Reactive Energy Research Articles

Reducing reactive energy consumption by optimizing operating modes of irrigation pumping stations

The article develops energy and resource efficiency modes of the technological process that meet the requirements of the “frequency converter - asynchronous motor - pump - pressure pipeline” system as a control object. Methods for regulating water pressure and water flow produced by pumping units are considered in order to ensure a schedule for water supply to irrigation pumping stations. The active, reactive and total electrical energies received by an asynchronous motor to change water flow by changing the position of the pressure valve in the pressure pipeline are calculated. In order to optimize water flow and water pressure by changing the speed of rotation of the pump impeller, the frequency converter system of an asynchronous motor with a squirrel-cage rotor was calculated and compared with the method of changing water flow using a push valve, by calculating active, reactive and total electrical energy from the network.

Benchmark ab initio characterization of the multi-channel Cl + CH3X [X = F, Cl, Br, I] reactive potential energy surfaces.

We determine benchmark geometries and relative energies for the stationary points of the Cl + CH3X [X = F, Cl, Br, I] reactions. We consider four possible reaction pathways: hydrogen abstraction, hydrogen substitution, halogen abstraction, and halogen substitution, where the substitution processes can proceed via either Walden inversion or front-side attack. We perform geometry optimizations and obtain harmonic vibrational frequencies at the explicitly-correlated UCCSD(T)-F12b/aug-cc-pVTZ level of theory, followed by UCCSD(T)-F12b/aug-cc-pVQZ single-point computations to make finite-basis-set error negligible. To reach chemical (<1 kcal mol-1), or even subchemical (<0.5 kcal mol-1) accuracy, we include core-correlation, scalar relativistic, post-(T), spin-orbit-splitting and zero-point-energy contributions, as well, in the relative energies of all the stationary points. Our benchmark 0 K reaction enthalpies are compared to available experimental results and show good agreement. The stationary-point structures and energetics are interpreted in terms of Hammond's postulate and used to make predictions related to the dynamical behavior of these reactive systems.

A Multi-Objective Stochastic Framework for Coupled Reactive Power and Energy Market Settlement for Wind Energy Integrated System

A Multi-Objective Stochastic Framework for Coupled Reactive Power and Energy Market Settlement for Wind Energy Integrated System

Optimal Day Ahead Active and Reactive Power Management in Residential Buildings using Particle Swarm Optimization

This paper introduces an innovative Active and Reactive Residential Building Energy Management (AR-RBEM) system, seamlessly integrated with a smart microgrid and the Electrical Power Grid (EPG) supplier. The proposed AR-RBEM computes optimal active and reactive power setpoints for each energy source over the next 24 hours based on predicted PV power generation and load consumption. The proposed strategy aims to achieve two primary objectives: minimize overall energy expenses, considering both active and reactive power consumption, and account for Energy Storage System (ESS) degradation costs. Additionally, it seeks to reduce the Peak-to-Average Ratio (PAR) and carbon emissions from the supplier’s perspective. To tackle this multi-objective problem, a metaheuristic algorithm which is Particle Swarm Optimization (PSO), is employed. To assess the effectiveness of the proposed approach, a comparative analyse is conducted, emphasizing the significance of concurrently managing active and reactive power compared to a system exclusively managing active power (A-RBEM). Both strategies are evaluated using Time of Use Tarriff (TOU) and real world data. The comparative analysis underscores the advantages of the proposed AR-RBEM, showcasing benefits in terms of cost reduction for both active and reactive power of 59,6%, a substantial 25.3% reduction in carbon emissions compared to the alternative strategy.

Hollow spherical Ni/ZrO2 as a superior catalyst for syngas production from photothermal synergistic dry reforming of methane

Dry reforming of methane (DRM) is appealing for syngas production yet challenging due to its high reactive energy barrier and catalyst deactivation.

A robust PQ-controlled three-phase SAPF-based SVM and PI anti-windup for power quality improvement

The use of power electronics devices in electrical energy conversion installations has significantly contributed to improving the performance and efficiency of these systems. On the other hand, static converters have contributed to the deterioration of the quality of current and voltage in distribution networks. These converters consume non-sinusoidal currents, and they behave like harmonic current generators. The power factor and overall system efficiency are weakened by these injected harmonics, reactive power, and other issues. Meanwhile, those loads’ harmonic difficulties must be resolved. The research presented in this paper particularly concerns the study of a shunt active power filter (SAPF) intended to compensate for the harmonic currents generated by nonlinear loads and the compensation of reactive energy. The theory of PQ control is the subject of this research. A robust structure is proposed for that purpose. To sustain the DC-link voltage at the desired interval, a robust PI-regulator-based antiwindup is inserted to ensure active power control. Besides, space vector modulation (SVM) is designed to replace classical pulse width modulation (PWM). The principal benefits of this technique are that it supplies a unitary power factor and sinusoidal network current with no requirement for a PWM generator and fewer harmonics. A comparison between the conventional and the proposed control is established to demonstrate the superiority of the PQ-SVM scheme. The comparison mainly concerns the bus voltage as it is the responsible for the whole system stability Expanded simulation results obtained from the transient and steady-state have demonstrated high performance and have confirmed the robustness and effectiveness of the suggested method.

Numerical studies on the propagation of iron dust flames in confinement

Iron powder is a promising alternative fuel owing to its high energy density, non-volatile combustion, and recyclability using green hydrogen. For the safe storage, transportation, and use of iron powder as a reactive energy source, the physical mechanisms behind pressurising dust flames in explosions must be understood. Here, we study the propagation of one-dimensional iron dust flames in confinement using an Euler–Lagrange framework with reflective adiabatic boundary conditions. The gas phase is described by the compressible Navier–Stokes equations, and heterogeneously burning iron particles follow the parabolic rate law for solid-phase oxidation, and diffusion-limited combustion during liquid-phase combustion. We demonstrate that the pressurisation in a closed vessel leads to an increase in unburnt gas density, decelerating flame propagation via a decrease in thermal diffusivity. Flames through dust suspensions at concentrations above the thermodynamic limit are quenched, while for concentrations near the quenching limit, flames are quenched before re-ignition. The trajectories of particles and gas parcels demonstrate that particles are strongly entrained in the gas streams, causing fluctuations in local dust concentrations. The transient evolution of flame speeds in confinement is used to inform a pressure-rise model, which predicts the time to peak pressure in a 20-L vessel with good agreement to experimental measurements. Our insights provide mechanistic understandings about dust flame propagation under pressurising conditions, particularly relevant to the informed design of explosion protection measures.

Optimization of the Powers Exchanged between a Cascaded Doubly Fed Induction Generator and the Grid with a Matrix Converter

The use of an electromagnetic converter of great reliability in wind energy, like the cascaded doubly fed induction generator (CDFIG), can solve several problems compared to the doubly fed induction generator (DFIG), among them the elimination of the brushes and copper rings. The work consists of studying the performance of a CDFIG-based wind system using a matrix converter and a variable pitch control system. The traditional PI is able to successfully solve a number of linear control problems. The fuzzy logic controller used can dramatically improve the system's performance. The active and reactive energy transferred between the CDFIG and the grid can be controlled by adjusting the machine inverter. The DC bus voltage has been kept stable by controlling the grid inverter. The pitch controller's objective is to maximise aerodynamic power while staying within the converter's power limits. Numerical simulation results implemented in the MATLAB/Simulink package will be provided in this study to show that the suggested control strategy is both possible and effective.

Renewable Energy in Portugal - Legislation, Incentives and Suggestions.

This paper presents the evolution of renewable energy generation in Portugal in the last decade, and explains the legislation and incentives in existence. The paper also presents suggestions that could incentive small and medium consumers to install renewable energy power plants (namely of wind power and photovoltaic types) in their facilities. These power plants would have to accomplish present legislation regarding “reactive energy” production during peak power consumption period, and besides, would have to assure power quality levels. On the other hand, reversible energy meters should be used, so that it would be registered the consumption of energy from the energy company, or the delivery of energy to the energy company. The equipments which could implement these characteristics are briefly described.

Determination of the Optimal Level of Reactive Power Compensation That Minimizes the Costs of Losses in Distribution Networks

The objective of the presented paper is to verify economically justified levels of reactive energy compensation in the distribution network in the new market conditions, including the extensive use of smart metering systems, new types of load, or distributed generation. The proposed methodology is based on the minimization of annual costs of losses caused by the flow of reactive energy to the supplied loads through the equivalent resistance of the distribution system determined on the basis of statistical energy losses in this network. The costs of losses are compared to the costs of using compensating devices expressed by the levelized costs of reactive energy generation. The results are the relations describing the optimal annual average value of the tgφ factor to be maintained by customers to optimize the cost of loss of the distribution network caused by reactive energy flows. The dependence of the optimal tgφ value on the analyzed load and network parameters is also discussed. The resulting optimal tgφ levels should be considered in the tariffication process of services offered by distribution system operators to improve capacity and limit the costs of power network operation due to reactive energy transmission.

Density functional theory study of adsorption and dissociation of CH2Cl2 on the surfaces of transition metal (Fe, Co, Ni, and Cu)-doped carbon nanotubes

A DFT study was carried out on the adsorption capacity and dissociation mechanism of dichloromethane by intrinsic carbon nanotubes and Fe, Co, Ni and Cu adsorption-modified carbon nanotube systems. The calculated results indicated the dopant of transition metal can effectively enhance the electronic properties of carbon nanotubes. The adsorption of carbon nanotubes for CH2Cl2 is also enhanced, and the dissociations of CH2Cl2 on the four metal-decorated carbon nanotubes are exothermic. Especially in the case of Fe adsorption modification (adsorption energy reaches -1.91 eV), which is much higher than the intrinsic carbon nanotube adsorption energy of CH2Cl2 (-0.38 eV), and the reactive energy barrier of the dissociation process of the CH2Cl2 is as low as 0.493 eV Meanwhile, the dissociation process can occur spontaneously at room temperature, which contributes to the dissociation of CH2Cl2 on Fe adsorption-modified carbon nanotube systems, providing theoretical support for the subsequent governance process of CH2Cl2.

Role of mitochondria in the regulation of ferroptosis and disease.

Ferroptosis is a distinctive form of iron-dependent cell death characterized by significant ultrastructural changes in mitochondria. Given the crucial involvement of mitochondria in various cellular processes such as reactive oxygen species production, energy metabolism, redox status, and iron metabolism, mounting evidence suggests a vital role of mitochondria in the regulation and execution of ferroptosis. Furthermore, there exists a strong correlation between ferroptosis and various diseases. In this review, we aim to summarize the mechanisms underlying the induction and defense of ferroptosis, emphasizing the influence of mitochondria on this intricate process. Additionally, we provide an overview of the role of ferroptosis in disease, particularly cancer, and elucidate the mechanisms by which drugs targeting mitochondria impact ferroptosis. By presenting a theoretical foundation and reference point, this review aims to contribute to both basic cell biology research and the investigation of clinically relevant diseases.

The role of melatonin in preventing amiodarone-induced rat liver damage.

Long-term exposure to amiodarone, an antiarrhythmic drug, can induce different organ damage, including liver. Cell damage included by amiodarone is a consequence of mitochondrial damage, reactive oxygen species production, and cell energy depletion leading to programmed cell death. In the present study, hepatoprotective potential of neurohormone melatonin (50mg/kg/day) was evaluated in a chronic experimental model of liver damage induced by a 4-week application of amiodarone (70mg/kg/day). The obtained results indicate that amiodarone induces an increase in xanthine oxidase activity, as well as the content of the lipid and protein oxidatively modified products and p53 levels. Microscopic analysis further corroborated the biochemical findings revealing hepatocyte degeneration, apoptosis, and occasional necrosis, with the activation of Kupffer cells. Coadministration of melatonin and amiodaron prevented an increase in certain damage associated parameters, due to its multiple targets. In conclusion, the application of melatonin together with amiodarone prevented an increase in tissue oxidative damage parameters and moderately prevented liver cell apoptosis, indicating that the damage of hepatocytes provoked by amiodarone supersedes the protective properties of melatonin in a given dose.

Coffee charcoal as a green catalyst for oxidative dehydrogenation

Nitrogen heterocyclic (N-heterocyclic) compounds are extensively present in natural products and active drug molecules. However, their synthesis typically requires the use of noble metal catalysts and harsh conditions. In this study, we report a novel approach using coffee charcoal as a green catalyst for the mildly mechanically driven oxidative dehydrogenation of N-heterocyclic compounds. A one-step strategy was employed to construct a microporous coffee charcoal catalyst with C defects, in-situ co-doping of multi-atoms (N, O), and abundant modification of functional groups (–OH and –COOH). Under room temperature and with H2O as solvent, the coffee charcoal catalyst demonstrates a high yield of approximately 94 % and allows for gram-scale reactions with H2O being the only by-product. Moreover, the catalyst exhibits excellent reusability, maintaining its activity for over 13 cycles. Results indicate that the defects and atom doping can enhance the adsorption of O2, which is then activated to singlet oxygen (1O2) through the conversion of –OH functional groups on the catalyst’s surface. The singlet oxygen (1O2) can strengthen the oxidative dehydrogenation of N-heterocyclic compounds. Furthermore, the mechanically driven technique both activates the reaction interface and supplies the necessary reactive energy, thus providing a comprehensive explanation for the mild reaction conditions observed. In conclusion, our research proposes a sustainable and environmentally friendly method for the oxidative dehydrogenation of N-heterocyclic compounds, which can drive crucial reactions with significant relevance to sustainability.

A Holistic P2P market for active and reactive energy trading in VPPs considering both financial benefits and network constraints

Due to the rise of distributed energy resources (DERs), virtual power plants (VPPs) have gained intensive academic and industrial attention. While current centralized VPPs benefit from their large aggregated capacity in the wholesale market, they lack effective incentivizing participants for asset investment and market participation. Conversely, the peer-to-peer (P2P) market offers better profits for prosumers through direct trading; however, transactions hinge on approval from associated authorities, such as electricity retailers, who read their power meters. Such an approval procedure becomes complex, even impossible, when more retailers are involved. Hosting the P2P market in a VPP environment can conveniently resolve this issue. This paper proposes a novel holistic P2P (H-P2P) market model hosted in a VPP environment to provide the VPP participants with financial incentives and optimize the network and market operations simultaneously. The proposed new market structure and trade principle allow the proposed H-P2P market to trade active and reactive energies for both financial benefits and network constraints. Using the novel transactive voltage/VAR control (TVVC) method proposed in this paper, the reactive energy trading through the P2P market can support effective voltage regulation and incentivize the VPP participants to provide such voltage control ancillary services actively. Finally, a novel multi-stage P2P negotiation process is introduced to divide the VPP regulatory, active and reactive energy convergence stages, which allows faster P2P decision-making with improved computing efficiency than the conventional multi-period alternating direction method of multipliers (ADMM). The case study validates that the proposed H-P2P market can increase the financial incentives of the prosumers who install PVs and batteries by a large extent and the pure load units by a relatively smaller percentage.

Hydrogen-rich water treatment improves fresh-cut Gastrodia elata quality by regulating reactive oxygen species metabolism and energy metabolism during low temperature storage

The application of hydrogen-rich water (HRW) can extend the shelf life of fruits and vegetables. This study aims to investigate the effects of HRW on the quality of fresh-cut Gastrodia elata during low-temperature (4°C) storage. The results indicate that HRW treatment inhibited the decrease in weight loss and the increase in respiration rate, and also slowed down the decrease in total acidity and total soluble solids. HRW treatment could reduce the generation of superoxide anions and hydrogen peroxide, enhance the activity and expression of antioxidant-related enzymes such as superoxide dismutase, catalase, ascorbate peroxidase, glutathione reductase, monodehydroascorbate reductase, and dehydroascorbate reductase, and increase the content of non-enzymatic antioxidant substances, ascorbic acid and glutathione, while decreasing the activity and expression of peroxidase and polyphenol oxidase. Furthermore, HRW treatment not only inhibited the reduction in ADP and ATP content and energy charge but also attenuated the increase in AMP content. It also slowed down the decrease in the activities of H+-ATPase, succinate dehydrogenase, Ca2+-ATPase, and cytochrome C oxidase. In conclusion, these results demonstrate that HRW treatment enhances the energy levels and effectively mitigates oxidative damage, thereby preserving postharvest quality of fresh-cut G. elata.

Artificial intelligent control of energy management PV system

Renewable energy systems, such as photovoltaic (PV) systems, have become increasingly significant in response to the pressing concerns of climate change and the imperative to mitigate carbon emissions. When static converters are used in solar power systems, they change the current, which uses reactive energy. A proportional-integral controller regulates active and reactive powers, whereas energy storage batteries enhance energy quality by storing current and voltage as they directly affect steady-state error. The utilization of artificial intelligence (AI) is crucial for improving the energy generation of PV systems under various climatic circumstances, as conventional controllers do not effectively optimize the energy output of solar systems. Nevertheless, the performance of PV systems can be influenced by fluctuations in meteorological conditions. This study presents a novel approach for integrating solar PV systems with high input performance through adaptive neuro-fuzzy inference systems (ANFIS). A fuzzy neural inference-based controller regarding energy generation and consumption aspects was designed and examined. This study examines the importance of artificial intelligence in facilitating continuous power supply to clients using a battery system, hence emphasizing its significance in energy management. Moreover, the findings demonstrated promising outcomes in energy regulation and management.

Gossypol Affects Viral Replication by Inhibiting Pseudorabies Virus Adsorption

Pseudorabies virus (PRV) has spread widely in swine herds since it was introduced into China, and PRV variants have appeared in many regions in China since 2011. Owing to the ongoing emergence of viral mutations, the protective efficacy of PRV vaccines faces great challenges. Gossypol is a polyphenolic natural compound purified from cotton roots, stems, or seeds. This study found that gossypol has a notable inhibitory effect on PRV. Based on the biological effects of gossypol, the anti-PRV effect of gossypol was studied from the aspects of mitochondrial damage, reactive oxygen species, energy metabolism, and autophagy. The results showed that gossypol had no direct inactivation effect on PRV in solution within concentrations of ≤3 µM; gossypol did not stimulate interferon-β to exert an anti-PRV effect by triggering mitochondrial DNA leakage; the anti-PRV effect of gossypol was reactive oxygen species and ATP-independent in cells; and the PRV-inhibitory effect of gossypol was independent of its induced autophagy. By adding gossypol to PRV-infected cells at different stages of infection and performing virus titer detection, atomic force microscopy, qPCR, fluorescence microscopy, and firefly luciferase activity assays, we found that gossypol exerted an anti-PRV effect by inhibiting the adsorption of PRV on the cell surface.

Improved Active and Reactive Energy Forecasting Using a Stacking Ensemble Approach: Steel Industry Case Study

The prevalence of substantial inductive/capacitive loads within the industrial sectors induces variations in reactive energy levels. The imbalance between active and reactive energy within the network leads to heightened losses, diminished network efficiency, and an associated escalation in operating costs. Therefore, the forecasting of active and reactive energy in the industrial sector confers notable advantages, including cost reduction, heightened operational efficiency, safeguarding of equipment, enhanced energy consumption management, and more effective assimilation of renewable energy sources. Consequently, a range of specialized forecasting methods for different applications have been developed to address these challenges effectively. This research proposes a stacked ensemble methodology, denoted as Stack-XGBoost, leveraging three distinct machine learning (ML) methods: extra trees regressor (ETR), adaptive boosting (AdaBoost), and random forest regressor (RFR), as foundational models. Moreover, the incorporation of an extreme gradient boosting (XGBoost) algorithm as a meta-learner serves to amalgamate the predictions generated by the base models, enhancing the precision of the active/reactive energy consumption forecasting using real time data for steel industry. To assess the efficacy of the proposed model, diverse performance metrics were employed. The results show that the proposed Stack-XGBoost model outperformed other forecasting methods. Additionally, a sensitivity analysis was conducted to assess the robustness of the proposed method against variations in input parameters.

A Gaussian Process Based Δ-Machine Learning Approach to Reactive Potential Energy Surfaces.

The Gaussian process (GP) is an efficient nonparametric machine learning (ML) method. A distinct advantage of the GP is its ability to provide an estimate of statistical uncertainties. This is particularly useful in constructing high-dimensional potential energy surfaces (PESs) from ab initio data as it offers an optimal way to add new geometries to reduce the overall error. In this work, GP is employed in the context of Δ-machine learning (Δ-ML), in which a correction PES to an inaccurate low-level PES is constructed using a small number of high-level ab initio calculations. This new method is tested in three prototypical reactive systems, namely, the H + H2 → H + H2, OH + H2 → H2O + H, and H + CH4 → H2 + CH3 reactions. The results show that the GP-based Δ-ML approach is more efficient than its direct application in constructing high-level PESs. We also compare the new method to a previously proposed neural-network-based Δ-ML approach [Liu and Li J. Phys. Chem. Lett. 2022, 13, 4729-4738]. The results indicate that the two Δ-ML methods have comparable efficiencies in constructing accurate PESs.