Yazar "Celik, Ozgur" seçeneğine göre listele

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    A comprehensive review of potential protection methods for VSC multi-terminal HVDC systems
    (Pergamon-Elsevier Science Ltd, 2024) Farkhani, Jalal Sahebkar; Celik, Ozgur; Ma, Kaiqi; Bak, Claus Leth; Chen, Zhe
    High voltage direct current (HVDC) transmission systems represent a significant development for future power systems due to presenting promising solutions for long-distance power transmission. However, the protection of HVDC systems is becoming one of the most significant challenges due to the extremely high short-circuit current within a short time span and the lack of zero crossing in the DC systems. DC protection schemes must detect and isolate the fault within 4-6 ms, while AC protection systems operate with considerably longer response times. Therefore, the techniques utilized for HVDC protection systems require more attention to enhance their performance in the future. This paper provides a comprehensive review of various protection techniques for HVDC systems, highlights recent advances, and analyzes the pros and cons of each method. Initially, different challenges of HVDC protection systems are presented, and then various HVDC protection schemes are discussed and classified into two groups based on the operation time. After that existing protection schemes are investigated in detail by including practical examples. The review aims to close the gap between the presented HVDC protection methods and technical challenges in order to overcome future issues of power system protection. Moreover, signal processing methods and artificial intelligence (AI) techniques, which play a key role in the future of protection systems, are extensively investigated to highlight the possible solutions. Finally, various recommendations, key challenges, and future trends with respect to the development of HVDC protection schemes have been presented for researchers and engineers studying in this field.
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    A Deep GMDH Neural-Network-Based Robust Fault Detection Method for Active Distribution Networks
    (Mdpi, 2023) Celik, Ozgur; Farkhani, Jalal Sahebkar; Lashab, Abderezak; Guerrero, Josep M.; Vasquez, Juan C.; Chen, Zhe; Bak, Claus Leth
    The increasing penetration of distributed generation (DG) to power distribution networks mainly induces weaknesses in the sensitivity and selectivity of protection systems. In this manner, conventional protection systems often fail to protect active distribution networks (ADN) in the case of short-circuit faults. To overcome these challenges, the accurate detection of faults in a reasonable fraction of time appears as a critical issue in distribution networks. Machine learning techniques are capable of generating efficient analytical expressions that can be strong candidates in terms of reliable and robust fault detection for several operating scenarios of ADNs. This paper proposes a deep group method of data handling (GMDH) neural network based on a non-pilot protection method for the protection of an ADN. The developed method is independent of the DG capacity and achieves accurate fault detection under load variations, disturbances, and different high-impedance faults (HIFs). To verify the improvements, a test system based on a real distribution network that includes three generators with a capacity of 6 MW is utilized. The extensive simulations of the power network are performed using DIgSILENT Power Factory and MATLAB software. The obtained results reveal that a mean absolute percentage error (MAPE) of 3.51% for the GMDH-network-based protection system is accomplished thanks to formulation via optimized algorithms, without requiring the utilization of any feature selection techniques. The proposed method has a high-speed operation of around 20 ms for the detection of faults, while the conventional OC relay performance is in the blinding mode in the worst situations for faults with HIFs.
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    A Hybrid MPPT method for grid connected photovoltaic systems under rapidly changing atmospheric conditions
    (Elsevier Science Sa, 2017) Celik, Ozgur; Teke, Ahmet
    The modest changes in operating current and voltage of photovoltaic (PV) panel due to the temperature and radiation fluctuation constitute visible variations in the output power. In this paper, a hybrid method to optimize the performance of the maximum power point tracking (MPPT) controller for mitigating these variations and forcing the system to operate on maximum power point (MPP) is developed. The presented Hybrid MPPT method consists of two loops: (i) artificial neural network (ANN) based reference point setting loop and (ii) perturbation and observation (P&O) based fine tuning loop. To assess robustness of the proposed method, a comparison is performed using the conventional P&O, incremental conductance (INC) and ANN based MPPT methods under both rapidly changing radiation and partially shaded conditions by using PSCAD/EMTDC program. The results obtained from the test cases explicitly demonstrate that the presented MPPT method not only achieves an increase in speed of MPP tracking, but also reduces the steady state oscillations and prevents the possibility of the algorithm from confusing its perturbation direction. The system efficiency more than 98.26%, 120 ms improvement in convergence speed and 1.16 V decrease in the rate of overshoot are obtained with proposed Hybrid MPPT method under the rapidly changing environmental conditions. (C) 2017 Elsevier B.V. All rights reserved.
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    A hybrid random parameters modification to MPPT algorithm to mitigate interharmonics from single-phase grid-connected PV systems
    (Elsevier, 2022) Hussein, Ibrahem; Celik, Ozgur; Teke, Ahmet
    As the on-grid photovoltaic (PV) system's penetration level increases, the utility grid power quality (PQ) becomes a vital emerging issue to spot the lights on. The recent studies confirmed that one source of interharmonics emission from PV inverters is the maximum power point tracking (MPPT) algorithms. Following this issue, a strong relationship has been found between the interharmonics generating characteristics and the MPPT parameters, such as the sampling rate and perturbation step size. Utilizing a big step size and fast sampling rate will enhance the tracking performance but will raise the level of interharmonics injected into the grid and lower the overall efficiency. Therefore, there is a trade-off between MPPT parameters, efficiency, and interharmonics characteristics in PV systems. To alleviate mentioned issue, this paper investigates the mechanism of interharmonics generation and emission under different power operating conditions. Accordingly, a new mitigation technique is presented for interharmonics generation in grid-connected PV systems. The proposed method is based on modifying the MPPT algorithm in a way that maintains its performance characteristic while effectively minimizing the generated interharmonics by a random selection of big or small perturbation step sizes and fast or slow sampling rate, respectively. Thereby, the frequency spectrum distribution is modified, and interharmonics peaks are reduced in the output injected current to the grid by 27 % compared to the other conventional and modified perturb and observe (P&O) MPPT algorithms found in the literature. The effectiveness of the proposed method is verified by simulation studies on a single-phase grid-connected PV system. (c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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    A novel gene expression programming-based MPPT technique for PV micro-inverter applications under fast-changing atmospheric conditions
    (Pergamon-Elsevier Science Ltd, 2022) Celik, Ozgur; Zor, Kasim; Tan, Adnan; Teke, Ahmet
    The erratic behavior of the atmospheric conditions adversely affects efficient energy harvesting and the stable operation of photovoltaic systems. It is therefore critical to draw maximum power from photovoltaic modules regardless of atmospheric conditions. The maximum power point tracking techniques have crucial impacts on both efficient and stable operation of photovoltaic systems as being the controller part of the power converters. In this paper, a novel gene expression programming-based maximum power point tracking technique is proposed for micro-inverter applications under fast-changing atmospheric conditions. In this context, the main objective of this study is to improve the significant performance indices of maximum power point tracking technique including convergence speed during transients, tracking accuracy, steady-state oscillations, and rate of overshoots for ensuring the stable and efficient operation of the photovoltaic micro-inverter system. The proposed maximum power point tracking technique is integrated to a two-stage grid-connected micro-inverter system and tested in terms of the aforementioned performance parameters. The performance analyses of the developed technique are performed under various scenarios by utilizing the PSCAD/EMTDC platform. The obtained results reveal that the rate of overshoots is decreased by 0.6 A while the convergence speed is accelerated by 1.4 s. In comparison with traditional MPPT techniques, tracking accuracy, steady-state stability, and robustness of the whole system are remarkably improved along with increasing overall system efficiency by 4%. It is also worth pointing out that the complexity level of the control technique is significantly reduced by the equation obtained through the symbolic regression analysis.
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    Application of Statistical and Artificial Intelligence Techniques for Medium-Term Electrical Energy Forecasting: A Case Study for a Regional Hospital
    (Int Centre Sustainable Dev Energy Water & Env Systems-Sdewes, 2020) Timur, Oguzhan; Zor, Kasim; Celik, Ozgur; Teke, Ahmet; Ibrikci, Turgay
    Electrical energy forecasting is crucial for efficient, reliable, and economic operations of hospitals due to serving 365 days a year, 24/7, and they require round-the-clock energy. An accurate prediction of energy consumption is particularly required for energy management, maintenance scheduling, and future renewable investment planning of large facilities. The main objective of this study is to forecast electrical energy demand by performing and comparing well-known techniques, which are frequently applied to short-term electrical energy forecasting problem in the literature, such as multiple linear regression as a statistical technique and artificial intelligence techniques including artificial neural networks containing multilayer perceptron neural networks and radial basis function networks, and support vector machines through a case study of a regional hospital in the medium-term horizon. In this study, a state-of-the-art literature review of medium-term electrical energy forecasting, data set information, fundamentals of statistical and artificial intelligence techniques, analyses for aforementioned methodologies, and the obtained results are described meticulously. Consequently, support vector machines model with a Gaussian kernel has the best validation performance, and the study revealed that seasonality has a dominant influence on forecasting performance. Hence heating, ventilation, and air-conditioning systems cover the major part of electrical energy consumption of the regional hospital. Besides historical electrical energy consumption, outdoor mean temperature and calendar variables play a significant role in achieving accurate results. Furthermore, the study also unveiled that the number of patients is steady over the years with only small deviations and have no significant influence on medium-term electrical energy forecasting.
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    Data-driven MPPT techniques for optimizing vehicular fuel cell performance in hybrid DC microgrid
    (Pergamon-Elsevier Science Ltd, 2024) Celik, Ozgur
    This paper aims to apply data-driven maximum power point tracking (MPPT) techniques specifically tailored for fuel cell vehicle (FCV) supported hybrid DC microgrids to enhance the power harvesting capability of fuel cell (FC) stacks. Compared to existing MPPT techniques, the current study focuses on developing and evaluating data- driven approaches for maximum power extraction by dynamically determining the operating point of FC power sources through a Zeta converter. An in-depth analysis is conducted by considering parameters such as efficiency, tracking accuracy, response time, and robustness to variations in load demand and operating conditions. The performance results validate that the developed three-layer neural network (TNN)-based MPPT gives better performance findings than Gaussian process regression (GPR), support vector regression (SVR), decision tree regression (DTR), and bagging ensemble decision tree (BEDT). In the performance evaluation phase, a vehicular FC with a rating of 1.26 kW is designed and operated within the temperature range of 320 K to 343 K for hydrogen pressure values ranging from 1 bar to 1.8 bar. For these operational conditions, the prediction accuracy value of the proposed TNN method is 99.6% while the performance values GPR, SVR, DTR, and BEDT are 99%, 98.6%, 97.2%, and 96%. In addition, system efficiency is increased by 0.98%, 1.25%, 2.51%, and 3.02% compared to GPR, SVR, DTR, and BEDT, respectively.
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    Design and analysis of a novel adaptive learning control scheme for performance promotion of grid-connected PV systems
    (Elsevier, 2022) Ozbek, Necdet Sinan; Celik, Ozgur
    This paper addresses a hybrid adaptive iterative learning control strategy for controlling power converters that are used in photovoltaic systems to enhance maximum power point tracking capability in the presence of variable atmospheric conditions. The adaptation of the controller to the fast-changing environmental conditions is provided by a fractional-order proportional-integral type learning control mechanism. The developed control scheme is integrated into a grid-connected current-source flyback inverter to highlight the improvements in performance criteria such as convergence speed during transients, tracking accuracy, steady-state oscillations, and robustness. The performance analyses are carried out under various scenarios. The obtained results reveal that the dynamic response of the system is considerably increased under erratic atmospheric conditions while steady-state oscillations are decreased for stable operation conditions. The maximum absolute error that indicates the robustness of the proposed controller is decreased from 2.3704 to 2.1920. In addition, the error deviations of the proposed control algorithm are below 10%. The variance of the error, which shows steady-state stability, is reduced from 2.5123 to 1.6152. Also, the proposed controller reduces the amount of control energy by 20% when compared to the PI controller. Furthermore, the values of IAE and ISE are reported 10% lower in the proposed controller.
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    Estimating daily Global solar radiation with graphical user interface in Eastern Mediterranean region of Turkey
    (Pergamon-Elsevier Science Ltd, 2018) Yildirim, H. Basak; Celik, Ozgur; Teke, Ahmet; Barutcu, Burak
    It is important to have accurate knowledge on global solar radiation for optimum design of solar energy conversion systems. However, global solar radiation measurement is very rare in meteorological stations in all around the world. Hence, modeling global solar radiation is an crucial issue to fill the gaps in database and to estimate global solar radiation in places where global solar radiation measurement is not available. This paper presents a detailed description and analysis of various global solar radiation modeling methods. The efficiency and accuracy of ten models from different functions to estimate daily solar radiation in EMR are investigated. Also an optimized model based on Artificial Neural Network (ANN) method and Angstrom-Prescott model for the estimation of daily global solar radiation are presented. The essence of this paper is to investigate the performance of the ANN model and Angstrom-Prescott model in order to ensure the most feasible solution for estimating daily global solar radiation for Eastern Mediterranean Region (EMR) of Turkey. 11 years solar radiation data from 4 stations are utilized in training and testing of developed ANN model and parametric model which is based on Angstrm-Prescott method. In order to ensure a simple application of the model that most accurately predicts the desired target value, a new graphical user interface is developed with MATLAB GUI.
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    Evaluation and performance comparison of different models for the estimation of solar radiation
    (Pergamon-Elsevier Science Ltd, 2015) Teke, Ahmet; Yildirim, H. Basak; Celik, Ozgur
    The rapid depletion of energy resources, increasing energy demand and degeneration of ecological values need an urgent solution in this age. Solar energy as the most important energy resource has become part of the solution to the world's energy challenges. Solar radiation data that provides the information on how much energy strikes to the earth is needed for utilization, planning and designing of solar power plants. The measurement of solar radiation data is generally available in some specific areas due to difficulty in solar radiation measurements in terms of its initial and maintenance costs. Therefore, solar energy modelling techniques are becoming more and more important due to the increasing need for the design, performance evaluation and improvement of the solar energy applications. The primary aim of this paper is to overview solar radiation modelling techniques to identify optimum models available and to classify research fields in the literature. In this paper, the modelling techniques, data information, accuracy tests of models used in around 90 papers were reviewed and the most accurate models were suggested. (C) 2015 Elsevier Ltd. All rights reserved.
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    Evaluation of artificial neural network methods to forecast short-term solar power generation: a case study in Eastern Mediterranean Region
    (Tubitak Scientific & Technological Research Council Turkey, 2022) Bozkurt, Helin; Macit, Ramazan; Celik, Ozgur; Teke, Ahmet
    Solar power forecasting is substantial for the utilization, planning, and designing of solar power plants. Global solar irradiation (GSI) and meteorological variables have a crucial role in solar power generation. The ever-changing meteorological variables and imprecise measurement of GSI raise difficulties for forecasting photovoltaic (PV) output power. In this context, a major motivation appears for the accurate forecast of GSI to perform effective forecasting of the short-term output power of a PV plant. The presented study comprises of four artificial neural network (ANN) methods; recurrent neural network (RNN) method, feedforward backpropagation neural network (FFBPNN) method, support vector regression (SVR) method, and long short-term memory (LSTM) for daily total GSI prediction of Tarsus by using meteorological data. Moreover, this study proposes a model that utilizes the predicted daily GSI for output power forecasting of a grid-connected PV plant. The obtained results are compared with the output power generation data of a 350 kW solar power plant. The results are evaluated with the performance indices as mean absolute percentage error (MAPE), normalized root mean squared error (NRMSE), weighted mean absolute error (WMAE), and normalized mean absolute error (NMAE). FFBPNN method is chosen with the best results of MAPE 7.066%, NMAE 3.629%, NRMSE 4.673%, and WMAE 5.256%.
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    Grid code requirements - A case study on the assessment for integration of offshore wind power plants in Turkey
    (Elsevier, 2022) Celik, Ozgur; Yalman, Yunus; Tan, Adnan; Bayindir, Kamil Cagatay; Cetinkaya, Uemit; Akdeniz, Mevlut; Chaudhary, Sanjay K.
    The increasing role of offshore wind power plants in the electricity generation mix in Turkey raises some critical grid operation issues. In this context, the grid code regulation concerning penetration of large-scale offshore wind power plants into Turkey's power system has become a prominent factor in the development of a reliable grid operation. In this paper, a comprehensive benchmark for grid codes of the European countries that have large-scale offshore wind power plants and Turkey is performed by considering voltage regulation, frequency regu-lation, fault ride-through, and power quality features. The compatibility of the grid codes in terms of the min-imum technical requirements is discussed to show the pros and cons. An elaborated assessment of the Turkish grid code reveals the technical properties that need to be improved. The rigorous state-of-the-art review indicates that active power control & frequency regulation, reactive power control & voltage regulation, and voltage ride-through capabilities should be clarified in detail for the Turkish grid code. With this background, various rec-ommendations, key challenges, and future trends related to the improvement of technical requirements for the Turkish grid code for the integration of offshore wind power plants are highlighted to help researchers, plant owners, and system operators.
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    Impacts of Large-Scale Offshore Wind Power Plants Integration on Turkish Power System
    (IEEE-Inst Electrical Electronics Engineers Inc, 2022) Yalman, Yunus; Celik, Ozgur; Tan, Adnan; Bayindir, Kamil Cagatay; Cetinkaya, Umit; Yesil, Merden; Akdeniz, Mevlut
    In this paper, the impacts of large-scale OWPPs penetration on the Turkish power system are addressed. The grid compliance analyses for the large-scale OWPP integration are carried out by using the grid connection criteria defined in the Turkish grid code. PV and QV curves are obtained to assess the effect of OWPP on the static voltage stability limit. Eight scenarios are conducted to analyze the effect of the OWPP on the static and dynamic characteristics of the power grid. To observe the large-scale OWPP impact on the voltage and frequency stability, transient events such as the outage of conventional power plants and three-phase to ground faults are applied. The results of the voltage and frequency stability analysis reveal that the Turkish grid remains stable after the integration of an 1800 MW OWPP. Furthermore, the Turkish system remains stable even in the event of an outage of the international transmission lines to Bulgaria and Greece.
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    Improvement of energy harvesting capability in grid-connected photovoltaic micro-inverters
    (Taylor & Francis Inc, 2024) Celik, Ozgur; Tan, Adnan; Inci, Mustafa; Teke, Ahmet
    In this paper, a multi-stage micro-inverter system depending on a dual neutral point clamped (D-NPC) inverter is developed for low power photovoltaic (PV) applications. The primary objectives of this study are determined as improving the performance of the proposed D-NPC inverter-based micro-inverter and its controller to ensure better system reliability and promote the overall efficiency. In comparison with conventional systems, the designed system provides many advantages: (1) utilization of lower rating switching components, (2) reduced switching frequency and voltage stress, (3) reduced the size of filter components, (4) higher efficiency, and (5) lower total harmonic distortion (THD). The weighted efficiency of the system is remarkably increased by the value of 2.15% compared to the conventional micro-inverter and reached 93.73%. Furthermore, the THD value of the output current is measured below 3% for the proposed system with the small size of passive filtering elements. Further, dynamic grid support and anti-islanding detection capabilities are provided through the utilized controller structure to fulfill grid codes. Consequently, obtained results demonstrate that the improved system can be a considerable candidate for photovoltaic micro-inverter applications in terms of weighted efficiency, output current THD, and reasonable cost.
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    Integrating electric vehicles as virtual power plants: A comprehensive review on vehicle-to-grid (V2G) concepts, interface topologies, marketing and future prospects
    (Elsevier, 2022) Inci, Mustafa; Savrun, Murat Mustafa; Celik, Ozgur
    Global factors such as energy consumption and environmental issues encourage the utilization of electric vehicles (EVs) as alternative energy sources besides transportation. Recently, the development of virtual power plants integrated with clean energy sources has also enhanced the role of EVs in the transportation industry. Vehicle -grid integration (VGI) provides a practical and economical solution to improve energy sustainability and feed consumers on the user side. Although technical developments in the field show that the energy sector supports the effective use of EVs in stationary applications, the research studies confirm that scientific and industrial developments continue to improve the performance of using EVs as virtual power plants. However, a compre-hensive study is needed to demonstrate the concepts, interfacing, and marketing of virtual power plants inte-grated with EVs for researchers and scientists working in this field. To this end, the current study aims to provide an extensive overview on the system configurations, interface topologies, marketing, and future perspectives in integrating EVs as virtual power plants. In this context, the integration concepts of VGI are investigated under the headings of stand-alone, grid-connected, transitional, and grid-supported operations. Then, VGI topologies are examined in terms of energy generation/storage units used in EVs, single-stage/two-stage/hybrid-multi-stage based systems, and grid-connection types & parameters. In the following section, the research projects and marketing values based on a large number of target data are introduced to show the current status of the VGI field. Lastly, future aspects, including charging strategies, intelligent technologies, and technical issues, are addressed and clarified.
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    Mitigation of power oscillations for energy harvesting capability improvement of grid-connected renewable energy systems
    (Elsevier Science Sa, 2022) Celik, Ozgur; Buyuk, Mehmet; Tan, Adnan
    The increasing penetration of renewable energy sources in the power system raises several challenges from the power quality and output power fluctuations point of view. However, the output power oscillations and power quality problems caused by the low order harmonic oscillations at the second-order frequency of the utility grid on the dc-link capacitor are emerging as major issues for the efficient operation of power converters. In this manner, a novel method based on modification in the control algorithm to alleviate the output power oscillation and reduce the output current total harmonic distortion is proposed. The proposed control approach seems as a promising solution for the satisfactory grid operation of renewable energy sources regarding not requiring any additional circuit and increment in the capacity of the dc-link capacitor. Moreover, mitigation of oscillations enhances the active power reference tracking capability of the controller that enables the overall efficiency improvement of the system. The performed case studies reveal that the overall efficiency rate of the system is increased by 1.3%. On the other hand, the output current harmonic distortion is decreased below 3% even under low power operating conditions. Also, the complexity level of the control algorithm is quite low for real-time implementation.
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    Overview of micro-inverters as a challenging technology in photovoltaic applications
    (Pergamon-Elsevier Science Ltd, 2018) Celik, Ozgur; Teke, Ahmet; Tan, Adnan
    One of the key components of the photovoltaic (PV) system is inverters due to their function as being an operative interface between PV and the utility grid or residential application. In addition, they can be employed as power quality conditioners at the point of common coupling (PCC). It should be noted that in inverter technologies, there has been an increasing interest to achieve robust output power injection capabilities with lesser design complexity in terms of controller part and power circuit topology. Micro-inverters (MIs) are module based type of inverters that have aroused much interest in recent years. Owing to their distributed architecture mounted with individual PV modules, system reliability can be improved remarkably by using MIs. Furthermore, a module based nature of the MI architecture provides a number of advantages, such as low converter power rating, low power losses, accurate maximum power point tracking (MPPT) ability against partially shading conditions and elimination of PV panel mismatches. However, there is still known weighted conversion efficiency of MIs ranges between 90% and 95%. Therefore, novel designs focus on the known weak aspects of traditional MIs and their failure mechanisms. In this paper, state-of-the-art technologies for MIs with a detailed survey on the technical features consisting of power circuit configuration, control structures, grid compatibility abilities, decoupling capacitor placement, energy harvesting capabilities, and safety mechanisms are presented. Additionally, elaborated comparison on MIs topologies is realized and some future research fields on MIs are summarized.
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    Power Quality Enhancement in Hybrid PV-BES System based on ANN-MPPT
    (Tubitak Scientific & Technological Research Council Turkey, 2024) Bozkurt, Helin; Celik, Ozgur; Teke, Ahmet
    Battery energy systems (BESs) assisted photovoltaic (PV) plants are among the popular hybrid power systems in terms of energy efficiency, energy management, uninterrupted power supply, grid-connected and off-grid availability. The primary objective of this study is to enhance the power quality of a grid-tied PV-BES hybrid system by developing an operational strategy based on artificial neural network (ANN) based maximum power point tracking (MPPT) method. A test system comprising a 10-kWh BES and a 12.4 kW PV plant is structured and simulated on the MATLAB/Simulink platform. The hybrid system is validated with three different cases: constant radiation, rapid changing radiation, and real-day solar radiation data from the Turkish State Meteorological Service of Tarsus (Mersin, Turkiye) employing the developed operational strategy. These cases involve the examination of three distinct MPPT methods, analyzing DC-link voltage, battery state of charge (SOC), current, voltage, and system total harmonic distortion (THD). The simulation results indicate that the developed operational strategy with the ANN-MPPT method yields superior THD results in output current and a more stable DC-link voltage. Furthermore, the strategy shows improved convergence speed and reduced oscillations to achieve diverse reference operating points under varying atmospheric conditions compared to conventional MPPT methods. Numerical results demonstrate that the developed operational strategy with the ANN-MPPT consistently maintains THD values below 3% and exhibits a stable DC-link voltage deviation of 1.42% in various charging modes for both rapidly changing radiation and real-day solar radiation data.
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    Power System Integration of Electric Vehicles: A Review on Impacts and Contributions to the Smart Grid
    (Mdpi, 2024) Inci, Mustafa; Celik, Ozgur; Lashab, Abderezak; Bayindir, Kamil Cagatay; Vasquez, Juan C.; Guerrero, Josep M.
    In recent years, electric vehicles (EVs) have become increasingly popular, bringing about fundamental shifts in transportation to reduce greenhouse effects and accelerate progress toward decarbonization. The role of EVs has also experienced a paradigm shift for future energy networks as an active player in the form of vehicle-to-grid, grid-to-vehicle, and vehicle-to-vehicle technologies. EVs spend a significant part of the day parked and have a remarkable potential to contribute to energy sustainability as backup power units. In this way, EVs can be connected to the grid as stationary power units, providing a range of services to the power grid to increase its reliability and resilience. The available systems show that EVs can be used as alternative energy sources for various network systems like smart grids, microgrids, and virtual power plants besides transportation. While the grid-EV connection offers various contributions, it also has some limitations and effects. In this context, the current study highlights the power system impacts and key contributions of EVs connected to smart grids. Regarding the power system impacts in case of EV integration into smart grids, the challenges and difficulties are categorized under the power system stability, voltage/current distortions, load profile, and power losses. Voltage/current distortions like sags, unbalances, harmonics, and supraharmonics are also detailed in the study. Subsequently, the key contributions to the smart grid in terms of energy management, grid-quality support, grid balancing, and socio-economic impacts are explained. In the energy management part, issues such as power flow, load balancing, and renewable energy integration are elaborated. Then, the fault ride-through capability, reactive power compensation, harmonic mitigation, and grid loss reduction are presented to provide information on power quality enhancement. Lastly, the socio-economic impacts in terms of employment, net billing fees, integration with renewable energy sources, and environmental effects are elucidated in the present study.
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    Real-Time HIL Simulation for Frequency Regulation in DFIG with AGC: An Egyptian Case Study
    (IEEE Computer Society, 2023) Abubakr, Hussein; Lashab, Abderezak; Celik, Ozgur; Vasquez, Juan C.; Guerrero, Josep M.
    A sudden change in loads creates troubles in grids/microgrids (MGs) such as frequency fluctuations. For balancing between generation and demand, a Doubly fed induction generator (DFIG) has been incorporated into the Egyptian power system (EG-EPS) to provide additional inertia. The studied EG-EPS consists of three conventional power plants (reheat, non-reheat, and hydro) as the main sources of generation, with 20% DFIG participation. The existing literature highlights that the control methods employ for LFC and DFIG in MGs are insufficiently coordinated for minimizing deviations in frequency and power. This limitation arises due to narrow control constraints and inadequate sensitivity to disturbances. To this regard, this paper proposes an adaptive control-based balloon effect modulation (BE) to tune the PID controller for the Automatic Generation Control (AGC) and to tune the PI controller of the DFIG speed controller. The balloon effect tool is used to minimize the impact of system load disturbance by increasing the optimizer sensitivity and traceability. The EG-EPS is examined under the influence of a 2% step load perturbation and random load variations using dSPACE DS1006 and real-time digital simulator (RTDS) as a Hard-In-Loop (HIL) test platform. The proposed controller demonstrates superior performance compared to the conventional one in terms of overshoot, undershoot, and stabilizing time. © 2023 IEEE.