Search Results (1,004)

Hybrid hydro–solar microgrids offer a practical electrification option for remote and weak-grid communities by combining run-of-river hydropower with photovoltaic generation. However, their performance depends strongly on coordinated decisions across three layers: (i) system sizing and architecture, (ii) turbine selection and rating under variable river flow, and (iii) operational energy dispatch under time-varying solar resource and demand. This paper develops an optimization-driven planning framework for a run-of-river hydro–PV microgrid that co-optimizes component capacities and turbine-related design choices while enforcing time-series operational feasibility. Physics-based component models translate river discharge into hydroelectric output via turbine efficiency characteristics and operating limits, and compute PV generation and storage trajectories under dispatch and state-of-charge constraints. The planning problem is formulated as a multi-objective optimization that quantifies trade-offs among life-cycle cost, supply reliability (e.g., unmet-load metrics), and sustainability indicators (e.g., diesel-free operation or emissions when backup generation is present). A Pareto-optimal set of designs is obtained using a population-based multi-objective algorithm, and representative knee-point (balanced) solutions are selected to illustrate how turbine choice and dispatch strategy interact with seasonal hydrology and solar variability. The proposed approach supports transparent and robust design decisions for hybrid hydro–solar microgrids. Full article

Vehicle-to-grid (V2G) technology has emerged as a key enabler for coupling large-scale electric vehicle (EV) deployment with the operation of smart energy systems. By allowing bidirectional power and information exchange between EVs and the grid, V2G transforms EVs from passive loads into distributed energy resources capable of supporting grid flexibility, reliability, and renewable energy integration. However, the practical realization of V2G remains challenged by technical complexity, system coordination, user participation, and regulatory constraints. This paper presents a comprehensive review of V2G integration from a system-level perspective. Rather than focusing solely on individual technologies, the review examines how V2G is embedded within smart energy systems, emphasizing the interactions among EVs, aggregators, grid operators, energy markets, and end users. Key enabling technologies, including bidirectional charging, aggregation mechanisms, communication frameworks, and data-driven control strategies, are discussed in relation to their system-level roles and limitations. The impacts of V2G on grid operation, energy management, and market participation are analyzed, with particular attention to reliability, battery lifetime, and user trust. Furthermore, this review identifies critical open issues that hinder large-scale deployment, spanning infrastructure readiness, standardization, economic incentives, and cybersecurity. Emerging application scenarios, such as building-integrated V2G, fleet-based services, and artificial intelligence (AI) supported coordination, are also discussed to illustrate potential evolution pathways. By synthesizing technological developments with system-level impacts and unresolved challenges, this paper aims to provide a structured reference for researchers, system planners, and policymakers seeking to advance the integration of V2G into future smart energy systems. Full article

It is critical to accurately estimate the state of charge (SOC) and state of health (SOH) of lithium-ion batteries to ensure the safety and reliability of marine power systems, where the inherent symmetry of lithium-ion battery charge–discharge dynamics is often disrupted. However, the accuracy of conventional methods significantly deteriorates under dynamic current rates induced by fluctuating electrical loads, leading to unreliable SOC and SOH estimates. This article proposes a novel SOC and SOH joint estimation method based on a long short-term memory network with a rate awareness attention mechanism (RAAM-LSTM) and support vector regression optimized by greylag goose algorithm (GGO-SVR). RAAM-LSTM improves SOC estimation accuracy by adaptively weighting enhanced rate-related features. For SOH estimation, the GGO-SVR model incorporates the SOC as a coupling feature and applies physical constraints to ensure consistency with irreversible battery degradation. The comparative experimental results show that the error of the SOC is less than 1.6%, and that of the SOH is less than 0.5%, which are much smaller compared with those of conventional methods. Full article

Electric field distortion remains a fundamental challenge to the operational reliability of HVDC cable accessories, where localized stress intensifies space charge injection and accelerates insulation degradation. While nonlinear conductive composites incorporating functional fillers such as ZnO have shown potential for adaptive field grading, their dynamic interaction with space charge under non-uniform fields has yet to be fully resolved. This study experimentally examines the spatiotemporal evolution of space charge in double-layer dielectric structures comprising linear low-density polyethylene (LLDPE) and ZnO-based nonlinear composites, using the laser-induced pressure pulse (LIPP) technique. Localized field enhancement is introduced via metallic pin defects embedded on the cathode side. Comparative analysis reveals that composites with 40 vol% ZnO microvaristors markedly suppress charge injection compared to conventional semiconductive ethylene-vinyl acetate (EVA) layers. Specifically, interfacial charge accumulation during polarization is reduced by 71%, and residual charge density after depolarization decreases by 88%, leading to a more uniform internal field distribution. These findings provide direct experimental evidence of the field-regulating mechanism of nonlinear composites from the perspective of charge dynamics, supporting their application in intelligent HVDC insulation systems. Full article

Rapid expansion of electric vehicle adoption has led to increased dependence on a charging infrastructure that is tightly integrated with energy distribution systems and digital communication networks. As electric vehicle charging stations evolve into complex cyber–physical systems, cybersecurity risks pose a growing threat to grid reliability and user trust. This paper presents a hybrid decision-support framework for cybersecurity risk assessment in EV charging infrastructure that advances beyond prior multi-criteria decision-making approaches by combining interpretability with data-driven validation. Specifically, the framework integrates the Analytic Hierarchy Process (AHP) for expert-driven weighting of cybersecurity attributes with PROMETHEE for flexible threat prioritization, enabling transparent and auditable risk rankings. The framework categorizes cybersecurity criteria across four infrastructure layers—transmission, distribution, consumer, and electric vehicle charging stations—and assigns relative weights through expert-driven pairwise comparisons. PROMETHEE is then applied to rank potential cyber threats based on these weights, allowing for flexible prioritization of cybersecurity interventions. The methodology is validated using the real-world WUSTL-IIoT-2018 SCADA dataset, which includes simulated reconnaissance (network scanning), device identification, and exploitation attacks. While this dataset does not natively include OCPP 2.0 or ISO 15118 protocols, the experimental results demonstrate strong discrimination power (AUC = 0.99, recall = 95%) and provide a basis for extension to modern EVSE communication standards. The results identify critical metrics such as anomalous source packet behavior and encryption reliability as key vulnerability markers, aligning with documented EV charging attack scenarios. By bridging expert judgment with empirical traffic data, the proposed framework offers both technical robustness and explainability, supporting grid operators, SOC teams, and infrastructure planners in systematically assessing risks, allocating resources, and enhancing the resilience of EV charging ecosystems against evolving cyber threats. Full article

The paper proposed an adaptive preference-based multi-objective optimization framework of intelligent energy management in smart microgrids that are dynamically adapted to operational priorities with regard to real-time grid conditions, stakeholder preferences, and environmental constraints. The suggested hierarchical algorithm combines an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II) with an advanced dynamic preference weight distribution system that can trade off between minimization of operational cost. Reduction of carbon emission, enhancement of voltage stability, enhancement of power quality and maximization of system reliability and adaptability to different operational conditions, such as renewable energy intermittency, demand response schemes and emergencies. The framework presents a new multi-layered preference-learning module that represents the intricate stakeholder priorities in terms of more sophisticated fuzzy logic-based decision matrices, neural network preference prediction, and adaptive reinforcement learning methods and transforms them into dynamic optimization weights with feedback mechanisms. Large-scale simulations on a modified IEEE 33-bus test system coupled with various renewable energy sources, energy storage facilities, electric vehicle charging points, and smart appliances demonstrate superior improvements in performance: 23.7% operational costs reduction, 31.2% carbon emissions reduction, 18.5% system reliability improvement, 15.3% voltage stability increase and 12.8% reduction of deviations in power quality. The proposed system has an adaptive nature with better performance in a variety of operating conditions such as peak demand times, renewable energy intermittency events, grid-connected and islanded operations, emergency load shedding situations, and cyber–physical security risks. The framework is shown to be highly effective under different conditions of uncertainty and variation in parameters and communication delay through intense sensitivity analysis and robustness testing, thus demonstrating its practical applicability in real-world applications of smart grids. Full article

Predicting loan defaults is a significant challenge for financial institutions; however, current machine learning techniques often encounter issues in areas such as data privacy, cross-institutional cooperation, and model transparency. The restrictions on the practical implementation of advanced predictive models are centralized training paradigms, which limit the application of advanced models because of regulatory and confidentiality issues, and black-box decision making, which diminishes confidence in automated credit risk tools. This study mitigates these problems by adopting a federated-inspired decentralized ensemble learning model combined with explainable artificial intelligence (XAI) in predicting loan defaults. Various machine learning classifiers are trained on partitioned institutional data without the need to share any data; they include K-Nearest Neighbors, support vector machine, random forest, and XGBoost. They use a prediction-level aggregation strategy to simulate the collaborative decision-making process without losing locality of data. SHAP and LIME are used to promote model interpretability by giving both global and local explanations of the consequences of prediction. The proposed framework was tested on a large public dataset of loans that contains more than 116,000 records, including various financial and borrower-related features. The experimental findings show that XGBoost has high and reliable predictive accuracy in both centralized and decentralized scenarios, achieving 99.7% accuracy under federated-inspired evaluation. The explanation analysis shows interest rate spread and upfront charges as the most significant predictors of loan default risk. The main contributions of this research are as follows: (i) a privacy-preserving decentralized ensemble learning framework that is applicable in multi-institutional financial contexts, (ii) a detailed analysis of centralized and decentralized predictive performances, and (iii) the pipeline of the XAI, which can be used to increase its transparency and regulatory confidence in automated credit risk evaluation. These results prove that decentralized learning combined with explainable AI can provide high-performing, transparent and privacy-sensitive loan default prediction systems in practice in real-world banking systems. Full article

A reliable and well-planned charging infrastructure is an essential pillar for enabling the widespread adoption of electric vehicles (EVs) and realizing their environmental and economic benefits. Car rental companies are increasingly transitioning towards EV fleets to support sustainability objectives, reduce emissions, and lower operational costs. However, EV charging management in rental car facilities presents unique challenges, including limited parking space, strict vehicle availability requirements, and unpredictable charging demand patterns. This study introduces a data-driven and probabilistic framework to estimate EV charging demand in rental car fleets. The proposed model integrates rental mobility data, vehicle technical specifications, and charging standards and employs Monte Carlo simulation to capture uncertainties in user behavior and charging processes. In addition, a priority-based charging management framework is developed to minimize technical disruptions in the power system, reduce infrastructure costs, and ensure efficient load distribution. The results demonstrate that the proposed framework supports sustainable charging infrastructure planning by improving charger utilization, enhancing grid compatibility, and enabling cost-effective EV fleet operations. Full article

Dynamic Wireless Power Transfer (DWPT) represents a promising solution to advance sustainable electric mobility by reducing vehicle downtime, extending driving range, and mitigating the need for battery oversizing. However, the lack of integrated and flexible experimental testbeds still limits the validation of emerging technologies. This paper presents DEXTER (Development of an Enhanced eXperimental proTotype of wirEless chargeR), a 1:2-scale open platform specifically designed for research on DWPT systems. The setup integrates a three-axis motion control for coil misalignments and trajectory emulation, digitally regulated TX/RX converters, a programmable battery emulator, and electromagnetic shielding coils equipped with field probes. A MATLAB-based interface enables automated testing and Hardware-in-the-Loop (HiL) integration. By combining modularity, scalability, and reproducibility, DEXTER provides a comprehensive framework for experimental optimization of power electronics and electromagnetic design while ensuring compliance with international safety standards. The case studies analyzed here demonstrate the capability of such a platform to validate and optimize the DWPT design choices, checking their impact on the overall performance of these systems. The platform constitutes a reference environment for both academia and industry, supporting the development of next-generation wireless charging systems and contributing to the sustainability and reliability of future electric mobility infrastructures. Full article

Accurate capacity prediction is paramount for ensuring the operational safety and reliability of lithium-ion battery management systems (BMS). Nevertheless, contemporary data-driven approaches often grapple with limited feature representation—frequently relying solely on aggregate charging duration or noise measures—which compromises the robustness of these approaches. To address these limitations, this study proposes a robust framework integrating multi-point voltage temporal sampling (MVTS) with an adaptive gated hybrid ensemble learning strategy. The MVTS method is first used to extract high-dimensional geometric features from the constant-current (CC) charging phase (3.9 V–4.15 V), effectively capturing subtle degradation patterns. Subsequently, an unsupervised isolation forest algorithm is incorporated for automated anomaly detection and rectification, thereby augmenting data stability prior to training. In the fusion stage, a heterogeneous hybrid model comprising eXtreme gradient boosting (XGBoost) and long short-term memory (LSTM) is constructed. An adaptive gating mechanism based on random forest (RF) is added to dynamically weight the base learners. To mitigate data leakage during the stacking process, this study employs an out-of-fold (OOF) training strategy based on leave-one-battery-out (LOBO) cross-validation to generate unbiased meta-features for the gating model. This mechanism dynamically modulates fusion weights contingent upon the multi-point voltage features and model discrepancies, thereby accommodating diverse aging stages and capacity degradation patterns. Experimental results from the NASA battery aging dataset demonstrate that the proposed framework significantly outperforms single-model baselines in terms of RMSE and ${\mathrm{R}}^2$, exhibiting superior adaptability and predictive precision. Full article

The variability of solar energy limits its reliability as a thermal resource, motivating the use of thermal energy storage (TES) to extend heat availability beyond periods of direct irradiance. This study numerically compares latent and sensible TES integrated into a solar dish system from a resource-oriented perspective across representative Mexican climates. Rather than focusing only on stored energy, the analysis evaluates how each storage strategy affects the temporal availability and post-irradiation persistence of usable thermal energy over 24 h charge–discharge cycles. A salt-based PCM (58.1LiNO₃–41.9KCl) was assessed against steel-based sensible storage under identical operating conditions. Under average-day forcing, the minimum PCM mass required to effectively utilize latent heat while sustaining a 320 W test load was found to be 13 kg. Under these conditions, the PCM case showed smoother thermal transients and longer post-irradiation energy availability, enabling nocturnal operation. In contrast, a mass-matched 13 kg steel store showed negligible post-irradiation availability, while a volume-matched 55 kg steel configuration achieved similar nocturnal operation only by substantially increasing mass, with limited improvement in accumulated energy. Hot-day forcing extended the operating window, whereas cold-day forcing yielded negligible charging so that operation could not be sustained within a single daily cycle. Full article

Reliable estimation of the State of Health (SOH) in lithium-ion batteries is critical to battery system security and dependability. However, existing Physics-Informed Neural Networks (PINNs) have drawbacks like single-feature physical constraints, rigid fixed-weight fusion of multi-feature constraints and insufficient time-series degradation modeling. To solve these problems, this study proposes an Adaptive-Weight PINN (AW-PINN) optimized by the Grey Wolf Optimizer (GWO) algorithm, which features a dual-LSTM parallel structure and takes incremental capacity peaks and charged capacity as dual physical constraints. A weight generator LSTM adaptively learns weights for monotonicity losses without manual intervention, and GWO globally optimizes physical loss weights to balance data fitting accuracy and prediction physical consistency. Validated on ${\mathrm{LiCoO}}_2$, NCA, and NCM batteries from CALCE and Tongji University datasets via comparative, ablation, and small-sample experiments, AW-PINN shows superior predictive performance (average RMSE = 0.0076; MAE = 0.0065; MAPE = 0.0072), robustness, and generalization. It integrates battery degradation physics with deep learning, retaining strong fitting capability while enabling physical interpretability. Full article

Lithium-ion batteries (LiBs) are fundamental to modern energy systems, particularly in electric vehicle (EV) applications, due to their high energy density, long cycle life, and low self-discharge characteristics. Accurate State-of-Charge (SoC) estimation is essential for ensuring reliable performance, efficient energy usage, and the safety of Battery Management Systems (BMSs). However, the nonlinear and time-varying characteristics of LiBs, along with the difficulty in directly measuring internal states, pose significant challenges for parameter identification and SoC estimation. This study presents an advanced approach based on the Weighted Mean of Vectors optimization algorithm to simultaneously identify the unknown parameters of an extended Thevenin Equivalent Circuit Model (ECM) and estimate the SoC. Unlike previous methods that use static parameters for specific battery modes, the proposed technique accounts for dynamic changes during both charging and discharging operations. The algorithm demonstrates superior adaptability by continuously adjusting model parameters to reflect real-time battery behavior under varying operational conditions. The algorithm also models the relationship between SoC and open-circuit voltage (Voc) using data collected from real lithium-ion cells tested under a controlled load profile in the laboratory. This experimental validation ensures the practical applicability and robustness of the proposed methodology. The simulation results confirm the effectiveness and precision of the proposed approach, showing excellent agreement between measured and estimated values, with minimal errors in both voltage and SoC prediction. The enhanced accuracy achieved through this dynamic parameter identification framework represents a significant advancement in battery state estimation technology. Full article

The global shift toward electric vehicles (EVs) has accelerated as governments pursue low-carbon transport systems and sustainable mobility transitions. In emerging economies such as Thailand, however, consumer adoption remains influenced by a complex interplay of policy incentives, perceived benefits, and charging-related uncertainties. This study investigates the determinants of EV adoption intention by integrating ecosystem and policy support with perceived value and perceived risk within a unified analytical framework. Grounded in customer perception theory and technology adoption perspectives, this research addresses the fragmented treatment of these factors in prior studies. Data were collected from 400 respondents with prior EV experience and analyzed using structural equation modeling to examine both direct and mediated relationships. The findings reveal that ecosystem and policy support significantly strengthen adoption intention, primarily by enhancing perceived value and reducing perceived risk. These results highlight the pivotal role of perception-based mechanisms in translating policy initiatives into consumer commitment. The study suggests that effective EV promotion in Thailand and similar emerging markets requires coordinated ecosystem development, clear policy communication, and reliable charging infrastructure to sustain long-term adoption momentum. Full article

It is of great significance in real time to accurately monitor the internal state parameters of lithium-ion batteries toy ensure the safety, reliability and lasting efficiency of battery energy storage systems. The battery management system can monitor the working state, prevent overcharge or overdischarge, and make the working process more safe and reliable. The state of charge (SOC) is one of the most important indicators to monitor a working battery, and its accurate estimation is the most important work at present. SOC cannot be measured directly, so the state estimation problem of batteries is transformed into a state estimation problem of time-varying nonlinear systems, the core of which is how to obtain a more accurate and reasonable state estimation value in real time. This paper introduces the definition of battery charge state, summarizes common estimation methods and disadvantages of the ampere-hour integration method and open-circuit voltage method, and finally points out the future development direction of battery charge state estimation methods. Full article