Browsing by Author "Ustun, Taha Selim"

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Artificial Intelligence Based Intrusion Detection System for IEC 61850 Sampled Values Under Symmetric and Asymmetric Faults
(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC445 HOES LANE, PISCATAWAY, NJ 08855-4141, 2021) Ustun, Taha Selim; Hussain, S. M. Suhail; Yavuz, Levent; Onen, Ahmet; AGÜ, Mühendislik Fakültesi, Elektrik - Elektronik Mühendisliği Bölümü; Yavuz, Levent; Onen, Ahmet
Modern power systems require increased connectivity to implement novel coordination and control schemes. Wide-spread use of information technology in smartgrid domain is an outcome of this need. IEC 61850-based communication solutions have become popular due to a myriad of reasons. Object-oriented modeling capability, interoperable connectivity and strong communication protocols are to name a few. However, power system communication infrastructure is not well-equipped with cybersecurity mechanisms for safe operation. Unlike online banking systems that have been running such security systems for decades, smartgrid cybersecurity is an emerging field. A recent publication aimed at equipping IEC 61850-based communication with cybersecurity features, i.e. IEC 62351, only focuses on communication layer security. To achieve security at all levels, operational technology-based security is also needed. To address this need, this paper develops an intrusion detection system for smartgrids utilizing IEC 61850's Sampled Value (SV) messages. The system is developed with machine learning and is able to monitor communication traffic of a given power system and distinguish normal data measurements from falsely injected data, i.e. attacks. The designed system is implemented and tested with realistic IEC 61850 SV message dataset. Tests are performed on a Modified IEEE 14-bus system with renewable energy-based generators where different fault are applied. The results show that the proposed system can successfully distinguish normal power system events from cyberattacks with high accuracy. This ensures that smartgrids have intrusion detection in addition to cybersecurity features attached to exchanged messages.
Artificial neural networks based harmonics estimation for real university microgrids using hourly solar irradiation and temperature data
(ELSEVIER, 2023) Yarar, Nurcan; Yagci, Mustafa; Bahceci, Serkan; Onen, Ahmet; Ustun, Taha Selim; 0000-0001-7086-5112; AGÜ, Mühendislik Fakültesi, Elektrik - Elektronik Mühendisliği Bölümü; Onen, Ahmet
The need for renewable energy is increasing day by day due to different factors such as increasing energy demand, environmental considerations as well as the will to decrease the share of fossil fuel-based generation. Due to their relative low-cost and ease of installation, PV systems are leading the way for renewable energy deployments around the globe. However, there are meticulous studies that need to be undertaken for realization of such projects. Studying local weather and load patterns for proper panel sizing or considering grid components to determine cable and transformer sizing can be named as some examples for pre-installation studies. In addition to these, post-installation impact studies, e.g. accurate harmonic analysis contribution, is more important to ensure safe and secure operation of the overall system. These steps need to be taken for all PV installation projects. The aim of this study is to show a step-by-step analysis of the effect of a real PV system on the network and to improve the prediction and give a new perspective to the harmonic estimation by using the hourly temperature and radiation data together. At the first phase of the study, a detail real-time 250 kW PV system was modeled for real university campus, and then harmonic estimation based on hourly solar irradiation and hourly temperature was performed with artificial neural networks (ANN) and nonlinear autoregressive exogenous (NARX). The accuracy of the prediction made with ANN was 0.98, and the accuracy of the prediction made with NARX was 0.96.Researchers in PV sizing and control field as well as engineers in power quality area would find these findings beneficial and useful. Use of ANNs and NARX for such analysis indicates the trend in this field that can be targeted by new research projects.
Dynamic rolling horizon control approach for a university campus
(Elsevier Ltd, 2022) Yoldas, Yeliz; Goren, Selcuk; Onen, Ahmet; Ustun, Taha Selim; 0000-0002-5320-4213; 0000-0001-7086-5112; 0000-0002-2413-8421; AGÜ, Mühendislik Fakültesi, Elektrik - Elektronik Mühendisliği Bölümü; Yoldaş, Yeliz; Gören, Selçuk; Önen, Ahmet
An energy management system based on the rolling horizon control approach has been proposed for the grid-connected dynamic and stochastic microgrid of a university campus in Malta. The aims of the study are to minimize the fuel cost of the diesel generator, minimize the cost of power transfer between the main grid and the micro grid, and minimize the cost of deterioration of the battery to be able to provide optimum economic operation. Since uncertainty in renewable energy sources and load is inevitable, rolling horizon control in the stochastic framework is used to manage uncertainties in the energy management system problem. Both the deterministic and stochastic processes were studied to approve the effectiveness of the algorithm. Also, the results are compared with the Myopic and Mixed Integer Linear Programming algorithms. The results show that the life span of the battery and the associated economic savings are correlated with the SOC values.
Energy Trading on a Peer-to-Peer Basis between Virtual Power Plants Using Decentralized Finance Instruments
(MDPI, 2022) Seven, Serkan; Yoldas, Yeliz; Soran, Ahmet; Alkan,Gulay Yalcin; Jung, Jaesung; Ustun, Taha Selim; Onen, Ahmet; 0000-0003-2611-720X; 0000-0003-3929-8126; 0000-0001-7086-5112; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü; Seven, Serkan; Yalçın Alkan, Gülay; Önen, Ahmet
Over time, distribution systems have begun to include increased distributed energy resources (DERs) due to the advancement of auxiliary power electronics, information and communication technologies (ICT), and cost reductions. Electric vehicles (EVs) will undoubtedly join the energy community alongside DERs, and energy transfers from vehicles to grids and vice versa will become more extensive in the future. Virtual power plants (VPPs) will also play a key role in integrating these systems and participating in wholesale markets. Energy trading on a peer-to-peer (P2P) basis is a promising business model for transactive energy that aids in balancing local supply and demand. Moreover, a market scheme between VPPs can help DER owners make more profit while reducing renewable energy waste. For this purpose, an inter-VPP P2P trading scheme is proposed. The scheme utilizes cutting-edge technologies of the Avalanche blockchain platform, developed from scratch with decentralized finance (DeFi), decentralized applications (DApps), and Web3 workflows in mind. Avalanche is more scalable and has faster transaction finality than its layer-1 predecessors. It provides interoperability abilities among other common blockchain networks, facilitating inter-VPP P2P trading between different blockchain-based VPPs. The merits of DeFi contribute significantly to the workflow in this type of energy trading scenario, as the price mechanism can be determined using open market-like instruments. A detailed case study was used to examine the effectiveness of the proposed scheme and flow, and important conclusions were drawn.
Enhancing Cybersecurity in Smart Grids: False Data Injection and Its Mitigation
(MDPIST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND, 2021) Unsal, Derya Betul; Ustun, Taha Selim; Hussain, S. M. Suhail; Onen, Ahmet; AGÜ, Mühendislik Fakültesi, Elektrik - Elektronik Mühendisliği Bölümü; Onen, Ahmet
Integration of information technologies with power systems has unlocked unprecedented opportunities in optimization and control fields. Increased data collection and monitoring enable control systems to have a better understanding of the pseudo-real-time condition of power systems. In this fashion, more accurate and effective decisions can be made. This is the key towards mitigating negative impacts of novel technologies such as renewables and electric vehicles and increasing their share in the overall generation portfolio. However, such extensive information exchange has created cybersecurity vulnerabilities in power systems that were not encountered before. It is imperative that these vulnerabilities are understood well, and proper mitigation techniques are implemented. This paper presents an extensive study of cybersecurity concerns in Smart grids in line with latest developments. Relevant standardization and mitigation efforts are discussed in detail and then the classification of different cyber-attacks in smart grid domain with special focus on false data injection (FDI) attack, due to its high impact on different operations. Different uses of this attack as well as developed detection models and methods are analysed. Finally, impacts on smart grid operation and current challenges are presented for future research directions.
Machine Learning-Based Intrusion Detection for Achieving Cybersecurity in Smart Grids Using IEC 61850 GOOSE Messages
(MDPIST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND, 2021) Ustun, Taha Selim; Hussain, S. M. Suhail; Ulutas, Ahsen; Onen, Ahmet; Roomi, Muhammad M.; Mashima, Daisuke; AGÜ, Mühendislik Fakültesi, Elektrik - Elektronik Mühendisliği Bölümü; Onen, Ahmet
Increased connectivity is required to implement novel coordination and control schemes. IEC 61850-based communication solutions have become popular due to many reasons-object-oriented modeling capability, interoperable connectivity and strong communication protocols, to name a few. However, communication infrastructure is not well-equipped with cybersecurity mechanisms for secure operation. Unlike online banking systems that have been running such security systems for decades, smart grid cybersecurity is an emerging field. To achieve security at all levels, operational technology-based security is also needed. To address this need, this paper develops an intrusion detection system for smart grids utilizing IEC 61850's Generic Object-Oriented Substation Event (GOOSE) messages. The system is developed with machine learning and is able to monitor the communication traffic of a given power system and distinguish normal events from abnormal ones, i.e., attacks. The designed system is implemented and tested with a realistic IEC 61850 GOOSE message dataset under symmetric and asymmetric fault conditions in the power system. The results show that the proposed system can successfully distinguish normal power system events from cyberattacks with high accuracy. This ensures that smart grids have intrusion detection in addition to cybersecurity features attached to exchanged messages.
Neuro-Fuzzy-Based Model Predictive Energy Management for Grid Connected Microgrids
(MDPI, ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND, 2020) Ulutas, Ahsen; Altas, Ismail Hakki; Onen, Ahmet; Ustun, Taha Selim; 0000-0001-7086-5112; 000-0002-7715-3246; AGÜ, Mühendislik Fakültesi, Elektrik - Elektronik Mühendisliği Bölümü
With constant population growth and the rise in technology use, the demand for electrical energy has increased significantly. Increasing fossil-fuel-based electricity generation has serious impacts on environment. As a result, interest in renewable resources has risen, as they are environmentally friendly and may prove to be economical in the long run. However, the intermittent character of renewable energy sources is a major disadvantage. It is important to integrate them with the rest of the grid so that their benefits can be reaped while their negative impacts can be mitigated. In this article, an energy management algorithm is recommended for a grid-connected microgrid consisting of loads, a photovoltaic (PV) system and a battery for efficient use of energy. A model predictive control-inspired approach for energy management is developed using the PV power and consumption estimation obtained from daylight solar irradiation and temperature estimation of the same area. An energy management algorithm, which is based on a neuro-fuzzy inference system, is designed by determining the possible operating states of the system. The proposed system is compared with a rule-based control strategy. Results show that the developed control algorithm ensures that microgrid is supplied with reliable energy while the renewable energy use is maximized.
Optimization of multiple battery swapping stations with mobile support for ancillary services
(FRONTIERS MEDIA SA, 2022) Kocer, Mustafa Cagatay; Onen, Ahmet; Ustun, Taha Selim; Albayrak, Sahin; 0000-0001-7086-5112; AGÜ, Mühendislik Fakültesi, Elektrik - Elektronik Mühendisliği Bölümü; Önen, Ahmet
The recent developments in electric vehicles (EVs) causes several issues that have not been satisfactorily addressed. One of the foremost problems is the charging–discharging processes of EV batteries with diverse characteristics. Although a charging station is the first choice in this regard, a battery swap station (BSS) is also a suitable alternative solution as it eliminates long waiting periods and battery degradation due to fast charging. BSS has the capability to ensure prompt and efficient service for electric vehicles. Since BSS has a large number of battery systems, optimum planning of the charging–discharging operations of the batteries is critical for both BSS and the grid. This study presents an optimal charging–discharging schedule for multiple BSSs based on the swap demand of privately owned EVs and electric bus (EB) public transportation system. In addition, BSSs reinforce the power grid by providing ancillary services such as peak shaving and valley filling with demand response programs. In order to increase the flexibility of the operation, the mobile swapping station (MSS) concept, an innovative and dynamic service, is introduced to the literature and added to the model. The results indicate that BSS is an essential agent in the ancillary services market and the MSS concept is a yielding solution for both BSSs and power networks. Last, the data utilized in the study for swap demand calculation and power grid analysis are real-world data from Berlin, Germany.
Review on Energy Application Using Blockchain Technology With an Introductions in the Pricing Infrastructure
(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC445 HOES LANE, PISCATAWAY, NJ 08855-4141, 2022) Al-Abri, Tariq; Onen, Ahmet; Al-Abri, Rashid; Hossen, Abdulnasir; Al-Hinai, Amer; Jung, Jaesung; Ustun, Taha Selim; 0000-0001-9471-997X; 0000-0001-7086-5112; 0000-0002-7200-9974; 0000-0001-8762-637X; 0000-0002-2413-8421; AGÜ, Mühendislik Fakültesi, Elektrik - Elektronik Mühendisliği Bölümü; Önen, Ahmet
With the rapid transformation of the energy sector towards modern power systems represented by smart grids (SGs), microgrids (MG), and distributed generation, blockchain (BC) technology has shown the capability for solving security, privacy, and reliability challenges that hinder progress. Currently, the energy structure is forming a decentralized system that prioritizes customer satisfaction. BC technology undertakes power network stockholders in a secure energy market, transparent transactions, and fair competition and offers promising energy solutions. This paper is a comprehensive review of energy applications using BC integration. Firstly, we introduce the drivers of BC leverage that make it a potentially important component of the power network. Following that, we provide background information on BC and its application in areas other than the energy sector. Subsequently, we discuss studies and sort potential energy applications from various recent papers and surveys that have already adopted BC technology in the energy sector. Then, we summarize the pricing infrastructure for applying BC in the energy sector and identify the requirements to build it. Finally, energy security and privacy challenges based on BC are highlighted, along with potential drawbacks and concerns related to the pricing infrastructure.