Search Results (16,204)

To further improve the economic benefits of operators and the low-carbon performance within the system, this paper proposes a hierarchical distributed low-carbon economic dispatch strategy for regional integrated energy systems (RIESs) based on the Alternating Direction Method of Multipliers (ADMM). First, the energy coupling relationships among conversion devices in RIESs are analyzed, and a structural model of RIES incorporating an energy generation operator (EGO) and multiple load aggregators (LAs) is established. Second, considering the stepwise carbon trading mechanism (SCTM) and the average thermal comfort of residents, economic optimization models for operators are developed. To ensure optimal energy trading strategies between conflicting stakeholders, the EGO and LAs are embedded into a master–slave game trading framework, and the existence of the game equilibrium solution is rigorously proven. Furthermore, considering the processing speed of the optimization problem by the operators and the operators’ data privacy requirement, the optimization problem is solved in a hierarchical distributed manner using ADMM. To ensure the convergence of the algorithm, the non-convex feasible domain of the subproblem bilinear term is transformed into a convex polyhedron defined by its convex envelope so that the problem can be solved by a convex optimization algorithm. Finally, an example analysis shows that the scheduling strategy proposed in this paper improves the economic efficiency of energy trading participants by 3% and 3.26%, respectively, and reduces the system carbon emissions by 10.5%. Full article

To address the problem of flow-induced resonance in the valve core assembly of a pilot-operated molten salt regulating valve in a concentrated solar thermal power generation molten salt energy storage system under high pressure differential and high flow rate conditions, the flow-induced vibration characteristics of the pilot-operated molten salt regulating valve were analyzed using computational fluid dynamics (CFD) and fluid–structure interaction modal analysis. The vibration characteristics of the valve core assembly under the excitation force of the molten salt medium were analyzed using the harmonic response method, and the influence of different parameters on the valve core assembly’s vibration characteristics was studied. The results show that under typical operating openings, the first six modal frequencies of the valve core motion assembly are not close to the fluid excitation frequency, indicating that flow-induced resonance does not occur. The maximum vibration stress and displacement of the valve core assembly decrease with increasing damping ratio. With increasing pressure differential, the maximum stress and maximum amplitude of the valve core assembly increase. By changing the valve stem constraint conditions, the vibration stress of the valve core assembly can be reduced. This study provides a reference for the design of flow-induced vibration suppression for pilot-operated molten salt regulating valves and provides guidance for the safe operation of concentrated solar thermal power generation molten salt regulating valves under high pressure differential and high flow rate conditions. Full article

Unmanned aerial vehicles (UAVs) are increasingly favored for emergency search and rescue operations due to their high adaptability to harsh environments and low operational costs. However, the dynamic nature of search path endpoints, influenced by target movement, limits the applicability of shortest path models between fixed points in moving target search problems. Consequently, the moving target search problem using UAVs in complex environments presents considerable challenges, constituting an NP-hard problem. The Grey Wolf Optimizer (GWO) is known for addressing such problems. However, it suffers from limitations, including premature convergence and instability. To resolve these constraints, a Grey Wolf Optimizer with variable population and strategy (GWO-VPS) is developed in this work. GWO-VPS implements a variable encoding scheme for UAV movement patterns, combining motion-based encoding with path-based encoding. The algorithm iteratively alternates between global optimization and local smoothing phases. The global optimization phase incorporates: (1) a Q-learning-based strategy selection; (2) position updates with obstacle avoidance and energy consumption reduction; and (3) adaptive exploration factor. The local smoothing phase employs four path smoothing operators and Q-learning-based strategy selection. Experimental results demonstrate that GWO-VPS outperforms both enhanced GWO variants and standard algorithms, confirming the algorithm’s effectiveness in UAV-based moving target search simulations. Full article

The strategic development of the low-altitude economy requires efficient urban logistics solutions. The existing Unmanned Aerial Vehicle (UAV) truck delivery system faces severe challenges in dealing with dynamic weather constraints and multi-agent coordination. This article proposes a neural network-based optimisation framework that integrates constrained K-means clustering and a three-stage neural architecture. In this work, a mathematical model for heterogeneous vehicle constraints considering time windows and UAV energy consumption is developed, and it is validated through reference to the Solomon benchmark’s arithmetic examples. Experimental results show that the Truck–Drone Cooperative Traveling Salesman Problem (TDCTSP) model reduces the cost by 21.3% and the delivery time variance by 18.7% compared to the truck-only solution (Truck Traveling Salesman Problem (TTSP)). Improved neural network (INN) algorithms are also superior to the traditional genetic algorithm (GA) and Adaptive Large Neighborhood Search (ALNS) methods in terms of the quality of computed solutions. This research provides an adaptive solution for intelligent low-altitude logistics, which provides a theoretical basis and practical tools for the development of urban air traffic under environmental uncertainty. Full article

Power engineering construction serves as the cornerstone of modern social development. Against the backdrop of new power system development, this study employs field investigations, case analysis, and expert discussions to conduct an in-depth analysis of the current status, existing problems, and characteristics of safety risk control in China’s power engineering construction during the 14th Five-Year Plan period. Through systematic analysis of 59 accident cases, 66 distinct causes are identified across 14 categories. Chi-squared testing quantitatively determines the top three risk factors: hollowing out of construction units’ own workforce (χ² = 10.22), deficiencies in risk classification and hierarchical implementation (χ² = 9.0), and inadequate hazard identification (χ² = 6.25). Through brainstorming and expert discussions, 11 critical risks in China’s power engineering construction have been identified, and a set of countermeasures has been formulated. These include nine enterprise-level initiatives such as deepening engineering procurement construction management, improving training systems, optimizing bidding methods, and implementing management principles, along with four regulatory measures targeting the National Energy Administration of China and its regulatory agencies. This study innovates by combining quantitative chi-squared analysis with expert-derived countermeasures, offering a model for transitioning economies. While the sample size imposes limitations on generalizability, this research can significantly improve the intrinsic safety management level of power construction enterprises in China and provides valuable reference experience for similar transitioning countries developing energy infrastructure. Full article

This study investigates the asymmetric trade-off between cost and reliability in the optimal sizing of stand-alone Hybrid Renewable Energy Systems (HRESs) composed of photovoltaic panels (PV), wind turbines (WT), battery storage, a diesel generator (DG), and an inverter. The optimization is formulated as a multi-objective problem with Cost of Energy (CoE) and Loss of Power Supply Probability (LPSP) as conflicting objectives, highlighting that those small gains in reliability often require disproportionately higher costs. To ensure practical feasibility, the installation roof area limits both the number of PV panels, wind turbines, and batteries. Two metaheuristic algorithms—NSGA-II and MOPSO—are implemented in a Python-based framework with an Energy Management Strategy (EMS) to simulate operation under real-world load and resource profiles. Results show that MOPSO achieves the lowest CoE (0.159 USD/kWh) with moderate reliability (LPSP = 0.06), while NSGA-II attains a near-perfect reliability (LPSP = 0.0008) at a slightly higher cost (0.179 USD/kWh). Hypervolume (HV) analysis reveals that NSGA-II offers a more diverse Pareto front (HV = 0.04350 vs. 0.04336), demonstrating that explicitly accounting for asymmetric sensitivity between cost and reliability enhances the HRES design and that advanced optimization methods—particularly NSGA-II—can improve decision-making by revealing a wider range of viable trade-offs in complex energy systems. Full article

In a large wind power base, it becomes unrealistic to rely only on synchronous generators to resist the uncertainty of wind power. A feasible way is to make wind turbines (WTs) and battery energy storage systems (BESSs) participate in frequency regulation. Taking into account the frequency regulation service of WTs and BESSs, the Coordinated Operation Strategy (COS) of the Wind–BESS–Thermal power model will become difficult to solve due to strong nonlinearity. To cope with this challenge, an improved Primary Frequency Regulation (PFR) model is first established considering the frequency regulation of WTs and BESSs. Based on the improved PFR model, the analytical expression of frequency stability constraints is deduced. Next, in view of the wind power uncertainty, the box-type ensemble robust optimization theory is introduced into the day-ahead optimal scheduling, and a robust COS model considering wind power uncertainty and frequency stability constraints is proposed. Then, a linear equivalent transformation method is designed, based on which the original COS model is transformed into a Mixed Integer Linear Programming (MILP) problem. Finally, a modified IEEE 39-bus system is adopted to test the effectiveness of the proposed method. Full article

Wave energy is a highly concentrated energy resource with five times higher energy density than wind and at least ten times the power density of solar energy. It is expected to make a major contribution to addressing climate change and to help end our dependency on fossil fuels. Many ingenious wave energy conversion methods have been put forward, and a large number of wave energy converters (WECs) have been developed. However, to date, wave energy conversion technology is still in the demonstration application stage. Key issues such as survivability, reliability, and efficient conversion still need to be solved. The major hurdle is the fact that ocean waves provide a slow-moving, high-magnitude force, whereas most electric generators operate at high rotary speed and low torque. Coupling the slow-moving, high-magnitude force of ocean waves normally requires conversion to a high-speed, low-magnitude force as an intermediate step before a rotary generator is applied. This, in general, tends to severely limit the overall efficiency and reliability of the converter and drives the capital cost of the converter well above an acceptable commercial target. Magnetohydrodynamic (MHD) wave energy conversion makes use of MHD generators in which a conducting fluid passes through a very strong magnetic field to produce an electric current. In contrast to alternatives, the relatively slow speed at which the fluid traverses the magnetic field makes it possible to directly couple to ocean waves with a high-magnitude, slowly moving force. The MHD generator provides an excellent match to the mechanical impedance of an ocean wave, and therefore, an MHD WEC has no rotating mechanical parts with high speeds, no complex control process, and has good response to low sea states and high efficiency under all working conditions. This review introduces the system composition, working process, and technical features of WECs based on MHD generators first. Then, the research development, key points, and issues of wave energy conversion technology based on MHD generators are presented in detail. Finally, the problems to be solved and the future research directions of wave energy conversion based on MHD generators are pointed out. Full article

This work aims to present tes-thermo, a Python library developed to solve thermodynamic equilibrium problems using the Gibbs energy minimization approach. The library is a variant of TeS v.3, a standalone executable developed for the same purpose. The tool formulates the chemical equilibrium problem of combined phases as a nonlinear programming problem, implemented using Pyomo (Python Optimization Modeling Objects) and solved with IPOPT (Interior Point OPTimizer). To validate the tool and demonstrate its robustness, the supercritical water gasification (SCWG) of methanol and ethanol was investigated. The Peng–Robinson equation of state was employed to account for non-idealities in the gas phase. Experimental and simulated data from the literature were used for validation, and, in both cases, the results were satisfactory, with root mean square errors consistently below 0.23. The SCWG processes studied revealed that hydrogen production is favored by increasing temperature and decreasing pressure. For both methanol and ethanol, increasing the carbonaceous substrate fraction in the feed promotes hydrogen formation; however, it also leads to reduced hydrogen relative yield due to the enhanced formation of methane and carbon monoxide under these conditions. Consequently, although hydrogen production increases, the hydrogen molar fraction in the dry gas stream tends to decrease with the higher substrate content. As expected, the SCWG of methanol produces more hydrogen and less carbon monoxide compared to ethanol under similar conditions. This behavior is consistent with the higher carbon content in ethanol, which favors reactions leading to carbon oxides. In summary, tes-thermo proves to be a robust and reliable tool for conducting research and studies on topics related to thermodynamic equilibrium. Full article

Power batteries are one of the important components of electric vehicles, but the manufacturing process of vehicle power batteries is complex and diverse. Traditional scheduling methods face challenges such as low production efficiency and inadequate quality control in complex production environments. To address these issues, a multi-objective optimization model with makespan, total machine load, and processing quality as the established objectives, and a Multi-objective Particle Swarm Energy Valley Optimization (MPSEVO) is proposed to solve the problem. MPSEVO integrates the advantages of Multi-objective Particle Swarm Optimization (MOPSO) and Energy Valley Optimization (EVO). In this algorithm, the particle stability level is combined in MOPSO, and different update strategies are used for particles of different stability to enhance both the convergence and diversity of the solutions. Furthermore, a local search strategy is designed to further enhance the algorithm to avoid the local optimal solutions. The Hypervolume ($HV$) and Inverted Generational Distance ($IGD$) indicators are often used to evaluate the convergence and diversity of multi-objective algorithms. The experimental results show that MPSEVO’s $HV$ and $IGD$ indicators are better than other algorithms in 10 computational experiments, which verifies the effectiveness of the proposed strategy and algorithm. The proposed method is also applied to solve the actual battery workshop scheduling problem. Compared with MOPSO, MPSEVO reduces the total machine load by 7 units and the defect rate by 0.05%. In addition, the effectiveness of each part of the improved algorithm was analyzed by ablation experiments. This paper provides some ideas for improving the solution performance of MOPSO, and also provides a theoretical reference for enhancing the production efficiency of the vehicle power battery manufacturing workshop. Full article

The problem of end-of-life (EoL) fibre-reinforced polymer (FRP) wind turbine blades (WTBs) poses a growing challenge due to the absence of an integrated circular value chain currently available on the market. A key barrier is the information gap between the EoL condition of WTB components and their second-life application requirements. This study addresses this question by focusing on the spar cap, which is an internal structural component with high repurposing potential. A framework has been developed to determine the as-received mechanical properties of spar caps from different EoL WTB models, targeting repurpose in the construction sector. The experimental programme encompasses fibre architecture assessment, calcination processes and mechanical tests in both longitudinal and transverse directions of three different WTB models. Results suggest that the spar caps appear to retain their strength and stiffness, with no evidence of degradation from previous service life. However, notable variation in properties is observed. To account for this, a prediction tool is proposed to estimate the as-received mechanical properties based on practically accessible parameters, thereby supporting decision-making. The results of this study contribute to enabling the repurposing of EoL spar cap beams from the wind energy sector for applications in the construction sector. Full article

Aiming at the demands for low-carbon transformation in multi-energy-coupled industrial parks, a model is proposed that incorporates a carbon trading system incorporating incentives and penalties. This model includes joint combined heat and power (CHP) units, carbon capture technologies, and power-to-gas (P2G) conversion equipment. Firstly, we develop a modeling framework for the joint operation of cogeneration units to establish a comprehensive energy system within the industrial park that integrates electricity, heat, gas, and cold energy sources. Subsequently, we introduce a reward and punishment carbon trading mechanism into an industrial park to regulate carbon emissions effectively. With an optimization objective focused on minimizing the overall operating costs of the system while considering relevant constraints, we formulate an optimization model. The Gurobi solver is employed through the Yalmip toolkit to address this optimization problem. Finally, four operational scenarios are established to compare and validate the feasibility of our proposed optimization strategy. The results from our computational example demonstrate that integrating combined heat and power along with carbon capture and P2G technologies—coupled with a tiered reward and punishment carbon trading mechanism—can significantly enhance the energy consumption structure of the system. Under this model, the overall expenses are decreased by 12.36%, CO₂ emissions decrease by 33.37%, and renewable energy utilization increases by 36.7%. This approach has effectively improved both wind power consumption capacity and low-carbon economic benefits within the system while ensuring sustainable economic development in alignment with “dual carbon” goals. Full article

Sixth-generation (6G) communication systems, with ultra-wide bands, energy-autonomous end nodes, and dense connectivity, challenge existing network designs. Optimizing time resources with energy harvesting, backscatter communication, and relays is essential to maximize the total bit rate in multi-user symbiotic radio networks (SRNs) with blocked direct paths. The literature lacks a unified optimization treatment that explicitly accounts for imperfect successive interference cancellation (SIC). This study addresses this gap by proposing the first optimization framework to maximize total bit rate for energy-harvesting TDMA/PD–NOMA-based multi-cluster and relay-assisted peer-assisted SR networks. The two-phase architecture defines a tractable constrained optimization problem that jointly adjusts cluster-specific time slots ($\tau$ and $\lambda$). Incorporating QoS, signal power, and reflection coefficient constraints, it provides a compact formulation and numerical solutions for both perfect and imperfect SIC. Detailed simulations performed under typical 6G power levels, bandwidths, and energy-harvesting efficiencies demonstrate graphically that imperfect SIC significantly limits total throughput due to residual interference, while perfect SIC completely eliminates this ceiling under the same conditions, providing a significant capacity advantage. Furthermore, the gap between the two scenarios rapidly closes with increasing relay time margin. The findings demonstrate that network capacity is primarily determined by the triad of base station output power, channel noise, and SIC accuracy, and that the proposed framework achieves strong performance across the explored parameter space. Full article

In the present article, we study a nonlinear mathematical model for the steady-state non-isothermal flow of a dilute solution of flexible polymer chains between two infinite horizontal plates. Both plates are assumed to be at rest and impermeable, while the flow is driven by a constant pressure gradient. The fluid rheology model used is FENE-P type. The flow energy dissipation (mechanical-to-thermal energy conversion) is taken into account by using the Rayleigh function in the heat transfer equation. On the channel walls, we use one-parameter Navier’s conditions, which include a wide class of flow regimes at solid boundaries: from no-slip to perfect slip. Moreover, we consider the case of threshold-type slip boundary conditions, which state the slipping occurs only when the magnitude of the shear stresses overcomes a certain threshold value. Closed-form exact solutions to the corresponding boundary value problems are obtained. These solutions represent explicit formulas for the calculation of the velocity field, the temperature distribution, the pressure, the extra stresses, and the configuration tensor. The results of the work favor better understanding and more accurate description of complex dynamics and energy transfer processes in FENE-P fluid flows. Full article

Background: Obesity is a multisystemic health problem causing chronic diseases like diabetes or cardiovascular diseases, but also reproductive dysfunctions like infertility in adults or altered puberty onset in children. Exercise is a recognized intervention to control or prevent energy imbalance, thus deeply contributing to metabolic health in physiological and pathological conditions. The kisspeptin system (KS), the main gatekeeper of reproduction and puberty onset in mammals, is also an upcoming “metabolic sensor”, linking energy homeostasis to reproductive ability both centrally and peripherally. Objectives: This narrative review aims at summarizing recent evidence from animal models and human studies on the role of the KS in energy homeostasis, with a focus on the upcoming role of the KS as a metabolic sensor able to modulate the functionality of the hypothalamus–pituitary–gonad axis in males as an adaptive response to exercise. Methods: PubMed and Scopus search (date: 2015–2025; keywords: kisspeptin and metabolism, male reproduction or exercise; kisspeptin and doping). Results and Conclusions: This review article illustrates the crucial role of the KS in linking energy homeostasis and male reproduction at the central and peripheral levels, and modulation of the KS by exercise in physiological and pathological conditions. Due to the large amount of data from animal models, knowledge gaps occur in the analysis of the relationship among KS, energy homeostasis, male reproduction and exercise in humans, particularly in the case of overtraining. Lastly, kisspeptin inclusion in the doping list is also discussed. Full article