Transformer, analyzing quantile ranges and error distributions, and conducting a sensitivity analysis. The findings demonstrate that the TSS-CGAN surpasses benchmark models like DeepAR by lowering the continuous ranked probability score by 50% while effectively accounting for market-specific factors, such as the generation of intermittent renewable energies, and responding appropriately to variations in the relevant variables.

With the rise of the Smart Home sector and the growing adoption of Machine Learning (ML), the acquisition of power consumption data is becoming increasingly significant. Gathering real-world energy measurements using sensors is often time-intensive, costly, error-prone, and, in some cases, entirely infeasible. To address this, we introduce the VA-Creator, a framework designed for generating Virtual Appliances (VAs). These VAs produce synthetic power consumption patterns (PCPs) using Neural Networks (NNs) that dynamically adjust their architecture according to the structure of the training data, thereby facilitating the development of additional VAs. To cover every appliance category commonly found in a typical household, we employ a range of neural network types—including Multilayer Perceptrons (MLPs), Long Short-Term Memory networks (LSTMs), and a tailored Generative Adversarial Network (GAN)—alongside other ML approaches such as XGBoost, choosing the most suitable method based on the specific properties of each appliance. The outputs of these ML models are subsequently compared with actual measurements and evaluated using Dynamic Time Warping (DTW) and MLP-based discriminator classification accuracy as evaluation metrics. Furthermore, to verify that the VAs enable effective training of ML models, we generate synthetic datasets with them and conduct an in-depth evaluation by training Non-Intrusive Load Monitoring (NILM) models. The results of this comprehensive assessment confirm that the VA models yield realistic and practically useful outputs.

The swift expansion of electric vehicle adoption demands significant improvements in durability, safety, and dependability for advanced high-capacity battery systems. Consequently, there is a strong need for data-oriented battery management strategies capable of decoding intricate battery signals and guiding appropriate control of the coupled multi-physical processes inside batteries. At present, battery informatics is gaining traction as an effective strategy, harnessing sophisticated machine learning methods to generate reliable forecasts of battery capability, degradation state, and hazard levels—yet its advancement is severely constrained by insufficient data availability. In response, this work offers one of the pioneering contributions to synthetic and experimental data creation, integrating lab-based measurements with comprehensive three-dimensional (3-D) multi-physics simulations to establish a robust deep learning pipeline accompanied by detailed evaluation of performance metrics and thermal hazard anticipation. In particular, Part I addresses the construction and rigorous verification of the battery simulation framework, conducted in two main phases to confirm its predictive fidelity: initially, validation of the multi-physics approach was performed using measurements from two widely used commercial Li-ion cells—Panasonic NCR18650B and 18650BD; subsequently, an in-depth investigation of the thermal–electrochemical coupling was carried out, yielding valuable model-derived understanding such as temporal voltage profiles and identification of peak local temperature regions (hot spots). This carefully validated model produces high-quality, interpretable datasets suitable for training the hybrid convolutional neural network and long short-term memory architecture (CNN-LSTM), which will be detailed in Part II.

Over the past few years, integrating Distributed Energy Resources (DERs) with advanced communication infrastructures has created major challenges for the control and protection of electrical power systems. These difficulties stem mainly from the rapid evolution of smart grids and the growing threat of cyberattacks. As grid-tied solar Photovoltaic (PV) installations—equipped with smart inverters—become more widespread, their exposure to malicious cyber activities and the resulting risks to power network stability have become a pressing issue, especially given the inverter control logic. The present study examines the outcomes of targeted cyber intrusions targeting key PV system elements, with particular attention to the inverter and the Overcurrent Protection Relay (OCR). It further analyzes how these two components interact across a range of cyber-attack conditions. Simulations are conducted using a three-phase radial Electromagnetic Transients Program (EMTP) model to represent the grid and study transient responses during various cyber intrusions. Analysis results uncover the tight coupling between weaknesses in inverters and protection relays, demonstrating how disruption of one can severely affect the performance of the other. The work also quantifies how cyber incidents degrade the efficiency and operational reliability of PV systems connected to the grid. As a concrete illustration, when PV inverters were subjected to a brief 0.1 s pulse signal attack, the OCR failed to detect the abnormality and isolate the fault. This breakdown caused pronounced harmonic distortion together with considerable active power losses, stemming directly from the protection scheme’s inability to recognize and counteract the irregular disturbance. Overall, this investigation provides important new insights into cybersecurity challenges in grid-integrated solar PV installations. It underscores the urgent need to adopt stronger defensive strategies and build greater system resilience as power networks continue evolving into intelligent, interconnected smart grids.

The Smart Grid is an advanced electricity network deeply intertwined with cyber technologies that oversees power generation, long-distance transmission, and final delivery to end users. At its core lies the substation. However, embedding digital systems within substations has dramatically widened the range of potential vulnerabilities. Advanced adversaries, such as those behind the PipeDream APT, deliberately exploit several industrial protocols, including Modbus, DNP3, and IEC61850. Substations face strict operational constraints that complicate the adoption of conventional protective mechanisms, such as data encryption or network intrusion detection systems. To address these difficulties, we present an integrated, trust-centric architecture that significantly strengthens substation defenses. The proposed architecture integrates three main components: a trust evaluation model, a dynamic risk assessment model, and a trust portability model. The trust evaluation model can uncover protocol-specific malicious activities targeting Intelligent Electronic Devices and SCADA human-machine interfaces. The dynamic risk assessment model continuously determines the substation’s current security risk exposure. The trust portability model analyzes whether a device, together with its accumulated trust profile, can be effectively migrated and reused in another substation environment. For a realistic evaluation, we constructed a containerized (Docker) substation replica organized around a multi-agent framework and equipped with a live dashboard modeled after modern Security Operations Centers. Effectiveness was measured by simulating a broad spectrum of adversary behaviors aligned with the MITRE ICS ATT&CK matrix. Experimental results indicate robust resistance to various attack vectors, reliable detection of malicious actions, and appropriate incentivization of well-behaved, trustworthy components. 

In today’s rapidly advancing landscape of information and communication technologies, metropolitan areas face a pressing challenge: water shortages combined with the urgent need for smart, sustainable water distribution systems. Deploying an Internet of Things (IoT)-driven monitoring solution can substantially strengthen oversight of water supply networks and help address common distribution problems such as leaks, pipe bursts, unauthorized withdrawals, tank overflows, and pump dry-running. Nevertheless, the proliferation of sensors and communication endpoints across smart urban environments is placing considerable strain on conventional networks, leading to higher latency and greater power consumption. This research outlines several practical approaches to minimize both delay and energy consumption in IoT-supported water distribution infrastructure in intelligent cities. A range of IoT-compatible network topologies is introduced, each shaped by specific hydraulic design features, surface land cover characteristics, and prevailing wireless propagation conditions. After assessing these layout options together with all critical communication metrics, the most suitable network structure for ongoing surveillance of a municipal water supply system in an Indian metropolitan setting is selected. As a practical illustration, the study develops and evaluates an IoT architecture tailored to a secondary-tier metropolitan city in India—namely, Kochi. Using location-specific conditions and the prevailing land-use/land-cover distribution, detailed delay and energy performance models for the IoT-enabled distribution network are constructed and examined. The paper also describes algorithms for classifying nodes and assigning tasks across edge and fog layers, supported by quantitative assessments of the resulting delays and energy consumption. These models yield approximate values for overall network latency and power consumption. Drawing on the derived outcomes and the associated state transition representations, the optimal positioning of fog computing nodes relative to edge devices and the central cloud platform can be determined. Moreover, evaluations across multiple configurations show that increasing the number of operational states in the transition diagram can deliver energy savings of up to 40%. The proposed techniques offer a roadmap for realizing low-latency, energy-conscious IoT communication infrastructures customized to particular geographic and functional contexts. 

Ongoing progress in intelligent systems and state-of-the-art technology has significantly shaped the evolution of intelligent vehicles. In recent years, the integration of multiple sensors into automobiles has enhanced and expanded advanced driver-assistance systems (ADAS), bringing the goal of full autonomy closer to reality. Despite existing self-driving techniques and implementations, autonomous driving remains an unresolved research challenge that must ensure driver safety and system dependability. This study uses images from two cameras and Global Positioning System (GPS) data to introduce a 3D vision-based approach for object localization and classification, aimed at supporting vehicle navigation. The experimental setup consists of a prototype two-seater electric vehicle, specifically developed and built for campus navigation under controlled driving conditions. At the same time, color and depth images from the main camera are fused to derive 2D features, which are then projected back into 3D space. Road detection combined with depth information isolates point clouds corresponding to objects, enabling the creation of an environmental occupancy map. A convolutional neural network was trained to categorize common urban objects within the color images. Experimental evaluations confirm the accuracy of vehicle and object positioning within the occupancy map across various scenarios, strengthening the visually derived vehicle position through integration with GPS measurements. 

With the rapid expansion of urban power grids and the widespread adoption of renewable energy sources, conventional fault-diagnosis methods for power grids face significant challenges in handling the complexities of modern, highly interconnected systems. While artificial intelligence approaches offer promising alternatives for power grid fault diagnosis, the scarcity of labeled grid data severely hinders the application of AI techniques in this domain. To tackle these issues, this study introduces a semi-supervised learning framework featuring self-supervised learning and an adaptive threshold mechanism (SAT-SSL) designed for fault detection and classification in power grids. Compared with existing approaches, the proposed method substantially reduces reliance on labeled data while maintaining excellent recognition performance. First, a frequency-domain analysis is applied to power grid data to identify and filter anomalous events, which are then classified and labeled based on their visual characteristics, thereby constructing a dedicated power grid dataset. Next, the Yule–Walker algorithm is employed to extract features from the power grid data. A semi-supervised learning framework is subsequently developed, integrating a self-supervised loss and a dynamic threshold mechanism to enhance the model’s information extraction capacity and adaptability across diverse operational scenarios. Finally, the constructed power grid dataset, together with two standard benchmark datasets, is utilized to evaluate the model’s effectiveness. Experimental results demonstrate that the proposed model maintains a low error rate across multiple scenarios and varying label proportions. Specifically, on the power grid dataset, retaining only 5% of the labels yields an error rate of just 6.15%, confirming the method’s ability to achieve precise fault detection and classification even with very limited labeled data.

The shift of electrical power systems toward intelligent smart-grid architectures delivers better dependability, operational performance, and real-time visibility, yet simultaneously heightens exposure to cyber threats. Thanks to the massive amounts of data now generated in smart grids, machine learning techniques have emerged as a particularly promising avenue for bolstering defensive capabilities. While the strengths of machine-learning solutions for cybersecurity improvement are clear, these same models form part of the attack surface and remain especially susceptible to carefully crafted adversarial perturbations. This work analyzes the resilience of autoencoder-based intrusion detection mechanisms in smart grids against adversarial examples. We begin by presenting an original iterative procedure for synthesizing adversarial instances. Next, we demonstrate that a white-box adversary with full knowledge of the autoencoder detector can reliably produce stealthy, misclassifying samples using the proposed approach. Experimental results show that simply starting from basic adversarial seeds is insufficient to generate effective evasions, underscoring the considerable difficulty of constructing potent adversarial examples targeting autoencoder-based anomaly detectors deployed in smart-grid environments.

Effective energy management plays a vital role in improving greenhouse (GH) performance and advancing sustainability. This study introduces an innovative multi-objective optimization strategy specifically developed for GH energy management, aiming to reduce reliance on grid energy while maximizing the battery state of charge (SOC) over a defined time period. The optimization framework incorporates decision variables, including network power, battery power, and battery energy, while respecting constraints on battery capacity, initial energy levels, and the minimum and maximum energy limits of the battery storage system. The performance of a smart energy management system (EMS) is assessed by comparing it against conventional optimization techniques. Critical hyperparameters for the optimization process—such as plateau time, prediction time, and optimization time—are identified using the ellipse optimization technique. By modeling the GH as a microgrid, the study examines relevant energy management metrics and load profiles. Simulations performed in MATLAB Simulink (R2021b) reveal substantial improvements, with the smart EMS reducing the objective function by more than 50% relative to traditional EMS approaches. Furthermore, the proposed EMS demonstrates strong robustness under varying load power and irradiation patterns. Even in partial-shading scenarios, the system remains adaptable, with a maximum increase in the objective function of only 0.35553%. Matching the photovoltaic (PV) system output power to realistic field conditions further confirms the EMS’s practical effectiveness. These results highlight the smart EMS’s capability to optimize energy use in GH settings, paving the way for more sustainable energy management solutions. This work advances energy optimization techniques in agricultural contexts, supporting greater resource efficiency and environmental responsibility.

This investigation explores ways to enhance the performance of ground source heat pump (GSHP) systems and energy piles as part of sustainable urban planning, with special attention to conditions in Southeast Asia. By drawing on stable geothermal heat, GSHPs provide an environmentally friendly alternative to conventional HVAC systems, capitalizing on the near-constant underground temperatures for efficient space heating and cooling. A key factor in boosting system effectiveness lies in thoroughly understanding how heat migrates through soil—particularly the soft marine clay deposits widespread in Southeast Asia. The research uses a one-dimensional finite-difference numerical model to assess the influence of soil thermal conductivity and density on operational outcomes. Results demonstrate that GSHP installations, especially those coupled with energy piles, can dramatically reduce electricity demand and cut greenhouse gas emissions, offering meaningful relief from the urban heat island effect in crowded metropolitan areas. Although the persistently warm and humid regional climate may pose challenges to sustained performance, the work calls for expanded research—including real-world field testing and more refined simulation methods—to fine-tune GSHP arrangements and unlock the full potential of shallow geothermal resources in city settings. These findings on subsurface thermal processes and optimized system configurations help advance climate-resilient, resource-efficient urban infrastructure. 

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.

A wide range of strategies has been proposed to identify and prevent non-technical electricity losses resulting from fraudulent activities. Among these, machine learning algorithms and data-driven methods have become increasingly prominent compared to conventional approaches, owing to their superior performance, leading to growing adoption in recent years. This paper introduces a novel two-step method for detecting fraudulent Non-technical losses (NTLs) in smart grids. The initial step is to transform the time-series data by incorporating additional features extracted from the publicly available State Grid Corporation of China (SGCC) dataset. These features are derived following the detection of sudden changes in electricity consumption patterns, using the sum of finite differences, the Auto-Regressive Integrated Moving Average model, and the Holt-Winters model. Subsequently, five classification models are applied to train and evaluate a fraud detection system using the SGCC dataset. The performance evaluation shows that the Gradient Boosting Machine delivers the best results among the five models. This two-step procedure enables classification models to outperform previously reported top-performing techniques in terms of accuracy, F1-score, and other key metrics for non-technical loss detection.