Search Results (49)

The integration of photovoltaic (PV) generation into distribution systems supports decarbonization and cost reduction but introduces challenges for secure and efficient operation due to voltage fluctuations and power flow variability. Traditional centralized optimal power flow (OPF) methods require full system observability and significant computational resources, limiting their real-time applicability in active distribution systems. This paper proposes a deep neural network (DNN)-based OPF control framework designed for active distribution systems with high PV penetration under limited measurement availability. The proposed method leverages offline convex chance-constrained OPF (convex-CCOPF) solutions, generated through iterative simulations across a wide range of PV and load conditions, to train the DNN to approximate optimal control actions, including on-load tap changer (OLTC) positions and inverter reactive power dispatch. To address observability constraints, the DNN is trained using a reduced set of strategically selected measurement points, making it suitable for real-world deployment in distribution systems with sparse sensing infrastructure. The effectiveness of the proposed framework is validated on the IEEE 33-bus test system under varying operating conditions. The simulation results demonstrate that the DNN achieves near-optimal performance with a significantly reduced computation time compared to conventional OPF solvers while maintaining voltage profiles within permissible limits and minimizing power losses. Full article

Phase-shifting transformers play a crucial role in power grid stability and efficiency. They adjust phase differences between loads, improve transmission efficiency, and balance loads during large-scale power transmission and grid integration. However, traditional mechanical phase-shifting transformers use fixed-tap designs with limited taps, preventing continuous and precise adjustments. This discrete adjustment method affects control accuracy and optimal tap position selection for proper power flow. This paper proposes a hybrid open-loop and closed-loop control strategy. This strategy maintains the phase-shifting transformer at its optimal tap position, enhancing system regulation precision and control effectiveness. Full article

The war between humans and bacteria started centuries ago. With the advent of antibiotics, there was a temporary ceasefire in this war, but the scenario soon started becoming worse with the emergence of drug-resistant strains within years of the deployment of antibiotics in the market. With the surge in the misuse of antibiotics, there was a drastic increase in the number of multidrug-resistant (MDR) and extensively drug-resistant bacterial strains, even to antibiotics like Methicillin and vancomycin, aggravating the healthcare scenario. The threat of MDR ESKAPE pathogens is particularly high in nosocomial infections, where biofilms formed by bacteria create a protective barrier that makes them highly resistant to antibiotics, complicating the treatment efforts. Scientists are looking at natural and sustainable solutions, as several studies have projected deaths contributed by drug-resistant bacteria to go beyond 50 million by 2050. Many plant-derived metabolites have shown excellent antibacterial and antibiofilm properties that can be tapped for combating superbugs. The present review explores the current status of various studies on antibacterial plant metabolites like alkaloids and flavonoids and their mechanisms in disrupting biofilms and killing bacteria by way of inhibiting key survival strategies of bacteria like motility, quorum-sensing, reactive oxygen species production, and adhesion. These mechanisms were found to be varied in Gram-positive, Gram-negative, and acid-fast bacteria like Mycobacterium tuberculosis, which will be discussed in detail. The successful tapping of the benefits of such plant-derived chemicals in combination with evolving techniques of nanotechnology and targeted drug delivery can go a long way in achieving the goal of One Health, which advocates the unity of multiple practices for the optimal health of people, animals, and the environment. Full article

To enhance the operational stability of distribution networks during peak periods, this paper proposes a multi-timescale operational method considering load forecasting impacts. Firstly, the Crested Porcupine Optimizer (CPO) is employed to optimize the hyperparameters of long short-term memory (LSTM) networks for an accurate prediction of the next-day load curves. Building on this foundation, a multi-timescale optimization strategy is developed: During the day-ahead operation phase, a conservation voltage reduction (CVR)-based regulation plan is formulated to coordinate the control of on-load tap changers (OLTCs) and distributed resources, alleviating peak-shaving pressure on the upstream grid. In the intraday optimization phase, real-time adjustments of OLTC tap positions are implemented to address potential voltage violations, accompanied by an electrical distance-based control strategy for flexible adjustable resources, enabling rapid voltage recovery and enhancing system stability and robustness. Finally, a modified IEEE-33 node system is adopted to verify the effectiveness of the proposed multi-timescale operational method. The method demonstrates a load forecasting accuracy of 93.22%, achieves a reduction of 1.906% in load power demand, and enables timely voltage regulation during intraday limit violations, effectively maintaining grid operational stability. Full article

The proactive involvement of photovoltaic (PV) smart inverters (PVSIs) in grid management facilitates voltage regulation and enhances the integration of distributed energy resources (DERs) within distribution networks. However, to fully exploit the capabilities of PVSIs, it is essential to achieve optimal control of their operations and effective coordination with voltage-regulating devices in the distribution network. This study developed a dual strategy approach to forecast the optimal setpoints of onload tap changers (OLTCs), PVSIs, and distribution static synchronous compensators (DSTATCOMs) to improve the voltage profiles in power distribution systems. The study began by running a centralized AC optimal power flow (CACOPF) and using the hourly PV output power and the load demand to determine the optimal active and reactive power of the PVSIs, the setpoint of the DSTATCOM, and the optimal tap setting of the OLTC. Furthermore, Machine Learning (ML) models were trained as controllers to determine the reactive-power setpoints for the PVSIs and DSTATCOMs as well as the optimal OLTC tap position required for voltage stability in the network. To assess the effectiveness of the method, comprehensive evaluations were carried out on a modified IEEE 33 bus with a high penetration of PV energy. The results showed that deep neural networks (DNNs) outperformed other ML models used to mimic the coordination method based on CACOPF. Furthermore, when the DNN-based controller was tested and compared with the optimizer approach under different loading and PV conditions, the DNN-based controller was found to outperform the optimizer in terms of computational time. This approach allows predictive control in power systems, helping system operators determine the action to be initiated under uncertain PV energy and loading conditions. The approach also addresses the computational inefficiency arising from contingencies in the power system that may occur when optimal power flow (OPF) is run multiple times. Full article

This paper proposes a robust voltage control strategy for grid-forming (GFM) inverters in distribution networks to achieve power support and voltage optimization. Specifically, the GFM control approach primarily consists of a power synchronization loop, a voltage feedforward loop, and a current control loop. A voltage feedforward control circuit is presented to achieve error-free tracking of voltage amplitude and phase. In particular, the current gain is designed to replace voltage feedback for improving the current response and simplifying the control structure. Additionally, in order to optimize voltage and improve the power quality at the terminal of the distribution network, an optimization model for distribution transformers is established with the goal of the maximum qualified rate of the load-side voltage and minimum switching times of transformer tap changers. An enhanced whale optimization algorithm (EWOA) is designed to complete the algorithm solution, thereby achieving the optimal system configuration, where an improved attenuation factor and position updating mechanism is proposed to enhance the EWOA’s global optimization capability. The simulation results demonstrate the validity and feasibility of the proposed strategy. Full article

Microalgae are an effective solution for the treatment and valorization of wastewater generated in hydroponic systems. In the current context of sustainability and resource management, the search for ecological alternatives in agriculture is essential. This study investigated the use of wastewater from hydroponic systems, purified by microalgae, for the irrigation of Pelargonium × hortorum. An experiment was designed under controlled conditions in which different irrigation treatments were applied. Hydroponic leachates treated by microalgae were used at 100%, 75%, and 50% (diluted using tap water), in addition to tap water as a negative control and nutrient solution as a positive control. The treatment system was established in a raceway photobioreactor, which allowed the proliferation of microalgae that act as bioremediators for the elimination of pollutants and the removal of nitrogen and phosphorus. The growth parameters, biomass, and general health of the Pelargonium × hortorum plants were evaluated, complemented with physicochemical analyses of the water carried out during the experimental period. These analyses showed that the water obtained after the purification process retained nutrients that can be reused for irrigation. The results indicated that plants irrigated with treated water showed significant improvements in height, diameter, number of leaves, leaf area, leaf dry weight, and flower dry weight compared to those irrigated with tap water. In conclusion, the study shows that the treatment of hydroponic wastewater by means of microalgal cultivation represents a viable and ecological alternative for the irrigation of ornamental plants such as Pelargonium × hortorum. The implementation of this system contributes both to the reduction of pollutants and to the optimal use of water resources, establishing a solid basis for future research in which additional nutrients could be incorporated to balance the nutrient solution studied. Full article

The development of smart grids has led to an increased focus by transmission and distribution network operators on the Optimal Power Flow (OPF) problem. The solutions identified for an OPF problem are vital to ensure the real-time optimal control and operation of electric networks and can help enhance their efficiency. In this context, this paper proposed an original solution to the OPF problem, represented by optimal voltage control in electric networks integrating wind farms. Based on a fuzzy inference system (FIS) built in the Fuzzy Logic Designer of the Matlab environment, where the fuzzification process was improved through fuzzy K-means clustering, two approaches were developed, representing novel tools for OPF analysis. The decision-maker can use these two approaches only successively. The FIS-based first approach considers the load requested at the PQ-type buses and the powers injected by the wind farms as the fuzzy input variables. Based on the fuzzy inference rules, the FIS determines the suitable tap positions for power transformers to minimise active power losses. The second approach (I-FIS), representing an improved variant of FIS, calculates the steady-state regime to determine power losses based on the suitable tap positions for power transformers, as determined with FIS. A real 10-bus network integrating two wind farms was used to test the two proposed approaches, considering comprehensive characteristic three-day tests to thoroughly highlight the performance under different injection active power profiles of the wind farms. The results obtained were compared with those of the best methods in constrained nonlinear mathematical programming used in OPF analysis, specifically sequential quadratic programming (SQP). The errors calculated throughout the analysis interval between the SQP-based approach, considered as the reference, and the FIS and I-FIS-based approaches were 5.72% and 2.41% for the first day, 1.07% and 1.19% for the second day, and 1.61% and 1.33% for the third day. The impact of the OPF, assessed by calculating the efficiency of the electric network, revealed average percentage errors between 0.04% and 0.06% for the FIS-based approach and 0.01% for the I-FIS-based approach. Full article

The integration of photovoltaic (PV) power generation systems has significantly increased the complexity of voltage distribution in power grids, making it challenging for conventional Load Ratio Control Transformers (LRTs) to manage voltage fluctuations caused by weather-dependent PV output variations. Power Conditioning Systems (PCSs) interconnected with PV installations are increasingly considered for voltage control to address these challenges. This study proposes a Machine Learning (ML)-based control method for sub-transmission grids, integrating long-term LRT tap-changing with short-term reactive power control of PCSs. The approach estimates the voltage at each grid node using a Deep Neural Network (DNN) that processes measurable substation data. Based on these estimated voltages, the method determines optimal LRT tap positions and PCS reactive power outputs using Deep Reinforcement Learning (DRL). This enables real-time voltage monitoring and control using only substation measurements, even in grids without extensive sensor installations, ensuring all node voltages remain within specified limits. To improve the model’s transparency, Shapley Additive Explanation (SHAP), an Explainable AI (XAI) technique, is applied to the DRL model. SHAP enhances interpretability and confirms the effectiveness of the proposed method. Numerical simulations further validate its performance, demonstrating its potential for effective voltage management in modern power grids. Full article

This paper proposes an optimal control method for the on-load tap changer (OLTC) of a substation’s main transformer (M.TR), to maximize the voltage compliance rate (VCR) in distribution system feeders. The conventional auto voltage regulator (AVR)’s line-drop compensation (LDC) control method struggles with accurately determining load centers and has limitations in managing voltage due to the variability of distributed energy resources (DERs). To address these challenges, this study defines sample number-based VCR (SNB-VCR) as the performance index function to be maximized. The optimal tap positions for the OLTC are obtained using the gradient ascent method. Since the SNB-VCR evaluates voltage compliance using 15 min interval data collected from all the load and DER connection points in the distribution system, the tap position obtained by the gradient ascent method maximizes voltage quality for every feeder included in the system. Using a simulation, it is verified that the proposed tap control method improves the overall voltage quality and reduces the occurrence of overvoltage or undervoltage compared to LDC control. The proposed control strategy offers a practical solution for enhancing voltage management efficiency in modern distribution systems, particularly those with high penetration of DERs. Full article

Quinoa is the only single plant that can meet all the nutritional needs of human, and its potential for feed utilization has been continuously explored, becoming a prosperous industry for poverty alleviation. In order to further tap the feeding value of whole quinoa, develop quinoa as a feed substitute for conventional crops such as corn, and improve its comprehensive utilization rate, this experiment analyzed the silage quality and mycotoxin content of mixed silage of whole-plant quinoa (WPQ) with whole-plant corn (WPC) or stevia powder(SP) in different proportions, and further improved the silage quality of mixed silage by using two lactic acid bacteria preparations (Sila-Max and Sila-Mix). The quality, microbial population, and mycotoxin levels of quinoa and corn silage, as well as that of the mixed silage of quinoa and stevia, were evaluated using single-factor analysis of variance. The impact of various lactic acid bacteria preparations on the quality of whole-quinoa and whole-corn mixed silage was investigated through two-factor analysis of variance. WPQ and WPC were mixed at the ratio of 5:5 (QB5), 6:4 (QB6), 7:3 (QB7), 8:2 (QB8), 9:1 (QB9) and 10:0 (QB10). SP was mixed with WPQ at the supplemental levels of 0.2% (QB10S2), 0.4% (QB10S4), 0.6% (QB10S6), 0.8% (QB10S8) and 1.0% (QB10S10). After 60 days of silage, the silage indexes, the number of harmful microorganisms, and the mycotoxin levels were measured, to explore the appropriate ratio of mixed silage. The membership function analysis showed that the quality of mixed silage of WPQ with SP was better, and the optimal addition amount of SP was 0.6%. The results of Max and Mix on the quality improvement test of WPQ with WPC mixed silage showed that the two lactic acid bacteria formulations increased CP and AA content, and reduced NH₃-N/TN; pH was significantly lower than the control group (p < 0.01), and LA was significantly higher than the control group (p < 0.01). The microbial count results showed that the addition of lactic acid bacteria preparation significantly reduced the number of molds and aerobic bacteria, and the effect of Mix was better than that of Max. When the mixing ratio was between QB7 and QB10, mold was not detected in the lactic-acid-bacteria preparation groups. Max and Mix significantly reduced the levels of mycotoxins, both of which were far below the range of feed safety testing, and 16S rRNA sequencing revealed that the silage microbiota varied with different mixing ratios and whether lactic acid bacteria preparations were used. Max and Mix increased the relative abundance of Firmicutes, with Mix having a more significant effect, especially in the QB6 (65.05%) and QB7 (63.61%) groups. The relative abundance of Lactobacillus was significantly higher than that of the control group (p < 0.05). The relative abundance of Enterobacteriaceae and Streptococcus were negatively and positively correlated with the addition level of quinoa, respectively. Comprehensive analysis showed that adding 0.6% SP to the WPQ and using Mix in mixed silage of WPQ and WPC with the proportion of WPQ no less than 70% had the best silage effect, and was more beneficial to animal health. Full article

The formulation process for some drugs can be challenging, due to their unfavorable physical and mechanical properties and poor water solubility. Powder technology has made a significant impact in regard to the modification of the particles in active pharmaceutical ingredients (APIs) to produce high-quality granules. Spherical particles are preferred over other shapes, due to their high tap and bulk density, reduced dustiness, better flowability, strong anti-caking properties, and better mechanical performance during tableting. The present study investigates the possibility of obtaining spherical crystals of ceritinib, a drug used for the treatment of anaplastic lymphoma kinase (ALK)-positive advanced non-small cell lung cancer, which belongs to BCS class IV drugs and has a platy crystal shape. Ceritinib spheres were prepared by spherical agglomeration, in a ternary system, and quasi-emulsion solvent diffusion, with the addition of polyvinylpyrrolidone, as well as a combination of these two methods. With the combined method of spherical crystallization, crystals with the most favorable morphology and the narrowest distribution of particle sizes were obtained, which was the reason for further optimization. The influence of different impeller geometries and mixing rates on the morphology of the obtained crystals was examined and the optimal conditions for the process were selected. Using empirical correlations and a visual criterion, the process was scaled up from a 0.1 L to a 1 L batch crystallizer. The obtained crystals were characterized by light and scanning electron microscopy. The addition of a bridging liquid and/or a polymer additive did not change the internal structure of the ceritinib crystals, which was confirmed by X-ray powder diffraction. Full article

Chronic copper intoxication via drinking water induces diseases and physiological toxicity. Bamboo charcoal has been applied in the treatment of copper (Cu²⁺) in water. However, the adsorption by micron bamboo charcoal (MBC) of trace Cu²⁺ in tap drinking water and the underlying factors behind it have not been sufficiently reported. In this study, to improve the adsorption by MBC of trace levels of Cu²⁺ in drinking water, MBC was thermo-modified and characterized. Through batch experiments, the adsorption equilibrium was analyzed, and isotherm models were simulated. The removal rates and the optimization were investigated through a general full factorial design including the thermo-modified temperature (MT), initial concentration (C₀), and dosage. The results indicated that the thermo-modification significantly improved the removal by MBC of Cu²⁺ at trace level C₀. The satisfactorily low level of 0.12 ± 0.01 mg⋅L⁻¹ was achieved in the range of C₀ from 0.5 to 2.0 mg⋅L⁻¹ within the short contact time of 0.5 h. The processes conformed to the Freundlich and Langmuir adsorption isothermal models at a C₀ lower than 4.0 mg⋅L⁻¹ and higher than 8.0 mg⋅L⁻¹. The correlation between C₀ and dosage played an important role in the removal of Cu²⁺. This work proposes the application of the ecofriendly material MBC and an optimization mode in the removal of trace Cu²⁺ from tap drinking water. It is also revealed that the positive and negative correlation and the “critical point” of the removal rate with dosage depend on the initial concentrations. Full article

A new energy industry represented by photovoltaic and wind power has been developing rapidly in recent years, and its randomness and volatility will impact the stable operation of the power system. At present, it is proposed to enrich the regulation of the power grid by tapping the regulation potential of load-side resources. This paper evaluates the overall voltage regulation capability of substations under the premise of considering the impact on network voltage security and providing a theoretical basis for the participation of load-side resources of distribution networks in the regulation of the power grid. This paper proposes a Zbus linear power flow model based on Fixed-Point Power Iteration (FFPI) to enhance power flow analysis efficiency and resolve voltage sensitivity expression. Establishing the linear relationship between the voltages of PQ nodes, the voltage of the reference node, and the load power, this paper clarifies the impact of reactive power compensation devices and OLTC (on-load tap changer) tap changes on the voltages of various nodes along the feeder. It provides theoretical support for evaluating the voltage regulation range for substations. The day-ahead focus is on minimizing network losses by pre-optimizing OLTC tap positions, calculating the substation voltage regulation boundaries within the day, and simultaneously optimizing the total reactive power compensation across the entire network. By analyzing the calculated examples, it was found that a pre-scheduled OLTC (on-load tap changer) can effectively reduce network losses in the distribution grid. Compared with traditional methods, the voltage regulation range assessment method proposed in this paper can optimize the adjustment of reactive power compensation devices while ensuring the voltage safety of all nodes in the network. Full article

Infill well location optimization poses significant challenges due to its complexity and time-consuming nature. Currently, determining the scope of infill wells relies heavily on field engineers’ experience, often using single indices such as the remaining oil saturation or abundance of remaining oil reserves to evaluate the potential of remaining oil. However, this approach lacks effectiveness in guiding the precise tapping of remaining oil in ultra-high water cut reservoirs. To address this, our study comprehensively considers the factors influencing the recoverable potential of remaining oil in such reservoirs. We characterize the differences in reservoir heterogeneity, scale of recoverable remaining oil reserves, water flooding conditions, and oil–water flow capacity to construct a quantitative evaluation index system for the recoverable potential of remaining oil. Recognizing the varying degrees of influence of different indices on the recoverable potential of remaining oil, we determine the objective weight of each evaluation index by combining an accelerated genetic algorithm with the projection pursuit model. This approach enables the construction of a recoverable potential index for remaining oil and forms a quantitative evaluation method for the recoverable potential of remaining oil in ultra-high water cut reservoirs. Subsequently, we establish a mathematical model for infill well location optimization, integrating and optimizing the infill well location coordinates, well length, well inclination angle, and azimuth angle. Using the main layer sand body of an oilfield in Bohai as a case study, we conducted evaluations of the remaining oil potential and infill well location optimization. Our results demonstrate that the assessment of the remaining oil potential comprehensively characterizes the influence of the reservoir’s physical properties and oil–water diversion capacity on the remaining oil potential across different regional positions. This evaluation can effectively guide the determination of infill well location ranges based on the evaluation results. Furthermore, infill well location optimization can effectively enhance reservoir development outcomes. Full article