Search Results (828)

The supply chain ecosystem (SCE) operating within an urban environment is characterised by continuous interactions among manufacturing, logistics, service, information, and energy flows across multiple smart city manufacturing-service clusters (SCMSCs). Within the SCE, intelligent production buildings (IPBs) emerge as multifunctional multistorey production-service infrastructures developed under conditions of limited urban land availability and increasing demand for localised manufacturing and service integration. These buildings operate under heterogeneous and dynamically changing energy-demand conditions, substantially complicating energy consumption management. This study develops a knowledge-driven coordination approach for the energy consumption management of IPBs operating within SCMSCs from the perspective of the urban SCE. IPBs are conceptualised as distributed environments with finite building-level power supply system capacity, where manufacturing, logistics, service, and digital processes dynamically compete for shared energy resources. A hierarchical representation of the SCMSC energy environment is proposed, capturing distributed interactions and heterogeneous electricity-demand profiles across interconnected clusters. An information-analytical system integrating monitoring, data acquisition, analysis, ML-based demand prediction, planning, and decision-support functions is developed to support predictive electricity-demand coordination. The proposed framework combines IoT-enabled monitoring with digital-twin-supported synchronisation of energy states for distributed coordination among IPBs. The proposed approach is evaluated through scenario-based analysis of an IPB operating within an urban manufacturing-service environment. The results indicate the potential of knowledge-driven coordination to improve energy-capacity utilisation, mitigate peak-load formation, and enhance operational stability within SCMSCs. Full article

Ports are progressively evolving from traditional logistics nodes into integrated energy ecosystems, characterised by increasing electrification of maritime and land-based operations, the deployment of renewable energy sources, and the emergence of new and highly variable energy demand profiles. In this context, the main challenge is not only the availability of renewable energy but also the capacity of port energy systems to provide sufficient electrical power, flexibility, and resilience under increasing operational constraints. These issues are particularly relevant in Mediterranean ports, where limited grid capacity, infrastructure constraints, load variability, and interactions with surrounding urban areas strongly influence energy planning strategies. This paper proposes an integrated framework for the development of sustainable port energy hubs based on renewable generation, energy storage, green hydrogen systems, port microgrids, and intelligent energy management strategies (EMS). The main novelty lies in the integration of these energy vectors within a unified framework that explicitly accounts for the specific operational and infrastructure constraints of Mediterranean ports. The proposed approach aims to optimise the interaction between energy production, distribution, storage, and consumption, with particular attention to the role of hydrogen as a long-duration energy storage vector and as an energy carrier for selected port logistics applications. Through a data-driven Port Energy Baseline Assessment (PEBA), port operational characteristics are translated into quantified energy demand and power requirements, providing the basis for power adequacy assessment and the evaluation of alternative transition pathways. An illustrative application to a representative Mediterranean port, characterized by a peak electricity demand of 42 MW, illustrates how the framework quantifies power requirements, assesses power adequacy under infrastructure constraints, and compares alternative transition pathways based on renewable generation, battery storage, and hydrogen. Full article

Electric vehicles (EVs) are crucial for mitigating greenhouse gas emissions in urban transportation. However, their integration requires efficient charging infrastructure and allocation strategies. In this paper, five heuristic algorithms were developed to allocate EVs to urban charging stations. This allocation process incorporates critical constraints, such as user preferences and charger type compatibility, while respecting station capacities governed by power output rules. The proposed methods include four initial allocation heuristics, ranging from capacity-centric and nearest-neighbor approaches to random assignments, complemented by a local search algorithm for solution refinement. To evaluate these heuristics, an optimization model minimizing station establishment and vehicle travel costs was adapted from the literature. Computational experiments were performed on both synthetic instances and real-world case studies. The results indicate that the developed heuristics, especially when enhanced by local search, deliver high-quality, near-optimal solutions within highly competitive computational times. Consequently, this study offers a scalable decision-support tool for urban planners, demonstrating how the joint optimization of infrastructure costs and user preferences can foster sustainable urban mobility and accelerate EV adoption. Ultimately, these findings offer actionable insights for scaling heterogeneous EV infrastructure, fostering urban sustainability and mitigating transport-related carbon emissions. Full article

Power quality degradation in low-voltage (LV) distribution networks remains insufficiently documented in many rapidly urbanizing African cities despite its significant impact on electrical equipment, service reliability, and network operation. This study investigates the following research question: To what extent does the power quality of an urban low-voltage distribution network comply with international standards, and which network characteristics are most strongly associated with the observed disturbances? To address this question, an extensive field measurement campaign was conducted from October 2024 to February 2025 on five radial feeders supplied by the Babemba medium-voltage/low-voltage (MV/LV) substation in Lubumbashi, Democratic Republic of the Congo. Electrical parameters were monitored using a Class B Chauvin Arnoux Qualistar C.A. 8331 power quality analyzer and evaluated against internationally recognized power quality standards. The measurements revealed persistent power quality degradation characterized by chronic under-voltage, with prolonged voltage levels below 207 V, typical deviations ranging from −20% to −30%, and voltage dips reaching 70–80% of the nominal voltage during peak loading conditions. Power supply continuity was also severely affected, with a System Average Interruption Frequency Index (SAIFI) of 7.85 interruptions/year and a System Average Interruption Duration Index (SAIDI) of 491 min/year, while a medium voltage outage lasting approximately 48 h highlighted the limited resilience of the distribution system. Additional disturbances included phase voltage imbalance reaching 18%, neutral currents up to 327 A, and short-term flicker values (Pst) approaching 1.5, exceeding the recommended comfort threshold. Overall, the observed disturbances were associated with heterogeneous feeder loading conditions, network configuration, non-standard electrical connections, and documented physical deterioration of the infrastructure. This study provides a comprehensive field-based assessment of power quality and service continuity in an urban LV distribution network in the Democratic Republic of the Congo and offers a quantitative basis for prioritizing feeder reinforcement, phase balancing, infrastructure rehabilitation, and the establishment of continuous local power quality monitoring. Full article

Triboelectric nanogenerators (TENGs) offer promising opportunities for distributed wind energy harvesting owing to their low-speed responsiveness, structural flexibility, and adaptability to non-stationary airflow. This review examines wind-driven TENGs from the perspective of realistic wind-field constraints, focusing on three representative scenarios: urban micro-winds, offshore wind–wave environments, and low-altitude complex flows. Scenario-specific advances in device architectures, materials and interfaces, environmental protection, power management, and system integration are systematically reviewed. Representative devices are further quantitatively compared in terms of wind-speed range, activation threshold, electrical output, power density, durability, and system-level energy delivery. Particular attention is given to inconsistent definitions of cut-in wind speed, output normalization, electrical loading, and validation conditions that limit cross-study comparison. Field-validation evidence is assessed from controlled laboratory tests to long-term field operation. Key challenges involving usable regulated energy, environmental reliability, lifetime prediction, array scaling, sustainability, and deployment economics are critically discussed. Finally, five grand challenges with actionable milestones are proposed to facilitate the transition of wind-driven TENGs from laboratory prototypes toward deployable distributed micro-energy systems. Full article

This paper aims to design and optimize a new type of Darrieus H-Rotor VAWT, specifically adapted to the conditions of urban and industrial environments, as a contribution to energy transition and the search for new sustainable solutions for decentralized electricity generation. It is designed to be a strong and efficient wind turbine capable of providing a nominal power of 2 kW at low and moderate wind speed. The methodology used is based on the analysis of wind resources (wind rose, Weibull distribution) and aerodynamic modelling using QBlade. The structural analysis with CATIA showed that the configuration of the third case (with 4 mm blade thickness and four supports of 2 mm) was an excellent compromise, with a reduced mass (13 kg) and a controlled maximum stress (0.854 MPa), far below the elastic limit of aluminum. From an aerodynamic point of view, CFD simulations in ANSYS Fluent 2023 R1 (k-ω SST model in transient regime) allowed us to visualize flow fields, pressure distribution and torque evolution. The results obtained showed a power coefficient (Cp) very close to 0.4, validating the configuration. This proves the chosen configuration to be effective. In this work, a suitable wind blade has been optimized with a strong and aerodynamically efficient design for operation in urban areas. Full article

Fuel cell electric vehicles (FCEVs) require energy management strategies that can balance hydrogen economy, battery utilization, component protection, and real-time control under varying driving conditions. This paper proposes an Adaptive-Modality Deep Deterministic Policy Gradient and Model Predictive Control hierarchical energy management strategy (AMDDPG–MPC HEMS). In the proposed architecture, the upper-level AMDDPG controller identifies driving-condition patterns and generates adaptive weights for hydrogen consumption, battery power, and state-of-charge regulation, while the lower-level MPC controller performs constrained power allocation between the fuel cell and battery. To improve adaptability, the AMDDPG algorithm incorporates an adaptive modality perception mechanism that extracts driving-condition features and a multi-scale reward mechanism that coordinates short-term energy-saving objectives with long-term component-protection requirements. A dedicated weight-scheduling and switching mechanism is also introduced to ensure smooth transitions between operating conditions. The proposed strategy is evaluated under the World Light Vehicle Test Cycle and Urban Dynamometer Driving Schedule and compared with rule-based, equivalent consumption minimization, and fixed-weight MPC strategies. The results show that the AMDDPG–MPC HEMS achieves the lowest equivalent hydrogen consumption, with reductions of 18.853% and 11.732% relative to the rule-based strategy under the two driving cycles, respectively. It also improves fuel-cell operating efficiency and maintains feasible battery SOC regulation. These results demonstrate the effectiveness and engineering potential of the proposed hierarchical energy management strategy. Full article

Reliable pack-level battery systems capable of safely handling instantaneous high-C-rate discharge above 10C during take-off, climb, and hovering are required for the commercialization of urban air mobility (UAM) aircraft. However, pack-level studies on wide-range C-rate characteristics of battery systems for UAM applications remain very limited, and most previous studies have been restricted to single-cell experiments or battery-pack simulations. In this study, a 24S1P test battery pack using nickel–cobalt–manganese (NCM) pouch cells, with a nominal voltage of 88.8 V and a capacity of 22 Ah, was designed and fabricated. A two-level battery management system (BMS) based on the LTC6803G-4 was also developed. To evaluate the charge–discharge characteristics of the battery system, constant-current discharge tests were conducted under five conditions ranging from 0.2C (4.4 A) to 10.68C (235 A), and charging tests were performed over the range of 0.2C–2C. The discharge test results showed that the capacity retention remained within 97.5–100.0% in the 1C–5C range, confirming excellent power capability. Continuous discharge operation was confirmed at 10.68C, the maximum discharge condition considered for vertical take-off and climb. Under this condition, the capacity decreased to 16.26 Ah, corresponding to 74.2% of the rated capacity, owing to internal-resistance-induced voltage drop, electrochemical polarization, and early attainment of the cut-off voltage. The Peukert exponent was estimated to be 1.113. An apparent pack-level direct-current internal resistance (DCIR) of approximately 40.3 mΩ was estimated from the initial voltage-drop analysis under different discharge-current conditions. In addition, the maximum temperature during 10.68C discharge was measured as 55.1 °C, providing a thermal margin of 4.9 °C relative to the operational temperature limit of 60 °C adopted in this study. Finally, a 24S4P battery system with a capacity of 88 Ah, consisting of four 24S1P battery packs connected in parallel, was installed in the VS-210, a 210 kg-class maximum take-off weight (MTOW) eVTOL aircraft. An in-flight test was conducted by repeating six take-off–hovering–landing cycles during a total test session of 15 min 20 s, and a stable propulsion power supply was maintained throughout all flight cycles. This study provides experimental baseline data for the design and preliminary safety assessment of high-power battery systems for UAM applications by presenting both the electrical and thermal characteristics of a 24S NCM battery pack over a wide discharge-rate range of 0.2C–10.68C and in-flight eVTOL data. Full article

Urban road transport policies must simultaneously address climate mitigation, local air quality, and the infrastructure requirements associated with vehicle electrification. However, these dimensions are rarely evaluated within a unified city-level framework. This study develops an integrated assessment framework that combines a bottom-up co-source inventory of CO₂ and seven air pollutants, Long-range Energy Alternatives Planning (LEAP)-based scenario modeling, policy contribution analysis, elasticity-based co-benefit assessment, and electric vehicle charging demand estimation for Zhengzhou, China. In 2022, the road transport sector consumed 10,178 ktce of energy and emitted 27.8 Mt of CO₂. Private cars contributed 66.7% of CO₂ emissions, whereas heavy- and medium-duty trucks and light-duty trucks contributed 48.1% and 27.9% of NOx emissions, respectively, collectively accounting for 76.0% of the total. Under the existing policy scenario (EPS), CO₂ emissions increase to 45 Mt in 2030 and 55 Mt in 2040. Under the dual carbon scenario (DCS), emissions peak at approximately 36 Mt in 2030 and decline to 32 Mt by 2040, representing reductions of 20% and 42% relative to the EPS, respectively. Electric vehicle promotion and green transport development contribute 42% and 32% of peak-year CO₂ mitigation. Policy effectiveness differs across emission types. Electric vehicle promotion and green public transport are relatively more effective for CO₂ mitigation, whereas old vehicle retirement, motorcycle phase-out, light-truck electrification, and tighter emission standards provide greater air pollutant reduction benefits. Supporting an electric vehicle stock of approximately 1.22 million in 2030 would require about 610,000 charging piles at a vehicle-to-charger ratio of 2:1. The principal contribution of this study is to demonstrate how complementary vehicle technology, transport structure, emission control, power sector, and infrastructure policies can be combined to support city-level carbon peaking and air pollution co-control. Full article

Underground cables are critical infrastructure for electrical power and communication transmission, and their reliable operation is of paramount importance to urban public safety. Although Distributed Acoustic Sensing (DAS) enables wide-range, continuous, and real-time monitoring, traditional DAS signal processing methods suffer from poor intrusion discrimination and weak anti-interference capability. To address these limitations, we propose a dual-branch network based on multi-domain feature fusion, integrating a Multilayer Perceptron, a Long Short-Term Memory network (LSTM), and an attention mechanism. Vibration signals corresponding to four representative high-risk intrusion events were acquired through controlled field experiments, and a standardized, category-balanced dataset was constructed accordingly. Time-domain, frequency-domain and joint time-frequency features were extracted and mapped through a time-frequency weighting transformation to form one branch of the network, while the parallel branch employed an LSTM to capture long-range temporal dependencies. A multi-head attention mechanism enables deep adaptive fusion of two types of modal information and overcomes the limitations of conventional simple feature concatenation. Comparative experiments against KNN, 1D-CNN and LSTM baselines demonstrate that the proposed model achieves a test accuracy of 98.89%, outperforming all reference methods. Ablation studies further validate the necessity and effectiveness of each constituent module within the proposed architecture. The results indicate that this approach provides reliable support for DAS-based online monitoring of power cables against external damage. Full article

The transport sector contributes nearly 20% of New Zealand’s total greenhouse gas emissions, making it crucial for interventions to meet the 2050 net-zero target. Transitioning to electric vehicles (EVs) presents a sustainable solution but poses challenges in electricity distribution due to unpredictable EV charging behaviours. This research addresses these challenges by developing three mathematical models that optimise EV charging patterns, manage power flow along distribution lines and incorporate battery storage systems. Using the Tāmaki area as a case study, the models analyse total energy demand and optimal battery storage size, revealing that a 3.49 MWh battery system could mitigate the projected 2040 peak daily grid energy demand of 541.5 MWh and avoid costly power line upgrades. The study also introduces a vehicle-to-grid (V2G) integration model, showcasing its potential to reduce grid dependence and improve energy utilisation. The findings provide critical insights for Auckland’s electricity distribution companies, supporting strategic asset upgrades and offering evidence-based guidance for government policies on EV adoption. In summary, this research provides innovative solutions for optimising EV charging infrastructure, benefiting utility companies and policymakers by informing data-driven decisions. The comprehensive approach, which includes power flow, battery storage, and V2G technology, presents a scalable framework for international cities facing similar challenges, promoting global sustainable transport solutions towards achieving international climate targets and sustainable urban development. Full article

Cooling electricity consumption in central air-conditioning systems of large public buildings accounts for a substantial share of urban electricity use and is strongly influenced by outdoor meteorological conditions. Under increasingly frequent extreme summer heat events, accurate cooling-load forecasting is important for HVAC operation, building energy management, urban electricity security, and power-system planning. This study investigates the effects of measured outdoor meteorological inputs on cooling-load forecasting for a large public building in Guangzhou. Consecutive hourly cooling-load data and measured meteorological data, including outdoor air temperature, relative humidity, solar radiation, wind speed, and wind direction, were collected from June to September 2022. The corresponding 2023 dataset was analyzed separately using the same modeling and evaluation procedure to assess cross-year repeatability; data from the two years were not combined. Correlation and univariate linear regression analyses were first used for preliminary candidate-input screening. Nine Long Short-Term Memory sub-models with different meteorological input combinations were then developed and compared using the 2022 dataset, and the selected input configuration was subsequently re-evaluated using the separate 2023 dataset. Solar radiation exhibited the strongest marginal association with cooling load, followed by outdoor air temperature and relative humidity. The negative association of relative humidity reflected its coupled variation with temperature and solar radiation during the investigated summer period. For the 2022 dataset, the model using outdoor air temperature, relative humidity, and solar radiation achieved the lowest MAPE. Compared with the model using all five meteorological variables, it reduced MAE, RMSE, and MAPE by 14.55%, 7.24%, and 19.07%, respectively, while R² increased from 0.9542 to 0.9601. Evaluation using the 2023 dataset showed corresponding reductions of 20.01%, 18.37%, and 25.81% in MAE, RMSE, and MAPE, respectively, together with an increase in R² from 0.9592 to 0.9708. Full article

The growing reliance of Internet of Energy (IoE)-enabled smart city infrastructures has significantly intensified the complexity of modern urban energy environments due to the integration of renewable resources, electric vehicles, and interconnected IoT devices. These highly dynamic environments introduce critical challenges related to power stability, operational reliability, and intelligent energy management. Therefore, developing accurate, adaptive, and explainable monitoring frameworks has become essential for ensuring resilient urban energy infrastructures. This paper proposes an Explainable Artificial Intelligence (XAI)-driven deep learning framework integrating Multi-Head Attention and SHapley Additive exPlanations (SHAP) for intelligent power stability monitoring in IoE-enabled smart cities. The proposed framework employs an attention-based deep learning architecture to classify stable and unstable operational conditions using multivariate operational power parameters. Furthermore, SHAP-based explainability analysis is incorporated to improve model transparency and identify influential operational factors affecting stability behavior. Using the Electrical Grid Stability Simulated Dataset, the proposed framework achieved 97.35% accuracy and 0.997 ROC-AUC, outperforming several traditional machine learning and deep learning baselines. The explainability results show that temporal response parameters have the strongest impact on stability decisions. This work offers not only high predictive performance but also valuable interpretability, which is essential for practical deployment in real-world smart city energy systems. Full article

This study investigates the long-run relationship between socio-economic inequality, public service provision, infrastructure development, structural transformation, and quality of life in Central Asia, focusing on Kazakhstan, Kyrgyzstan, Tajikistan, and Uzbekistan over the 2000–2024 period. Using panel data from the World Development Indicators, the analysis applies a comprehensive econometric strategy including panel unit root tests, Pedroni cointegration tests, FMOLS, DOLS, CCR estimators, and a Panel ARDL-PMG framework for robustness. The study examines how inequality, education and health expenditure, ICT development, electricity access, economic growth, and urbanization jointly shape quality-of-life outcomes measured by life expectancy. The empirical results confirm the existence of a stable long-run equilibrium relationship among the variables. Urbanization emerges as the most powerful determinant of quality of life, indicating a highly elastic welfare response to spatial structural transformation. ICT development and education expenditure consistently exert positive effects, highlighting the importance of digital connectivity and human capital accumulation in improving welfare outcomes. In contrast, health expenditure, electricity access, and economic growth display negative long-run relationships, suggesting that these variables often reflect structural inefficiencies, transitional dynamics, or non-inclusive growth patterns in the region. The Gini coefficient shows a positive association with quality of life, which is interpreted in the context of structural transformation and sectoral reallocation processes in transition economies. Robustness checks using Panel ARDL-PMG confirm both long-run equilibrium relationships and heterogeneous short-run dynamics, with evidence of adjustment processes toward equilibrium. The findings suggest that improvements in quality of life in Central Asia are driven primarily by structural transformation, digital development, and education investment rather than aggregate economic growth alone. The study contributes to the literature by providing a comprehensive multi-country panel analysis of Central Asia, integrating infrastructure, inequality, and structural change within a unified framework. The results offer important insights into the mechanisms through which development processes translate into welfare outcomes in transition economies. Full article

Electric scooters (e-scooters) and electric bicycles (e-bikes) are established urban micromobility modes, and children and adolescents form a growing share of riders. This narrative review synthesises peer-reviewed evidence on e-scooter and e-bike injuries in patients aged 18 years or younger, covering epidemiology, mechanisms, injury patterns, clinical outcomes, comparisons with conventional devices, and prevention. Each source was classified as dedicated paediatric evidence, mixed-age evidence with extractable paediatric results, adult or mixed-age evidence used only for context, or an adult-dominated systematic review, so that the basis of every claim is explicit. The reported burden has risen across national surveillance data and single-centre series, and one population-adjusted analysis found injury rates increasing by 293% for e-bikes and by 88% for powered scooters between 2019 and 2022. Most studies lack exposure denominators, so exposure-adjusted paediatric risk remains poorly quantified and rising counts cannot be equated with rising risk. Injuries affect adolescent boys disproportionately and peak at ages 11 to 14 years. Extremity and soft-tissue injuries predominate, while a clinically important minority sustain traumatic brain injury, craniofacial and dental trauma, or severe multisystem injury. Low helmet use and higher device speeds are consistently associated with more severe outcomes, although observational designs cannot establish causal effects and device type is confounded with rider age and road exposure in every available paediatric comparison. Powered devices are associated with greater severity than non-powered counterparts. Speed limitation and helmet promotion appear promising, but paediatric-specific effectiveness evidence remains limited. Prospective paediatric research incorporating exposure denominators is the priority. Full article