An effective strategy for unit commitment of microgrid power systems integrated with renewable energy sources including effects of battery degradation and uncertainties.

The large use of renewable sources and plug-in electric vehicles (PEVs) would play a critical part in achieving a low-carbon energy source and reducing greenhouse gas emissions, which are the primary cause of global warming. On the other hand, predicting the instability and intermittent nature of wind and solar power output poses significant challenges. To reduce the unpredictable and random nature of renewable microgrids (MGs) and additional unreliable energy sources, a battery energy storage system (BESS) is connected to an MG system. The uncoordinated charging of PEVs offers further hurdles to the unit commitment (UC) required in contemporary MG management. The UC problem is an exceptionally difficult optimization problem due to the mixed-integer structure, large scale, and nonlinearity. It is further complicated by the multiple uncertainties associated with renewable sources, PEV charging and discharging, and electricity market pricing, in addition to the BESS degradation factor. Therefore, in this study, a new variant of mixed-integer particle swarm optimizer is introduced as a reliable optimization framework to handle the UC problem. This study considers six various case studies of UC problems, including uncertainties and battery degradation to validate the reliability and robustness of the proposed algorithm. Out of which, two case studies defined as a multiobjective problem, and it has been transformed into a single-objective model using different weight factors. The simulation findings demonstrate that the proposed approach and improved methodology for the UC problem are effective than its peers. Based on the average results, the economic consequences of numerous scenarios are thoroughly examined and contrasted, and some significant conclusions are presented.

Leveraging opposition-based learning for solar photovoltaic model parameter estimation with exponential distribution optimization algorithm.

Given the multi-model and nonlinear characteristics of photovoltaic (PV) models, parameter extraction presents a challenging problem. This challenge is exacerbated by the propensity of conventional algorithms to get trapped in local optima due to the complex nature of the problem. Accurate parameter estimation, nonetheless, is crucial due to its significant impact on the PV system's performance, influencing both current and energy production. While traditional methods have provided reasonable results for PV model variables, they often require extensive computational resources, which impacts precision and robustness and results in many fitness evaluations. To address this problem, this paper presents an improved algorithm for PV parameter extraction, leveraging the opposition-based exponential distribution optimizer (OBEDO). The OBEDO method, equipped with opposition-based learning, provides an enhanced exploration capability and efficient exploitation of the search space, helping to mitigate the risk of entrapment in local optima. The proposed OBEDO algorithm is rigorously verified against state-of-the-art algorithms across various PV models, including single-diode, double-diode, three-diode, and photovoltaic module models. Practical and statistical results reveal that the OBEDO performs better than other algorithms in estimating parameters, demonstrating superior convergence speed, reliability, and accuracy. Moreover, the performance of the proposed algorithm is assessed using several case studies, further reinforcing its effectiveness. Therefore, the OBEDO, with its advantages in terms of computational efficiency and robustness, emerges as a promising solution for photovoltaic model parameter identification, making a significant contribution to enhancing the performance of PV systems.

Optimal estimation of parameters of the three-diode commercial solar photovoltaic model using an improved Berndt-Hall-Hall-Hausman method hybridized with an augmented mountain gazelle optimizer.

It is absolutely necessary to extract the photovoltaic (PV) model parameters to anticipate the energy production of PV systems accurately. In the literature, many studies have analyzed and discussed various strategies for handling the parameter computation of the PV model. However, very few studies have been conducted to formulate the fitness function, and no studies have been presented on the methodologies to solve the nonlinear, multivariable, and complicated PV models based on empirical data. As a result, the key objective is to investigate the traditional methods for solving the equations of PV models. An improved variant of the Mountain Gazelle Optimizer (MGO) called Augmented Mountain Gazelle Optimizer (AMGOIB3H) is proposed to guarantee MGO convergence based on an improved Berndt-Hall-Hall-Hausman method. This AMGOIB3H highlights key advancements in the literature regarding improving the exploration and exploitation phases of MGO and the design of objective functions. Finally, a hybrid method has been established for effectively identifying unknown parameters of the three-diode PV model. This method uses actual measured laboratory data gathered under various environmental conditions. The simulation results show that the AMGOIB3H reduces errors to zero under various statistical standards and environmental variables. In addition, the AMGOIB3H outperforms the state-of-the-art algorithm in the research literature regarding reliability, accuracy, and convergence rate with a reasonable processing time.

Dietary supplementation of curcumin-loaded chitosan nanoparticles stimulates immune response in the white leg shrimp Litopenaeus vannamei challenged with Vibrio harveyi.

Pathogen infections in shrimps trigger the release of reactive oxygen species (ROS) as a part of immune response. The excessive accumulation of ROS causes the production of oxidative stress, which leads to oxidative damage of the biomolecules in the host cells. The inclusion of dietary antioxidants is known to mitigate oxidative stress and stimulate immunity. Curcumin, a potential antioxidant was encapsulated in chitosan nanoparticles to surge its bioavailability and was administered orally to Vibrio harveyi challenged and non-challenged Litopenaeus vannamei. The non-challenged shrimps fed with curcumin-loaded chitosan nanoparticles (Cur-CSNPs) showed a significant increase (p ≤ 0.05) in the specific growth rate, daily growth coefficient and survival rate. A significant increase (p ≤ 0.05) in the phenoloxidase activity, total hemocyte count and superoxide dismutase activity was observed in both the challenged and non-challenged shrimps fed with Cur-CSNPs. Additionally, a significant increase (p ≤ 0.05) in the relative mRNA expression of lysozyme, cMnSOD and lectin was observed in the Cur-CSNPs fed shrimps. The findings of this research suggest that Cur-CSNPs reinforce the immune system of L. vannamei against V. harveyi infection. Moreover, the non-challenged shrimps showed improvement in the growth parameters in addition to immunostimulation. Thereby a routine inclusion of dietary Cur-CSNPs could mitigate the oxidative damage caused by the incidence of environmental or pathogen-mediated oxidative stress.

Curcumin loaded chitosan nanoparticles fortify shrimp feed pellets with enhanced antioxidant activity.

Chitosan nanoparticles (CSNPs) have been recently explored as a potential drug carrier to enhance the bioavailability and aqueous solubility of drugs. Curcumin, an antioxidant with a remarkable antiradical scavenging activity was encapsulated in CSNPs to revamp its bioavailability. While changes in the optimal farming condition can induce oxidative stress in the animals, curcumin loaded chitosan nanoparticles (Cur-CSNPs) were amalgamated into shrimp feed pellets to ameliorate its antioxidant content in an attempt to bolster the organisms against oxidative stress. Cur-CSNPs were synthesized in two different concentrations of curcumin as Cur-CSNPs A and B. Characterization of the synthesized Cur-CSNPs revealed asymmetrical nanoparticles with semispherical geometry and a zeta potential ˃50 mV. HPLC studies substantiated encapsulation efficiencies of 77.53% and 80.35% for Cur-CSNPs A and B respectively. DPPH, ABTS and FRAP assays manifested a significant enhancement in the antioxidant property of the Cur-CSNPs fortified feed pellets. This is the first study to investigate and demonstrate the ability of Cur-CSNPs to enhance the antioxidant property of aquaculture feed pellets. These findings substantiate that Cur-CSNPs fortified feed may be applied to reinforce aquaculture animals against oxidative stress.

Mineral and Trace Metal Concentrations in Seaweeds by Microwave-Assisted Digestion Method Followed by Quadrupole Inductively Coupled Plasma Mass Spectrometry.

This study reports the total concentrations of mineral and trace metals sodium, potassium, calcium, magnesium, phosphorus, iron, copper, zinc, and manganese in the seaweeds Padina tetrastromatica, Turbinaria ornate, Sargassum wightii, Sargassum swartzii, Gracilaria edulis, Ulva lactuca, Chaetomorpha antennina, and Halimeda opuntia collected from mandapam coastal regions, Southeast coast of India. Microwave-assisted digestion was used for sample preparation prior to mineral and trace metal analysis. Mineral and trace metal analyses were determined by inductively coupled plasma mass spectrometry. The ranges of concentrations of mineral and trace metals in algae were 27.04 ± 2.54-194.08 ± 2.36 mg/kg for manganese, 1.88 ± 0.10-121.5 ± 0.70 mg/kg for sodium, 6.5 ± 0.56-90.5 ± 2.12 mg/kg for magnesium, 59.07 ± 0.34-672 ± 2.82 mg/kg for potassium, 13.15 ± 2.08-135.13 ± 1.59 for sulfur, 0.003 ± 0.001-3.44 ± 0.13 mg/kg for cobalt, 0.39 ± 0.19-8.95 ± 0.38 mg/kg for copper, 0.72 ± 0.28-25.72 ± 0.39 mg/kg for zinc, and 6.01 ± 0.27-188.47 ± 1.92 mg/kg for iron.The results were evaluated statistically, and the significant difference was observed in the mean concentrations of all mineral and trace elements, except Co, Cu, and Zn, among the type of seaweeds.