Search Results (2,496)

Port operators must now reduce emissions without weakening the reliability of cargo-handling and logistics services. Two load groups are especially important in this setting: vessels connected to shore-side facilities during berthing and heavy-duty vehicles working inside the terminal area. Their energy-use patterns shape both dispatch stability and the carbon intensity of the port energy system. This paper therefore proposes an integrated port energy management model that jointly schedules wind power, photovoltaic generation, hydrogen production and storage, shore power, conventional purchases, berthed-vessel demand, and low-carbon heavy-duty transport demand. The model combines price-based demand response with a tiered carbon-trading penalty so that flexible electricity consumption and emission costs are reflected in the dispatch decision. Numerical simulations show that the joint use of demand response and the carbon-penalty mechanism lowers total economic dispatch cost by about $11.05\%$ and reduces carbon emissions by $24.52\%$. The results indicate that coordinated renewable-energy and logistics-aware scheduling can improve the economic and environmental performance of port operations. Full article

This study investigated the feasibility and biomechanical effects of volitional electromyography (EMG)-based control of a powered transtibial ankle prosthesis. Four male participants completed static and dynamic EMG assessments and gait analysis while using both their prescribed passive prosthesis and an EMG-controlled powered prototype during level walking at self-selected and fast speeds, as well as ramp ascent and descent. Selective activation of residual tibialis anterior and gastrocnemius muscles was quantified using a co-contraction index, and lower-limb kinematics and kinetics were compared between prosthetic conditions. Participants were able to generate task-dependent residual muscle activity, supporting the feasibility of EMG-based volitional control. However, muscle selectivity was reduced during dynamic tasks, with higher co-contraction during gait than during seated static contractions, and substantial inter-subject variability was observed. Compared to the prescribed passive prosthesis, the EMG-controlled prototype generally produced lower prosthetic-side ankle range of motion and ankle power, although ankle moments were sometimes slightly greater. These findings suggest that EMG control is feasible, but that future controller design must remain flexible to individual users’ neuromuscular abilities and dynamic control limitations. The results provide important guidance for the development and testing of more adaptive, personalized, and functionally effective EMG-controlled prosthetic ankle systems. Full article

As the complexity, scale, and nonlinearity of modern engineering optimization problems continue to increase, traditional optimization algorithms face significant challenges in achieving high solution accuracy, fast convergence, and robust performance. To address these issues, this paper proposes a Rural Tourism Migration-based Improved Equilibrium Optimizer (RTM-IEO), aiming to enhance the global search capability and adaptive balance between exploration and exploitation. Specifically, an adaptive lens imaging opposition-based learning strategy is introduced to effectively expand the search space and maintain population diversity. A dynamic elite-guided elimination mechanism is designed to strengthen exploitation capability and accelerate convergence by reconstructing inferior individuals using high-quality solutions. In addition, a multi-stage rural tourism migration strategy is developed to dynamically regulate the search behavior across different optimization phases, enabling a more flexible and efficient search process. The effectiveness of the proposed algorithm is comprehensively validated on the CEC2021 and CEC2022 benchmark suites, where RTM-IEO demonstrates superior performance in terms of convergence accuracy, convergence speed, and robustness compared with several representative state-of-the-art algorithms. The statistical superiority of the proposed method is further confirmed through Friedman mean ranking and Wilcoxon rank-sum tests. To further evaluate its practical applicability, RTM-IEO is applied to the sustainable economic dispatch problem of a microgrid integrating renewable energy sources, including wind power and photovoltaic generation, along with energy storage systems and controllable units. The optimization objective simultaneously considers economic cost minimization and sustainable operation requirements, such as improving renewable energy utilization and reducing dependence on fossil-fuel-based generation. Experimental results indicate that the proposed method achieves a significant reduction in daily operating cost (exceeding 52% compared with benchmark algorithms), while effectively promoting low-carbon energy utilization and enhancing overall system sustainability. Overall, the proposed RTM-IEO provides an efficient and reliable optimization framework for addressing complex global optimization problems, particularly in scenarios requiring a coordinated balance between economic performance and sustainable development. Full article

The integration of collaborative robots into industrial environments requires rigorous safety validation under the Power and Force Limiting (PFL) regime. This review article systematically maps the technological and normative development of certified Pressure and Force Measurement Devices (PFMDs) and experimental biofidelic instruments for Physical Human–Robot Interaction (pHRI) between the years 2011 and 2026. A quantitative screening of 68 studies revealed a publication peak in impact metrology in 2021. This peak occurred with a five-year latency after the release of the ISO/TS 15066 technical specification. Although global interest in collaborative robotics steadily grows, the publication trend indicates a gradual shift in scientific focus from reactive testing toward proactive prevention. A methodological deconstruction of four Research Questions (RQs) identifies persistent limitations in safety evaluation. The findings demonstrate that the internal structure of conventional sensors induces nonlinear shock filtering and parasitic oscillations (RQ1). Furthermore, the rigid fixation of test stands generates unrealistic pressure spikes. This physical limitation forces a transition to flexible and pendulum-based configurations (RQ2). Commercial flat films physically fail due to sensor saturation and introduced stiffness. Such failures accelerate the development of conformable electronic skins (e-skins) and multimodal test manikins (RQ3). To ensure interlaboratory reproducibility within the current ISO 10218-2:2025 standard, the text defines imperative metrological parameters. These parameters strictly include frequency response, calibration protocols, and volumetric mapping of inertial masses (RQ4). Furthermore, the analysed publications were systematically stratified into distinct technological categories, strictly reflecting their primary engineering domains, ranging from empirical metrological evaluation and sensor hardware design to advanced numerical modeling. Finally, the vision for future research anticipates a definitive shift toward proactive anti-collision technologies, encompassing Artificial Intelligence (AI), machine vision, and Augmented Reality/Virtual Reality/Mixed reality (AR/VR/MR). Future methodologies must also consider demographic anisotropies and the cognitive fatigue of the human operator. Full article

The reliable operation of grid-connected distributed generation converters is challenged by severe unbalanced conditions and stringent fault ride-through requirements. To address these issues, this paper presents a sensorless flexible predictive direct power control (SF-PDPC) strategy for converters operating under severe asymmetrical grid faults. The proposed approach combines a frequency-adaptive neural network quadrature signal generator (FANN-QSG)-based virtual-flux estimator with a flexible power-reference generation scheme, enabling predictive control without grid-voltage sensors, conventional synchronization units, or cascaded filtering stages. The key feature of the proposed method lies in a flexible power-reference formulation that exploits the degrees of freedom associated with positive- and negative-sequence power components, allowing continuous regulation of the trade-off among current quality, active-power oscillations, and reactive-power oscillations under unbalanced grid conditions. This enables a unified control framework adaptable to different grid support objectives. The effectiveness of the proposed strategy is validated under a severe type-C voltage sag, grid frequency deviation, and harmonic distortion. Compared with the conventional PDPC, the proposed method reduces current total harmonic distortion from 57.78% to 0.44% while maintaining satisfactory active power tracking performance. Furthermore, the FANN-QSG-based estimator and the overall control structure demonstrate strong robustness under highly disturbed operating conditions. The proposed SF-PDPC enhances the operational flexibility of predictive power control for grid-connected converters operating under highly disturbed and unbalanced grid conditions. Full article

With the large-scale integration of renewable energy and the deepening of electricity market reform, uncertainty in power system operation has increased significantly. This creates new challenges for multiple generation companies when they work together to develop generation schedules that balance economic efficiency and low-carbon goals. Most existing studies assume fixed loads and ignore the active regulation capability of the demand side under price signals and incentive signals. To address this gap, this paper proposes a low-carbon generation schedule optimization method for multiple generation companies. The method considers heterogeneous flexible loads. First, the paper decomposes flexible load adjustability into two components: price elasticity-based load shifting and incentive-based adjustable capacity. Using the price elasticity matrix method, the market clearing price serves as a known input. The load shifting amount under price elasticity regulation is pre-calculated for each park and treated as an exogenous parameter in the generation schedule model. This allows generation companies to directly use demand-side flexibility information during the planning stage. Second, the paper uses the proportion of residential and industrial loads as a core parameter. It characterizes the heterogeneity of four parks along two dimensions: elasticity coefficients and upper limits of adjustable capacity. Parks with a higher proportion of industrial loads have stronger flexible regulation capability. This result is consistent with real physical characteristics. It also provides a quantitative basis for generation companies to utilize flexible resources differently across parks and optimize their output arrangements. Finally, the paper uses the upward and downward adjustable capacity of each park as decision variables. It builds a multi-generator low-carbon generation schedule optimization model with heterogeneous flexible loads. Generator output constraints, power balance constraints, flexible load adjustable capacity constraints, and carbon quota constraints are all integrated into a single-level mixed-integer linear programming framework. This framework can be solved efficiently using commercial solvers. It helps generation companies develop optimal generation schedules that balance economic efficiency and low-carbon targets. Case study results show that combining price elasticity regulation with incentive-based adjustable capacity can effectively improve both the economic performance and low-carbon performance of generation schedules. Full article

The development of low-emission and reliable propulsion systems is essential for extending the operational capability of unmanned aerial vehicles (UAVs). Although aviation decarbonization is widely recognized as an important objective, it must be considered within the broader context of limited renewable-energy availability. Recent system-level analyses of transportation decarbonization have shown that the allocation of renewable electricity and sustainable fuels should prioritize sectors where direct electrification is most efficient, while hard-to-electrify sectors require alternative pathways. Aviation is one of the most difficult transport sectors to electrify because of strict energy-density requirements, especially for long-endurance airborne platforms. Therefore, sustainable liquid fuels and hybrid propulsion systems should not be considered universal replacements for electrification, but rather complementary solutions for applications where batteries alone cannot provide the required endurance, payload capacity or operational flexibility. In this context, the present study focuses on alcohol–kerosene blends for hybrid UAV power systems, where liquid-fuel energy density and partial emission reduction remain relevant engineering requirements. This work provides one of the first systematic experimental evaluations of ethanol–, butanol– and octanol–kerosene blends in a micro-turboprop engine operating as part of a hybrid UAV power-generation architecture. Unlike previous studies focused mainly on micro-turbojet thrust response, the present work evaluates the coupled influence of alcohol chain length and blending ratio on exhaust gas temperature, gaseous emissions, electrical output and operational stability under multi-load conditions representative of UAV operation. Jet-A and nine alcohol–kerosene blends containing 10%, 20% and 30% ethanol, butanol or octanol by volume were tested over four operating regimes, from idle to 2500 W electrical load. The results show that ethanol blends provided the strongest CO reduction, with E30 reducing CO by 24.9% relative to Jet-A under R3, while E10 offered the most balanced behavior across the full operating range. Higher ethanol fractions improved CO suppression but introduced NOx and low-load stability penalties. Octanol blends, particularly O20, exhibited the most kerosene-like and stable response, supporting reliable power delivery with reduced operational variability. Butanol blends showed intermediate behavior without providing a dominant advantage. A multi-criteria evaluation combining emissions, EGT behavior, relative performance, operational stability and cost identified E10 as the best overall compromise for hybrid UAV use. The study demonstrates that alcohol chain length produces nonlinear system-level effects in hybrid micro-turboprop architectures and provides an experimental basis for fuel selection in low-emission UAV power systems. Full article

Accurate plant-level photovoltaic (PV) power estimation is important for performance monitoring, model benchmarking, and grid-integration studies. In bifacial horizontal single-axis tracking (HSAT) systems, this task is complicated by the coupled effects of front-side irradiance, rear-side irradiance, tracker position, and module temperature. This study proposes an algorithmic framework for same-time-step AC power estimation in a bifacial HSAT PV plant using field measurements of irradiance, tracker angle, module temperature, and inverter active power. The framework is not intended as an operational forecasting model because future irradiance and weather conditions are not predicted; instead, it evaluates how compact physics-based structure, interpretable nonlinear learning, and ensemble learning estimate measured AC power under nominal operating conditions. An empirical rear-to-front irradiance relationship was derived using solar-elevation bins and incorporated into a compact physics-based benchmark. This benchmark was compared with an additive Explainable Boosting Machine (EBM) and a Random Forest (RF) on a common test subset of 3916 observations. The physics-based model achieved an RMSE of 19.6 kW, an R² of 0.72, and an NRMSE of 0.38. The EBM improved these values to 17.09 kW, 0.786, and 0.334, respectively, while the RF achieved 15.96 kW, 0.814, and 0.312. Chronological validation showed weaker and more variable performance than randomized validation, indicating that temporal generalization remains challenging. Overall, the results support the use of interpretable PV-domain-guided learning as a transparent intermediate approach between compact physics-based modeling and more flexible ensemble regression. Full article

Towards achieving carbon neutrality, it is important to produce carbon-free hydrogen from renewables at an acceptable cost. At the same time, power retailers that own renewables must manage their imbalances between planned and actual generation. This paper proposes an economically viable carbon-free hydrogen method for such retailers, utilizing both positive imbalances of renewables and electricity from the market with non-fossil certificates. The proposed method enables geographically flexible hydrogen production through the power grid while utilizing renewable imbalances within actual power business operations. This paper develops solutions to an optimization problem that minimizes the hydrogen variable cost and offsets the imbalances using an electrolyzer and a battery while accounting for imbalance uncertainty. The case study in Tokyo, Japan demonstrates that imbalance compensation reduces the hydrogen variable cost by 30%. The minimum levelized cost of hydrogen (LCOH) is approximately 60 JPY/Nm³ when the electrolyzer operates at a 40% capacity factor. Furthermore, sensitivity analysis of market prices indicates that the LCOH can decline to 50 JPY/Nm³ under lower price conditions. The results suggest that market-independent cost components, such as wheeling and renewable energy charges and non-fossil certificates, remain major obstacles to further reducing hydrogen costs. Full article

Oscillating water column (OWC) wave energy converters equipped with dielectric elastomer generators (DEGs) represent a promising technology for harnessing ocean wave energy. This study emphasises the critical role of functional specifications in guiding the development of these devices from initial concept to full-scale deployment. A comprehensive analysis of key design parameters that influence the performance and efficiency of flexible OWCs with DEG-based power take-off systems is presented. This investigation focuses on the effects of draft, membrane diameter, deformation characteristics, number of layers, and membrane thickness on power output. Utilising a combination of analytical tools, including Wave Venture software, MATLAB, and Abaqus, detailed simulations and analyses are conducted to optimise these parameters. Our results demonstrate that increasing the DEG diameter significantly enhances power output, with diameters between 5 and 12 m showing optimal efficiency. A critical strain threshold of approximately 32% is identified, beyond which power output efficiency diminishes. Furthermore, the study reveals that multi-layer DEG configurations can substantially increase energy production, with thinner membranes generally yielding higher outputs. These findings provide valuable insights for developing functional specifications that balance performance, manufacturability, and long-term reliability in marine environments. This research advances OWC technology by offering a parameter-screening framework to guide device design towards optimised configurations and to accelerate the path to commercial viability in the wave energy sector. Full article

With the increasing penetration of renewable energy generation (REG) in novel distribution systems, active distribution networks (ADNs) integrated with microgrids (MGs) play a crucial role in enhancing the flexibility of regulation resources and promoting the accommodation of REG. To meet the operational requirements for efficient collaboration between ADNs and MGs under different dispatch time scales, this paper proposes a collaborative optimal dispatch strategy for multi-microgrid active distribution systems based on a hybrid game and variable time scales. Firstly, a transaction operation framework is constructed for the distribution network operator (DNO) and a multi-microgrid alliance (MMA), considering the peer-to-peer (P2P) transaction mode. On this basis, a day-ahead hybrid game model with a two-layer structure is constructed, the upper layer is a master–slave game with the DNO as the leader and the MMA as the follower, while the lower layer is a cooperative game for MGs within the MMA. An asymmetric Nash bargaining strategy based on contribution degree in P2P transactions is introduced to ensure equitable benefit allocation among cooperative MGs. Secondly, an intra-day rolling optimization model for reactive power and voltage based on variable time scales is proposed, which enhances the system’s responsiveness to real-time source–load power fluctuations by dynamically adjusting the dispatch time scale. Finally, the alternating direction method of multipliers (ADMM), integrated with a strategy separation mechanism, is adopted to efficiently solve the hybrid game model involving numerous 0–1 variables. The case study results indicate that, under the proposed strategy, the MMA’s power purchase cost from the DNO and ESS operational cost are decreased by 9.7% and 11.6%, respectively, while the system’s average deviation rate of node voltage decreases by 0.82%. Therefore, the proposed collaborative dispatch strategy can not only effectively reduce the system’s operational cost and ensure voltage stability but also significantly promote the accommodation of REG. Full article

The rapid development of wind and solar energy necessitates a solution to the problem of the optimal spatial placement of wind farms (WFs) and solar farms (SFs) within electric power systems. The non-stationary generation schedules of WFs and SFs place increased demands on the flexibility of conventional generation, determined by the intensity of net load fluctuations. This paper proposes a methodology for the spatial optimization of WF and SF location, in which the optimization criteria include net load indicators (rate of net load change and net load increment), the base power of the RES system, and the economic criterion of maximum electricity generation. Unlike existing approaches, in which the geographical smoothing effect on power fluctuations is treated as an incidental outcome, the proposed methodology employs it as an explicit optimization criterion for RES placement. The algorithm provides for the preliminary ranking of candidate sites based on the maximum electricity generation criterion, followed by the redistribution of generating capacities among sites with an acceptable capacity factor in accordance with the selected optimization criterion. The methodology was tested on a model comprising six potential wind farm sites and two solar farm sites with a total installed capacity of 600 MW and a maximum power system load of 3000 MW. The obtained results show that the optimal redistribution of installed capacities among sites allows a 31.5% reduction in net load variability intensity to be achieved with an 11.6% reduction in electricity generation relative to the maximum possible. The study is based on idealized daily generation and consumption profiles and is theoretical in nature, proposing a pre-screening tool for RES siting that complements rather than replaces subsequent network-constrained planning studies, including power-flow analysis and grid verification, and establishes a methodological foundation for further development using real multi-year retrospective data. Full article

Weak grid infrastructure and the absence of flexible storage are among the principal barriers to reliable, low-carbon energy access in sub-Saharan transmission systems. This paper proposes a hierarchical multi-objective framework for the optimal siting and sizing of battery energy storage systems (BESSs), applied to the 130-bus Mali transmission network within the EMERGE project. The upper level employs NSGA-II to simultaneously maximize daily price arbitrage revenue and minimize active power losses; the lower level solves a network-constrained DC optimal power flow with thermal branch limits enforced as hard linear inequalities via the Power Transfer Distribution Factor (PTDF) matrix. Over 500 generations, the framework identifies Bus 91 (SIRAKORO II, 150 kV) as the dominant storage location, achieving a maximum daily revenue of approximately €10,033 at a marginal loss increment of $6.7\times {10}^{-3}$ MWh. The resulting Pareto front gives Mali system planners a quantitative tool for trading off private investment returns against grid-level environmental impact, demonstrating that rigorous network-constrained BESS planning is technically tractable and economically viable in the resource-constrained context of sub-Saharan energy transitions. Full article

The efficient management of power systems requires balancing electricity generation costs with associated environmental emissions under dynamically varying demand. This paper proposes a two-stage approach that combines machine learning (ML) with a metaheuristic optimization algorithm to address the dynamic economic–environmental load dispatch (DEELD) challenge. In the first stage, electricity consumption data are enriched with temporal features to capture demand patterns and enable accurate forecasting. In the second stage, the daily scheduling horizon is divided into multiple periods, and dispatch solutions are generated sequentially while enforcing ramp-rate constraints. To enhance operational realism, a priority-based generator scheduling mechanism is explicitly introduced, enforcing hierarchical unit commitment and reflecting practical dispatch policies. Rather than focusing on a single optimal solution, the proposed framework generates multiple feasible dispatch solutions and evaluates them using economic, environmental, and operational performance indicators. These solutions are then ranked according to predefined decision profiles, enabling system operators to select dispatch strategies that align with specific priorities. This transforms the dispatch process into a flexible decision-support tool capable of addressing diverse real-world requirements. Full article

A recent generalized Bilal (G-Bilal) model demonstrates remarkable flexibility in capturing a wide spectrum of failure behaviors, including monotonic and non-monotonic (upside-down bathtub-shaped) hazard patterns, outperforming several existing models such as the Weibull, gamma, and exponential families. This paper develops several inferential frameworks for different G-Bilal parameters of life using samples gathered by improved Type-II adaptive progressive censoring. This enhanced design ensures optimal control of test duration while maintaining high inferential precision. Expressions for the model parameters, reliability, and hazard rate functions are derived, followed by the development of maximum likelihood (ML) and maximum product of spacing (MPS) estimators with their asymptotic confidence intervals using the observed Fisher information with the delta approach. Furthermore, Bayesian estimators and two associated credible intervals are proposed under independent gamma priors and computed through Markov iterations, with both ML and MPS posteriors considered. Extensive Monte Carlo experiments confirm the consistency, robustness, and precision of the proposed estimators, with Bayesian spacing-based methods exhibiting superior accuracy and coverage. The model’s practical potential is further verified through two real applications: one involving mechanical system lifetimes and another analyzing health insurance premium data, representing physical and actuarial domains, respectively. Using the introduced censoring, the proposed G-Bilal model outperforms all competing models in terms of goodness-of-fit and reliability estimates in both cases. The results underscore the G-Bilal model’s adaptability, computational stability, and empirical superiority, establishing it as a powerful tool for modern reliability and actuarial risk assessments. Full article