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This thesis focuses on conducting a comparative study to analyze the techno-economic performance of various energy storage solutions for fast-acting grid balancing applications, specifically in frequency control markets. The goal of this research is to provide insights into the suitability of different energy storage technologies for specific frequency control markets, namely FCR-D down-regulation, FCR-D up-regulation, FCR-N, and FFR. By evaluating key parameters such as charge/discharge power, Capex, Opex, and NPV, the study aims to inform stakeholders and market participants about the most appropriate energy storage options for each market. The findings will help guide decision-making processes, optimize energy storage investments, and contribute to the enhancement of grid stability in dynamic electricity markets.
High voltage generation, is one of the main applications of the Cockcroft-Walton voltage multiplier (CWVM), however recently this structure is investigated to be used for high step-up DC-DC applications. This paper discusses dynamic behaviour and small-signal modelling of a CWVM based DC-DC converter and investigates how switching strategy can affect the dynamic performance of the converter. This study firstly presents, a new switching method, the steady-state relations are derived and compared to the conventional switching strategy, which shows that the proposed method equilibrates the voltage and current stress of the switches and decreases the current ripple of the input inductor. Then, the converter is dynamically modelled and analyzed using the pole-zero map. The analysis shows that the proposed switching strategy improves the dynamic behaviour of the converter. The effect of the passive elements on dynamic performance is also discussed. Experimental results are presented for a 160 W prototype to validate the evaluated performance and the dynamic analysis.
This study presents a new approach to modeling and control the current-fed Dickson voltage multiplier (CF-DVM). The capacitor voltage relation and the input current are obtained. As all switching intervals are considered in detail, a highly accurate dynamic model is obtained, which can be easily extended for a CF-DVM with an arbitrary number of stages. Using the precise extracted model, the Takagi-Sugeno fuzzy model (TSFM) of the CF-DVM is provided, which is an exact equivalent representation of the CF-DVM nonlinear model. Then, a highly accurate and responsive model predictive controller (MPC) is designed based on an obtained TSFM of the CF-DVM to control the output voltage in an optimal and constrained manner. To obtain the control signal, the suggested optimization problem is converted to a quadratic programming (QP)-based problem which has a low online computational burden. Moreover, the performance of the proposed MPC is compared with the PI controller and the linear MPC. Finally, the simulation and experimental results demonstrate the promising merits of the proposed model and control approaches.
Increasing energy demand has created the challenge of supplying safe, economical, and durable energy with minimal impact on the environment. Therefore, governments have developed and executed several strategies such as increasing efficiency in energy systems in addition to replacing existing sources with renewable energies. One of the most important renewable energy sources that have a competitive advantage compared with other resources is solar energy and its related technologies. However, development of this technology, its related products, and their competitiveness in the market has created a plethora of challenges. In this study, the focus is on the analysis of photovoltaic technology development in the context of different technology generations. The S-shape curve of each generation and sub-technologies of photovoltaic is designed and analyzed. Results show that the first generation of photovoltaic technology is in growth and early maturity stage. The second generation is also in growth stage, but the third generation is mainly in the introduction stage.
Achieving Universal Health Coverage (UHC) is a strategic objective of the Jordanian government and has been prioritized in its strategies and plans. However, there are several challenges affecting primary healthcare in Jordan and the health system in general that prevent Jordan from achieving UHC. This paper highlights the importance of team-based care in the form of Family Health Teams (FHTs) to realize Jordan’s goal of achieving UHC. FHTs are a team-based approach that brings together diverse professionals to provide a comprehensive, efficient, patient-centered primary care system that meets the changing needs of Jordan’s population and refugees. However, the implementation of FHT may encounter obstacles, including individual, organizational, institutional, and external barriers. To overcome such obstacles, several actions and processes need to be taken, including political commitment and leadership, implementing good governance and policy frameworks, allocating resources and funding, multisectoral collaboration, and engagement of communities and stakeholders. The successful implementation of FHTs requires participation from government officials, parliamentarians, civil society, and influential community, religious, and business leaders. A strategic policy framework, effective oversight, coalition building, regulation, attention to system design, and accountability are also essential. In conclusion, adopting the FHT approach in Jordan’s Primary Healthcare system offers a promising path towards achieving UHC, improving healthcare access, quality, and efficiency while addressing the unique challenges faced by the country’s healthcare system.
Abstract In this paper, a high-gain annular ring with meander slots antenna array is presented. The proposed design is realized on two different substrate materials separated by a foam layer of 7.5 mm to enhance the operating bandwidth. The antenna is designed to operated as UHF-RFID reading antenna over center frequency of 915 MHz with operating bandwidth of 49.25 MHz (around 5.38%). The overall antenna optimized dimensions are 240×240×11.56 mm3. An overall total realized gain of 12.5 dBi is achieved at the intended center frequency. The proposed antenna exhibits stable radiation capabilities over the operating band. Good agreement is obtained between both CSTMWS, and HFSS simulators.
A significant challenge for designing a coordinated and effective protection architecture of a microgrid (MG) is the aim of an efficient, reliable, and fast protection scheme for both the grid-connected and islanded modes of operation. To this end, bidirectional power flow, varying short-circuit power, low voltage ride-through (LVRT) capability, and the plug-and-play characteristics of distributed generation units (DGUs), which are key issues in a MG system must be considered; otherwise, a mal-operation of protection devices (PDs) may occur. In this sense, a conventional protection system with a single threshold/setting may not be able to fully protect an MG system. To tackle this challenge, this work presents a comprehensive coordinated adaptive protection scheme for AC MGs that can tune their protection setting according to the system states and the operation mode, and is able to switch the PDs’ setting. In the first step of the proposed adaptive algorithm, an offline setting will be adopted for selective and sensitive fault detection, isolation, and coordination among proposed protective modules. As any change in the system is detected by the proposed algorithm in the online step, a new set of setting for proposed modules will be performed to adapt the settings accordingly. In this way, a new set of settings are adapted to maintain a fast and reliable operation, which covers selective, sensitive, and adaptive requirements. The pickup current (Ip) and time multiple settings (TMS) of directional over-current relays (DOCR), as well as coordinated time delays for the proposed protection scheme for both of the grid-connected and islanded modes of operation, are calculated offline. Then, an online adaptive protection scheme is proposed to detect different fault types in different locations. The simulation results show that the proposed method provides a coordinated reliable solution, which can detect and isolate fault conditions in a fast, selective and coordinated adaptive pattern.
Nanomaterials or nanoparticles are commonly used in the cosmetics, medicine, and food industries. Many researchers studied the possible side effects of several nanoparticles including aluminum oxide (Al2O3-nps) and zinc oxide nanoparticles (ZnO-nps). Although, there is limited information available on their direct or side effects, especially on the brain, heart, and lung functions. This study aimed to investigate the neurotoxicity, cardiotoxicity, and lung toxicity induced by Al2O3-nps and ZnO-nps or in combination via studying changes in gene expression, alteration in cytokine production, tumor suppressor protein p53, neurotransmitters, oxidative stress, and the histological and morphological changes. Obtained results showed that Al2O3-nps, ZnO-nps and their combination cause an increase in 8-hydroxy-2 acute accent -deoxyguanosine (8-OHdG), cytokines, p53, oxidative stress, creatine kinase, norepinephrine, acetylcholine (ACh), and lipid profile. Moreover, significant changes in the gene expression of mitochondrial transcription factor-A (mtTFA) and peroxisome proliferator activator receptor-gamma-coactivator-1 alpha (PGC-1 alpha) were also noted. On the other hand, a significant decrease in the levels of antioxidant enzymes, total antioxidant capacity (TAC), reduced glutathione (GSH), paraoxonase 1 (PON1), neurotransmitters (dopamine - DA, and serotonin - SER), and the activity of acetylcholine esterase (AChE) in the brain, heart, and lung were found. Additionally, these results were confirmed by histological examinations. The present study revealed that the toxic effects were more when these nanoparticle doses are used in combination. Thus, Al2O3-nps and ZnO-nps may behave as neurotoxic, cardiotoxic, and lung toxic, especially upon exposure to rats in combination.
Para-coumaric acid (p-CA) is a plant derived secondary metabolite belonging to the phenolic compounds. It is widely distributed in the plant kingdom and found mainly in pizza, vegetables, and cereals. Various in vivo and in vitro studies have revealed its scavenging and antioxidative properties in the reduction of oxidative stress and inflammatory reactions. This evidence-based review focuses on the protective role of p-CA including its therapeutic potential. p-CA and its conjugates possesses various bioactivities such as antioxidant, anti-inflammatory, anti-cancer, anti-diabetic, and anti-melanogenic properties. Due to its potent free radical scavenging activity, it can mitigate the ill effects of various diseases including arthritis, neurological disorders, and cardio-vascular diseases. Recent studies have revealed that p-CA can ameliorate the harmful effects associated with oxidative stress in the reproductive system, also by inhibiting enzymes linked with erectile function.
Abstract Dry laboratories (dry labs) are laboratories dedicated to using and creating data (they are data-centric). Several aspects of the minerals industry (e.g., exploration, extraction and beneficiation) generate multi-scale and multivariate data that are ultimately used to make decisions. Dry labs and digitalization are closely and intricately linked in the minerals industry. This paper focuses on the instrumentation and infrastructure that are required for accelerating digital transformation initiatives in the minerals sector. Specifically, we are interested in the ability of current and emerging instrumentation, sensors and infrastructure to capture relevant information, generate and transport high-quality data. We provide an essential examination of existing literature and an understanding of the 21st century minerals industry. Critical analysis of the literature and review of the current configuration of the minerals industry revealed similar data management and infrastructure needs for all segments of the minerals industry. There are, however, differences in the tools and equipment used at different stages of the mineral value chain. As demand for data-driven approaches grows, and as data resulting from each segment of the minerals industry continues to increase in abundance, diversity and dimensionality, the tools that manage and utilize such data should evolve in a way that is more transdisciplinary (e.g., data management, artificial intelligence, machine learning and data science). Ideally, data should be managed in a dry lab environment, but minerals industry data is currently and historically disaggregated. Consequently, digitalization in the minerals industry must be coupled with dry laboratories through a systematic transition. Sustained generation of high-quality data is critical to sustain the highly desirable uses of data, such as artificial intelligence-based insight generation.