Search Results (1,448)

With the penetration of renewable power generation (RPG) in the distribution network (DN), power quality issues caused by RPG fluctuations have become more prominent than ever, let alone the integration of new types of power loads like electrified trains and electric vehicles that are major harmonic sources. Traditional power quality enhancement approaches are mostly dedicated to the smoothing of RPG power output or active compensation of harmonics, but fail to incorporate both routines into one single power quality enhancement scheme. Out of this research motivation, this paper aims to propose a synergetic allocation scheme for the hybrid energy storage system (HESS) and the unified power quality conditioner (UPQC) to achieve superior power quality enhancement. Firstly, a novel comprehensive vulnerability index of the DN suited for the power quality issues is presented to reflect the key factors that may impact the bus voltage security. Afterwards, the capacity specifications of HESS and UPQC for power smoothing and load side harmonic compensation are deduced with variational mode decomposition and inverter capacity configurations. Subsequently, the synergetic allocation method of HESS and UPQC are proposed by formulating an optimization problem, with the former obtained capacity specifications acting as the main constraints. After that, a dynamic hourly network reconfiguration approach is proposed to enhance the vulnerability level of the DN by dynamically changing its topology, and ensuring better power quality with the optimally allocated HESS and UPQC. Finally, simulation tests and comparative studies are conducted to evaluate the effectiveness and performance of the proposed scheme by comparing with existing methods. The comparative study has shown that the proposed method can reduce bus voltage deviation by 2.63%; meanwhile, it can reduce the total harmonic distortion by 1.83%. Full article

The electrification of public transport with battery electric buses (BEBs) poses technical, regulatory, and environmental challenges. This paper analyzes the impact of BEB charging on a Brazilian urban medium-voltage (MV) feeder using a novel methodology to convert utility GIS data into OpenDSS simulation models. The study utilizes Geographic Database of the Distribution Company (BDGD) data from the Brazilian Electricity Regulatory Agency (ANEEL) and OpenDSS simulations. Motivated by Cuiabá’s proposal to electrify its public bus fleet, four realistic scenarios were simulated, incorporating distributed photovoltaic (PV) generation and vehicle-to-grid (V2G) operation. Results show that up to 118 BEBs can be charged simultaneously without voltage violations. However, thermal overload occurs beyond 56 units, requiring conductor upgrades or load redistribution. PV systems can supply up to 64% of the daily energy demand but introduce reverse power flows and overvoltages, indicating the need for dynamic control. V2G operation enables peak shaving but also leads to overvoltages when more than 33 buses inject power concurrently. The findings suggest that while the current infrastructure partially supports fleet electrification, future scalability depends on integrating smart grid features and reinforcing the system. Although focused on Cuiabá, the methodology offers a replicable approach for low-carbon urban mobility planning in similar developing regions. Full article

The penetration rate of battery electric buses (BEBs) continues to rise, and the design of BEB networks has become the foundation for establishing efficient and sustainable public transportation systems. Improving the equity of bus network and reducing the total cost of the bus system are taken as the targets, a multi-objective programming model for TNDP is proposed in this study. Among them, the Gini coefficient of bus travel times during peak hours and the direct travel proportion of the elderly during non-peak hours are used to describe the equity of the bus network. When calculating the comprehensive cost, factors such as the fleet size of battery electric buses, charging facilities requirements, and charging costs are taken into account. To enhance the reliability of the obtained results, the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is adopted to generate the Pareto-optimal solution set. The Mandl’s benchmark network is used for comparative validation, and a case study based on the road network of Zhengzhou is undertaken. Calculation results indicate that the proposed model not only minimizes the total travel costs but also significantly reduces the Gini coefficient of the transportation mode distribution. Under the constraint of overall expenses, it effectively improves the equity and the direct travel proportion of the elderly served by the bus system. The results can provide quantitative support to formulate livelihood transportation policies for local government and optimize the allocation of public transportation resources. Full article

Electricity theft is a major source of non-technical losses in distribution networks, threatening both economic revenues and power supply reliability. This study addresses the identification of nodes exhibiting anomalous load behavior (anomalous nodes) in 10 kV distribution feeders. Based on the IEEE-33 bus benchmark system, the disturbance patterns induced by abnormal consumption are analyzed. The results show that voltage and current fluctuations intensify with increasing electrical distance from the power source, while branch loss peaks localize at the affected terminals and propagate unidirectionally along the power flow path. Building on these findings, an improved density-based spatial clustering of applications with noise (DBSCAN) method is proposed, integrating five spatial network features and sixteen temporal electrical features extracted from voltage, current, and power series. Prior to clustering, the features are standardized and reduced via principal component analysis (PCA), retaining over 90% of the cumulative variance. Validation on a hybrid dataset demonstrates that the proposed method achieves 90.7% accuracy, 87.5% recall, and an F1-score of 0.895, outperforming traditional K-means and approaching supervised CNN models without requiring labeled data. These results confirm the method’s robustness and suitability for practical deployment in distribution networks. Full article

Cities are increasingly using electric buses as a viable alternative to diesel buses. This is a crucial undertaking to achieve sustainability in the transport sector. However, integrating them in transport systems in developing countries such as Sri Lanka, which is characterized by environmental and economic challenges, is complex. This work examines the factors that influence the shift from diesel to electric buses with particular attention to the stakeholders, their motivations, and how they seek to achieve their objectives regarding each other, both conflicting and cooperative angles. This study adopts a comprehensive stakeholder-centric methodology to analyze electric bus adoption in the public transit system in Sri Lanka. The research employs a mixed-methods approach that combines qualitative stakeholder analysis with quantitative barrier prioritization, following established project management principles. Based on the case study of Sri Lanka, the research investigates how the electric bus transition can be expedited by leveraging such alliances while considering local challenges like infrastructural deficits, policy gaps, and funding limitations. Lessons learned and best practices from international case studies are considered to provide strategic recommendations to policymakers and other stakeholders to promote the electric bus. By mapping out the interactions between various stakeholders and outlining where key leverage exists, the research provides a roadmap for introducing electric buses. This will be aligned with the sustainability targets and the vision to deliver sustainability goals for the long term. Full article

The increasing integration of decentralized technologies such as photovoltaic (PV) systems and electric vehicles (EVs) poses significant challenges to the reliable operation of radial distribution grids. In this paper, we study Active Power Curtailment (APC), which is a cost-effective method that maintains grid safety by temporarily reducing power injections. However, APC can place disproportional curtailment burden on grid buses that may in fact undermine the continuous adoption of PVs and EVs. In this work, we propose different novel APC methods that incorporate fairness properties for radial grids with PVs, EVs, and battery energy storage systems (BESSs). In addition, we integrate BESSs and show their benefits in lowering APC levels and achieving better PV and EV utilization while enhancing fairness. The proposed APC designs allow for fast decision making and can be generalized to unseen grids. To do so, a two-step solution is adopted, where in the first step, a reinforcement learning (RL)-based agent determines uniform per-feeder APC and BESS actions, and in the second step, heuristic controllers disaggregate these actions into tailored per-bus decisions while incorporating fairness features. Through simulations, the controllers are shown to mitigate over $99\%$ of constraint violations and significantly enhance fairness in curtailment distribution. BESSs are shown to improve the violations count and APC trade-off, leaning towards reduced APC percentages. Finally, we exemplify how the solution generalizes effectively to unseen grid configurations. Full article

With the rapid development of modern society and the continuous growth of electricity demand, the stability of power systems has become increasingly critical. In particular, Transient Stability Assessment (TSA) plays a vital role in ensuring the secure and reliable operation of power systems. Existing studies have employed Graph Attention Networks (GAT) to model both the topological structure and vertex attributes of power systems, achieving excellent results under ideal test environments. However, the continuous expansion of power systems and the large-scale integration of renewable energy sources have significantly increased system complexity, posing major challenges to TSA. Traditional methods often struggle to handle various disturbances. To address this issue, we propose a graph attention network framework with multi-branch feature aggregation. This framework constructs multiple GAT branches from different information sources and employs a learnable mask mechanism to enhance diversity among branches. In addition, this framework adopts an uncertainty-aware aggregation strategy to efficiently fuse the information from all branches. Extensive experiments conducted on the IEEE-39 bus and IEEE-118 bus systems demonstrate that our method consistently outperforms existing approaches under different disturbance scenarios, providing more accurate and reliable identification of potential instability risks. Full article

In this article, an approach to balance the State of Charge (SoC) of two batteries connected to the DC bus of a fuel cell (FC) electric aircraft by Droop-controlled converters is described. The proposed algorithm is based on shifting the Droop reference voltages and prevents the simultaneous charging and discharging of the batteries. This approach is not only practical but also highly versatile, as it is compatible with all converters as long as the Droop voltage can be changed remotely, and a current measurement is provided to a central controller. No further programming access to the DC/DCs is necessary. There is no need for nonlinear or different-valued Droop resistances for charging and discharging. The balancing approach is validated via simulation in MATLAB/Simulink 2024a.The results show that the proposed approach achieves SoC balancing without degrading the dynamic performance of the grid. The delays added by the slower communication with the central controller have a minimal impact on performance. Full article

Automated, electric shuttles are expected to be key for the future of public transportation, providing a safe, efficient, and robust operation with a minimum carbon footprint. However, in complex, urban environments, their reliable operation is particularly challenging and shows a lack of performance and comfort. This study presents a quantitative benchmark of an automated shuttle compared to a conventional, human-operated bus on the same route. Speed and acceleration across geofenced segments are systematically analyzed based on over 12 million GNSS and IMU data points. The results show that the automated shuttle operates at about half the average speed of the bus. Furthermore, frequent abrupt decelerations are reducing passenger comfort, while the main distributions and mean values of the measured acceleration indicate a smooth operation of the automated shuttle; outliers reveal critical braking events. The presented methodology enables objective performance tracking and supports the iterative improvement of autonomous shuttles through datadriven optimization. Full article

Modern power distribution systems are increasingly stressed as they operate closer to their voltage stability limits, driven by growing electricity demand, complex load behaviors, and the evolving structure of power networks. Radial distribution systems, in particular, are highly susceptible to voltage instability under critical loading conditions, where even minor load increases can trigger voltage collapse. Such events threaten the continuity and quality of power supply and can cause damage to infrastructure and sensitive equipment. While large-scale cascading failures are typically associated with transmission systems, localized cascading effects such as sequential voltage drops, feeder outages, and protective device operations can still occur in distribution networks, especially under high loading. Therefore, reliable and timely voltage stability assessment is essential to maintain system reliability and prevent disruptions. This study presents a comprehensive comparative analysis of four voltage stability indices designed for radial distribution networks. The performance of these indices is evaluated on the IEEE 33-bus and 69-bus test systems under various critical loading conditions and multiple static load models, including Constant Power Load (CPL), Constant Current Load (CIL), Constant Impedance Load (CZL), Composite Load (COML), and Exponential Load (EXL). The analysis investigates each index’s effectiveness in identifying voltage collapse points, estimating critical load levels, and calculating load margins, while also evaluating their robustness across diverse operating scenarios. The findings offer practical insights and serve as a valuable benchmark for selecting suitable voltage stability indicators to support monitoring and planning in modern distribution networks. Full article

European energy and climate policies have enabled reductions in greenhouse gas emissions across many sectors, with transport standing out as an exception. In this area, one of the most promising solutions is the electrification of vehicles. In urban contexts, the shift towards electrifying transport—particularly local public transport (LPT)—can yield significant benefits, especially when paired with an increasingly decarbonized electricity mix, effectively reducing tailpipe emissions of both greenhouse gases and other pollutants. Nevertheless, it is essential to assess whether eliminating tailpipe emissions simply shifts environmental impacts to other stages of a vehicle’s life cycle. The Life Cycle Assessment (LCA), employing a comprehensive cradle-to-grave approach, serves as the principal tool for such evaluations. In this framework, this study focuses on the Italian situation by using a dynamic LCA for the electricity mix. Results show that the electric bus reduces the impact on climate change (28.5 gCO₂eq/pkm vs. 66.7 gCO₂eq/pkm for Diesel, −57%), acidification, photochemical ozone formation, particulate matter, and the use of fossil resources. However, it presents higher impacts in terms of human toxicity (both carcinogenic and non-carcinogenic) and the use of mineral and metal resources, mainly due to battery production and the use of metals such gold, silver, and copper. Full article

This study aims to provide an efficient framework for the coordinated integration of AC and DC chargers, intermittent solar Photovoltaic (PV) Distributed Generation (DG) units, and a Distribution Static Compensator (DSTATCOM) across residential, commercial, and industrial zones of a Radial Distribution Network (RDN) considering the benefits of various stakeholders: Electric Vehicle (EV) charging station owners, EV owners, and distribution network operators. The model uses a multi-zone planning method and healthy-bus strategy to allocate Electric Vehicle Charging Stations (EVCSs), Photovoltaic Distributed Generation (PVDG) units, and DSTATCOMs. The proposed framework optimally determines the numbers of EVCSs, PVDG units, and DSTATCOMs using Harris Hawk Optimization, considering the maximization of techno-economic benefits while satisfying all the security constraints. Further, to showcase the benefits from the perspective of EV owners, an EV waiting-time evaluation is performed. The simulation results show that integrating EVCSs (with both AC and DC chargers) with solar PVDG units and DSTATCOMs in the existing RDN improves the voltage profile, reduces power losses, and enhances cost-effectiveness compared to the system with only EVCSs. Furthermore, the zonal division ensures that charging infrastructure is distributed across the network increasing accessibility to the EV users. It is also observed that combining AC and DC chargers across the network provides overall benefits in terms of voltage profile, line loss, and waiting time as compared to a system with only AC or DC chargers. The proposed framework improves EV owners’ access and reduces waiting time, while supporting distribution network operators through enhanced grid stability and efficient integration of EV loads, PV generation, and DSTATCOM. Full article

This research addresses the complex challenge of integrating modern public transport into historic medieval city centers. These unique urban environments are characterized by narrow streets, protected heritage status, and topographical constraints, which are incompatible with conventional transit vehicles. The introduction of standard bus routes often aggravates traffic congestion and fails to meet the specific mobility needs of residents and visitors. This paper suggests that autonomous electric buses represent a viable and sustainable solution, capable of navigating these constrained environments while aligning with modern energy efficiency goals. The central challenge lies in the optimal selection of an autonomous electric bus that can operate safely and efficiently within the tight streets of historic city centers while satisfying the travel demands of passengers. To address this, a comprehensive study was conducted, analyzing resident mobility patterns—including key routes and hourly passenger loads—and the specific geometric constraints of the road network. Based on this empirical data, a vehicle dynamics model was developed in Matlab^®. This model simulates various operational scenarios by calculating the instantaneous forces (rolling resistance, aerodynamic drag, inertial forces) and the corresponding power required for different electric bus configurations to follow pre-established speed profiles. The core of this research is an optimization analysis, designed to identify the balance between minimizing total energy consumption and maximizing the quality of passenger service. The findings provide a quantitative framework and clear procedures for urban planners to select the most suitable autonomous transit system, ensuring that the chosen solution enhances mobility and accessibility without compromising the unique character of historic cities. Full article

In the realm of sustainable public transportation, the integration of intelligent electric bus propulsion systems represents a novel and promising approach to reducing environmental impact—particularly through the mitigation of NOx emissions and overall exhaust pollutants. This emerging technology underscores the growing need for advanced drive control architectures that ensure not only operational safety and reliability but also compliance with increasingly stringent emissions standards. The present article introduces an innovative analysis of energy-optimized dual-drive electric propulsion systems, with a specific focus on their potential for real-world application in emission-conscious urban mobility. A detailed dynamic model of a dual-drive electric bus was developed in MATLAB Simulink, incorporating a Fuzzy Logic-based decision-making algorithm embedded within the Transmission Control Unit (TCU). The proposed control architecture includes a torque-limiting safety strategy designed to prevent motor overspeed conditions, thereby enhancing both efficiency and mechanical integrity. Furthermore, the system architecture enables supervisory override of the Fuzzy Inference System (FIS) during critical scenarios, such as gear-shifting transitions, allowing adaptive control refinement. The study addresses the unique control and coordination challenges inherent in dual-drive systems, particularly in relation to optimizing gear selection for reduced energy consumption and emissions. Key areas of investigation include maximizing efficiency along the motor torque–speed characteristic, maintaining vehicular dynamic stability, and minimizing thermally induced performance degradation. The thermal modeling approach is grounded in integral formulations capturing major loss contributors including copper, iron, and mechanical losses while also evaluating convective heat transfer mechanisms to improve cooling effectiveness. These insights confirm that advanced thermal management is not only vital for performance optimization but also plays a central role in supporting long-term strategies for emission reduction and clean, efficient public transportation. Full article

The transition to sustainable urban mobility requires innovative solutions optimising electric vehicle (EV) ecosystems while integrating seamlessly with smart urban grids. This paper proposes a decentralised framework leveraging adaptive algorithms, vehicle-to-grid (V2G) technology, and renewable energy prioritisation to enhance urban sustainability without requiring new infrastructure. By integrating federated learning (FL) for privacy-preserving coordination, multi-objective optimisation for load balancing, and predictive models for renewable energy integration, our approach addresses energy demand, grid stability, and environmental impact in urban areas. Validated through simulations on an IEEE 39-bus urban feeder and real-world urban mobility case studies, the framework achieves a 40% reduction in carbon emissions, improves grid reliability by 20%, and enhances renewable utilisation by 25% compared to an uncoordinated charging baseline. These outcomes support urban planning by informing smart grid design, reducing urban heat island effects, and promoting equitable mobility access. This work provides actionable strategies for policymakers, urban planners, and energy providers to advance more sustainable, electrified urban ecosystems. Full article