WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Article Machine Learning for V2X-Enabled Microgrids: A Bibliometric and Thematic Review of Intelligent Energy Management Applications(Springer Heidelberg, 2026-03-09) Dogan, Yasemin; Unlu, RamazanModern power systems are evolving due to convergence of electric mobility, artificial intelligence, and renewable energy integration. Electric vehicles serve as dynamic, mobile energy storage units playing a vital role in ensuring resilient microgrid operations, via vehicle-to-everything (V2X) technology. However, despite the rise of machine learning (ML) in energy management, much of the existing literature remains fragmented lacking a holistic perspective across all facets of V2X-enabled microgrids. This study fills this gap by conducting a systematic bibliometric and thematic analysis of 310 articles obtained from Web of Science (2013-2024). By combining bibliometric mapping with thematic synthesis, the research identifies dominant and emerging ML techniques-ranging from reinforcement learning to federated learning-and evaluates their roles in microgrid management. The study highlights underexplored areas, including decentralized coordination, encouraging prosumer participation, understanding user behavior, safeguarding cybersecurity, improving real-time optimization, and the effective integration and adaptation of V2X technology within microgrid ecosystems. These gaps emphasize the need for interdisciplinary research and policy frameworks to address the social dimensions of future energy systems. Beyond a comprehensive overview, this paper proposes a research roadmap integrating technical, social, and policy dimensions. It offers actionable guidance for researchers, stakeholders aiming to unlock the potential of intelligent, human-centered, and socially inclusive energy ecosystems. Furthermore, the findings align with UN Sustainable Development Goals (SDG 7, 11, and 13), while also creating a positive impact on humanity by supporting the well-being of both society and the planet. Ultimately, this reinforces the indispensable role of ML in advancing the zero-carbon transition.Article Modeling and Simulation of Dynamic Energy Management Systems for Smart Buildings(TÜBİTAK, 2025-11-25) Ozel, O.; Rıfat Boynueğrİ, A.; Yigit, H.; Tekgun, B.; Boynuegri, Ali RifatThis study presents a dynamic energy management system tailored for smart residential buildings, integrating thermal and electrical models to achieve both natural gas and electricity bill cost reduction. By harnessing wind and solar energy sources, the system aims to meet the diverse energy needs of modern homes. Through load shifting and thermal storage strategies, known as power-to-heat (P2H) approaches, the system ensures efficient renewable energy utilization while maintaining resident comfort. Validation of the proposed system was conducted using real-world data from the Yıldız Technical University Smart Home Laboratory, demonstrating its practical applicability and effectiveness. Results indicate significant reductions in both natural gas and electricity consumption, leading to substantial cost savings. Specifically, the proposed system reduced natural gas consumption by 3.79% and electricity consumption by 35.62%, highlighting its potential to enhance energy efficiency and sustainability in residential settings. © This work is licensed under a Creative Commons Attribution 4.0 International License.Conference Object Citation - WoS: 1Citation - Scopus: 1Microgrid Environmental Impact(Institute of Electrical and Electronics Engineers Inc., 2020-09-28) Al-Agtash, Salem Y.; al-Hashem, Mohammad; Batarseh, Mohanad; Bintoudi, Angelina D.; Tsolakis, Apostolos Charalampos; Tzovaras, Dimitrios K.; Hadjidemetriou, Lenos; Khiat, MounirPower plants have bad impacts on the environment. One of these impacts is Carbon Dioxide (CO2) emission resulted from power plants that depend on fossil fuel, oil and natural gas. Renewable energy is considered as an important solution for this problem since it is classified as clean and environmentally friendly source of energy and helps reducing the dependency on conventional power plants. High renewable energy penetration into power systems is a big challenge that can be solved by deploying the concept of smart Micro-Grids. This paper presents a study on how much reduction of CO2 emission can be resulted from deploying smart micro-grid concept on a university campus, German Jordanian University (GJU) campus was taken as a pilot. The micro-grid is meant to operate according to an optimum resource scheduling framework that guarantee a minimum operational cost while achieving high local power availability. © 2020 Elsevier B.V., All rights reserved.Article Citation - WoS: 47Citation - Scopus: 68Artificial Intelligence Based Intrusion Detection System for IEC 61850 Sampled Values Under Symmetric and Asymmetric Faults(IEEE-Inst Electrical Electronics Engineers Inc, 2021) Ustun, Taha Selim; Hussain, S. M. Suhail; Yavuz, Levent; Onen, AhmetModern 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.Article Citation - WoS: 42Citation - Scopus: 46Analyzing the Role of Renewable Energy and Energy Intensity in the Ecological Footprint of the United Arab Emirates(MDPI, 2021-12-27) Dogan, Eyup; Shah, Syed FaisalEven though a great number of researchers have explored the determinants of environmental pollution, the majority have used carbon emissions as an indicator while only recent studies have employed the ecological footprint which is a broader and more reliable indicator for the environment. The present study contributes to the literature by exploring for the first time in the literature the role of real output, energy intensity (technology), and renewable energy in the ecological footprint under the STIRPAT framework for a Gulf Cooperation Council (GCC) country-the United Arab Emirates. By applying the novel bounds testing with dynamic simulations on the data from 1992-2017, the findings of this paper reveal that energy intensity and renewable energy have a negative and significant influence on the ecological footprint but real output has a positive and significant impact on it. In other words, the empirical results indicate that a rise in the real income increases environmental pollution while increases in renewable energy and advances in technology mitigate the level of emissions. The findings also suggest that the government should establish new programs, investment opportunities, and incentives in favor of energy intensity-related technology and renewable energy for the sake of environmental sustainability. The outcomes from this research analysis are useful for policymakers, industrial partners, and project designers in the United Arab Emirates.Article Citation - WoS: 114Citation - Scopus: 118Analysis of CO2 Emissions and Energy Consumption by Sources in MENA Countries: Evidence From Quantile Regressions(Springer Heidelberg, 2021-03-20) Alharthi, Majed; Dogan, Eyup; Taskin, DilvinThe development of economies and energy usage can significantly impact the carbon dioxide (CO2) emissions in the Middle East and North Africa (MENA) countries. Therefore, this study aims to analyze the factors that determine CO2 emissions in MENA under the environmental Kuznets curve (EKC) framework by applying novel quantile techniques on data for CO2 emissions, real income, renewable and non-renewable energy consumption, and urbanization over the period from 1990 to 2015. The results from the estimations suggest that renewable energy consumption significantly reduces the level of emissions; furthermore, its impact increases with higher quantiles. In addition, non-renewable energy consumption increases CO2 emissions, while its magnitude decreases with higher quantiles. The empirical results also confirm the validity of EKC hypothesis for the panel of MENA economies. Policymakers in the region should implement policies and regulations to promote the adoption and use of renewable energy to mitigate carbon emissions.