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Scopus İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395

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Subject: search.filters.subject.Renewable EnergyWoS Q: search.filters.wosQuartile.Q2

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Now showing 1 - 5 of 5
  • Article
    Citation - WoS: 1
    Citation - Scopus: 1
    Machine Learning for V2X-Enabled Microgrids: A Bibliometric and Thematic Review of Intelligent Energy Management Applications
    (Springer Heidelberg, 2026-03-09) Dogan, Yasemin; Unlu, Ramazan
    Modern 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
    Tapered Curved-Beam Hinges for Electret-Based Vibration Energy Harvesting Devices
    (IOP Publishing Ltd, 2024-12-01) Hah, Dooyoung
    Interest in vibration energy harvesting have been growing recently for various applications. One of the major development goals for vibration energy harvesters has been improvement in energy conversion efficiency. To pursue that goal, one of the main approaches has been to broaden the spectra of harvesters. Employment of nonlinear springs, such as curved-beam hinges, has proven to be effective for that purpose. The main contribution of the current study is to introduce a lateral taper to the curved beam so as to further optimize the harvester performances. Via numerical analysis by using stochastic differential equations, the study shows that at 0.05g of vibration strength, tapered curved-beam hinges can result in higher electric power output than the non-tapered ones. Deformation-induced stress was taken into consideration as well, in reference to the fracture strength of the material (single-crystal silicon). At lower vibration strength (0.02g), spring nonlinearity becomes weaker, and as a result, the narrowest curved-beam hinge produces the highest output power. Overall, the current study demonstrates that tapering of the curved beam can be a useful addition in the vibration energy harvester design.
  • Article
    Citation - WoS: 50
    Citation - Scopus: 73
    Artificial 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, 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.
  • Article
    Citation - WoS: 45
    Citation - Scopus: 51
    Analyzing 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 Faisal
    Even 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: 153
    Citation - Scopus: 175
    Analyzing the Determinants of Carbon Emissions from Transportation in European Countries: The Role of Renewable Energy and Urbanization
    (Springer, 2020-08-13) Amin, Azka; Altinoz, Buket; Dogan, Eyup
    The continuous growth of transport sector and the increase in carbon emissions from transportation attract the attention of policy makers in sustainable transportation. Therefore, it is of great importance to understand the determinants of pollution from transportation. The aim of this study is to analyze the impacts of economic growth, renewable energy consumption and urbanization on CO2 emissions from transport sector in an Environmental Kuznets Curve (EKC) framework for European countries. To end this, second-generation panel long-run estimates and non-causality test are applied on the dataset from 1980-2014. Empirical pieces of evidence show that increases in renewable energy consumption mitigate carbon emissions from transportation, while urbanization has statistically insignificant positive impact on pollution. An increase in renewable energy consumption reduces CO2 from transportation by about 12 percent. The EKC hypothesis is validated. Moreover, unidirectional causality runs from renewable energy, economic growth and urbanization to emissions in transport sector. The findings of this study suggest strengthening the sustainable transportation system by promoting eco-friendly and energy-efficient modes of transportation and increase the environmental awareness of urban population and their overall concerns related to environmental issues caused by transportation. This study provides concrete evidence to the policy makers of European countries for especially sector-based renewable energy projects, drawing attention to the greenhouse gas impact of European transportation sector. [GRAPHICS] .