Recent Submissions

  • Power Quality Disturbances Elucidation in Hybrid Systems Based on Event-Driven Variational Mode Decomposition in The Smart Grid Prospect

    Mian Qaisar, Saeed; Alghazi, Omnia Sameer; Master of Science in Energy Engineering
    Recently renewable energy (RE) sources were combined with a utility network to establish a hybrid power system to accomplish the stability of power generations. Integrating a renewable-resources-based distributed generation (DG) system into the current power grid could cause problems with power quality (PQ), system dependability, and other challenges. The power quality (PQ) disruptions assessment is essential to guarantee high-quality power generations in smart grids. The research collected effectively models different types of PQ disturbances signals. After that, these can be used for the PQ disturbances interpreting systems training and performance quantification. The recording and collection of such signals is not an easy task. A common trend is the generation of real-like signals from mathematical models to overcome this limitation. This thesis will determine the PQ disturbances model based on IEEE 1159-2019 standard. The outcome of the model will evaluate the performance of the devised system. Firstly, the signal reconstruction will be performed to realize analog-quasi signals. In this context, mature and precise cubic-spline interpolators-based signal reconstruction algorithms are sued. In the next step, the reconstructed signals will be acquired by using the MATLAB-based event-driven sensing models. The acquired signals will be segmented by using novel event-driven activity selection techniques. Afterward, the segments will be decomposed in oscillatory modes by using the adaptive-rate Variational mode decomposition (VMD). This decomposition will result in Mode updates. The pertinent features will be extracted from modes. These features used to prepare templates, testing instances and to prepare and evaluate the considered classification algorithms. The outcome of the model evaluated the performance of the devised system. In this work, we evaluated 11 classes as final results, including the following cases transient, oscillatory transient, flicker, harmonics, interruption, sag, swell, notch, harmonics with sag with flicker, harmonics with swell with flicker, and swell with oscillatory transient signals by using the MATLAB-based event-driven sensing models. The comparison of the trained data between instants PQDs and extracted data using VMD feature extraction has been studied. The average results of the VMD feature extraction method of classifiers showed an improvement percentage between 10.05% to 60.38%.Accuracy and speed have been raised. The most significant improvement in the linear SVM classifier has been shown by 60.38% of average measurements. That will help the smart grid to reach maximum power and increase its affection. Assuming that it will result in rising energy efficiency and simple hardware along with lower latency realizations compared to the classical counter sensing and processing-based approaches. This Study is well aligned with the 2030 vision of Saudi Arabia and can be well integrated into the NEOM smart metering system. The solution has potential and could be commercialized in collaboration with the authorities and industrial partners in Saudi Arabia.
  • The Potential for harvesting wave energy offshore NEOM region, Northern Red Sea

    Brahimi, Tayeb; Hoteit, Ibrahim; Alkhayyat, Misaa Abduljbbar Ibrahim; Graduate Studies and Research (Effat University, 2020)
    The Red Sea region represents a challenge for wave modeling and analysis due to its distinct wave structures induced by the spatially and temporally varying forcing wind fields. By incorporating wind and wave data series from 1985 to 2015 in the Advanced Research Weather and Forecasting model and WAVEWATCH III, the present study attempts to find the best site for installing wave energy converters (WEC) in the North Red Sea along the eastern Gulf of Aqaba and the NEOM bay in the northern Red Sea. Besides, the analysis determines the most suitable wave energy converter system using available wind and wave model data provided by KAUST. A total of 8 points were selected and analyzed to test the potential of wave energy at NEOM coastlines along the Gulf of Aqaba and NEOM Bay. The highest peak period found in the selected area was 4 seconds based on the wave hindcast generated on a 1-km resolution grid, and the highest wave found was 0.79 m. Based on the present results, the Gulf of Aqaba, with a mean wave power of 1.98 kW/m at P2 is a good candidate for a WEC system. Possible installation of wave energy converters in the selected areas is discussed in this thesis, including farms of point absorbers with the integration of wave and solar sources (DEIM). Based on preliminary information regarding the NEOM region, potential environmental and social challenges were also identified in this study for the viability of wave energy exploitation.
  • Techno-economic and Environmental Analysis of a Hybrid Renewable Energy System for Residential Homes

    Hussein, Aziza I.; Atlam, Hazem; Graduate Studies and Research (Effat University, 2022)
    Hybrid renewable energy systems (HRESs) are becoming more prevalent as they are viewed as economic off-grid sources of clean energy that could help reduce rural electrification and global warming problems. This thesis aims to provide a techno-economic feasibility and environmental analysis of a HRES to be designed for meeting a daily load requirement of 389.4 kWh/day with a peak load of 82.71 kW, represented by the energy demand of thirty houses located in Al-Qurayyat city (near the Technical College for Boys), Al Jouf Province, KSA. Thus, the aim of this thesis coincides with the 2030 and 2060 visions of KSA, which promote sustainable energy solutions and net zero CO2 emissions, respectively. Moreover, the objectives of the research could be divided over two stages. First, a HRES consisting of PV, WT, a DG, converter and lead-acid BSS is considered. Simulation of the system is achieved by HOMER software to obtain the optimum configuration. After considering six arrangements, the results reveal that the ideal arrangement is indeed the PV/WT/DG/Converter/BSS with an optimized NPC and COE of $358,616, and $0.166/kWh while attaining a RF percentage of 92.8%. An alternative configuration, consisting of PV/WT/Converter/BSS would yield a 100% RF but with a NPC of $475,374 and COE of $0.22/kWh. The technical results show that the proposed HRES produces a total annual energy of 285,750 kWh/year with the PV, WT, and DG contributing 91.2%, 5.21%, and 3.58%, correspondingly. Regarding the environmental assessment, the optimized hybrid system saves a total of 206,678 kg of greenhouse gases each year. In the second stage, the proposed HRES will be compared with another HRES comprised of CPV, WT, DG and BSS. The objective is to determine the effectiveness of utilizing CPVs in hybrid systems over regular PVs in order to determine the more economic solar technology to be incorporated in the HRES, and this is where the novelty of the study lies. The CPV-HRES has a NPC and COE of $878,042 and $0.406/kWh, correspondingly with an annual energy generation of 434,731 kWh/year. Hence, the PV technology is the more economic choice even though it has a lower energy yield than CPV.
  • Techno-Economic Study for Adding Hydrogen Storage to the Photovoltaic Plant in Neom City

    Mousa, Mohamed F. El-Amin; Rajeh, Mashael; Graduate Studies and Research (Effat University, 2022)
    The status and future potential of renewable energy in Saudi Arabia for Vision "2030" seeks clean energy, an essential input into most industrial sector production processes. Neom city is 100% dependent on renewable energy to limit climate change caused by increased CO2 emissions. Hydrogen (H2) is a clean energy carrier; it can store a large amount of energy. The use of hydrogen as an energy source has gained much attention in recent years. This work aims to study the techno-economic of adding hydrogen storage to a photovoltaic (PV) plant in Neom City and build a mathematical model to simulate metal hydride hydrogen storage. Therefore, this study proposes to create a mathematical model of heat and mass transfer inside a metal hydride hydrogen storage and solve it numerically. Furthermore, a techno-economics comparison of the two different plants is provided. On the grid, the system has a capacity of 30 MW each year. The first application of PV plants without hydrogen storage and the second application of PV plants with hydrogen storage were compared using the system Advisor model (SAM). To reach the economic-study parameters such as the payback period (PP), intern rate of return IRR), and other economic parameters.
  • The Life Distribution of Commercial Concentrator III-V Triple-Junction Solar Cells in View of Inverse Power law and Arrhenius Life

    Kittaneh, Omar; Abdulmajid, Mohammed; Dennah, Dunya; Graduate Studies and Research (Effat University, 2022)
    Renewable energy is one of the essential clean power sources, especially solar energy. The world is shifting toward increasing production from clean energy with a vast transformation level of integrating solar energy with grid production. This comprehensive transformation leads the researchers to focus more on solar reliability, which is considered one of the most challenging elements of solar power to increase production with the highest quality. Different factors affect the PV panel's estimated lifetime before the failure accrues, such as temperature stress, voltage, irradiation, and many other factors. PV panel output power will be a certain percentage of its original Standard Test Condition (STC), rated DC power, depending on its working hours, or we can say PV panel age. The consumer must trust and be confident in such PV manufacturers' companies to ensure the system will remain stable and reliable for a long time before the decision is taken, which is what they call a warranty. PV panels are still being replaced and repaired before the expiration of their warranty period. Studying the PV model's reliability will limit the PV panel's failure and ensure a long lifetime for the system to operate and produce efficient and reliable power. The accelerated lifetime ALT for the PV will simulate the failure under different stressor conditions with the minimum period. This research will use the Inverse power Law module based on statistical analysis with different distributions such as Weibull and Lognormal. Previous studies were done in the same field using the Arrhenius model. Authors usually used Arrhenius as the best life-stress relationship; others favored the inverse power law in many accelerated life-testing experiments even when the stress is thermal. The same procedures and analysis were done in this research by testing the result of ALT space solar cells to support this theory; more details are described in the below report sections.
  • Modeling and event-driven processing based elucidation of the power quality disturbances in smart grids

    Mian Qaisar, Saeed; Aljefri, Raheef Ahmed; Graduate Studies and Research (Effat University, 2020)
    Power Quality (PQ) disturbances cause rigorous issues in classical and smart grids, combine both renewable and conventional power sources, and industries. The performance of power networks can be noticeably affected by these intermittent events. The sustainability of the energy supply can be significantly disturbed. Moreover, it can cause damage to appliances and industrial machines. The identification of PQ disturbances and effective prevention of such events are essential. The first research step in these studies is to collect or effectively model different types of PQ disturbances signals. Later on, these can be used for the PQ disturbances interpreting systems training and performance quantification. The recording and collection of such signals is not an easy task. To overcome this limitation a common trend is the generation of real-like signals from the mathematical models. The working steps of this thesis are to firstly identify and employ the existing potential PQ disturbances mathematical models. In second step these models are implemented in MATLAB. The output of this MATLAB model serves as the database and is used for the proposed system parameterization and performance quantification. In third step, signal reconstruction is performed in order to realize quasi analog signals. In this context mature, precise, cubic-spline and interpolators based signal reconstruction algorithms are evaluated. In fourth step, the reconstructed signals are acquired by using the MATLAB based event-driven sensing models. The acquired signal is segmented by using novel event- driven signal selection techniques. Afterwards, the segmented signal pertinent features are extracted by using an effective time-domain and hybrid parameters extractor. These features are later on used to prepare templates, testing instances and to train the considered classification algorithms. 7 The idea is based on smartly combining the event-driven signal acquisition and segmentation along with local features extraction and classification for realizing an efficient and high precision solution. The system performance is tested and the findings are reported in four case studies. It is shown that the solution proposed achieves the first order of magnitude reduction in the total sample count as compared with conventional equivalents. It indicates a major performance advantage and reliability when compared to peers in terms of power consumption and data transmission of the suggested solution. The suggested system achieves high automatic accuracy in reconnaissance of PQ signals. It shows the advantages of using the suggested approach to realize computationally efficient and cost- effective elucidators for automated PQ disturbances. A novel development is the use of event- sensing and processing techniques to contribute to the realization of new computationally effective strategies for detecting and minimizing PQ disturbances. We assume that it will result in energy efficient and simple hardware realizations as compared to the counter classical sensing and processing based approaches. This Study is well aligned with the 2030 vision of Saudi Arabia and can be well integrated in the NEOM smart metering system. The solution has a potential and it could be commercialized in collaboration with the authorities and the industrial partners in Saudi Arabia. The summary of the thesis consists of 490 words and written in 1.5 spaced between the rows
  • Modeling and Evaluation of MPPT Methods for a Three-Port Converter Used in PV Applications

    Hussein, Aziza; Ahmed, Marwa M.; Alzharani, Amani; Graduate Studies and Research (Effat University, May 2022)
    Worldwide energy demand is growing fast because of the population explosion. Technological advancement paved the way toward utilizing renewable energy sources instead of fossil fuels as they cause harmful effects on the environment. Among all renewable sources, solar energy is a promising source. Solar energy is captured by photovoltaic (PV) arrays that convert the sunlight into electricity that powers the load. Since sunlight is not always available, a battery is utilized to power the load as a backup power source. Both PV and battery are interfaced with the load using a power converter. A Three-Port Converter (TPC) has recently gained interest in the literature. The TPC interfaces the PV and the battery with the load. It is less expensive and more efficient than using two separate power converters for the PV and the battery. However, a system with TPC connecting a PV array and a battery to a load could be further enhanced by applying Maximum Power Point Tracking (MPPT) techniques to the TPC. By applying MPPT, the PV arrays are guaranteed to operate at the maximum power they can deliver. This thesis aims to simulate a system with a TPC and apply different MPPT techniques (Perturb and Observe P&O and Incremental Conductance (IC) to the TPC. Based on the resultant simulation, these techniques are compared based on their response to the environmental changes (radiation and temperature) to identify the most suitable MPPT technique for a TPC. The topological structure of the TPC is identified based on a comparison with the available TPC topologies. The comparison is based on topology complexity, efficiency, and control complexity. To achieve this aim, three steps are followed. The first step is simulating a case study of an existing PV system is considered to illustrate the importance of applying MPPT to PV systems. Namely, the PV system installed at Effat library rooftop is simulated using MATLAB/SIMULINK with and without the MPPT. The simulation shows that applying MPPT increases the system efficiency up to 99%. The second step compares the PV power of a system of the two-port converters and a system of TPC when the solar irradiance changes. In the last step, TPC is simulated in MATLAB/SIMULINK, and MPPT methods (P&O and IC) are applied. Based on the simulation, both MPPT methods exhibit similar results when the radiation and temperature change. However, the IC method performs slightly better than P&O. Hence, IC has a better response to environmental changes than P&O. The significance of this work relies on enhancing TPC used in PV applications by applying MPPT. This field has not been much investigated in the literature.
  • Load recognition by interpreting the smart meter data

    Mian Qaisar, Saeed; Alsharif, Futoon; Graduate Studies and Research (Effat University, 2020)
    The technological advancements have evolved the deployment of smart meters in place of the conventional ones. These smart meters are the vital elements of smart grids and are offering significant advantages for various stakeholders in terms of the social, environmental and economical constraints. The extensive installations of smart meters allow an enormous amount of data collection with a wanted granularity. Automatic data acquisition, transmission, processing and analysis are key factors behind the success of smart meters. The usage of smart meters is increasing in modern societies. A fine–grained metering data collection and analysis is necessary to bring benefits to multiple smart grid stakeholders such as energy providers, distributors, consumers and governments. The classical sensing mechanism is time–invariant. Therefore, it results in the collection, transmission, and processing of a large amount of unnecessary data. This work employs the event–driven sensing mechanism to achieve real–time data compression. Afterward, the novel adaptive rate techniques are employed for the data conditioning, segmentation, and extraction of features. The pertinent features regarding the appliances' consumption patterns are afterward used for their identification. It is realized by employing the mature Support Vector Machine, k–Nearest Neighbor, Naïve Bias and Artificial Neural Network classifiers. The applicability of the devised solution is evaluated with the help of five case studies. Final results confirm a significant compression gain and the computational effectiveness of the suggested solution while securing high classification precisions. This study works in alignment with the 2030 vision of Saudi Arabia and the goals of NEOM city. It contributes in realizing modern smart–energy related services such as detailed electricity consumption billing systems, effective load identification, decision–support for dynamic load management, support to hourly price charges, activity of daily living recognition, occupancy detection, and monitoring of user–appliance interaction.
  • Islanding Modelling and Detection for a Grid Connected Renewable Energy System

    Attia, Abla; Altowairqi, Lujain Arif; Graduate Studies and Research (Effat University, 2020)
    Islanding is one of the issues that are related to grid – connected photovoltaic systems that affects the voltage and frequency of the system. Islanding is defined as the situation in which a distribution system becomes electrically isolated from the remaining power system and continues to be energized by the distributed generation resources. This study presents a method of detecting islanding phenomena in a distribution system equipped with renewable energy resources. The modeling of islanding phenomena was designed and simulated using MATLAB Simulink to extract the three–phase voltage signals at the common coupling point. Discrete Fast Fourier Transform and wavelet analysis techniques are used in this study to analyze the voltage on the loads to detect Islanding occurrence. Comparing between the results of both techniques show the effectiveness of Wavelet analysis over Discrete Fourier transform for different levels of loading. The fast response of wavelet analysis in Islanding detection can be added as a part of relay algorithm. The result will be helpful in development protection system design for a robust, fast response and effective protection system equipped with renewable energy sources.
  • Monitoring system for Li-Ion batteries state of health estimation for smart grid

    Mian Qaisar, Saeed; Al-Guthmi, Maram Omar; Graduate Studies and Research (Effat University, 2020)
    The recent technological developments have evolved the remote and integrated power systems related technologies and tools. The render novel developments of smart and micro grids. The battery is a key element of the modern power systems and is frequently employed in various important applications like hybrid cars, drones, avionics, satellites, and mobile phones. The Li-Ion batteries are extensively employed because of their ever- wanted features like compact size, high power supply capability, a higher number of charge-discharge cycles, etc. Batteries are quite expensive, therefore for an effective utilization of batteries and in order to assure their longer life the Battery Management Systems (BMSs) are frequently employed. The modern BMSs require extensive processing resources which can render into higher power consumption overhead. In this context, several embedded and integrated systems-based solutions have been proposed. This thesis focuses on enhancing the existing Li-Ion BMSs by redesigning their associative data acquisition and processing chain. The focus is to ameliorate the data acquisition and the Li-Ion batteries State of Health (SoH) estimation mechanisms. In this framework, event-driven sensing and processing approaches are used. In contrast to the traditional counterparts, the battery cell parameters are no more captured periodically but are acquired based on events. It results in significant real-time data compression. Afterward, this non-uniformly partitioned information is employed by original event- driven voltage base and Coulomb counting algorithms for a real-time determination and calibration of the cell State of Health (SoH). The system performance is studied with the help of four case studies. Results are used to compare the devised system performance with the traditional counterparts. Preliminary results demonstrate anot able outperformance in terms of compression gain and computational efficiency, for the studied case, while assuring an analogous SoH estimation precision. 7 This work is well aligned with the 2030 vision of Saudi Arabia and the goals of future smart cities like NEOM. It contributes in realizing modern smart – energy related services such as electric vehicles, hybrid power systems, integration of renewable energy sources in smart grid, mitigation of power quality issues, effective dimensioning of renewable energy systems, efficient cell-balancing, and energy storage automatic management and maintenance.
  • Numerical Simulation and Analysis of Harvesting Atmospheric Water Using Porous Materials

    Brahimi, Tayeb; Mian Qaisar, Saeed; Alkinani, Sadeem; Graduate Studies and Research (Effat University, 2022)
    The UN reported that in 2020, 26% of people lack safely managed to drink water. Although water covers more than 70% of the Earth's surface, 97.5% of the water on Earth is saltwater. The scientific community explores three main techniques: groundwater extraction, desalination of saltwater, and rainwater collection to resolve water scarcity. All these techniques require the availability of liquid water; however, in areas with a lack of liquid water, such as in Saudi Arabia, harvesting water from the atmosphere could be a viable option for supplying freshwater as water is abundant in the humid air. This study aims to build a robust mathematical model describing the water moisture absorption to simulate and analyze harvesting atmospheric water (HAW) using porous materials. Furthermore, a rigorous mathematical model was developed to describe the dehydration of absorbed moisture from porous materials. The mathematical model consists of a set of governing partial differential equations, including mass conservation equation, momentum equation (Darcy’s law), heat (energy) equation, associated parameterizations, and initial/boundary conditions. Moreover, the model represents a two-phase fluid flow that contains phase-change gas-liquid physics. The mathematical model has been solved numerically. In the simulated model, different times, thicknesses, and other critical parameters are considered. A dataset has been collected from the literature containing 11 porous materials that have been experimentally used in water generation from the air. A group of empirical models to relate the relative humidity and water content have been suggested and combined with the governing to close the mathematical system. Furthermore, a comparison with experimental findings was made to demonstrate the validity of the simulation model, and the relative error was calculated. The results show that the proposed mathematical model well predicts water content during the absorption and dehydration process. Also, the simulation results show that; during the absorption process, when the depth is smaller, the water content reaches a higher saturation point quickly and at a lower time i.e., quick process. However, during the dehydration process, a lower thickness reaches a higher heat faster, as heat increases, the water content decreases; thus, it has an opposite relationship, and water content decreases over time. Finally, the highest average error of the HAW model is around 4.43% compared to experimental data observed in MnO2-2. Thus, the HAW model is applicable.
  • Online Prediction of the Li-Ion Battery Remaining Useful Life for Smart Grid by Using Event-Driven Approach

    Mian Qaisar, Saeed; Akbar, Muhammed; AbdelGawad, Amal; Graduate Studies and Research (Effat University, 2021)
    The latest innovative advancements have included renewable energy sources based on smart grids and electric vehicles (EVs). Technologies and equipment related to remote and integrated power systems offer improved developments for smart and microgrids. The battery is a crucial element of modern power systems; it is indispensable in many vital applications such as EVs, drones, avionics, satellites, mobile phones, and energy storage for renewable smart grids. Due to their superior characteristics, including but not limited to having a compact size, high power supply capability, and a higher number of charging/discharging cycles, the Lithiumion (Li-ion) batteries are one of the most dominant energy storage technologies. On the other hand, due to one of their most significant disadvantages being expensive, and to optimize their performance and ensure they last longer in the smart grid, their use is monitored using battery management systems (BMSs). The extensive processing resources that modern BMSs need can result in higher overhead power consumption. Regarding this, several embedded and integrated systems-based solutions have been proposed. This thesis focuses on upgrading the present Liion BMSs, reconstructing their associative data acquisition by employing the event-driven sensing mechanism. It aims at efficiently predicting the Li-ion battery's remaining useful life (RUL) online using machine learning (ML). Firstly, a suitable current high-power Li-ion battery real dataset has been distinguished and utilized during offline processing. Then, this dataset is reconstructed to make its form compatible with MATLAB. Using MATLAB, the proposed system is modeled. The eventdriven peak sensing model efficiently extracted the features from the studied battery consumption parameters based on the phenomenon of shape context feature extraction. The feature extractor is sufficiently achieved via embedding the event-driven peak sensing phenomenon in the system. These features are used for training the proposed event-driven remaining useful life (RUL) predictor using robust ML classifiers, namely k-Nearest Neighbor (kNN), Artificial Neural Network (ANN), Linear Regression (LR), Random Tree (RT), and Random Forest (RF). During online processing, the event-driven sensing-based acquired parameters are used to predict the battery capacity. Besides, by applying an event-driven peak sensing approach, the shape context features are extracted. Beyond, those fused features are used for online RUL prediction of the studied Li-ion battery cell by employing Weka software. The devised solution excelled its counterparts in terms of power consumption with a compression gain of 437.5-fold on average. The results showed superior performance for the LR among its studied counterparts, followed by the ANN, kNN, RF, then RT. The achieved MAE, RMSE, RAE, and RRSE by the LR based on the training and testing split of 70:30% are 0.0019, 0.0023, 1.0411%, and 1.1606%, respectively, and by the 5-fold cross-validation are 0.00289, 0.0051, 1.6405%, and 2.6656%, respectively. The proposed solution is parametrizable and can be utilized in different expected applications such as smart and microgrids, hybrid electric vehicles (HEVs), drones, distributed sensors, and satellites.
  • Investigating The Best Statistical Lifetime Model for Commercial Lithium-Ion Batteries

    Kittaneh, Omar; Abdulmajid, Mohammed; Mouais, Talal Ali; Graduate Studies and Research (Effat University, 2021)
    One of the significant drawbacks of renewable energy is that renewable energy sources such as solar and wind are intermittent and operate with different degrees of intermittency. In other words, they only generate power when the sun is shining or when the wind is blowing. One of the most promising methods of overcoming the problem intermittent renewable energy supplies is the use batteries that can store renewable energy until it is needed. Batteries are known for their high commercial potential, fast response time, modularity, flexible installation, and short construction cycles. Consequently, the battery is an attractive option for storing renewable energy and peak shaving during intensive grid loads, and it can serve as a back-up system to control voltage drops in the energy grid. The lithium-ion battery is regarded as one of the most promising battery technologies because it has a high specific energy density, a high volumetric energy density, a low and falling cost, and a long lifetime. The failure mechanism of the Li-ion battery is a highly complex phenomenon produced by a complex interplay of many physical and chemical mechanisms. Three main approaches are used to modeling the lifetime of batteries: the physics-based model (Electrochemical modeling); the half empirical model (statistical methods), which is based on conducting and analyzing battery aging experiments; and the data-driven model, which is based on numerous battery aging experiments that require data analysis and machine learning. This study employs the statistical method to understand and uncover hidden failure interactions inside the cell. It is used here because of its relative simplicity. Based on Accelerated lifetime data of Li-ion adapted from [10], we examine the fitting of three lifetime distributions; the Weibull, lognormal and normal distributions. We conclude that the lognormal distribution is the best lifetime model for Li-ion batteries. Also, this study shows that the electrode physical parameters, such as thickness, play an important role in the lifetime model of Li-ion battery.
  • Feasibility analysis of wind power plants in GCC connected to GCC super grid

    Shehata, Mohammad; Alharthey, Sarah S.; Graduate Studies and Research (Effat University, 2019)
    The energy demand and the level of consumption in the Gulf Cooperation Council (GCC) nations are increasing significantly due to socio-economic growth and rapid moving the populace forward out of traditional living into the lifestyle of a developed nation. Therefore, the concept of electrical interconnection was raised by GCC in early 80s to ensure security and stability of their local electrical networks. As energy security is an essential for the council members, GCC grid interconnections was built to cover in case of emergency outages. However, the balance of the capacity of the interconnector is not adequate to allow major exchanges of electrical energy in base load. The underutilization of the connection is a wasted opportunity in seizing the low hanging fruit such as renewable energy resources. Since the GCC region has enormous undeveloped solar and wind power resources, there is a potential to increase the capacity of the existed GCC interconnected grid to utilize it efficiently. Most of the work in this area are focused on utilizing solar energy while wind energy did not receive the same attention although of the great optional of wind power in GCC region. For example, countries, which are located at the central zone of Arabian Gulf waters (such as Saudi Arabia, Bahrain and Qatar) have annual wind power of 277, 300, 275 w/m2, respectively. This order of wind power is attractive for large-scale power production Therefore, the aim of this thesis is to provide the Gulf Cooperation Council Interconnection Authority (GCCIA) with a methodology of feasibility study for the wind power plant that could be connected to the GCC interconnected grid. The proposed feasibility study in this thesis is focused on utilizing the potential wind energy in GCC region to increase the capacity of the GCC interconnected grid to have it well- exploited. The methodology utilizes technical and economic study to select the best wind power plant suitable for connecting to the grid. Technically, wind power plant models and specifications were determined to guarantee efficient operation which was verified by using the software RETscreen. Also, it is important to check the economic validity of the wind power plants, therefore, economic modeling software; NPV, PBP and IRR, were used to make sure of its economic feasibility. Since the environmental impact has a crucial concern, GHG model was used to measure impact of wind power plant on the environment. In this thesis, eleven candidate locations around GCC countries were tested to check their feasibility, and nine out of them were successfully chosen with complete technical and economical specifications. The results were verified using RETScreen and Vestas was used to analyze the wind turbines. The result of the proposed study concludes that using the proper wind energy in GCC can contribute by about 13% of the energy demand in 2022 in GCC region. The contribution of this thesis is to provide an efficient systematic methodology for selecting, sizing, determining g the wind power plants in GCC region which can be hocked to the GCC integrated grid and operated efficiently and economically.
  • Design of an efficient event-driven battery monitoring system

    Mian Qaisar, Saeed; Al-Shaiban, Amani Nasser; Graduate Studies and Research (Effat University, 2019)
    The remote and integrated Power Systems (PS) are experiencing the new development of the concept of smart and micro-grids. The battery is an elementary part of the PS and is frequently employed in many key applications like hybrid cars, drones, avionics, satellites, mobile phones, renewable powered homes, etc. Among the rechargeable batteries, the Li- Ion ones are extensively employed because of their ever-wanted features like compact size, high power supply capability, a higher number of charge-discharge cycles, etc. For effective utilization of batteries and in order to assure their longer life the Battery Management Systems (BMSs) are frequently employed. The modern BMSs require extensive processing resources which can render into higher power consumption overhead. In this context, several embedded and integrated systems-based solutions have been proposed. The focus of this work is to enhance the existing BMSs, especially in terms
  • Evaluation and Simulation of Reif’s Concentrated Solar Collector

    Alhalabi, Wadee; Samkari, Baraah; Graduate Studies and Research (Effat University, 2019)
    Water and electricity are one of the highest demanding elements within the Kingdom of Saudi Arabia. There are several effective systems are accessible on the area. The most common issue that these systems face a difficult way to generate power. Another important issue that these systems face is a way to deliver the water or electricity to neighborhoods. The main objective of this thesis is to exploit the current knowledge of the power systems in different fields and contribute with a novel approach for the concentrated solar power system capable to generate the required energy by different power systems. The main approach adopts the scientific method that includes the comparative analysis of related literature results to the specification of the research problem. Then the justification for a Reif’s system simulation for concentrated solar collector is provided. The work concludes with the main findings that prove the capacity of the new approach to provide a sound solution to the well- defined research problem. The added value of the approach is anchored around two pillars: • The development of the system is using immobile primary concentrators and a mobile secondary concentrator. This system is specialized for practical uses in the Kingdom of Saudi Arabia. • The key deliverables of the scientific methods represent a strong action towards the improvement of the sustainable energy through the vision of the Kingdom to replace the consumption of the oil with the clean and renewable resources to generate power in different places in the kingdom that have difficulties in accessing electricity and water.