Search Results (106)

Driven by the global energy transition, the rapid expansion of photovoltaic (PV) capacity—particularly in China’s “sand-Gobi-desert” mega-bases—demands highly efficient collection technologies. DC collection, offering low losses, compactness, and high reliability, is emerging as a critical solution for large-scale integration. This paper provides a comprehensive review of PV DC step-up collection systems. First, it analyzes typical network architectures, compares AC versus DC schemes, and examines design constraints imposed by DC bus voltage levels. Second, control strategies are summarized across device, equipment, and system levels. Third, based on engineering practices in ultra-large-scale bases, key challenges regarding fault detection, efficiency optimization, economic viability, and grid code compatibility are identified alongside representative solutions. Finally, future trends in high-voltage hardware maturation, protection bottlenecks, real-time artificial intelligence, and specialized standardization are proposed. This study serves as a vital reference for the topology design and engineering standardization of PV DC collection systems. Full article

Electric buses can help cities address environmental concerns, such as air quality and greenhouse gas emissions, and contribute to a cleaner city. The transition process from conventional fuel buses to electric buses is a growing concern for stakeholders, as industries and governments struggle to nurture the initial phase maturity of electric buses in the marketplace. This research examines the current state and development of electrification in public transport within a city, as well as the challenges and barriers encountered in adopting electric buses for electrification. Present research connects to the experience of cities that have already electrified their urban bus fleets. It relates to the role of charging technologies in cost and the implementation of battery and grid infrastructure in developing countries. It briefly presents the context of the Bus Rapid Transit System use and the electrification of public transport in Ahmedabad. Furthermore, policy recommendations for electric vehicle purchases are outlined based on service levels for sustainable transportation. Full article

Amid the increasing demand to reduce carbon emissions, replacing diesel buses with electric buses has become a key development direction in public transportation. However, a significant challenge in this transition lies in developing efficient charging strategies and accurately determining the required fleet size, as existing research often fails to adequately account for the impact of real-time traffic congestion on energy consumption. To address this gap, in this study, an optimized charging strategy is proposed, and the necessary fleet size is calculated using a deep reinforcement learning (DRL) approach, which integrates actual route characteristics and dynamic traffic congestion patterns into an electric bus operation model. Modeling is conducted based on Beijing Bus Route 400 to ensure the practical applicability of the proposed method. The results demonstrate that the proposed DRL method ensures operational completion while minimizing charging time, with the algorithm showing rapid and stable convergence. In the multi-route scenarios investigated in this study, the DRL-based charging strategy requires 40% more electric buses, with this figure decreasing to 24% when fast-charging technology is adopted. This study provides bus companies with valuable electric bus procurement and route operation references. Full article

The integration of electric buses into urban transportation networks is a priority for policymakers aiming to promote sustainable public mobility. Among available technologies, electric Bus Rapid Transit (eBRT) systems offer an environmentally friendly and operationally effective alternative to conventional modes. This study introduces a Machine Learning Decision Support Framework designed to assess the feasibility of deploying eBRT systems in urban environments. Using a dataset of 28 routes in the Athens Metropolitan Area, the framework integrates diverse variables such as land use, population coverage, proximity to public transport, points of interest, road characteristics, and safety indicators. The XGBoost model demonstrated strong predictive performance, outperforming traditional approaches and highlighting the significance of points of interest, land use diversity, green spaces, and roadway infrastructure in forecasting travel times. Overall, the proposed framework provides urban planners and policymakers with a robust, data-driven tool for evaluating the practical and environmental viability of eBRT systems. Full article

This paper develops and applies an ex-ante methodological framework to assess the societal optimisation of eBRT innovations within the Horizon Europe eBRT2030 project, using Multi-Criteria Decision Analysis (MCDA) and the PROMETHEE method. The study evaluates 11 eBRT innovations to be deployed in five demonstration sites in Europe and one in Colombia. Twenty social parameters, including 10 risks and 10 benefits, were weighted and scored through expert and stakeholder engagement, to calculate the Societal Optimisation Index (SOI). Positive SOI values indicate that societal benefits outweigh risks, and negative values indicate the opposite, while close-to-zero values indicate socially neutral or ambiguous options requiring case-specific judgement. The results indicate that innovations such as Adaptive Fleet Scheduling and Planning, Intelligent Driver Support Systems, and IoT Monitoring Platforms provide strong societal benefits with manageable risks, while charging-related innovations are associated with social concerns. The study emphasises the importance of social impact assessment prior to implementing innovations, to enable inclusive decision-making for policymakers and transport planners and enable the development of socially optimised eBRT systems. Embedding experts’ perspectives and social criteria ensures that technological innovations are aligned with societal needs, assisting the transition towards more equitable, low-carbon transport systems. Full article

The effectiveness of transit-oriented development (TOD) in achieving emission reductions and energy savings is highly influenced by policy frameworks, the accessibility of sustainable transport systems, and the degree of land use integration. This study investigated the implementation of TOD in Dukuh Atas along the Sudirman–Thamrin corridor in Jakarta to assess its role in promoting energy efficiency and lowering emissions. The analysis incorporated carbon emission calculations, annualized traffic volumes, and emissions data, alongside land use metrics such as the floor area ratio (FAR), job-to-housing ratio, and point-of-interest (POI) density. The findings indicate that while TOD implementation in the corridor is still evolving, there were positive outcomes in several key areas. Energy efficiency measures have been partially realized through the operation of electric buses in the bus rapid transit (BRT) system, electrified rail modes, such as commuter lines, mass rapid transit (MRT), and light rail transit (LRT), and improved pedestrian infrastructure, as reflected in a favorable Pedestrian Environmental Quality Index (PEQI). Public transport ridership has significantly increased, contributing to a measurable reduction in emissions from private vehicle use. The land use analysis showed that medium- to high-density housing dominated (78.94% FAR), with a job-to-housing ratio of approximately 1:2. This study also found that the emission estimates were moderately sensitive to changes in both emission factors (EFs) and vehicle kilometers traveled (VKT). Overall, the results suggest that TOD can effectively contribute to energy savings and emission reductions by enhancing public transport usage and reducing dependence on motorcycles. Moreover, the efficacy of modal shifting in the Global South is significantly influenced by population mobility characteristics, which are intricately linked to socio-cultural factors, alongside government initiatives to improve the quality of mass public transportation systems (e.g., integration, availability, service coverage, affordable fares, and inclusive design). Full article

The first extensive Bus Rapid Transit (BRT) system in the Philippines, the EDSA Busway, was put into place as a result of Metro Manila’s ongoing traffic congestion. This study uses an integrated framework that combines cost–benefit analysis (CBA), commuter perception survey, and traffic simulation to assess its economic, social, and environmental implications. The operational viability and traffic impact of the planned Magallanes BRT station were evaluated through simulation using PTV VISSIM. A total of 385 commuters participated in a survey measuring their impressions of safety, accessibility, and satisfaction using a four-point Likert scale. The Busway’s excellent economic feasibility was confirmed by the CBA results, which showed a Benefit–Cost Ratio (BCR) of 15.38 and a Net Present Value (NPV) of ₱778.64 billion. Results from the simulation showed a 24% decrease in PM₂ emissions, a 75% increase in throughput, and a 64% reduction in bus trip time. According to survey results, 61% of commuters said accessibility had improved and 62% said travel satisfaction had increased. The study supports the EDSA Busway’s status as a feasible model for future BRT expansion in Metro Manila and other emerging metropolitan regions by showing how it greatly improves environmental sustainability and mobility efficiency. Full article

To mitigate voltage transients caused by power fluctuations in microgrid systems, this study investigates model predictive control and virtual inertia control for the voltage regulation strategy of energy storage unit converters. By drawing an analogy with the virtual synchronous machine equation in AC systems, the virtual capacitor inertia equation is derived for DC systems. Subsequently, model predictive control (MPC) is integrated with virtual inertia (VI) control, leading to the development of an MPC-VI cooperative control method. The reference value for the inner control loop is computed in real time using model prediction, enabling the injection of a counteracting signal opposite to the direction of DC bus voltage fluctuation during disturbances. This approach effectively suppresses rapid voltage variations and enhances system inertia. Furthermore, by incorporating a threshold-based mechanism, the issue of prolonged dynamic response time is mitigated. Simulation and experimental results demonstrate that, compared to conventional control strategies, the proposed MPC-VI method significantly attenuates instantaneous and severe voltage fluctuations, allowing for a more gradual voltage transition during transient events. Additionally, with the implementation of the threshold equation, the system returns to steady state without notable delay, preserving the droop characteristics of the control scheme. Full article

Grid-connected photovoltaic (PV)–battery systems require advanced control to maintain stable operation, efficient energy exchange, and minimal conversion losses under variable generation and load conditions. This study proposes a dual-loop Energy Management System (EMS) integrated with a Hybrid Grey Wolf Optimizer–Particle Swarm Optimization (GWO–PSO) algorithm for coordinated control of a low-voltage PV–battery–grid system (380 V AC, ≈800 V DC bus). The hybrid optimizer was chosen due to the limitations of standalone GWO and PSO methods, which frequently experience slow convergence and local stagnation; the integrated GWO–PSO strategy enhances both exploration and exploitation during the real-time adjustment of PI controller gains. The rapid inner loop effectively balances instantaneous power among the PV, battery, and grid, while the outer optimization loop aims to minimize the ITAE criterion to enhance transient response. Simulation outcomes validate stable DC-bus voltage regulation, quicker transitions between power import and export, and prompt power balance with deviations maintained below 2.5%, signifying reduced converter losses and improved power-sharing efficiency. The battery’s state of charge is sustained within the range of 20–80%, ensuring safe operational conditions. The proposed hybrid EMS offers faster convergence, smoother power regulation, and enhanced dynamic stability compared to standalone metaheuristic controllers, establishing it as an effective and reliable solution for grid-connected PV–battery systems. Full article

With the rapid development of distribution–microgrid (DN–MG) systems, they have become increasingly important in the construction of modern power systems. However, existing scheduling approaches often overlook the frequency security risks faced by microgrids when transitioning into unintentional islanding during contingencies. To address this issue, this paper proposes a projection-based coordinated scheduling method for DN–MG systems under microgrid frequency security constraints. First, an approximate frequency response curve is derived to characterize the maximum frequency deviation of microgrids after unintentional islanding, which is explicitly embedded into the microgrid optimization model to ensure frequency security. Second, to achieve efficient coordination, a power–energy boundary-based feasible region approximation is proposed for microgrids, which accurately characterizes the projection feasible region under inter-temporal coupling while reducing conservativeness. This enables a non-iterative coordination framework. Finally, case studies on a modified IEEE 33-bus system containing three microgrids demonstrate that the proposed method effectively limits the maximum frequency deviation to within 0.5 Hz, while the projection-based feasible region achieves 87.62% coverage, which is twice that of conventional box approximations. Overall, the proposed method ensures microgrid frequency security while balancing computational efficiency and privacy protection, highlighting its strong potential for practical engineering applications. Full article

The growth of e-commerce has imposed new challenges on urban supply chains, especially in the middle mile, which still lacks structured, sustainable and scalable logistics solutions. This study investigates the feasibility of using the Bus Rapid Transit (BRT) system, widely present in cities of emerging economies, as an urban logistics platform for the transport of light and traceable goods. This research adopts a qualitative approach, with analysis of international experiences and development of a methodological framework based on three main components: technical, economic and governance. The results reveal that the use of idle operating windows, load compatibility and institutional articulation are key factors for the implementation of the system. The proposal represents a logistical innovation aligned with the new paradigms of urban resilience and the multifunctionality of public infrastructure. This study suggests that BRT could serve as a potential logistics platform for the middle mile, under specific operational and governance conditions. Full article

The transportation sector is undergoing a rapid energy transition. Electric Vehicles (EVs) are gradually replacing conventional ones in many different sectors, but battery management still represents a critical limitation of this process. Consequently, research in this area is expanding, aiming to develop solutions that enhance performance while minimizing environmental impact. This study addresses the application of Machine Learning (ML) techniques to estimate the battery State of Charge (SoC) for a full-electric bus fleet operating public service. The methodology is built based on the available driving data disclosed from the fleet monitoring system. The ML methods are assessed starting from model-based (MB) observers assumed as reference and performances are compared upon this basis. The datasets are retrieved from a public repository or derived from real cases, particularly referring to an electric bus fleet operating for an urban public service. The most critical limitation is the absence of the electrical input data coming from the battery, typically required by model-based approaches. Despite this, the proposed ML model achieved sufficient accuracy levels (RMSE < 0.3%) comparable to those of traditional observers. These outcomes demonstrate the potential of data-driven approaches to provide scalable and more straightforward tools for battery monitoring. Full article

Traditional multi-modal dynamic transit assignment (DTA) models predominantly focus on bus and rail systems, overlooking the role of bike-sharing in passenger flow distribution. To bridge this gap, a multi-modal dynamic transit assignment model incorporating bike-sharing (MMDTA-BS) is proposed. This model integrates bike-sharing, buses, rail services, and walking into a unified framework. Represented by the variational inequality (VI), the MMDTA-BS model is proven to satisfy the multi-modal dynamic transit user equilibrium conditions. To solve the VI formulation, a projection-based approach with dynamic path costing (PA-DPC) is developed. This approach dynamically updates path costs to accelerate convergence. Experiments conducted on real-world networks demonstrate that the PA-DPC approach achieves rapid convergence and outperforms all compared algorithms. The results also reveal that bike-sharing can serve as an effective means for transferring passengers to rail modes and attracting short-haul passengers. Moreover, the model can quantify bike-sharing demand imbalances and offer actionable insights for optimizing bike deployment and urban transit planning. Full article

As an emerging technology for sustainable, safe, energy-efficient, and smooth traffic flow, autonomous public transport (APT) has been widely studied in recent years. However, the influence of conventional public transport (CPT) on behavioural intentions toward APT is largely overlooked. While APT is in its nascent phase, users’ choices may be shaped by their perceptions and attitudes toward CPT. Therefore, identifying these perceptions and examining their effect on behavioural intention is crucial. In this study, the Unified Theory of Acceptance and Use of Technology (UTAUT) is integrated with the satisfaction-loyalty model to analyze the key factors influencing behavioural intentions toward APT. To obtain more precise findings, this study examined public transport by type, including rubber-tired systems, urban rail, and bus rapid transit, rather than as a single mode, unlike many previous studies. A survey (n = 1271) was employed to validate the theoretical model among CPT users in Istanbul. The results indicate that loyalty to CPT significantly influences behavioural intention toward APT. Moreover, users of different CPT types have distinct priorities influencing their intention to use APT. While users of rubber-tired systems prioritize effort expectancy, social influence and facilitating conditions, users of urban rail systems consider social influence, trust and loyalty to CPT to be decisive factors. Furthermore, users of bus rapid transit systems consider performance expectancy, effort expectancy, trust, and loyalty to CPT as key factors influencing their behavioural intention. The findings are expected to enrich theoretical research on behavioural intention toward APT and guide future integration and transition between CPT and APT. Full article

This paper examines the advances and challenges of Bus Rapid Transit (BRT) systems in Brazil, considering their potential in promoting sustainable urban mobility. Rapid urbanization and the predominance of private motorized transport have intensified the need for efficient, accessible, and environmentally sound collective transport solutions. BRT has emerged as a cost-effective alternative to rail systems, combining high capacity, lower implementation costs, and operational flexibility. The study focuses on three Brazilian cities (Rio de Janeiro, Belo Horizonte, and Fortaleza) selected for their regional diversity and distinct BRT models. Using the Delphi method, the analysis was structured around three dimensions: road infrastructure, transport planning and networks, and system operation and performance. Results indicate significant progress in terms of exclusive corridors, integration terminals, express services, and the adoption of Intelligent Transport Systems. However, structural gaps persist, particularly regarding incomplete infrastructure, weak integration between trunk and feeder lines, limited monitoring of feeder services, and insufficient adaptation of networks to urban dynamics. The findings highlight that the effectiveness of Brazilian BRT systems depends on strengthening feeder lines, improving physical and fare integration, and expanding sustainable infrastructure. Full article