Navigating Power Paths: Assessing Line Losses in the IEEE 14-Bus System amidst Electric Vehicle and Renewable Energy Integration
DOI:
https://doi.org/10.47672/ejt.1769Keywords:
Electric Vehicles, Renewable Energy Sources, Line Losses, Power System Planning, Newton-Raphson MethodAbstract
Purpose: This study thoroughly investigates the impacts of Electric Vehicles (EVs) and Renewable Energy Sources (RES) on line losses within the IEEE 14-Bus System, with a particular emphasis on addressing challenges and optimizing power system planning. The primary purpose of this study is to assess and understand the intricate relationships between EVs, RES, and line losses within the IEEE 14-Bus System. The research aims to provide insights that contribute to the development of tailored strategies in power system planning, addressing challenges posed by EV adoption and RES integration. By exploring the impact on grid efficiency, sustainability, and reliability, the study aims to guide future power system planners in optimizing the integration of EVs and RES.
Materials and Methods: The study utilizes load flow analysis, specifically the Newton-Raphson method, to model and simulate scenarios reflecting EV charging and discharging dynamics alongside intermittent RES integration within the IEEE 14-Bus System. A 24-hour dynamic load flow analysis is conducted to capture the diverse and dynamic impacts under varying load conditions. This comprehensive approach allows for a detailed assessment of line losses in the presence of EVs and RES.
Findings: The study reveals nuanced impacts, indicating higher EV adoption and increased RES integration result in notable escalations in line losses. This highlights challenges associated with grid efficiency, emphasizing the need for tailored strategies in power system planning.
Implications to Theory, Practice and Policy: The study advances theoretical understanding of dynamic interactions between EVs, RES, and power systems. In practice, it underscores the importance of adaptive control mechanisms and optimized strategies for accommodating EVs and RES while minimizing line losses. On a policy level, the findings suggest the need for regulatory frameworks incentivizing sustainable energy practices and technological research to mitigate challenges posed by EV adoption and RES integration.
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Benti, N. E., Chaka M. D., & Semie A. G. (2023) Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects. Sustainability, 15(9), 7087. https://doi.org/10.3390/su15097087
Kene, R. O., & Olwal T. O. (2023). Energy Management and Optimization of Large-Scale Electric Vehicle Charging on the Grid. World Electr. Veh. J, 14(4), 95. https://doi.org/10.3390/wevj14040095
Liu, C., Chau, K. T., Wu, D., & Gao, S. (2013). Opportunities and challenges of vehicle-to-home, vehicle-to-vehicle, and vehicle-to-grid technologies. Proceedings of the IEEE, 101 (11), 2409-2427. http://hdl.handle.net/10722/202856
Mohammed, A., Saif, O., & Abo-Adma, M. (2024). Strategies and sustainability in fast charging station deployment for electric vehicles. Sci Rep., 14, 283. https://doi.org/10.1038/s41598-023-50825-7
Shair, J., Li, H., Hu, J., & Xie, X. (2021). Power system stability issues, classifications and research prospects in the context of high-penetration of renewables and power electronics. Renewable and Sustainable Energy Reviews, 145. https://doi.org/10.1016/j.rser.2021.111111.
Theodoropoulos, T., Pantazopoulos, P., Karfopoulos, E., Lytrivis, P., Karaseitanidis, G., & Amditis, A. (2022) Proportionally fair and scalable EV charging under distribution line voltage constraints. Electric Power Systems Research, 208, https://doi.org/10.1016/j.epsr.2022.107797.
Thirunavukkarasu, M., Sawle, Y., & Lala, H. (2023) A comprehensive review on optimization of hybrid renewable energy systems using various optimization techniques. Renewable and Sustainable Energy Reviews, 176, https://doi.org/10.1016/j.rser.2023.113192.
Tinney W. F. & Hart C. E. (1967) Power Flow Solution by Newton's Method, IEEE Trans. Power App. Syst., 86, 1449-1460.
Worku, M.Y. (2022) Recent Advances in Energy Storage Systems for Renewable Source Grid Integration: A Comprehensive Review. Sustainability, 2014(10), 985. https://doi.org/10.3390/su14105985
Yao, J., Zang, Y., & Yan, Z. (2018). A Group Approach of Smart Hybrid Poles with Renewable Energy, Street Lighting and EV Charging Based on DC Micro-Grid. Energies, 11(12), 3445. https://doi.org/10.3390/en11123445.
Zhang, J., Yan J., Yongqian, L., Zhang, H. & Lv, Guoliang. (2020). Daily electric vehicle charging load profiles considering demographics of vehicle users. Applied Energy, 274(2), 115063. https://doi.org/10.1016/j.apenergy.2020.115063.
Zhong, X., Xin, Li. G., & Zhng, C. (2021) False data injection in power smart grid and identification of the most vulnerable bus; a case study 14 IEEE bus network. Energy Reports, 7, 8476-8484. https://doi.org /10.1016/j.egyr.2021.08.029.
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Copyright (c) 2024 Ozan Gül
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