Search Results (252)

This study evaluates the impact of four drying methods—open sun drying, solar drying, infrared drying, and microwave drying—on the quality attributes and elemental retention of apricots (Prunus armeniaca L.). Experimental trials were conducted in June 2024 at the Tashkent Institute of Chemical-Technology using equal quantities of fresh apricots. Drying was continued until the moisture content, measured gravimetrically, dropped below 20% (wet basis), followed by spectroscopic analysis to determine macro- and microelement concentrations. Solar-dried apricots showed higher retention of essential nutrients in this experimental trial: potassium (2.37%), silicon (0.538%), magnesium (0.145%), calcium (0.176%), and sulfur (0.152%). In contrast, open sun drying led to significant nutrient degradation and poor visual quality. Microwave drying preserved some micronutrients but resulted in surface scorching due to uneven heating. Infrared drying yielded acceptable results but required substantial energy input. Among all methods, solar drying provided the optimal balance of high product quality and energy efficiency. The drying process required negligible electrical energy owing to exclusive reliance on solar radiation. This method supports sustainable food processing by reducing energy demand and greenhouse gas emissions while preserving nutritional quality. The results highlight solar drying as a promising, eco-friendly technique for preserving the nutritional integrity of agricultural products. These findings offer valuable scientific guidance for selecting appropriate drying technologies in the food processing industry, especially in regions with high solar potential. However, the study is limited to a single fruit variety and seasonal conditions. Full article

Wastewater treatment plants increasingly rely on anaerobic digestion and biogas utilization to reduce operational costs, enhance energy self-sufficiency, and support circular-economy objectives. This study provides a comprehensive, year-round assessment of sludge production, sludge characteristics relevant to digestion, biogas generation, and energy performance at a municipal wastewater treatment plant. The plant generated on average 68.0 m³/d of thickened primary sludge and 24.0 m³/d of excessive sludge (total 92 m³/d), with low daily variability throughout the year. Biogas production remained highly stable, with an annual average of approximately 1300 m³/d and limited daily variation. Although monthly averages ranged from 1004 to 1728 m³/d, within-month variability was low to moderate, indicating that digestion processes responded consistently to changes in sludge quantity and composition. The weak correlation between sludge volume and biogas output (r = 0.29) showed that, besides sludge quantity, factors such as organic content and digester operating conditions also influence biogas yield. Energy performance indicators demonstrated strong self-sufficiency potential: the facility produced 1,095,047 kWh of electricity, covering 56.72% of its annual demand. The high coefficient of determination for self-sufficiency (R² = 0.871) confirmed a strong linear relationship between biogas-derived energy production and reduced grid dependence. Operational correlations further highlighted system coherence, with cogenerator and boiler usage strongly inversely related (r = −0.85) and biogas production positively associated with heat output (r = 0.66). Overall, the results demonstrate a stable and efficient sludge-to-energy system and provide a detailed dataset supporting future optimization of anaerobic digestion processes. Full article

The partial discharge (PD) caused by insulation defects of gas-insulated switchgear (GIS) threatens the secure and stable operation of power systems. Traditional PD pattern recognition methods exhibit limitations due to incomplete information utilization and unresolved correlations among characteristic parameters. Based on the partial discharge mechanisms of GIS, this paper establishes a GIS partial discharge simulation model using the finite element time-domain (FETD) method. The propagation rules and influence factors of ultra-high-frequency (UHF) signals are studied. Furthermore, a PD pattern recognition method based on a deep convolutional neural network (CNN) is proposed. Research results indicate that UHF signals generated by GIS partial discharge are significantly influenced by pulse current waveforms and discharge quantity. The peak-to-peak amplitude of the electric field (Epp) increases linearly with the current amplitude, while it decreases nonlinearly with increasing pulse width. The UHF signal remains a certain value while the pulse width exceeds a critical threshold (4 ns). The proposed CNN-based approach, utilizing full-wave UHF signals, overcomes the shortcomings of traditional methods reliant on manually extracted discrete feature parameters. Compared to other network architectures and optimization algorithms, the ConvNeXt-AdamW model demonstrates superior performance, achieving an average PD pattern recognition accuracy exceeding 96%. Full article

Virtual power plants efficiently aggregate distributed energy resources with small capacities but large quantities to participate in electricity market transactions through advanced control technologies. As the number of distributed power sources increases, issues such as output volatility and optimal decision-making need to be addressed. To tackle these problems, this paper proposes an optimal economic dispatch strategy for virtual power plants that accounts for flexible resource responses under uncertain scenarios. First, a combined prediction model based on variational mode decomposition (VMD) and an improved bidirectional multi-gated long short-term memory network is established to achieve accurate prediction of renewable energy output. On this basis, a price–demand elasticity matrix is constructed to characterize the spatiotemporal coupling effect of time-of-use electricity prices on load, and a demand response model based on optimal time-of-use electricity pricing is established. Meanwhile, an improved Particle Swarm Optimization (PSO) algorithm is employed to achieve efficient and precise solutions. Finally, the effectiveness and feasibility of the proposed method are validated and illustrated through an improved IEEE-33 bus test system. Full article

Silicone rubber optical fiber composite insulators introduce interface defects due to embedded optical fibers, and their structural design remains immature, resulting in inadequate interface sealing performance. In actual operation, the combined effects of high electric fields, high humidity and heat, and mechanical loads lead to frequent failures. This study proposes replacing conventional silicone rubber with cycloaliphatic epoxy resin (CEP), which exhibits superior aging resistance, to enhance long-term operational reliability. However, the correlation mechanism between the structural parameters of CEP optical fiber insulators and their electromechanical properties remains unclear, lacking corresponding design basis. Therefore, based on finite element simulation technology, this study systematically analyzed the influence patterns of core rod diameter, fiber implantation method, spiral groove angle, fiber implantation quantity, and voltage equalization ring structural parameters (outer diameter, circular tube radius, shielding depth) on their mechanical and electrical properties. Research findings indicate that in terms of mechanical properties, the helical groove structure with a 40 mm core rod diameter, a groove angle of 135°, and six embedded optical fibers exhibits the lowest optical fiber strain. In terms of electrical performance, the minimum peak electric field strength at the end of the insulator occurs when the equalizing ring has an outer diameter of 370 mm, the circular tube radius is 25 mm, and the shielding depth is 50 mm, reaching only 4.6 kV/cm, which meets the requirements of DL/T 1000.3-2015. This study establishes optimization principles for key structural parameters of CEP optical fiber composite insulators, offering significant engineering value for enhancing the overall performance of optical fiber composite insulators and improving the operational safety of power systems. Full article

This paper addresses the optimization of electric vehicle (EV) charging facility configuration on highways by proposing a collaborative planning method that integrates driver anxiety psychology, mixed traffic flow dynamics, and service area queuing characteristics. By abstracting the road travel and service area replenishment processes into an integrated queuing network, a system analysis framework is constructed to characterize the coupling relationship of “facility supply, traffic assignment, and state feedback.” On this basis, a bi-level optimization model is established with the objective of minimizing the generalized total social cost. The upper level makes decisions on the coordinated quantities of fixed charging piles and mobile charging vehicles, while the lower level describes the stochastic user equilibrium behavior of drivers under the influence of real-time congestion and anxiety. To tackle the high-dimensional nonlinear nature of the model, an efficient solution algorithm based on simultaneous perturbation stochastic approximation (SPSA) is designed. A case study of the Nei-Yi Expressway demonstrates that compared with the traditional peak demand proportional allocation method, the proposed approach can better balance construction costs, operation and dispatching costs, and user travel experience under limited investment, significantly reducing waiting times and psychological anxiety costs. It provides theoretical methods and decision support for planning a resilient energy replenishment network that achieves “fixed facilities ensuring base load and mobile resources responding to peak demands.” Full article

To address the significant scheduling challenges arising from high-penetration renewable integration and coupled multi-energy loads, this study examines the operational scheduling of an integrated energy system (IES) that incorporates system operators, user aggregators, electric vehicles, and other stakeholders. First, the flexibility demand and supply resources in the IES were analyzed, and flexibility indicators were quantified. Subsequently, a multi-objective bi-level optimization model considering flexibility and multi-entity participation was established for the IES’s low-carbon economic dispatch. The upper-level model considered the IES operator’s revenue and system flexibility, incorporating a green certificate carbon trading mechanism, while the lower-level model accounted for user aggregator costs and electric vehicle self-benefits, with interactions between the two levels through energy prices and purchase quantities. Finally, an improved Particle Swarm Optimization (PSO) algorithm was employed to solve the proposed upper-level model, and CPLEX 12.10 software was used for the lower-level model. A typical scenario in northern China was selected to validate the proposed model. The results demonstrated that the proposed model balanced system economy and flexibility compared to the traditional single-objective economic dispatch. Compared with only considering the benefits of operators, the proposed model can balance the interests of multiple parties. Additionally, compared to the traditional PSO algorithm, the improved PSO algorithm reduced the number of iterations at convergence by 52.0%, improved the closeness of the obtained optimal solution to the ideal solution by 7.5%, and had better convergence and optimization performance. Full article

The global shipping fleet uses vast quantities of fossil fuels and releases significant levels of pollution. Supplying ships moored at quays in ports with onshore power allows them to shut down onboard engines, cutting fossil fuel use and reducing emissions. This is particularly significant when ports utilize green electricity. Equipping ports to connect serviced ships to onshore power grids involves substantial investments, which must be carefully optimized. The aim of this article is to develop a methodology, grounded in probability theory, for determining the electrical power required to connect ships to onshore power grids in ports. The proposed methodology was developed and validated through a case study of container terminal operations. By applying this methodology and considering the conditions of ship service in ports, it is possible to estimate both the number of ships and their berthing durations at quays, as well as the electrical power required from onshore networks to connect the vessels. The results of this research may be of interest to port managers, terminal operators, shipowners, and other stakeholders involved in the development of onshore power grids for ship connections in ports. Full article

The increasing popularity of electric vehicles (EVs) as green alternatives to traditional internal combustion engine cars has highlighted the need for sustainable and environmentally friendly supply chain models. In particular, the handling of EV batteries, which are environmentally unfriendly and logistically critical due to their hazardous nature and short life cycle, requires a well-designed closed-loop supply chain (CLSC). This study proposes a new multi-objective optimization model of the CLSC, explicitly tailored to EV batteries under demand and return rate uncertainty. The proposed model incorporates three primary objectives that are typically in conflict with one another: minimizing the total cost, reducing carbon emissions throughout the entire supply chain network, and maximizing the recycling and reuse of batteries. The model employs a neutrosophic goal programming (NGP) methodology to address the uncertainties associated with demand and battery return quantities. The NGP model translates multiple objectives into non-monolithic goals with crisp aspiration levels (i.e., prescribed ideal levels for achieving the best of each goal) and thresholds that capture tolerances, thereby accounting for uncertainty. The efficiency of the proposed method is illustrated by a numerical example, solved using a IBM ILOG CPLEX Optimization Studio 22.1.2 solver. The findings demonstrate that the NGP can offer cost-effective, low-impact, and environmentally friendly solutions, thereby enhancing system robustness and flexibility to adapt to uncertainties. This study contributes to the emerging literature on sustainable operations research by developing a decision-making tool for EV-HV battery supply chain management. It also offers relevant suggestions for policymakers and industrialists who seek to co-optimize economic benefits, ecological sustainability, and logical feasibility in the emerging green society. Full article

This paper proposes an analysis of different logics (heuristic and linear) of managing renewables scenarios including two different operating conditions and their relative degradation: fixed and variable point. The synergy between two storage technologies, such as Li-ion batteries and the hydrogen power-to-power solution (electrolyzer, H₂ tank, and fuel cells), is evaluated to ensure the balance of the power grid. This paper presents a numerical model of the smart grid developed in MATLAB/Simulink. A detailed performance evaluation of each component was performed to meet an electrical load (30 kW-peak) of a smart renewable energy community. From the optimization process, a fuel cell of 6 kW, an electrolyzer of 18 kW, a tank of 40 m³ at 200 bars, as well as a battery of 75 kWh were selected. The fuel cell operates during autumn and winter due to the lack of photovoltaic power generation, while its contribution is reduced during the summer period. In the heuristic logic, the minimum and maximum hydrogen levels are 18% and 60% of the tank volume (40 m³), respectively, while in the linear logic, they are 33% and 65%. The average value of the state of charge (SOC) of the battery is similar in both logics (0.51 vs. 0.53). Regarding hydrogen produced from the electrolyzer, the linear logic allows it to produce a quantity 7% higher than the heuristic one; therefore, the linear logic allows it to properly manage the electrochemical systems. The dynamic operation results in more significant degradation of hydrogen systems, making them less suitable; thus, to preserve the devices (up to 25% of lifetime more), a fixed-point operation is recommended. The cost comparison does not show relevant differences between the two scenarios, while a steep increase in the costs is shown when the fuel cell is operated in dynamic mode. Finally, the total emissions associated with renewable microgrids are 30 times lower than the traditional grid scenario, demonstrating the potential of renewable energy communities. Full article

In the context of the rapid development of the new energy vehicle industry, how to achieve the mixed production of fuel vehicles and electric vehicles has become an important issue for the transformation and flexible manufacturing of automotive production lines. This paper addresses the balance problem of the mixed assembly line for electric vehicles and fuel vehicles and proposes a mathematical modeling method based on the product structure differences and workstation sharing. An improved genetic algorithm is designed for optimization. The established optimization model includes mathematical models of process priority relationships, cycle time constraints, synchronization constraints, and exclusive process co-placement constraints, with the optimization goals of minimizing workstation quantity and balancing workstation load. To solve such models, the decoding process of the genetic algorithm is redesigned in the algorithm design. The improved genetic algorithm can be well used to solve the workstation-sharing model. A case study of the chassis assembly line of an automotive manufacturing enterprise is used for verification. The results show that the method considering workstation sharing can effectively reduce the number of workstations, improve the distribution of workstation loads, and increase the utilization rate of the production line, while ensuring the cycle time constraints. The conclusions of this study expand the theoretical framework of the balance problem of mixed assembly lines and provide practical references for the transformation of fuel vehicle production lines into new energy vehicles. Full article

To address the issues of benefit coordination and energy supply risk management in energy trading between integrated energy parks and the main grid utility, this paper proposes a bi-level game-based optimal scheduling model for the electricity–heat–hydrogen integrated energy system considering energy supply risks. A bi-level game framework of the integrated energy park (IEP), aggregator, and utility is firstly built, where the aggregator acts as an intermediary coordination entity. The upper-level and lower-level game models, the trading strategies between the aggregator and the utility, as well as the trading strategies between the aggregator and the IEP, are, respectively, optimized after achieving the equilibrium. Furthermore, a conditional value-at-risk (CVaR)-based energy supply risk quantification model is introduced to characterize the operational risks caused by differences in traded energy quantities and then is incorporated into the proposed game-based optimal scheduling model. Finally, a bi-level game-based optimal scheduling model of the IEP, aggregator, and utility considering energy supply risk is proposed. Case studies demonstrate that the proposed model can effectively reduce the operating cost of the utility, reasonably allocate the benefit of the aggregator and the IEP, and can effectively balance energy supply risk and social welfare maximization of the electricity–heat–hydrogen integrated energy system. Full article

Silver nanoparticles are metallic particles with very small dimensions and excellent optical, electrical and biological properties. Lately, they have shown promising results in biosensing applications. In the material’s fabrication, the synthesis parameters remain the main aspect to be considered once a certain application is targeted. Therefore, this work presents the synthesis of silver nanoparticles using a chemical reduction based on various volumes of reducing and stabilizing agents. The multiple synthesis methods proposed were tested and optimized in order to achieve the best results for further biosensing applications. In this regard, sodium borohydride (NaBH₄) was used as reducing agent in volumes of 400 μL and 1 mL, while trisodium citrate (TSC) was proposed in much smaller volumes of 10, 20, and 50 μL. The optical and morphological analysis obtained from UV-VIS and TEM microscopy confirmed the formation of nanoparticles in case of all synthesis. The average diameters of silver nanoparticles were in the range between 21 and 27 nm, with high homogeneity for the samples with 20 and 50 μL of TSC. FT-IR analysis confirmed the TSC functionalization on the AgNPs’ surface. SERS analysis and the bulk sensitivity method also showed good surface results, leading to the assumption that both reducing and stabilizing agents can influence the final properties of the material. LSPR biosensing of para-aminothiophenol was tested, and was proven to have detection capabilities at concentrations as low as 10⁻⁷ M. Overall, the results proved that the synthesis method with a smaller amount of reducing agent and a moderate quantity of stabilizing agent has superior properties for biosensing applications. Full article

Modern electrical vehicles (EVs) are equipped with sizable batteries that possess significant potential as energy prosumers. EVs are poised to be transformative assets and pivotal contributors to the virtual power plant (VPP), enhancing the performance and profitability of VPPs. The number of household EVs is increasing yearly, and this poses new challenges to the optimization of VPP operations. The computational cost increases exponentially as the number of decision variables rises with the increasing participation of EVs. This paper explores the role of a large number of EVs as prosumers, interacting with a VPP consisting of a photovoltaic system and battery energy storage system. To accommodate the large quantity of EVs in the modeling, this research adopts the decentralized control structure. It optimizes EV operations by regulating their charging and discharging behavior in response to pricing signals from the VPP. A two-stage optimization framework is proposed for VPP-EV operation using a reinforcement algorithm and gradient-based programming. Action masking for reinforcement learning is explored to eliminate invalid actions, reducing ineffective exploration, thereby accelerating the convergence of the algorithm. The proposed approach is capable of handling a substantial number of EVs and addressing the stochastic characteristics of EV charging and discharging behaviors. Simulation results demonstrate that the VPP-EV operation optimization increases the revenue of the VPP and significantly reduces the electricity costs for EV owners. Through the optimization of EV operations, the charging cost of 1000 EVs participating in the V2G services is reduced by 26.38% compared to those that opt out of the scheme, and VPP revenue increases by 27.83% accordingly. Full article

The integration of LED lighting enables precise radiation control in plant factory cultivation systems. While LEDs offer energy efficiency and spectral tuning, achieving a uniform photosynthetic photon flux density (PPFD) remains a critical technical challenge. This study evaluated the impact of three spatial LED configurations on irradiance uniformity using commercial horticultural LEDs and a light recipe of 75% red and 25% blue. Optical simulations in TracePro^® 2017 were conducted to analyze radiant flux, optical efficiency, and uniformity, along with LED quantity, system cost, and electrical consumption under two environmental scenarios: open (without reflective walls) and closed (with reflective walls). Results show that distribution 3, which featured reduced central LED density, achieved 4–8% higher homogeneity in the open scenario, and 2.7–6.5% in the closed scenario, compared to symmetric layouts (distribution 1 and 2). Reflective walls increased average PPFD by up to 20% and optical efficiency by around 9%, with a minimal effect on uniformity. Lowering the lamp-to-canopy distance from 35 cm to 30 cm resulted in a 10% increase in PPFD. Despite a reduction in total photon flux, distribution 3 exhibited superior irradiance homogeneity. One-way ANOVA confirmed significant effects of environment, height, and LED model (p < 0.05), but not of spatial alone. This simulation-based methodology offers a robust framework for optimizing energy-efficient lighting systems. Future work will explore the integrating of non-visible wavelengths and experimental validations to extend practical applicability. Full article