Hybrid System Research Articles

On a fully coupled nonlocal multipoint boundary value problem for a dual hybrid system of nonlinear q-fractional differential equations

A new class of nonlocal multipoint boundary value problems involving a dual hybrid system of nonlinear Riemann–Liouville-type [Formula: see text]-fractional differential equations is studied in this paper. Existence and uniqueness results for the given problem are derived by applying the Leray–Schauder nonlinear alternative and the Banach contraction mapping principle. Examples are presented for illustrating the obtained results. The work established in this paper is a useful contribution to the existing literature on [Formula: see text]-fractional differential equations. Some interesting special cases are also discussed.

Hybrid machine learning system based on multivariate data decomposition and feature selection for improved multitemporal evapotranspiration forecasting

Evapotranspiration is an essential component of the hydrological cycle. Forecasting the reference crop evapotranspiration (ETo) using a reliable and generalized framework is crucial for agricultural operations, especially irrigation. This study was aimed at evaluating the performance of a hybrid system including the K-Best selection (KBest), multivariate variational mode decomposition (MVMD), and Machine learning (ML) models for 1-, 3-, 7-, and 10-day-ahead forecasting of the daily ETo in twelve stations of California. The analysis covered a span of 20 years, from 2003 to 2022. Three stand-alone ML models, namely Cascade Forward Neural Network (CFNN), Extreme Learning Machine (ELM), and Bagging Regression Tree (BRT) are used and were integrated with various preprocessing techniques to construct three hybrid models, i.e., MVMD-KBest-CFNN, MVMD-KBest-ELM, and MVMD-KBest-BRT. According to the results obtained in the testing phase, averaged across all stations, all three stand-alone models (CFNN, ELM, and BRT) yielded similar outcomes. In contrast, the hybrid models exhibited significantly enhanced performances compared with the standalone models, and MVMD-KBest-CFNN and MVMD-KBest-ELM models outperformed MVMD-KBest-BRT model. The BRT-based models were vulnerable to overfitting. The performance of the best models is superior compared to similar existing studies. Examining the variations across stations, it was found that the stations located further from the coast and in arid regions could be susceptible to prediction errors and necessitate more attention.

Room temperature photon-magnon coupling in YIG- electric field coupled resonator system

Room temperature photon-magnon coupling in YIG- electric field coupled resonator system

Techno-economic study of a hybrid photovoltaic-diesel system for a remote area in southwest Algeria

Exploiting natural sun and wind resources in remote and isolated areas is undoubtedly an excellent decision to generate electrical energy due to their availability and cleanliness. Various systems were used to generate this energy, such as photovoltaics (PV), wind turbine sand other energy systems. Moreover, for optimum energy use, some of these systems are combined either with each other or with other conventional systems, such as diesel generators with PV systems (i.e., hybrid systems). This work aims to present a technical-economic study of PV/diesel autonomous hybrid systems to supply electrical power for an isolated house located in a hot desert climate, Adrar. For optimizing the hybrid systems, hourly input data of solar radiation and load were used according to two configurations, where the annual load is 11.2 kWh/day. The findings showed that the diesel system had a high cost, with a cost for energy of $0.407/kWh and a fuel price of $0.140/l. Among the hybrid power systems but with significant pollution, the proposed hybrid system 2 kw photovoltaic and diesel generator with 2.3 kW has important economic feasibility, where the energy cost amounted to $0.172/kWh. In addition, CO2 emissions are reduced by approximately 5 tons every year compared to an independent diesel generator system.

The influence of diverse climate on the 3E analysis of energy, exergy, and environmental aspects of a hybrid module of thermoelectric generator and photovoltaic cells

The increasing demand for energy and the efforts to meet this demand cleanly have drawn experts' attention towards renewable energy sources such as solar energy. In this article, a novel hybrid system consisting of a thermoelectric module for converting the heat from a solar module into electrical energy, coupled with a solar cell, has been utilized to maximize energy utilization. This unique integration of thermoelectric and photovoltaic technologies has been shown to take advantage of the fact that the thermoelectric module reduces the temperature of the solar module, consequently increasing its efficiency. The results of this study have been examined in five different cities and for five different climate types through three energy, exergy, and environmental analyses, which were conducted under two distinct scenarios. The PVSyst software was used to simulate environmental conditions and solar cell performance, while MATLAB was employed to code and connect the solar cell and thermoelectric modules. In the first scenario, by varying the thermoelectric merit constant and the intensity of solar radiation, it has been observed that the placement of the thermoelectric module increases the energy and exergy efficiency by approximately 5 % and 4 % respectively, and carbon dioxide pollution is reduced by 194 tons on a specific day of the month (July). Moreover, in the second scenario, by examining the hybrid system in the city of Ahvaz and determining the material and quantity of the thermoelectric and solar cell modules, it has been found that the energy and exergy efficiency of the hybrid system (in the maximum state) reach 20.12 % and 21.23 % respectively, in May. This system in the city reduces carbon dioxide pollution by approximately 41 tons on a specific day of the month (maximum reduction).

A techno-economic assessment of the viability of a photovoltaic-wind-battery storage-hydrogen energy system for electrifying primary healthcare centre in Sub-Saharan Africa

Healthcare facilities in isolated rural areas of sub-Saharan Africa face challenges in providing essential health services due to unreliable energy access. This study examines the use of hybrid renewable energy systems consisting of solar PV, wind turbines, batteries, and hydrogen storage for the electrification of rural healthcare facilities in Nigeria and South Africa. The study deployed the efficacy of Hybrid Optimization of Multiple Energy Resources software for techno-economic analysis and the Evaluation based on the Distance from Average Solution method for multi-criteria decision-making for sizing, optimizing, and selecting the optimal energy system. Results show that the optimal configurations achieve cost-effective levelized energy costs ranging from $0.336 to $0.410/kWh for both countries. For the Nigeria case study, the optimal energy system includes 5 kW PV, 10 kW fuel cell, 10 kW inverter, 10 kW electrolyzer, and 16 kg hydrogen tank. South Africa’s optimal configuration has 5 kW PV, 10 kW battery, 10 kW inverter, and 7.5 kW rectifier. Solar PV provides more than 90 % of energy, with dual axis tracking yielding the highest output: 8,889kWh/yr for Nigeria and 10,470kWh/yr for South Africa. The multi-criteria decision-making analysis reveals that Nigeria’s preferred option is the hybrid system without tracking. In contrast, the horizontal axis, weekly adjustment tracking configuration is optimal for South Africa, considering technical, economic, and environmental criteria. The findings highlight the importance of context-specific optimization for hybrid renewable energy systems in rural healthcare facilities to accelerate Sustainable Development Goals 3 and 7.

Novel control of PV-wind-battery powered standalone power supply system based LSTM based ANN

Integrated wind-photovoltaic (PV) based standalone electric power supply systems are widely used for various applications. A battery storage system is needed to provide continuous power supply to loads despite changes in loads, wind speed, and solar irradiance. Power quality is crucial in these hybrid systems, as the battery needs to charge from surplus power when generation exceeds the load and discharge to meet load demand. A bidirectional DC to DC converter is used to connect the battery to the network, and maximum power point tracking devices with proper algorithms are incorporated for optimal utilization of PV and wind turbines. Multiple PV systems and wind turbines are considered for proper power supply system ratings. Long short-term memory (LSTM) based artificial neural network (ANN) controllers are implemented for various control units in the hybrid standalone power system. The proposed control techniques improve power quality under various situations. Results are presented using MATLAB/Simulink to evaluate the performance of the proposed method.

Fundamental frequency switching strategies of a seven level hybrid cascaded H-bridge multilevel inverter

This paper presents a novel hybrid cascaded H-bridge multilevel inverter (HCHB MLI) designed to address the growing importance of multilevel inverters in the context of renewable energy sources such as solar, wind, and fuel cells. The proposed topology features eight insulated-gate bipolar transistor (IGBT) switches and utilizes two distinct input direct current (DC) sources: a battery and a capacitor, making it a hybrid system. The control strategy employed in this topology is based on fundamental switching frequency techniques. Simulation results of the proposed topology are conducted using MATLAB/Simulink software, while hardware experimentation with a single-phase H-bridge inverter is also demonstrated in the paper. For pulse generation and IGBT switch control, an Arduino UNO microcontroller is utilized. The output voltage of the single-phase H-bridge inverter is verified through experimentation using a cathode-ray oscilloscope (CRO).

Electric vehicle and photovoltaic advanced roles in enhancing the financial performance of a manufacturing and commercial setup

Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network.

The implementation of an optimized neural network in a hybrid system for energy management

In the face of increasing global energy demand and volatile energy prices, many countries are searching for solutions to ensure their energy independence. One of the most popular solutions is to incorporate renewable energy sources in their energy systems. While there are many advantages to integrating renewable energy sources, it is important to note that their intermittent operation can present challenges. Energy storage and smart grid management systems are key solutions to overcome these challenges and ensure sustainable, reliable use of renewable energy sources. In this article, we present an intelligent electrical energy management system for hybrid energy systems. This management system is based on a multi-layer neural network that has undergone an architecture optimization phase to improve the accuracy of real-time energy management and simplify its implementation. The management model that was built demonstrated highly good performance across a range of test circumstances.

Common-Mode Voltage Reduction Method-Based Flexible Power Control for Multiport DC–AC Converter in PV–Battery Hybrid System

Common-Mode Voltage Reduction Method-Based Flexible Power Control for Multiport DC–AC Converter in PV–Battery Hybrid System

A unit commitment method of pumped storage in renewable energy system via a twofold particle swarm optimization algorithm

Abstract The problem of unit commitment of pumped storage power stations (UCPS) is rarely discussed in existing works of multi-energy hybrid systems scheduling. It goes against accurately evaluating the operation performance and reliability of the pumped storage unit (PSU). This study proposes a co-optimization scheduling method for a Wind-Photovoltaic-Pumped storage system based on a twofold particle swarm optimization (TPSO). At the system layer, a standard PSO algorithm is used to optimize the output of the pumped storage plant (PSP). At the PSP layer, an improved discrete PSO based on three discrete variables is used to solve the UCPS problem. It uses the constraint patching and the micro-increment rate method, respectively, with detailed consideration of the constraints on PSP and the characteristics of the PSU. Finally, the results of a case study show that refined UCPS can reduce the regulation intensity indicators such as operating duration by more than 25%, with better consistency with the characteristics of units. This work could provide a guideline for improving the operation benefits and reliability of the PSP.

Multi-DG qZSI based grid tied inverters

The increasing adoption of distributed generation (DG) systems, driven by their numerous advantages, has led to a growing utilization of hybrid systems. Multilevel converters have emerged as essential components in power electronics applications, offering benefits such as harmonic reduction. However, designing effective control strategies for multilevel inverters presents challenges. This paper proposes a multi-DG quasi-Z-Source Inverter (qZSI) designed with a cascaded H-bridge controller. The incorporation of qZSI in the system enhances reliability and component safety while enabling the implementation of additional shoot-through control. This feature facilitates the balancing of inequalities arising from the two DG sources, thereby improving disturbance rejection. Furthermore, a current control scheme is implemented on the AC side to seamlessly feed active power into the grid without introducing distortions in current injection. Overall, the proposed system addresses key challenges in multilevel inverter design and offers a promising solution for efficient and reliable grid integration of DG systems. This work is thus useful in variety of applications, including electric vehicles and environment sustainability.

Hybrid renewable/grid power systems, an essential for base transceiver station penetration in Rural Nigeria

The energy crisis in Nigeria has continued to impede the rapid expansion of the telecommunication industry, whose operating expenditure is galloping due to over-dependence on diesel generators as an alternative source of power to its base transceiver station (BTS). This fossil-fuel power source has also increased the industry’s carbon footprint. As a solution to these problems, the objective of this work is to provide a sustainable and quality hybrid DC power supply system for BTS that would increase access to information and communication technology or ICT infrastructure. This involves the integration of solar & wind energy with the grid. The sizing of the hybrid sub-systems was designed & simulated using MATLAB Simulink to test for functionality. A prototype of the design system was then implemented with the results showing an average power output that guarantees 21 hours/day of supply. By installing this hybrid system of 1.3 kW, approximately 2.55 kg of diesel (C10H20) would be un-utilized by one BTS, thereby preventing 3.6 kg of CO2 from been emitted to the atmosphere daily. Extrapolating these values shows 930.75 kg of diesel can be saved and reduce 1314 kg of CO2 emission within a year. Hence eliminating the need for diesel-backup generator for a grid connected or non-grid BTS sited in rural areas.

Combining solar panels with plants for sustainable energy and food production: state of the art

The need for alternative energy sources becomes extensive because of the escalating cost of fossil fuels. The goal of this paper is to examine the effectiveness of combining photovoltaics and agriculture for better yield. Photovoltaic (PV) solar plants will compete with farms for available land. In this study, the methodologies are discussed how it is possible to maximize land utilization by placing solar arrays and food crops on the same plot of land. The term is proposed "agrivoltaic system" to describe this setup. Conventional solutions (discrimination of agricultural and energy extracting) were compared to two agrivoltaic schemes with varying density of PV arrays using land equivalent ratios. We utilized a crop model to simulate the amount of sunlight reaching the crop from an array of solar panels and to speculate on the yield reduction that would result from the partial shading. These early findings suggest that agrivoltaic systems may be highly effective; the two densities of PV panels were anticipated to boost worldwide land production by 73%. One possible explanation for the success of these hybrid systems is the presence of facilitation mechanisms analogous to those seen in agroforestry. At the end it is suggested that in places where arable land is rare, new solar plants may find it beneficial to produce both power and food.

Understanding synergistic effects between MgO expansive agents and hydrophilic nano-silica in UHPC

Ultra-high performance concrete (UHPC) incorporating MgO expansive agents (MEAs) demonstrates reduced early shrinkage but can potentially compromise mechanical strength, which may lead to serious quality incidents during early construction. Nanomaterials, with small particles, large specific surface area, and high hydration activity, have proven effective in enhancing the mechanical properties of cementitious materials. Nonetheless, limited research exists on the impact of nanomaterials on the performance of MEA-containing UHPC. This study aims to enhance the mechanical strength of the hybrid system by incorporating nanomaterials, specifically hydrophilic nano-SiO2 (HNS). The effect of HNS and/or MEA on UHPC performance was systematically investigated through various tests, including heat of hydration, MIP, XRD, TG-DTG, FTIR, and SEM-EDS. The results show that HNS improves the hydration process of cement, and effectively increases the compressive strength of MEA-containing UHPC by up to 30.87% at 3 days. The improvement of the mechanical properties of MEA-UHPC was attributed to the filling effect of the unreacted HNS, which effectively enhanced the densification of the matrix. Additionally, the crystallization and nucleation effect of hydrated HNS produced more C-S-H gels with high stiffness, which resisted the stresses generated by the micro-expansion of MEA. Importantly, in terms of less shrinkage, HNS exhibits a synergistic effect with MEA, wherein MEA provides the necessary weakly alkaline environment for the dissolution of reactive silica, while HNS appropriately increases the initial hydration degree of MEA. These combined effects further reduce the early shrinkage of UHPC, consequently lowering the risk of cracking. The collective improvement of all the aforementioned results suggests that HNS holds significant potential for application in MEA-containing UHPC.

Enhancement Network Security Features Through Intrusion Detection Systems

Abstract: In pursuit of its objectives, the project "Enhancement Network Security features through Intrusion Detection Systems" undertakes a comprehensive exploration of the intricacies surrounding network security, delving into the nuances of intrusion detection methodologies. By dissecting the mechanisms behind both anomaly- and signature-based IDS, the initiative aims not only to implement these systems but also to grasp their underlying principles deeply. Through rigorous experimentation and analysis, the project endeavours to uncover the strengths and limitations of each approach, thereby paving the way for the development of a hybrid system that leverages the best of both worlds. This meticulous approach ensures that the resulting IDS is not only robust but also finely tuned to adapt to the ever-evolving landscape of cyber threats. Furthermore, the integration of machine learning techniques represents a pivotal advancement in the realm of intrusion detection. By harnessing the power of algorithms capable of autonomously learning from data, the IDS transcends static rule-based detection methods, ushering in a new era of adaptive security measures. Through continuous training on vast datasets comprising diverse threat scenarios, the machine learning-enabled IDS hones its ability to discern subtle patterns indicative of malicious activity, thus bolstering its efficacy in safeguarding network assets. Moreover, the implementation of machine learning algorithms fosters a proactive stance against emerging threats, enabling the system to anticipate and mitigate potential breaches before they materialize into fullfledged attacks. This proactive approach not only enhances the overall security posture of organizations but also instils confidence in their ability to stay ahead of the curve in an increasingly hostile digital landscape. The project's goal is to provide companies with a stronger protection against cyberattacks by guaranteeing the availability, confidentiality, and integrity of their networked systems.

Economic and operational assessment of solar-assisted hybrid carbon capture system for combined cycle power plants

The paper presents a post-combustion CO2 capture process which involves two configurations for enhancing the CO2 separation driving force in the conventional amine-based Carbon Capture System (CCS): a multi-stage membrane module for selective exhaust gas recirculation (SEGR case) and the combination of turbine exhaust gas recirculation with SEGR (EGR + SEGR case). Furthermore, the stripper reboiler in both configurations is integrated with a parabolic trough solar collector field with a 4-h thermal energy storage to provide the required thermal duty for solvent regeneration. Various system components are designed, simulated, and integrated with an economic model to evaluate the system's techno-economic performance across a wide range of loads. The results show that the proposed designs have efficient part-load performance although efficiency degradation is inevitable during partial-loads. A stable supply of solar thermal energy results during the partial operation of NGCC even during night. The EGR + SEGR case represents the case with the lowest levelized cost of electricity and CO2 avoided cost, 81.43 $/MWh and 101.66 $/tonneCO2, among the CCS-equipped designs, which highlight the advantages of this design over baseline and SEGR case. The proposed hybrid system shows promise for significantly decarbonizing fossil-fuel power plants and increasing power sector flexibility.

Optimal sizing of battery energy storage system for mitigation of RES fluctuation

The integration of renewable energy sources (RES), such as wind and solar, into the electricity grid is crucial in the pursuit of sustainable energy. However, their inherent intermittency poses challenges to grid stability and reliability. Battery Energy Storage Systems (BESS) offer promising solutions to buffer these fluctuations, providing flexibility and scalability. The study examines optimal sizing methods for BESS to mitigate the unpredictability of RES output. It identifies strategies such as forecasting models, stochastic optimization, and hybrid systems that enhance predictability. Additionally, it addresses challenges like battery degradation, safety concerns, and grid integration hurdles. Innovations in Battery Management Systems (BMS) are highlighted for their potential to extend battery life and improve cost-effectiveness. Ultimately, optimizing BESS integration is critical to maximize renewable energy utilization, reduce fossil fuel dependence, and support a sustainable energy future.

Development and characterization of antacid microcapsules to buffer the acidic intervertebral disc microenvironment.

During intervertebral disc (IVD) degeneration, microenvironmental challenges such as decreasing levels of glucose, oxygen, and pH play crucial roles in cell survival and matrix turnover. Antacids, such as Mg(OH)2 and CaCO3, entrapped in microcapsules are capable of neutralizing acidic microenvironments in a controlled fashion and therefore may offer the potential to improve the acidic niche of the degenerated IVD and enhance cell-based regeneration strategies. The objectives of this work were, first, to develop and characterize antacid microcapsules and assess their neutralization capacity in an acidic microenvironment and, second, to combine antacid microcapsules with cellular microcapsules in a hybrid gel system to investigate their neutralization effect as a potential therapeutic in a disc explant model. To achieve this, we screened five different pH- neutralizing agents (Al(OH)3, Mg(OH)2, CaCO3, and HEPES) in terms of their pH neutralization capacities, with Mg(OH)2 or CaCO3 being carried forward for further investigation. Antacid-alginate microcapsules were formed at different concentrations using the electrohydrodynamic spraying process and assessed in terms of size, buffering kinetics, cell compatibility, and cytotoxicity. Finally, the combination of cellular microcapsules and antacid capsules was examined in a bovine disc explant model under physiological degenerative conditions. Overall, CaCO3 was found to be superior in terms of neutralization capacities, release kinetics, and cellular response. Specifically, CaCO3 elevated the acidic pH to neutral levels and is estimated to be maintained for several weeks based on Ca2+ release. Using a disc explant model, it was demonstrated that CaCO3 microcapsules were capable of increasing the local pH within the core of a hybrid cellular gel system. This work highlights the potential of antacid microcapsules to positively alter the challenging acidic microenvironment conditions typically observed in degenerative disc disease, which may be used in conjunction with cell therapies to augment regeneration.