Search Results (252)

The increasing integration of renewable energy sources and the widespread adoption of electric vehicles have introduced considerable uncertainty into the operation of large-scale power systems. Traditional deterministic unit commitment models are insufficient for managing such variability in a reliable and cost-effective manner. This study proposes a two-stage stochastic unit commitment model that captures uncertainties in solar photovoltaic generation, electric vehicle charging demand, and load fluctuations using a mixed-integer linear programming framework with recourse. The model is applied to Thailand’s national power system, comprising 171 generators across five regions, to assess its scalability for sustainable large-scale planning. Results indicate that the stochastic model significantly enhances system reliability across most demand profiles. Under the Winter Weekday group, the number of lacking scenarios decreases by 76.92 percent and the number of missing periods decreases by 78.57 percent, while the average and maximum lack percentages are reduced by 56.32 percent and 72.61 percent, respectively. Improvements are even greater under the Rainy Weekday group, where lacking scenarios and periods decline by more than 92 percent and the maximum lack percentage falls by over 98 percent, demonstrating the model’s robustness under volatile solar output and load conditions. Although minor anomalies are observed, such as slight increases in average and maximum lack percentages in the Summer Weekday group, these are minimal and likely attributable to randomness in scenario generation or boundary effects in optimization. Overall, the stochastic model provides substantial advantages in managing uncertainty, achieving notable improvements in reliability with only modest increases in operational cost and computational time. The findings confirm that the proposed approach offers a robust and practical framework for supporting sustainable and resilient power systems in regions with high variability in both generation and demand. Full article

For electric vehicles widely used recently, lithium-ion batteries serve as the primary energy storage units, affecting the vehicles’ performance, safety, and lifespan. Accurate state of charge (SOC) estimation is pivotal for the battery management system (BMS) to enhance the predictability of the vehicle’s range and avert thermal runaway due to improper charging methods. In this study, an adaptive SOC estimation methodology was developed using parameter identification with forgetting factor recursive least squares (FFRLS). These parameters are then incorporated into a dual adaptive extended Kalman filter (DAEKF) for SOC estimation under varying load conditions. DAEKF is used to dynamically adjust the covariance matrices for process and measurement noises, significantly enhancing the filter’s adaptability and precision. The integration of FFRLS and DAEKF enables a robust SOC estimation of electric vehicles, featuring rapid computation speeds, high accuracy, and excellent adaptability, positioning them as ideal candidates for enhancements in battery management system technology. Full article

The global transition toward renewable energy and the electrification of transportation is imposing unprecedented power quality (PQ) challenges on modern distribution networks, rendering traditional governance models inadequate. To bridge the existing research gap of the lack of a holistic analytical framework, this review first establishes a systematic diagnostic methodology by introducing the “Triadic Governance Objectives–Scenario Matrix (TGO-SM),” which maps core objectives—harmonic suppression, voltage regulation, and three-phase balancing—against the distinct demands of high-penetration photovoltaic (PV), electric vehicle (EV) charging, and energy storage scenarios. Building upon this problem identification framework, the paper then provides a comprehensive review of advanced mitigation technologies, analyzing the performance and application of key ‘unit operations’ such as static synchronous compensators (STATCOMs), solid-state transformers (SSTs), grid-forming (GFM) inverters, and unified power quality conditioners (UPQCs). Subsequently, the review deconstructs the multi-timescale control conflicts inherent in these systems and proposes the forward-looking paradigm of “Distributed Dynamic Collaborative Governance (DDCG).” This future architecture envisions a fully autonomous grid, integrating edge intelligence, digital twins, and blockchain to shift from reactive compensation to predictive governance. Through this structured approach, the research provides a coherent strategy and a crucial theoretical roadmap for navigating the complexities of modern distribution grids and advancing toward a resilient and autonomous future. Full article

Accurate State of Charge (SOC) estimation for retired power batteries remains a critical challenge due to their degraded electrochemical properties and heterogeneous aging mechanisms. Traditional methods relying solely on electrical parameters (e.g., voltage and current) exhibit significant errors, as aged batteries experience altered internal resistance, capacity fade, and uneven heat generation, which distort the relationship between electrical signals and actual SOC. To address these limitations, this study proposes a surface temperature-assisted SOC estimation method, leveraging the distinct thermal characteristics of retired batteries. By employing infrared thermal imaging, key temperature feature regions—the positive/negative tabs and central area—are identified, which exhibit strong correlations with SOC dynamics under varying operational conditions. A Gated Recurrent Unit (GRU) neural network is developed to integrate multi-region temperature data with electrical parameters, capturing spatial–temporal thermal–electrical interactions unique to retired batteries. The model is trained and validated using experimental data collected under constant current discharge conditions, demonstrating superior accuracy compared to conventional methods. Specifically, our method achieves 64.3–68.1% lower RMSE than traditional electrical-parameter-only approaches (V-I inputs) across 0.5 C–2 C discharge rates. Results show that the proposed method reduces SOC estimation errors compared to traditional voltage-based models, achieving RMSE values below 1.04 across all tested rates. This improvement stems from the model’s ability to decode localized heating patterns and their hysteresis effects, which are particularly pronounced in aged batteries. The method’s robustness under high-rate operations highlights its potential for enhancing the reliability of retired battery management systems in secondary applications such as energy storage. Full article

Distributed generation strengthens distribution network reliability by placing generators close to load centers. The integration of electric vehicle charging stations (EVCSs) with PV systems mitigates the effects of EV charging burden. In this research, the objective is to combineEVCSs with distributed generation (DG) units in the Puducherry smart grid system to obtain optimized locations and enhance their reliability. To determine the right nodes for DGs and EVCSs in an uneven distribution network, the modified decision-making (MDM) algorithm and the model predictive control (MPC) approach are used. The Indian utility 29-node distribution network (IN29NDN), which is an unbalanced network, is used for testing. The effects of PV systems and EVCS units are studied in several settings and at various saturation levels. This study validates the correctness of its findings by evaluating the outcomes of proposed methodological approaches. DIgSILENT Power Factory is used to conduct the simulation experiments. The results show that optimizing the location of the DG unit and the size of the PV system can significantly minimize power losses and make a distribution network (DN) more reliable. Full article

The rapid growth of electric vehicles (EVs) and new energy systems has put lithium-ion batteries at the center of the clean energy change. Nevertheless, to achieve the best battery performance, safety, and sustainability in many changing circumstances, major innovations are needed in Battery Management Systems (BMS). This review paper explores how artificial intelligence (AI) and digital twin (DT) technologies can be integrated to enable the intelligent BMS of the future. It investigates how powerful data approaches such as deep learning, ensembles, and models that rely on physics improve the accuracy of predicting state of charge (SOC), state of health (SOH), and remaining useful life (RUL). Additionally, the paper reviews progress in AI features for cooling, fast charging, fault detection, and intelligible AI models. Working together, cloud and edge computing technology with DTs means better diagnostics, predictive support, and improved management for any use of EVs, stored energy, and recycling. The review underlines recent successes in AI-driven material research, renewable battery production, and plans for used systems, along with new problems in cybersecurity, combining data and mass rollout. We spotlight important research themes, existing problems, and future drawbacks following careful analysis of different up-to-date approaches and systems. Uniting physical modeling with AI-based analytics on cloud-edge-DT platforms supports the development of tough, intelligent, and ecologically responsible batteries that line up with future mobility and wider use of renewable energy. Full article

This paper suggests a perspective-controlled solution for an integrated Infrared micro-/nanoplastic spectroscopy/photometry-based detection, from the diffraction up to the geometry etendue, with the aim of yielding a universal spectrometer/photometer. Spectrophotometry, unlike spectroscopy that shows the interaction between matter and radiated energy, is a specific form of photometry that measures light parameters in a particular range as a function of wavelength. The solution, meant for diffraction grating and geometry etendue of the display unit, is provided by a controller that tunes the grating pitch to accommodate any emitted/transmitted wavelength from a sample made of microplastics, their degraded forms and their potential retention, and ensures that all the diffracted wavelengths are concentrated on the required etendue. The purpose is not only to go below the current Infrared limit of $20\mathsf{\mu }\mathrm{m}$ microplastic size, or to suggest an Infrared spectrophotometry geometry capable of detecting micro- and nanoplastics in the range of ($1\mathrm{nm}$–$20\mathsf{\mu }\mathrm{m}$) for integrated nano- and micro-scales, but also to transform most of the pivotal components to be directly wavelength-independent. The related controlled geometry solutions, from the controlled grating slit-width up to the controlled display unit etendue functions, are suggested for a wider generic range integration. The results from image-size characterization show that the following charge-coupled devices, nanopixel CCDs, and/or micropixel CCDs of less than $100\mathrm{nm}$ are required on the display unit, justifying the Infrared micro- and nanoplastic-integrated spectrophotometry, and the investigation conducted with other electromagnetic spectrum ranges that suggests a possible universal spectrometer/photometer. Full article

The rapid proliferation of electric vehicles necessitates accurate forecasting of charging pile capacity for urban power system planning, yet existing methods for medium- to long-term prediction lack effective mechanisms to capture complex multi-factor relationships. To address this gap, a hybrid cointegration–BiLSTM framework is proposed for medium- to long-term load forecasting. Cointegration theory is leveraged to identify long-term equilibrium relationships between EV charging capacity and socioeconomic factors, effectively mitigating spurious regression risks. The extracted cointegration features and error correction terms are integrated into a bidirectional LSTM network to capture complex temporal dependencies. Validation using data from 14 cities in Hunan Province demonstrated that cointegration analysis surpassed linear correlation methods in feature preprocessing effectiveness, while the proposed model achieved enhanced forecasting accuracy relative to conventional temporal convolutional networks, support vector machines, and gated recurrent units. Furthermore, a 49% reduction in MAE and RMSE was observed when ECT-enhanced features were adopted instead of unenhanced groups, confirming the critical role of comprehensive feature engineering. Compared with the GRU baseline, the BiLSTM model yielded a 26% decrease in MAE and a 24% decrease in RMSE. The robustness of the model was confirmed through five-fold cross-validation, with ECT-enhanced features yielding optimal results. This approach provides a scientifically grounded framework for EV charging infrastructure planning, with potential extensions to photovoltaic capacity forecasting. Full article

This study presents a comprehensive multi-objective optimization framework for optimal placement and sizing of distributed generation (DG) units in electric bus (E-bus) transit systems integrated with a high-power flash charging infrastructure. An enhanced Multi-Objective Grey Wolf Optimizer (MOGWO), utilizing Euclidean distance-based Pareto ranking, is developed to minimize power loss, voltage deviation, and voltage violations. The framework incorporates realistic E-bus operation characteristics, including a 31-stop, 62 km route, 600 kW pantograph flash chargers, and dynamic load profiles over a 90 min simulation period. Statistical evaluation on IEEE 33-bus and 69-bus distribution networks demonstrates that MOGWO consistently outperforms MOPSO and NSGA-II across all DG deployment scenarios. In the three-DG configuration, MOGWO achieved minimum power losses of 0.0279 MW and 0.0179 MW, and voltage deviations of 0.1313 and 0.1362 in the 33-bus and 69-bus systems, respectively, while eliminating voltage violations. The proposed method also demonstrated superior solution quality with low variance and faster convergence, requiring under 7 h of computation on average. A five-method compromise solution strategy, including TOPSIS and Lp-metric, enabled transparent and robust decision-making. The findings confirm the proposed framework’s effectiveness and scalability for enhancing distribution system performance under the demands of electric transit electrification and smart grid integration. Full article

Triboelectric nanogenerators (TENGs) hold significant potential for decentralized energy harvesting; however, their dependence on rotational mechanical energy often limits their ability to harness ubiquitous horizontal motion in real-world applications. Here, a single horizontal linear-to-rotational triboelectric nanogenerator (SHLR-TENG) is presented, designed to efficiently convert linear motion into rotational energy using a robust gear system, enabling a high voltage and reliable full cycle of alternating current (AC). The device features a radially patterned disk with triboelectric layers composed of polyimide. The SHLR-TENG achieves a peak-to-peak voltage of 1420 V, a short-circuit current of 117 µA, and an average power output of 41.5 mW, with a surface charge density of 110 µC/m². Moreover, it demonstrates a power density per unit volume of 371.2 W·m⁻³·Hz⁻¹. The device retains 80% efficiency after 1.5 million cycles, demonstrating substantial durability under mechanical stress. These properties enable the SHLR-TENG to directly power commercial LEDs and low-power circuits without the need for energy storage. This study presents an innovative approach to sustainable energy generation by integrating horizontal motion harvesting with rotational energy conversion. The compact and scalable design of the SHLR-TENG, coupled with its resilience to humidity (20–90% RH) and temperature fluctuations (10–70 °C), positions it as a promising next-generation energy source for Internet of Things (IoT) devices and autonomous systems. Full article

The rapid growth of electric vehicle (EV) adoption necessitates advanced energy management strategies to ensure sustainable, reliable, and efficient operation of charging infrastructure. This study proposes a hybrid AI-based framework for optimizing residential EV charging systems through the integration of Reinforcement Learning (RL), Linear Programming (LP), and real-time grid-aware scheduling. The system architecture includes smart wall-mounted chargers, a 120 kWp rooftop solar photovoltaic (PV) array, and a 60 kWh lithium-ion battery energy storage system (BESS), simulated under realistic load conditions for 800 residential units and 50 charging points rated at 7.4 kW each. Simulation results, validated through SCADA-based performance monitoring using MATLAB/Simulink and OpenDSS, reveal substantial technical improvements: a 31.5% reduction in peak transformer load, voltage deviation minimized from ±5.8% to ±2.3%, and solar utilization increased from 48% to 66%. The AI framework dynamically predicts user demand using a non-homogeneous Poisson process and optimizes charging schedules based on a cost-voltage-user satisfaction reward function. The study underscores the critical role of intelligent optimization in improving grid reliability, minimizing operational costs, and enhancing renewable energy self-consumption. The proposed system demonstrates scalability, resilience, and cost-effectiveness, offering a practical solution for next-generation urban EV charging networks. Full article

The accelerated growth of the transport sector has increased oil consumption and greenhouse gas (GHG) emissions, intensifying global environmental challenges. The electrification of transportation has emerged as a key strategy to achieve sustainability targets, with electric vehicles (EVs) expected to account for 50% of global car sales by 2035. However, widespread adoption requires smart infrastructure capable of enabling dynamic in-motion charging. In this context, Electric Road Systems (ERSs), particularly those based on Wireless Power Transfer (WPT) technologies, offer a promising solution by transferring energy between road-embedded transmitters and vehicle-mounted receivers. This study assesses the structural response and service life of conventional and electrified asphalt pavement sections representative of the Spanish road network. Several standard pavement configurations were analyzed under heavy traffic (dual axles, 13 tons) using a hybrid approach combining mechanistic–empirical multilayer modeling and three-dimensional Finite Element Method (FEM) simulations. The electrified designs integrate prefabricated charging units (CUs) placed at a 9 cm depth, disrupting the structural continuity of the pavement. The results reveal stress concentrations at the CU–asphalt interface and service life reductions of up to 50% in semiflexible pavements. Semirigid sections performed better, with average reductions close to 40%. These findings are based on numerical simulations of standard Spanish sections and do not include experimental validation. Full article

The increasing adoption of electric vehicles (EVs) presents a growing demand for efficient, real-time battery monitoring systems. Many existing Battery Management Systems (BMS) with built-in Controller Area Network (CAN) communication are often expensive or lack user-friendly interfaces for displaying data. Moreover, integrating such BMS units with standard Human–Machine Interface (HMI) displays remains a challenge in cost-sensitive applications. This article presents the design and development of an interface for integrating the BMS of electric vehicles with the ATD3.5-S3 display using the CANopen protocol. The system enables the real-time visualization of essential battery parameters, including voltage, current, temperature, and state of charge (SOC) percentage. The proposed system utilizes a JK BMS, an ESP32 microcontroller, and a TJA1051 CAN transceiver to convert UART data into CAN Open messages. The design emphasizes affordability, modular communication, and usability in EV applications. Testing under various load conditions confirms the system’s stability, reliability, and suitability for practical use in electric vehicles. Full article

Due to the high demand for fuel efficiency, electric vehicles have come into the picture, as they only use batteries to power the vehicle. This requires constant charging of the batteries at charging stations, which are costly and impractical to install. But it is possible to install charging stations by making use of photovoltaic (PV) cells and demagnetization currents to self-charge batteries under stand-still conditions. The design of a bidirectional converter with asymmetrical half-bridge converter based on a switched reluctance motor (SRM) drive, using PV for electric vehicles, is implemented in this paper. It consists of developing a control unit (GCU), Li-ion battery pack, and photovoltaic (PV) solar cells that are integrated with a bidirectional converter and asymmetrical half-bridge converter (AHBC) to provide power to the SRM drive. The solar-assisted SRM drive can be operated in either the motoring mode or charging mode. In the motoring-mode GCU, the battery or PV energy can be used in any combination to power the SRM. In the charging-mode PV, the GCU and AC grids are used to charge the battery under stand-still conditions. This work helps in the self-charging of batteries using either the GCU or PV cells, as well as aids in the improvement in the performance characteristics. Also, this work compares the performance metrics for the proposed system and conventional system. The performance of the drive system using PV cells/GCU is evaluated and verified through MatLab/Simulink and experimental results. Full article

This research focuses on optimizing a grid-connected hybrid microgrid system (HMGS) for The Neotia University (TNU), West Bengal, India, utilizing renewable energy sources to improve sustainability and energy efficiency. The system integrates solar panels, wind turbines, and an existing diesel generator (DG) to meet campus energy demands, including electric vehicle (EV) charging facilities for residents and staff. The pelican optimization algorithm (POA) is employed to determine the optimal capacity of PV and wind turbine units for reducing energy costs, enhancing reliability, and minimizing carbon emissions. The results reveal a substantial decrease in the cost of energy (COE) from INR 11.74/kWh to INR 5.20/kWh. Full article