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Mustafa Baysal
Smart Home Laboratory Yildiz Technical University Esenler/İstanbul Turkey

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Research article
Published: 21 June 2021 in International Journal of Energy Research
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In this study, the thermal and electrical performance analysis of an induction heating based-thermochemical reactor is investigated for high-temperature heat storage. The induction-heating model is built with Maxwell equations, and the surface-to-surface (S2S) radiation model is used for the induced and diffused thermal energy flow transport in the fluid phase within the reactor inner cavity. The effects of operating and structural parameters in terms of the coil turn number, coil current intensity and frequency, the conductive plate, and the coil’s relative permeability, electrical conductivity, and emissivity could affect the heat generation, input power demand, and energy consumption of the proposed reactor performance are sufficiently investigated. It is found that the reactor temperature distribution resulted from the homogenized multi-turn coil magnetomotive force and frequency with the current intensity 48% more effective in reaching the desired temperature at the heat storage medium. However, the conductive plate relative permeability, electrical conductivity, and surface emissivity significantly affect the induction heating system’s thermal power, with the relative permeability having the highest impact of 13% in the storage medium’s temperature increasing at low current intensity. Moreover, the surface emissivity shows remarkable effects when the inducting heating operates at a high current. Significant energy consumption, more than 159%, is observed when the induction heating generator operates at steady-state mode. The reactor heating region temperature increases when the reactor operating current and frequency get high. It is observed that the more the induced heat was applied to the reactor, the more the reactor is heating up to a steady-state. The instantaneous temperature distribution inside the reactor depicted the rise in the temperature is caused by convection and radiation heat transfer. Higher and more uniform temperature distribution inside the reactor is obtained by optimizing the reactor operating and structural parameters.

ACS Style

Karim Bio Gassi; Bachirou Guene Lougou; Mustafa Baysal; Clément Ahouannou. Thermal and electrical performance analysis of induction heating based‐thermochemical reactor for heat storage integration into power systems. International Journal of Energy Research 2021, 1 .

AMA Style

Karim Bio Gassi, Bachirou Guene Lougou, Mustafa Baysal, Clément Ahouannou. Thermal and electrical performance analysis of induction heating based‐thermochemical reactor for heat storage integration into power systems. International Journal of Energy Research. 2021; ():1.

Chicago/Turabian Style

Karim Bio Gassi; Bachirou Guene Lougou; Mustafa Baysal; Clément Ahouannou. 2021. "Thermal and electrical performance analysis of induction heating based‐thermochemical reactor for heat storage integration into power systems." International Journal of Energy Research , no. : 1.

Review paper
Published: 30 March 2020 in International Journal of Energy Research
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Over the past decades, electric power systems (EPSs) have undergone an evolution from an ordinary bulk structure to intelligent flexible systems by way of advanced electronics and control technologies. Moreover, EPS has become a more complex, unstable and nonlinear structure with the integration of distributed energy resources in comparison with traditional power grids. Unlike classical approaches, physical methods, statistical approaches and computer calculation techniques are commonly used to solve EPS problems. Artificial intelligent (AI) techniques have especially been used recently in many fields. Deep neural networks have become increasingly attractive as an AI approach due to their robustness and flexibility in handling nonlinear complex relationships on large scale data sets. Major deep learning concepts addressing some problems in EPS have been reviewed in the present study by a comprehensive literature survey. The practices of deep learning and its combinations are well organized with up‐to‐date references in various fields such as load forecasting, wind and solar power forecasting, power quality disturbances detection and classifications, fault detection power system equipment, energy security, energy management and energy optimization. Furthermore, the difficulties encountered in implementation and the future trends of this method in EPS are discussed subject to the findings of current studies. It concludes that deep learning has a huge application potential on EPS, due to smart technologies integration that will increase considerably in the future.

ACS Style

Asiye K. Ozcanli; Fatma Yaprakdal; Mustafa Baysal. Deep learning methods and applications for electrical power systems: A comprehensive review. International Journal of Energy Research 2020, 44, 7136 -7157.

AMA Style

Asiye K. Ozcanli, Fatma Yaprakdal, Mustafa Baysal. Deep learning methods and applications for electrical power systems: A comprehensive review. International Journal of Energy Research. 2020; 44 (9):7136-7157.

Chicago/Turabian Style

Asiye K. Ozcanli; Fatma Yaprakdal; Mustafa Baysal. 2020. "Deep learning methods and applications for electrical power systems: A comprehensive review." International Journal of Energy Research 44, no. 9: 7136-7157.

Journal article
Published: 22 February 2020 in Sustainability
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The inherent variability of large-scale renewable energy generation leads to significant difficulties in microgrid energy management. Likewise, the effects of human behaviors in response to the changes in electricity tariffs as well as seasons result in changes in electricity consumption. Thus, proper scheduling and planning of power system operations require accurate load demand and renewable energy generation estimation studies, especially for short-term periods (hour-ahead, day-ahead). The time-sequence variation in aggregated electrical load and bulk photovoltaic power output are considered in this study to promote the supply-demand balance in the short-term optimal operational scheduling framework of a reconfigurable microgrid by integrating the forecasting results. A bi-directional long short-term memory units based deep recurrent neural network model, DRNN Bi-LSTM, is designed to provide accurate aggregated electrical load demand and the bulk photovoltaic power generation forecasting results. The real-world data set is utilized to test the proposed forecasting model, and based on the results, the DRNN Bi-LSTM model performs better in comparison with other methods in the surveyed literature. Meanwhile, the optimal operational scheduling framework is studied by simultaneously making a day-ahead optimal reconfiguration plan and optimal dispatching of controllable distributed generation units which are considered as optimal operation solutions. A combined approach of basic and selective particle swarm optimization methods, PSO&SPSO, is utilized for that combinatorial, non-linear, non-deterministic polynomial-time-hard (NP-hard), complex optimization study by aiming minimization of the aggregated real power losses of the microgrid subject to diverse equality and inequality constraints. A reconfigurable microgrid test system that includes photovoltaic power and diesel distributed generators is used for the optimal operational scheduling framework. As a whole, this study contributes to the optimal operational scheduling of reconfigurable microgrid with electrical energy demand and renewable energy forecasting by way of the developed DRNN Bi-LSTM model. The results indicate that optimal operational scheduling of reconfigurable microgrid with deep learning assisted approach could not only reduce real power losses but also improve system in an economic way.

ACS Style

Fatma Yaprakdal; Mustafa Berkay Yılmaz; Mustafa Baysal; Amjad Anvari-Moghaddam. A Deep Neural Network-Assisted Approach to Enhance Short-Term Optimal Operational Scheduling of a Microgrid. Sustainability 2020, 12, 1653 .

AMA Style

Fatma Yaprakdal, Mustafa Berkay Yılmaz, Mustafa Baysal, Amjad Anvari-Moghaddam. A Deep Neural Network-Assisted Approach to Enhance Short-Term Optimal Operational Scheduling of a Microgrid. Sustainability. 2020; 12 (4):1653.

Chicago/Turabian Style

Fatma Yaprakdal; Mustafa Berkay Yılmaz; Mustafa Baysal; Amjad Anvari-Moghaddam. 2020. "A Deep Neural Network-Assisted Approach to Enhance Short-Term Optimal Operational Scheduling of a Microgrid." Sustainability 12, no. 4: 1653.

Journal article
Published: 15 May 2019 in Energies
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Passive distribution networks are being converted into active ones by incorporating distributed means of energy generation, consumption, and storage, and the formation of so-called microgrids (MGs). As the next generation of MGs, reconfigurable microgrids (RMGs) are still in early phase studies, and require further research. RMGs facilitate the integration of distributed generators (DGs) into distribution systems and enable a reconfigurable network topology by the help of remote-controlled switches (RCSs). This paper proposes a day-ahead operational scheduling framework for RMGs by simultaneously making an optimal reconfiguration plan and dispatching controllable distributed generation units (DGUs) considering power loss minimization as an objective. A hybrid approach combining conventional particle swarm optimization (PSO) and selective PSO (SPSO) methods (PSO&SPSO) is suggested for solving this combinatorial, non-linear, and NP-hard complex optimization problem. PSO-based methods are primarily considered here for our optimization problem, since they are efficient for power system optimization problems, easy to code, have a faster convergence rate, and have a substructure that is suitable for parallel calculation rather than other optimization methods. In order to evaluate the suggested method’s performance, it is applied to an IEEE 33-bus radial distribution system that is considered as an RMG. One-hour resolution of the simultaneous network reconfiguration (NR) and the optimal dispatch (OD) of distributed DGs are carried out prior to this main study in order to validate the effectiveness and superiority of the proposed approach by comparing relevant recent studies in the literature.

ACS Style

Fatma Yaprakdal; Mustafa Baysal; Amjad Anvari-Moghaddam. Optimal Operational Scheduling of Reconfigurable Microgrids in Presence of Renewable Energy Sources. Energies 2019, 12, 1858 .

AMA Style

Fatma Yaprakdal, Mustafa Baysal, Amjad Anvari-Moghaddam. Optimal Operational Scheduling of Reconfigurable Microgrids in Presence of Renewable Energy Sources. Energies. 2019; 12 (10):1858.

Chicago/Turabian Style

Fatma Yaprakdal; Mustafa Baysal; Amjad Anvari-Moghaddam. 2019. "Optimal Operational Scheduling of Reconfigurable Microgrids in Presence of Renewable Energy Sources." Energies 12, no. 10: 1858.

Journal article
Published: 21 June 2017 in Applied Sciences
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Demand response (DR) implementations have recently found wide application areas in the context of smart grids. The effectiveness of these implementations is primarily based on the willingness of end-users to be involved in such programs. In this paper, an interactive and user-friendly interface is presented in order to facilitate and accordingly to increase the participation of end-users in DR programs. The proposed interface has the capability of providing the targeted information about the DR events to end-users and system operators, as well as allowing end-users to interactively monitor and control the progress of their appliances. In addition to its benefits to system operators and thus to the improved operation of power systems, the proposed interface particularly aims to exploit the potential energy-related cost savings by providing the required information and resources to end-users via mobile phone. A separate interface apart from the mentioned end-user oriented interface has also been developed for the system operator to more effectively check the status of DR applications in detail. The capabilities of the proposed concept are evaluated in a real smart home in terms of various aspects.

ACS Style

Barış Yener; Akin Tascikaraoglu; Ozan Erdinç; Mustafa Baysal; João P. S. Catalão. Design and Implementation of an Interactive Interface for Demand Response and Home Energy Management Applications. Applied Sciences 2017, 7, 641 .

AMA Style

Barış Yener, Akin Tascikaraoglu, Ozan Erdinç, Mustafa Baysal, João P. S. Catalão. Design and Implementation of an Interactive Interface for Demand Response and Home Energy Management Applications. Applied Sciences. 2017; 7 (6):641.

Chicago/Turabian Style

Barış Yener; Akin Tascikaraoglu; Ozan Erdinç; Mustafa Baysal; João P. S. Catalão. 2017. "Design and Implementation of an Interactive Interface for Demand Response and Home Energy Management Applications." Applied Sciences 7, no. 6: 641.

Conference paper
Published: 01 August 2008 in 2008 Power Quality and Supply Reliability Conference
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Power quality is a considerable concern for power systems. The voltage-current characteristics of iron and steel industries are unsteady and especially in these industries nonlinear loads cause power quality problems. High power steel plants produce three-phase unbalanced operation, flicker and harmonics. This paper presents field measurement results of several steel industries in Turkish Electrical Power System. Also, the effects of these industries on power quality disturbances are investigated. Plant measurements are taken by members of the National Power Quality Project in Turkey. This project is supported by The Scientific and Technological Research Council of Turkey with reference number of 105G129.

ACS Style

Bedri Kekezoglu; C. Kocatepe; R. Yumurtaci; O. Arikan; M. Baysal; A. Bozkurt; Y. Akkaya; E. Özdemirci; R. Yumurtacı. Investigation of harmonic effect in Turkeys iron - steel industry. 2008 Power Quality and Supply Reliability Conference 2008, 29 -34.

AMA Style

Bedri Kekezoglu, C. Kocatepe, R. Yumurtaci, O. Arikan, M. Baysal, A. Bozkurt, Y. Akkaya, E. Özdemirci, R. Yumurtacı. Investigation of harmonic effect in Turkeys iron - steel industry. 2008 Power Quality and Supply Reliability Conference. 2008; ():29-34.

Chicago/Turabian Style

Bedri Kekezoglu; C. Kocatepe; R. Yumurtaci; O. Arikan; M. Baysal; A. Bozkurt; Y. Akkaya; E. Özdemirci; R. Yumurtacı. 2008. "Investigation of harmonic effect in Turkeys iron - steel industry." 2008 Power Quality and Supply Reliability Conference , no. : 29-34.

Conference paper
Published: 30 August 2021
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ACS Style

Mustafa Baysal; Mehmet Uzunoğlu; Celal Kocatepe. GÜÇ SİSTEM GERİLİM KARARLILIĞINDA YÜK MODELLEMELERİNİN ÖNEMİ. 2021, 1 .

AMA Style

Mustafa Baysal, Mehmet Uzunoğlu, Celal Kocatepe. GÜÇ SİSTEM GERİLİM KARARLILIĞINDA YÜK MODELLEMELERİNİN ÖNEMİ. . 2021; ():1.

Chicago/Turabian Style

Mustafa Baysal; Mehmet Uzunoğlu; Celal Kocatepe. 2021. "GÜÇ SİSTEM GERİLİM KARARLILIĞINDA YÜK MODELLEMELERİNİN ÖNEMİ." , no. : 1.

Conference paper
Published: 30 August 2021
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ACS Style

Mustafa Baysal; Celal Kocatepe; Mehmet Uzunoğlu. TRİSTÖR KONTROLLÜ SERİ KOMPANZASYON METODLARININ KARŞILAŞTIRILMASI. 2021, 1 .

AMA Style

Mustafa Baysal, Celal Kocatepe, Mehmet Uzunoğlu. TRİSTÖR KONTROLLÜ SERİ KOMPANZASYON METODLARININ KARŞILAŞTIRILMASI. . 2021; ():1.

Chicago/Turabian Style

Mustafa Baysal; Celal Kocatepe; Mehmet Uzunoğlu. 2021. "TRİSTÖR KONTROLLÜ SERİ KOMPANZASYON METODLARININ KARŞILAŞTIRILMASI." , no. : 1.