Department of Electrical Engineering

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https://hdl.handle.net/11073/8670

Work by the faculty and students of the Department of Electrical Engineering

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Flexible PDMS Composite Electrodes with Boronic Acid-Modified Carbon Dots for Surface Electrophysiological Signal Recording
(ACS Publications, 2024) Ali, Amaal Abdulraqeb; Al-Sayah, Mohammad H.; Al-Othman, Amani; Al Nashash, Hasan
Conventional surface electrodes are composed of rigid metals such as Ag/AgCl that are not only harsh to the skin but also irritating if used as wet electrodes. Furthermore, rigid, inflexible surface electrodes can cause patient discomfort when used for long term. To reduce the mechanical mismatch, flexible alternatives to metal electrodes are needed. This study reports the development of highly flexible composite electrodes fabricated from the conductive dopant boronic acid-modified carbon dots embedded in a polydimethylsiloxane matrix. The electrodes were characterized for their structural, electrochemical, and mechanical characteristics and ability to record electrophysiological signals. Furthermore, the composition of these electrodes was varied systematically to obtain the optimal electrochemical and mechanical properties. The best-performing electrode composed of 10% boronic acid-modified carbon dots, 16% glycerol, and 74% polydimethylsiloxane (8:1 elastomer to curing agent) had a smooth surface, a promising conductivity of 9.62×10⁻ᵌ S/cm, an impedance of 964 kΩ at 1 kHz, and a charge storage capacity of 21.4 μC/cm². This electrode had a Young’s modulus (0.0545 MPa), which is compatible with biological tissues’ elasticity. The fabricated electrodes recorded high-quality electrocardiography signals with a promising signal-to-noise ratio (SNR) of 36.75 dB that is comparable to the commercial Ag/AgCl, which had a SNR of 39.98 dB. A similarly good performance was observed with electromyography. Furthermore, the developed flexible surface electrodes maintained their ability to record high quality ECG and EMG over a period of three weeks.
A Comparative Analysis of Numerical Methods for Solving the Leaky Fire and Integrate Model
(MDPI, 2023) El Masri, Ghinwa; Ali, Asma; Abuwatfa, Waad Hussein; Mortula, Maruf; Husseini, Ghaleb
The human nervous system is one of the most complex systems of the human body. Understanding its behavior is crucial in drug discovery and developing medical devices. One approach to understanding such a system is to model its most basic unit, neurons. The leaky integrate and fire (LIF) method models the neurons’ response to a stimulus. Given the fact that the model’s equation is a linear ordinary differential equation, the purpose of this research is to compare which numerical analysis method gives the best results for the simplified version of this model. Adams predictor and corrector (AB4-AM4) and Heun’s methods were then used to solve the equation. In addition, this study further researches the effects of different current input models on the LIF’s voltage output. In terms of the computational time, Heun’s method was 0.01191 s on average which is much less than that of the AB-AM4 method (0.057138) for a constant DC input. As for the root mean square error, the AB-AM4 method had a much lower value (0.0061) compared to that of Heun’s method (0.3272) for the same constant input. Therefore, our results show that Heun’s method is best suited for the simplified LIF model since it had the lowest computation time of 36 ms, was stable over a larger range, and had an accuracy of 72% for the varying sinusoidal current input model.
Novel algorithms for accurate DNA base-calling
(Scientific Research Publishing, 2013) Mohammed, Omniyah Gul; Assaleh, Khaled; Husseini, Ghaleb; Majdalawieh, Amin
The ability to decipher the genetic code of different species would lead to significant future scientific achievements in important areas, including medicine and agriculture. The importance of DNA sequencing necessitated a need for efficient automation of identification of base sequences from traces generated by existing sequencing machines, a process referred to as DNA base-calling. In this paper, a pattern recognition technique was adopted to minimize the inaccuracy in DNA base-calling. Two new frameworks using Artificial Neural Networks and Polynomial Classifiers are proposed to model electropherogram traces belonging to Homo sapiens, Saccharomyces mikatae and Drosophila melanogaster. De-correlation, de-convolution and normalization were implemented as part of the pre-processing stage employed to minimize data imperfections attributed to the nature of the chemical reactions involved in DNA sequencing. Discriminative features that characterize each chromatogram trace were subsequently extracted and subjected to the chosen classifiers to categorize the events to their respective base classes. The models are trained such that they are not restricted to a specific species or to a specific chemical procedure of sequencing. The base- calling accuracy achieved is compared with the existing standards, PHRED (Phil’s Read Editor) and ABI (Applied Biosystems, version2.1.1) KB base-callers in terms of deletion, insertion and substitution errors. Experimental evidence indicates that the proposed models achieve a higher base-calling accuracy when compared to PHRED and a comparable performance when compared to ABI. The results obtained demonstrate the potential of the proposed models for efficient and accurate DNA base-calling.
Battery Energy Management Techniques for an Electric Vehicle Traction System
(IEEE Access, 2022) AbdelAziz, Ahmed Sayed AbdelAal; Mukhopadhyay, Shayok; Rehman, Habibur
This paper presents two battery energy management (BEM) techniques for an electric vehicle (EV) traction system which incorporates an indirect field-oriented (IFO) induction motor (IM) drive system. The main objective of the proposed BEM techniques is to regulate the IM's speed while minimizing the lithium-ion (Li-ion) battery bank state of charge (SOC) reduction and state of health (SOH) degradation. In contrast to most of the existing work, the proposed BEM techniques operate without any prior knowledge of driving profiles or road information. The first BEM technique incorporates two cascaded fuzzy logic controllers (CSFLC). In CSFLC, the first fuzzy logic controller (FLC) generates the reference current signal for regulating the motor speed, while the second FLC generates a variable gain that limits the current signal variation based on the battery SOC. The second BEM technique is based on model predictive control (MPC) which generates the current signal for the speed regulation. However, this work introduces a new way of tuning the MPC input weight using battery information. It features a fuzzy tuned model predictive controller (FMPC), where an FLC adjusts the input weight in the MPC objective function such that the battery SOC is considered while generating the command current signal. Furthermore, this work utilizes a model-in-loop strategy comprising a Chen and Mora (CM) battery model and the experimentally obtained battery bank power consumption to estimate the increase in battery bank runtime and lifetime. A real-time implementation is carried out on a prototype EV traction system using the New European Drive Cycle (NEDC) and the Supplemental Federal Test Procedure (US06) drive cycles. The experimental results validate that the proposed CSFLC and FMPC BEM techniques exhibit a lower reduction in the battery SOC and SOH degradation, thus prolonging the battery bank runtime and lifetime as compared to the conventional FLC and MPC speed regulators. Further experimentation demonstrates the superiority of the FMPC technique over the CSFLC technique due to the lesser computational burden and higher average energy saving.
Classifying Maqams of Qur'anic Recitations Using Deep Learning
(IEEE Access, 2021) Shahriar, Sakib; Tariq, Usman
The Holy Qur’an is among the most recited and memorized books in the world. For beautification of Qur’anic recitation, almost all reciters around the globe perform their recitations using a specific melody, known as maqam in Arabic. However, it is more difficult for students to learn this art compared to other techniques of Qur’anic recitation such as Tajwid due to limited resources. Technological advancement can be utilized for automatic classification of these melodies which can then be used by students for self-learning. Using state-of-the-art deep learning algorithms, this research focuses on the classification of the eight popular maqamat (plural of maqam). Various audio features including Mel-frequency cepstral coefficients, spectral, energy and chroma features are obtained for model training. Several deep learning architectures including CNN, LSTM, and deep ANN are trained to classify audio samples from one of the eight maqamat . An accuracy of 95.7% on the test set is obtained using a 5-layer deep ANN which was trained using 26 input features. To the best of our knowledge, this is the first ever work that addresses maqam classification of Holy Qur’an recitations. We also introduce the “Maqam-478” dataset that can be used for further improvements on this work.
Prediction of EV Charging Behavior Using Machine Learning
(IEEE Access, 2021) Shahriar, Sakib; Al-Ali, Abdul-Rahman; Osman, Ahmed; Dhou, Salam; NIJIM, MAIS
As a key pillar of smart transportation in smart city applications, electric vehicles (EVs) are becoming increasingly popular for their contribution in reducing greenhouse gas emissions. One of the key challenges, however, is the strain on power grid infrastructure that comes with large-scale EV deployment. The solution to this lies in utilization of smart scheduling algorithms to manage the growing public charging demand. Using data-driven tools and machine learning algorithms to learn the EV charging behavior can improve scheduling algorithms. Researchers have focused on using historical charging data for predictions of behavior such as departure time and energy needs. However, variables such as weather, traffic, and nearby events, which have been neglected to a large extent, can perhaps add meaningful representations, and provide better predictions. Therefore, in this paper we propose the usage of historical charging data in conjunction with weather, traffic, and events data to predict EV session duration and energy consumption using popular machine learning algorithms including random forest, SVM, XGBoost and deep neural networks. The best predictive performance is achieved by an ensemble learning model, with SMAPE scores of 9.9% and 11.6% for session duration and energy consumptions, respectively, which improves upon the existing works in the literature. In both predictions, we demonstrate a significant improvement compared to previous work on the same dataset and we highlight the importance of traffic and weather information for charging behavior predictions.
A Survey on the Integration of Blockchain With IoT to Enhance Performance and Eliminate Challenges
(IEEE Access, 2021) Al Sadawi, Alia; Hassan, Mohamed; Ndiaye, Malick
Internet of things IoT is playing a remarkable role in the advancement of many fields such as healthcare, smart grids, supply chain management, etc. It also eases people's daily lives and enhances their interaction with each other as well as with their surroundings and the environment in a broader scope. IoT performs this role utilizing devices and sensors of different shapes and sizes ranging from small embedded sensors and wearable devices all the way to automated systems. However, IoT networks are growing in size, complexity, and number of connected devices. As a result, many challenges and problems arise such as security, authenticity, reliability, and scalability. Based on that and taking into account the anticipated evolution of the IoT, it is extremely vital not only to maintain but to increase confidence in and reliance on IoT systems by tackling the aforementioned issues. The emergence of blockchain opened the door to solve some challenges related to IoT networks. Blockchain characteristics such as security, transparency, reliability, and traceability make it the perfect candidate to improve IoT systems, solve their problems, and support their future expansion. This paper demonstrates the major challenges facing IoT systems and blockchain's proposed role in solving them. It also evaluates the position of current researches in the field of merging blockchain with IoT networks and the latest implementation stages. Additionally, it discusses the issues related to the IoT-blockchain integration itself. Finally, this research proposes an architectural design to integrate IoT with blockchain in two layers using dew and cloudlet computing. Our aim is to benefit from blockchain features and services to guarantee a decentralized data storage and processing and address security and anonymity challenges and achieve transparency and efficient authentication service.
A Customer-Centered Smart Charging Strategy Considering Virtual Charging System
(IEEE Acess, 2022) Mokhtar, Mohamed; Shaaban, Mostafa; Zeineldin, H. H.; El-Saadany, Ehab
Electric vehicle (EV) charging is considered as one of the main issues that face EV drivers. Thus, there should be a facility to suggest the best charging station based on the customer requirements. However, the routing process of EVs in most of the literature was generally implemented centrally based on the charging station/operator perspective. On contrary, this paper proposes a smart charging strategy that routes EVs drivers to the best charging station based on their priorities. In the proposed smart strategy, various charging stations will cooperate through a virtual charging system (VCS) to serve all EVs charging requests with a high satisfaction level. The drivers’ requirements are achieved through a new scoring criterion which ranks the participating charging stations based on EV driver’s perspective. Then, the EV driver will select individually the charging station based on his priorities. The data required for the scoring criterion are computed through two stages: offline (day-ahead) and online stages. The expected waiting time at each charging station within the VCS is computed during the offline stage based on the forecasted arrivals. The integration between offline and online stages aims to reduce the data flow, calculated data, and finally the communication bandwidth during the online stage. Different case studies are introduced to evaluate the significance of the proposed strategy. The results demonstrate the superiority of the proposed strategy in achieving EVs requirements.
A Mobile Energy Storage Unit Serving Multiple EV Charging Stations
(MDPI, 2021) Elmeligy, Mohamed Mostafa Abdelazim; Shaaban, Mostafa; Azab, Ahmed; Azzouz, Maher A.; Mokhtar, Mohamed
Due to the rapid increase in electric vehicles (EVs) globally, new technologies have emerged in recent years to meet the excess demand imposed on the power systems by EV charging. Among these technologies, a mobile energy storage system (MESS), which is a transportable storage system that provides various utility services, was used in this study to support several charging stations, in addition to supplying power to the grid during overload and on-peak hours. Thus, this paper proposes a new day-ahead optimal operation of a single MESS unit that serves several charging stations that share the same geographical area. The operational problem is formulated as a mixed-integer non-linear programming (MINLP), where the objective is to minimize the total operating cost of the parking lots (PLs). Two different case studies are simulated to highlight the effectiveness of the proposed system compared to the current approach.
Design and Performance Analysis of Misalignment Tolerant Charging Coils for Wireless Electric Vehicle Charging Systems
(MDPI, 2021) ElGhanam, Eiman Ayman Mahmoud; Hassan, Mohamed; Osman, Ahmed; Kabalan, Hanin Hassan
n order to design a high efficiency Wireless Electric Vehicle Charging (WEVC) system, the design of the different system components needs to be optimized, particularly the design of a high-coupling, misalignment-tolerant inductive link (IL), comprising primary and secondary charging coils. Different coil geometries can be utilized for the primary and the secondary sides, each with a set of advantages and drawbacks in terms of weight, cost, coupling at perfect alignment and coupling at lateral misalignments. In this work, a Finite Element Method (FEM)-based systematic approach for the design of double-D (DD) charging coils is presented in detail. In particular, this paper studies the effect of different coil parameters, namely the number of turns and the turn-to-turn spacing, on the coupling performance of the IL at perfect alignment and at ±200 mm lateral misalignment, given a set of space constraints. The proposed design is verified by an experimental prototype to validate the accuracy of the FEM model and the simulation results. Accordingly, FEM simulations are utilized to compare the performance of rectangular, DD and DDQ coils. The FEM results prove the importance of utilizing an additional quadrature coil on the secondary side, despite the added weight and cost, to further improve the misalignment tolerance of the proposed inductive link design.
Review of Communication Technologies for Electric Vehicle Charging Management and Coordination
(MDPI, 2021) ElGhanam, Eiman Ayman Mahmoud; Hassan, Mohamed; Osman, Ahmed; Ahmed, Ibtihal Mohamed Taha
Recently, electric vehicles (EVs) have been introduced as an alternative method of transportation to help mitigate environmental issues, such as carbon emissions and fuel consumption, caused by conventional transportation systems. The implementation of effective EV charging systems is essential to motivate mass adoption of EVs. Accordingly, fast and reliable communications between the charging systems and the EVs are vital for efficient management of the charging process. Different radio access technologies (RATs) are discussed in the literature to enable communication between the highly mobile EVs and the charging subsystems, to collect and exchange information such as state of charge (SoC), users’ locations, and charging decisions between the different network entities. This information can be used to coordinate charging plans and select the optimal routes for moving vehicles. This paper presents a survey of existing literature on vehicular communications for EV charging coordination and management. The communication requirements and feasible communication technologies for vehicular communication are first discussed in details. A review of the physical layer security strategies is then presented and the role of the different RATs in EV charging coordination and management is described and studied.
Design of a High Power, LCC-Compensated, Dynamic, Wireless Electric Vehicle Charging System with Improved Misalignment Tolerance
(MDPI, 2021) ElGhanam, Eiman Ayman Mahmoud; Hassan, Mohamed; Osman, Ahmed
Dynamic wireless power transfer (DWPT) systems are becoming increasingly important for on-the-move electric vehicle (EV) charging solutions, to overcome range anxiety and compensate for the consumed energy while the EV is in motion. In this work, a DWPT EV charging system is proposed to be implemented on a straight road stretch such that it provides the moving EV with energy at a rate of 308 Wh/km. This rate is expected to compensate for the vehicle’s average energy consumption and allow for additional energy storage in the EV battery. The proposed charging system operates at an average power transfer efficiency that is higher than 90% and provides good lateral misalignment tolerance up to ±200 mm. Details of the proposed system’s design are presented in this paper, including EV specifications, inductive link and compensation network design and power electronic circuitry.
Non-Destructive Water Leak Detection Using Multitemporal Infrared Thermography
(IEEE, 2021) Yahia, Mohamed; Gawai, Rahul; Ali, Tarig; Mortula, Maruf; Albasha, Lutfi; Landolsi, Taha
Waterleakage detection and localization in distribution networks pipelines is a challenge for utility companies. For this purpose, thermal Infrared Radiation (IR) techniques have been widely applied in the literature. However, the classical analysis of IR images has not been robust in detecting and locating leakage, due to presence of thermal anomalies such as shadows. In this study, to improve the detection and location accuracy, a digital image processing tool based on multitemporal IR is proposed. In multitemporal IR analysis, the variation of soil's temperature due to field temperature can be obtained; and hence; estimating variations due to water leakage would be more accurate. An experimental setup was built to evaluate the proposed multitemporal IR water leak detection method. In order to consider the temporal temperature variation due to water leakage and mitigate the field temperature effects, a luminance transformation of the IRimages was introduced. To determine the temporal temperature variation of the soil's surface due to the leakage, several metrics have been considered such as the difference, the ratio, the log-ratio and the coefficient variation (CV) images. Based on the experimental results, the log-ratio and the CVimages were the most robust metrics. Then, based on log-ratio or the CV image, a temporal variation image (TVI) that traduces the temporal IR luminance variation was introduced. The analysis of the TVI image showed that the CV image is less noisy than the log-ratio image, and can more accurately locate the leakage. Finally, based on TVI histogram, a threshold was de ned to classify the TVI image into leakage/non-leakage areas. Results showed that the proposed method is capable of accurately detecting and locating water leakage, which is an improvement to the false detections of spatial thermal IR analysis.
Maximum-Service Channel Assignment in Vehicular Radar-Communication
(IEEE Access, 2021) Kafafy, Mai; Ibrahim, Ahmed S.; Ismail, Mahmoud
Spectrum sharing between different technologies has become a necessity as the RF spectrum has become more congested than ever due to the increasing number of connected devices and applications all wishing to access the spectrum simultaneously. Vehicular networks are one of many scenarios of spectrum sharing as vehicles are expected to share the same spectrum for radar sensing and communication purposes. Channel assignment among radar sensors and communication transceivers in automotive systems is crucial for the success of future vehicular networks. This paper proposes an optimization framework for the channel assignment to multiple automotive radars and communication transceivers aiming at maximizing the number of served vehicles under hard quality of service requirements. The channel assignment problem is formulated as an integer linear optimization with binary variables. A heuristic solution that is based on ordered sequential channel assignment (OSCA) is proposed and is shown to achieve at least 92% of the solution obtained from branch-and-cut based techniques with much less computational complexity and run-time; at most 2% of the run-time of branch-and-cut.
Cognitive Vigilance Enhancement using Audio Stimulation of Pure Tone at 250 Hz
(IEEE Access, 2021) Yahya, Fares; Tariq, Usman; Babiloni, Fabio; Al-Nashash, Hasan
In this paper, we propose a novel vigilance enhancement method based on audio stimulation of pure tone at 250 Hz. We induced two different levels of vigilance state; vigilance decrement (VD) and vigilance enhancement (VE). The VD state was induced by performing a modified version of the Stroop Color-Word Task (SCWT) for approximately 45 minutes. Likewise, the VE state was induced by incorporating audio stimulation of 250 Hz into the SCWT for 45 minutes. We assessed the levels of vigilance on 20 healthy subjects by utilizing Electroencephalogram (EEG) signals and machine learning. The EEG signals were analyzed using four types of entropies; Approximate Entropy (AE), Sample Entropy (SE), Fuzzy Entropy (FE), and Differential Entropy (DE). We then quantified vigilance levels using statistical analysis and support vector machines (SVM) classifier. We found that the proposed VE method has significantly reduced the reaction time (RT) by 44% and improved the accuracy of target detection by 25%, (p < 0.001) compared to VD state. Besides, we found that 30 min of audio stimulation has reduced the RT by 32% from the beginning to the end of VE phase of the experiment. The entropy measures show that the temporal profile of the EEG signals has significantly increased with VE. The classification results showed that SVM technique with DE features across all frequency bands can detect VE levels with accuracy varying between (92.10±02.24)% to (98.32±01.14)%, sensitivity of (92.50±02.33)% to (98.66±01.00)%, and specificity of (91.70±02.32)% to (97.99±01.05)%. Results also showed that the classification performance using DE has outperformed the other entropy measures by an average of +8.07%. Our results demonstrate the effectiveness of the proposed 250 Hz audio stimulation method in improving vigilance level and suggest using it for future cognitive enhancement studies.
Microwave Imaging for Early Breast Cancer Detection: Current State, Challenges, and Future Directions
(MDPI, 2022) AlSawaftah, Nour Majdi; Elabed, Salma Sami; Dhou, Salam; Zakaria, Amer
Breast cancer is the most commonly diagnosed cancer type and is the leading cause of cancer-related death among females worldwide. Breast screening and early detection are currently the most successful approaches for the management and treatment of this disease. Several imaging modalities are currently utilized for detecting breast cancer, of which microwave imaging (MWI) is gaining quite a lot of attention as a promising diagnostic tool for early breast cancer detection. MWI is a noninvasive, relatively inexpensive, fast, convenient, and safe screening tool. The purpose of this paper is to provide an up-to-date survey of the principles, developments, and current research status of MWI for breast cancer detection. This paper is structured into two sections; the first is an overview of current MWI techniques used for detecting breast cancer, followed by an explanation of the working principle behind MWI and its various types, namely, microwave tomography and radar-based imaging. In the second section, a review of the initial experiments along with more recent studies on the use of MWI for breast cancer detection is presented. Furthermore, the paper summarizes the challenges facing MWI as a breast cancer detection tool and provides future research directions. On the whole, MWI has proven its potential as a screening tool for breast cancer detection, both as a standalone or complementary technique. However, there are a few challenges that need to be addressed to unlock the full potential of this imaging modality and translate it to clinical settings.
Identifying Friction in a Nonlinear Chaotic System Using a Universal Adaptive Stabilizer
(IEEE, 2022-04) Wadi, Ali; Mukhopadhyay, Shayok; Romdhane, Lotfi
This paper proposes a friction model parameter identification routine that can work with highly nonlinear and chaotic systems. The chosen system for this study is a passively-actuated tilted Furuta pendulum, which is known to have a highly nonlinear and coupled model. The pendulum is tilted to ensure the existence of a stable equilibrium configuration for all its degrees of freedom, and the link weights are the only external forces applied to the system. A nonlinear analytical model of the pendulum is derived, and a continuous friction model considering static friction, dynamic friction, viscous friction, and the stribeck effect is selected from the literature. A high-gain Universal Adaptive Stabilizer (UAS) observer is designed to identify friction model parameters using joint angle measurements. The methodology is tested in simulation and validated on an experimental setup. Despite the high nonlinearity of the system, the methodology is proven to converge to the exact parameter values, in simulation, and to yield qualitative parameter magnitudes in experiments where the goodness of fit was around 85% on average. The discrepancy between the simulation and the experimental results is attributed to the limitations of the friction model. The main advantage of the proposed method is the significant reduction in computational needs and the time required relative to conventional optimization-based identification routines. The proposed approach yielded more than 99% reduction in the estimation time while being considerably more accurate than the optimization approach in every test performed. One more advantage is that the approach can be easily adapted to fit other models to experimental data.
A combined resource allocation framework for PEVs charging stations, renewable energy resources and distributed energy storage systems
(Elsevier, 2017) Kandil, Sarah M.; Farag, Hany E. Z.; Shaaban, Mostafa; El-Sharafy, M. Zaki
The massive deployment of plug-in electric vehicles (PEVs), renewable energy resources (RES), and distributed energy storage systems (DESS) has gained significant interest under the smart grid vision. However, their special features and operational characteristics have created a paradigm shift in distribution network resource allocation studies. This paper presents a combined model formulation for the concurrent optimal resource allocation of PEVs charging stations, RES and DESS in distribution networks. The formulation employs a general objective function that optimizes the total Annual Cost of Energy (ACOE). The decision variables in the formulation are the locations and capacities of PEVs charging stations, RES, and DESS units. A Markov Chain Monte Carlo (MCMC) simulation model is utilized to account for the uncertainties of PEVs charging demand and output generation of RES units. Also, in order to enhance the accuracy of the resource allocation problem, the coordinated control of PEVs charging, RES output power, and DESS charging/discharging are incorporated in the formulated model. The formulation is decomposed into two interdependent sub-problems and solved using a combination of metaheuristic and deterministic optimization techniques. A sample case study is presented to illustrate the performance of the algorithm.
Joint Planning of Smart EV Charging Stations and DGs in Eco-Friendly Remote Hybrid Microgrids
(IEEE, 2019) Shaaban, Mostafa; Mohamed, Sayed; Ismail, Muhammad; Qaraqe, Khalid; Serpedin, Erchin
This paper proposes an efficient planning algorithm for allocating smart electric vehicle (EV) charging stations in remote communities. The planning problem jointly allocates and sizes a set of distributed generators (DGs) along with the EV charging stations to balance the supply with the total demand of regular loads and EV charging. The planning algorithm specifies optimal locations and sizes of the EV charging stations and DG units that minimize two conflicting objectives: (a) deployment and operation costs and (b) associated green house gas emissions, while satisfying the microgrid technical constraints. This is achieved by iteratively solving a multi-objective mixed integer non-linear program. An outer sub-problem determines the locations and sizes of the DG units and charging stations using a non-dominated sorting Genetic algorithm (NSGA-II). Given the allocation and sizing decisions, an inner sub-problem ensures smart, reliable, and eco-friendly operation of the microgrid by solving a non-linear scheduling problem. The proposed algorithm results in a Pareto frontier that captures the trade-off between the conflicting planning objectives. Simulation studies investigate the performance of the proposed planning algorithm in order to obtain a compromise planning solution.
Stochastic Geometry-Based Model for Dynamic Allocation of Metering Equipment in Spatio-Temporal Expanding Power Grids
(IEEE, 2019) Atat, Rachad; Ismail, Muhammad; Shaaban, Mostafa; Serpedin, Erchin; Overbye, Thomas
With smart grids replacing conventional power grids and rapidly expanding in both space and time, ensuring an acceptable system observability becomes a challenge in spatio-temporal expanding power grids. In addition, system operators face another challenge, namely, financial budget constraints. To address these challenges, a metering equipment allocation strategy for monitoring of the power grid state needs to be dynamic in both space and time. Unfortunately, existing metering allocation strategies are quite limited. They usually deal with static power grid topologies, and hence, do not reflect the spatio-temporal expansion of the power grid. In this paper, a spatio-temporal power grid model is proposed based on stochastic geometry, which we show that it is in a good match with real-world power grids. The proposed model enables us to carry out tractable dynamic allocation of metering equipment in a large (city-wide) and structurally evolving power grid. Using the developed model, a multi-year algorithm for the allocation of metering equipment is proposed based on finite horizon dynamic programming, given budgetary and technical constraints on system observability. Several case studies for metering allocation are demonstrated through simulation results.

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