Search Results (1,169)

Permanent Magnet Synchronous Motors (PMSMs) are widely employed in electric vehicle (EV) propulsion systems because of their high efficiency, high power density, and superior dynamic performance. The performance of field-oriented control (FOC)-based PMSM drives strongly depends on accurate tuning of the proportional–integral (PI) controllers governing the speed and current loops. Conventional tuning approaches often optimize a single performance index and therefore fail to simultaneously enhance the dynamic behavior of all control loops. This paper proposes a multi-error Integral of Time-weighted Absolute Error (ITAE)-based optimization framework for simultaneous tuning of the PI controllers by minimizing a composite objective function that incorporates the time-weighted absolute errors of the rotor speed, q-axis current, and d-axis current. To validate the effectiveness and optimizer independence of the proposed framework, five metaheuristic optimization algorithms—Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Gray Wolf Optimizer (GWO), Gazelle Optimization Algorithm (GOA), and White Shark Optimization (WSO)—are evaluated under identical optimization settings. MATLAB/Simulink simulations are performed for reference-speed tracking, load disturbance rejection, and variable-speed operation. The results demonstrate that the proposed optimization framework consistently improves tracking accuracy and dynamic response regardless of the selected optimizer, while WSO provides the best overall performance. In the variable-speed tracking scenario, WSO achieved the lowest RMSE of 0.96 rad/s and the minimum ITAE value of 0.1716, confirming its effectiveness as the most suitable optimizer for the proposed framework in high-performance PMSM drive applications. Full article

The coordinated operation of interconnected multi-carrier energy hubs is a key enabler of cost-efficient and flexible energy management in modern smart cities. This paper develops a comprehensive optimization framework for the day-ahead scheduling of interconnected energy hubs in residential and commercial sectors. The problem is formulated as a mixed-integer linear programming (MILP) model that jointly manages electricity, natural gas, and thermal energy flows. To enhance operational flexibility, the proposed model incorporates demand response programs for both electrical and thermal loads, multiple energy storage technologies, and electric vehicles with vehicle-to-grid (V2G) capability. Six operating scenarios are defined to assess the impact of different resources and coordination levels, ranging from independent hub operation to fully integrated interconnected scheduling. Simulation results show that coordinated operation of the energy hubs, supported by flexible loads, storage systems, and electric vehicles, can significantly reduce total daily operating costs compared with conventional standalone configurations. The findings confirm that energy exchange among hubs, combined with demand-side flexibility and EV participation, improves both economic performance and system efficiency. The proposed framework offers a scalable scheduling approach for future integrated multi-energy systems. Full article

Continuous carbon anode roasting in industrial parks requires a stable high-temperature heat supply and remains highly dependent on grid electricity and natural gas. However, existing energy-system studies rarely coordinate hydrogen production and storage, volumetric hydrogen blending, and temperature-graded waste-heat recovery under continuous production constraints. To address this gap, this study proposes an electricity–heat–gas–hydrogen integrated energy system for carbon anode industrial parks and develops a 24 h multi-objective scheduling model. The model coordinates heat demands at different temperature levels with hourly electricity and hydrogen flows, using surplus photovoltaic power to produce hydrogen for later high-load periods. The selected scheme achieves a daily volumetric hydrogen-blending ratio of 10.79%, with an operating cost of 82,985.75 CNY and carbon emissions of 54,371.53 kg. Relative to an otherwise equivalent non-hydrogen configuration, hydrogen coupling provides additional reductions of 7.2% in operating cost and 2.3% in carbon emissions. Compared with a basic conventional configuration, operating cost and carbon emissions decrease by 27.9% and 26.0%, respectively. Cascade recovery also increases the daily waste-heat utilization rate by approximately 30 percentage points. These results show that the proposed scheduling framework can coordinate hydrogen utilization and graded waste-heat recovery while maintaining continuous carbon anode production. Full article

Integrated electricity–gas–heat systems require coordinated scheduling while limiting data sharing and representing network constraints. This paper develops a day-ahead model incorporating reactive power, voltage magnitudes, network losses, demand response, and CHP/P2G coupling. Piecewise linearization and second-order cone relaxation reformulate the model as a mixed-integer second-order cone program, while a hierarchical ATC–ADMM method coordinates the electricity–heat and natural gas subsystems by exchanging coupling variables. Residual checks verify approximation accuracy and original equation feasibility. In the test system, ATC–ADMM reached consensus within five iterations, with a total-cost deviation of 0.0075% from centralized optimization, whereas ATC did not converge within 500 iterations. Coordinated operation reduced the total cost by 1.13%, and Shapley allocation benefited both subsystems. Increasing demand-side flexibility from 5% to 9% reduced the total cost by 0.88% and wind curtailment from 6.02% to 4.86%; increasing reactive compensation from 40% to 60% reduced the total cost by 0.41% and wind curtailment to 5.70%. The results reveal non-monotonic penalty-update effects and diminishing marginal benefits of flexibility resources, providing guidance for parameter selection and capacity allocation. Full article

Food spoilage along the supply chain represents a major global challenge, contributing to economic losses, environmental impacts, and food safety concerns. Graphene-based materials have emerged as promising platforms for real-time food freshness monitoring owing to their high surface area, electrical conductivity, chemical sensitivity, and compatibility with flexible sensing architectures. This review critically examines graphene-based sensing strategies for food freshness and spoilage monitoring, including chemiresistive, dielectric, field-effect, optical/fluorescence, photoelectrochemical, mass-sensitive, and colorimetric approaches. Representative sensing platforms are compared in terms of analytical performance, including detection range, limit of detection, selectivity, response and recovery times, calibration, reproducibility, and stability. Particular attention is given to machine-learning-assisted sensing, multifunctional platforms for temperature, humidity, and gas monitoring, and the challenges associated with real-world implementation, including environmental interference, sensor fouling, signal drift, long-term stability, and cross-matrix validation. Finally, commercialization readiness, integration into intelligent packaging, and safety and regulatory considerations related to graphene-based food-contact applications are discussed. Overall, the review identifies the main technological gaps and future research priorities toward robust, scalable, and practical graphene-based systems for real-time food freshness monitoring. Full article

This research presents a comprehensive diagnosis of Ecuador’s electricity generation (2003–2024), focusing on structural vulnerabilities, fuel dependence, and Greenhouse Gas (GHG) emissions from electricity generation. The technological composition of the grid, including installed and effective capacity, as well as the share of fossil and organic fuels, is analyzed to construct a coherent analytical framework. GHGs (i.e., CO₂, CH₄, and N₂O) are estimated from fuel consumption in Non-Conventional Renewable Energy (NCRE) and Non-Renewable Energy (NRE), in accordance with the 2006 IPCC Guidelines. Conversion to CO₂ equivalent utilizes the AR5 GWP factors, in agreement with AR6. The results present annual series for each gas and their corresponding CO₂ equivalents, showing that NREs dominate the CO₂ profile, while NCREs contribute significantly to CH₄ and N₂O. Despite the expansion of installed capacity, a gap persists with effective capacity, reflecting the structural vulnerabilities of Ecuador’s electricity generation system, including exposure to hydrological variability (Kraftnōt, referring in this research to electricity shortages caused by reduced hydropower generation under adverse hydrological conditions), insufficient thermal backup, a high concentration of hydropower plants, fluctuating fossil fuel subsidies, and limited diversification. This manuscript provides a technical and quantitative basis using annual CO₂, CH₄, and N₂O values and their CO₂ equivalents to inform future decarbonization scenarios that strengthen NCRE integration within international climate commitments and a sustainable electricity transition. Full article

Accurate joint forecasting of electricity, cooling, heating, and gas loads is essential to the coordinated operation of integrated energy systems. However, multivariate energy load sequences exhibit strong cross-carrier coupling, non-stationarity, and heterogeneous fluctuation characteristics, which limits the performance of conventional independent forecasting and fixed-model approaches. To address these challenges, this study proposes a joint load forecasting framework that integrates tabular Q-learning-assisted multivariate variational mode decomposition, sample-entropy-based reconstruction, and dynamic model selection. First, tabular Q-learning is employed to select the MVMD penalty factor and the four load sequences are synchronously decomposed to preserve the coupling relationships among components with common center frequencies. Second, sample entropy is used to reconstruct the decomposed modes into high-frequency, low-frequency, and residual subsequences, thereby reducing forecasting complexity while retaining relevant temporal features. Third, a dynamic model selection mechanism evaluates SVR, BiLSTM, XGBoost, and LightGBM and assigns an appropriate predictor to each reconstructed subsequence according to its forecasting performance. The framework is evaluated using daily electricity, cooling, heating, and gas load data collected from the Tempe Campus of Arizona State University from 2016 to 2020. A rolling input window of 56 days is used to forecast the subsequent seven days. Compared with the benchmark methods, the proposed framework achieved the best overall composite performance and competitive forecasting accuracy across the four load types. These results provide a potentially useful forecasting basis for operational decision-making in integrated energy systems. Full article

With the acceleration of the global energy transition, hydrogen is increasingly considered a potential energy carrier for renewable-energy integration, large-scale energy storage, and industrial decarbonization. Proton exchange membrane (PEM) water electrolysis is well suited to variable renewable power because of its fast load response, wide operating range, and compact system structure. However, most existing studies focus on steady-state performance, materials, or model-based analysis, while system-level experimental data on the dynamic behavior of industrial-scale PEM water electrolysis systems remain limited. In this study, the dynamic performance of a 50 kW-class PEM water electrolysis system was experimentally investigated under stepwise load changes, pressure variation, and cold-start conditions. The responses of voltage, temperature, pressure, hydrogen-in-oxygen (HTO), oxygen-in-hydrogen (OTH), and system energy consumption were analyzed. The voltage followed current step changes within seconds, indicating a fast electrical response. In contrast, the thermal response was much slower, and the system required approximately 34 min to approach the rated thermal condition from a cold start. The gas-composition measurements exhibited minute-scale response delays and gradual settling after changes in operating conditions. When the operating pressure increased from 1.2 MPa to 2.9 MPa, the HTO content increased from 0.383% to 0.545%. When the current increased from 300 A to 1200 A, the OTH content decreased from 1001.77 ppm to 5.86 ppm. Energy-flow analysis showed that the total system power consumption under full-load operation was 69.7 kW, including the electrolyzer-related part and balance-of-plant consumption. These results clarify the different response time scales of electrical, thermal, and gas-composition variables in a 50 kW-class PEM water electrolysis system and provide experimental support for dynamic operation under variable renewable power input. Full article

The rapid adoption of electric vehicles, together with increased battery mass and altered load distribution, is placing greater demands on ride comfort and suspension adaptability, while controller optimization may still request forces beyond the instantaneous capability of the physical semi-active actuator if experimentally supported force limits are not explicitly enforced. This study proposes a unified experimentally constrained optimization framework for a magnetorheological damper (MRD)-based semi-active suspension system using a two-degree-of-freedom quarter-car model. The damper is characterized at eleven current levels and represented by a branch-dependent lookup model that provides the zero-current baseline and instantaneous feasible force range. Proportional–integral–derivative (PID) and linear quadratic regulator (LQR) controllers are independently tuned using a genetic algorithm (GA) and particle swarm optimization (PSO) under identical vehicle dynamics, objective functions, tuning excitation, and MRD force constraints. Each candidate force demand is projected onto the experimentally derived feasible range throughout optimization. The controllers are tuned on a composite B–C–D profile and subsequently evaluated over nine road–speed scenarios. PID-PSO reduces the RMS sprung-mass acceleration by 15.91% and achieves the best acceleration performance in six cases, whereas LQR-PSO provides more balanced improvements in body motion, suspension travel, tire response, and force feasibility. The proposed framework therefore provides a more physically constrained basis for the comparative design and evaluation of MRD-based semi-active suspension control. Full article

Climate change is altering energy-demand patterns through changing temperatures and heating and cooling requirements. Long-term energy-demand projections are essential for energy security, infrastructure planning, and preparing net-zero energy systems. However, integrated assessments of climate-sensitive electricity and gas demand trajectories in the UK under long-term climate-forcing pathways and net-zero transition assumptions remains limited. To address this gap, this study develops a scenario-based machine-learning framework to jointly project electricity and gas demand in the UK up to 2050 under climate-forcing pathways. CMIP6 daily temperature projections at 0.25° resolution are used to calculate Heating Degree Days and Cooling Degree Days under low-, intermediate-, and high-forcing pathways, labelled RCP2.6, RCP4.5, and RCP8.5. These indicators are used as inputs to Random Forest models for electricity and gas demand. By 2050, electricity demand under RCP8.5 is 4.1% higher than under RCP2.6, while gas demand is 6.8% lower. Under the net-zero adjustment, gas demand declines because of the assumed 75% reduction in gas use, while electricity demand rises as part of displaced gas demand shifts to electricity. Adjusted electricity demand differs by 2–3 TWh between the highest- and lowest-warming pathways, while adjusted gas demand differs by 7–8 TWh. The framework jointly assesses climate-sensitive electricity and gas demand and links these projections to net-zero gas-reduction and electrification assumptions. The results support electricity-capacity and storage planning, electrification strategies, hydrogen infrastructure investment, and decisions on the future role of gas networks in UK energy-security planning under changing climate and transition conditions across Britain. 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

Artificial intelligence (AI) data centers are creating large, power-dense loads, often faster than transmission lines, substations, transformers, and grid interconnections can be expanded. This review assesses whether solid oxide fuel cells (SOFCs) can provide dependable on-site power during these grid delivery constraints and remain competitive after grid capacity becomes available. We critically synthesized evidence on AI electricity demand, competing power supply options, SOFC efficiency and durability, commercial deployments, environmental impacts, thermal and electrical integration, and hybrid SOFC–battery–grid systems. We also performed a screening-level levelized cost of electricity sensitivity analysis covering natural gas prices, carbon costs, stack replacement, grid electricity prices, and the avoided cost of delayed grid access. The evidence indicates that commercial SOFC systems can achieve approximately 50–60% net electrical efficiency and scale modularly from 325 kW units to a planned deployment of up to 2.45 GW. A nominal 100 MW installation would require approximately 308 such modules and at least 3600 m² of direct equipment area, excluding auxiliary systems and safety setbacks. However, multi-year durability targets of about 40,000 h, fuel and carbon price exposure, slow transient response, lifecycle methane emissions, and limited opportunities to use high temperature exhaust heat remain important constraints. The economic analysis indicates that avoided grid delay costs can justify SOFCs as bridge assets, whereas long-term retention requires competitiveness without this temporary benefit. SOFCs are therefore most suitable for sites that prioritize rapid access to firm power, modularity, reliability, and low local air pollutant emissions, rather than as a universal alternative to grid expansion. Full article

Hydrogen is not intrinsically low-carbon; its environmental value depends on how, where, and with which energy system it is produced. This PRISMA 2020 systematic review synthesizes 28 peer-reviewed life cycle assessment (LCA) studies on major hydrogen production pathways, including steam methane reforming, electrolysis, biomass-based routes, thermochemical cycles, and emerging photoelectrochemical systems. Unlike reviews focused only on carbon intensity, this study jointly evaluates environmental performance, economic feasibility, and technology readiness to identify where apparent advantages remain robust and where they disappear under real deployment conditions. The evidence shows that renewable-powered electrolysis can deliver the lowest greenhouse gas emissions when supported by additional low-carbon electricity, but the same technology can lose its climate benefit in fossil-dominated grids. Biomass and emerging routes diversify supply options but introduce water, land, material, and maturity trade-offs that are often underrepresented in policy narratives. Regional conditions, especially grid carbon intensity and resource availability, explain much of the variation observed across studies. The review also identifies persistent methodological gaps, including inconsistent system boundaries, limited dynamic grid modelling, weak treatment of indirect land use effects, and insufficient accounting for system-level benefits from flexible electrolysis. Overall, the findings support performance-based carbon intensity standards, region-specific deployment strategies, and more transparent LCA methods capable of capturing hydrogen’s role in integrated energy systems. Full article

High renewable energy penetration and surging electrical demand challenge the operation of integrated electricity–gas systems (IEGS) due to source and load uncertainties. This paper proposes a multi-objective optimal scheduling framework that harnesses flexible resources within the IEGS to balance economic, environmental, and energy efficiency goals. First, a liquid storage tank is introduced to reform the traditional carbon capture, utilization, and storage system. Additionally, a hydrogen energy multi-utilization structure—integrating two-stage power-to-gas, hydrogen fuel cells, and hydrogen storage—is developed to improve operational flexibility under renewable fluctuations and carbon constraints. Second, electric vehicles (EVs) schedulability is quantitatively evaluated across different charging scenarios, defining carbon quotas and profit calculation methods to incentivize EV participation. To address source-load uncertainties, a two-stage robust optimization model utilizing a box uncertainty set and budget constraints is constructed to secure the optimal scheduling solution under worst-case scenarios. Finally, by introducing penalty factors for carbon emissions and energy loss, the multi-objective function is transformed into a single-objective problem to minimize operation costs, emissions, and energy wastage. The results show that the coupled CCUS–HEMU configuration reduces the total and environmental costs by 15.50% and 77.13%. Under the worst-case source–load scenario, bidirectional EV charging further reduces the total cost by 22.10%, increases renewable-energy utilization from 87.34% to 94.34%, and decreases load fluctuation and the maximum peak–valley difference by 68.95% and 14.85%, respectively, thereby enhancing the system’s low-carbon flexibility and robustness against operational uncertainties. Full article

The maritime sector is increasingly focused on green propulsion technologies to address stringent regulations on greenhouse gas emissions and other pollutants. In recent years, research has proposed novel electric and hybrid propulsion architectures and advanced energy management systems. This paper reviews the fundamentals and the most recent developments of hybrid and electric propulsion technologies, evaluating their environmental and economic implications. Particular attention is given to the various onboard energy storage, conversion, and generation technologies, outlining their potential and limitations. Through the analysis of numerous research studies in alternative marine propulsion, the suitability of Li-ion batteries, supercapacitors, flywheels, and different types of fuel cells for maritime transport needs is evaluated, along with the possibilities offered by renewable energy to reduce the environmental impact of marine energy systems. Additionally, it discusses important future directions, research gaps, and emerging paradigms in sustaining maritime eco-systems. Unlike previous reviews that mainly focus on individual technologies, this study provides an integrated analysis connecting propulsion architectures, energy storage systems, fuel cells, alternative fuels, renewable energy integration, and energy management strategies. The review also discusses technology limitations, operational suitability for different vessel categories, and future research challenges toward maritime decarbonization. In presenting these issues, the author’s intention is to promote interdisciplinary cooperation between shipbuilders, policymakers, and researchers for the benefit of more sustainable development of the maritime industry. Full article