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Shadi, Sh., Salehi, J., Safari, A.. (1402). Traffic Uncertainty Modeling and Energy Management of Smart Distribution Networks with the Presence of Parking Lots. سامانه مدیریت نشریات علمی, (), -. doi: 10.22098/joape.2024.12065.1894
Sh. Shadi; J. Salehi; A. Safari. "Traffic Uncertainty Modeling and Energy Management of Smart Distribution Networks with the Presence of Parking Lots". سامانه مدیریت نشریات علمی, , , 1402, -. doi: 10.22098/joape.2024.12065.1894
Shadi, Sh., Salehi, J., Safari, A.. (1402). 'Traffic Uncertainty Modeling and Energy Management of Smart Distribution Networks with the Presence of Parking Lots', سامانه مدیریت نشریات علمی, (), pp. -. doi: 10.22098/joape.2024.12065.1894
Shadi, Sh., Salehi, J., Safari, A.. Traffic Uncertainty Modeling and Energy Management of Smart Distribution Networks with the Presence of Parking Lots. سامانه مدیریت نشریات علمی, 1402; (): -. doi: 10.22098/joape.2024.12065.1894

Traffic Uncertainty Modeling and Energy Management of Smart Distribution Networks with the Presence of Parking Lots

Journal of Operation and Automation in Power Engineering
مقالات آماده انتشار، اصلاح شده برای چاپ، انتشار آنلاین از تاریخ 08 بهمن 1402    اصل مقاله (939.8 K)
نوع مقاله: Research paper
شناسه دیجیتال (DOI): 10.22098/joape.2024.12065.1894
نویسندگان
Sh. Shadi؛ J. Salehi* ؛ A. Safari
Department of Electrical Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran.
چکیده
Energy management (EM) in smart distribution networks (SDN) is to schedule the power transaction between the SDN and the existing distributed energy resources (DERs) e.g., distributed generations, especially renewable resources and electrical vehicles, from an eco-technical viewpoint. Due to the dual role of electric vehicles (EVs) acting as a power source and load, they presented both challenges and opportunities in EM. The complexity of EM increases as DERs become more prevalent in SDN. Moreover, the uncertainties of renewable resources, price, and load besides the uncertainties related to the place, amount, and time of EV’s charging makes EM a more intricate field. This supports the necessity of extensive tools and approaches to manage EM in SDNs. In this respect, this paper proposes an optimum scenario-based stochastic energy management scheme for intelligent distribution networks. The proposed approach is modeled as a MINLP problem and solved in GAMS software under the DICOPT solver. The test is conducted on a 33-bus SDN with and without factoring in uncertainties.
کلیدواژه‌ها
Energy management؛ distribution network؛ electrical vehicle؛ parking lot؛ traffic؛ uncertainty
مراجع
  1. Zare and S. Nojavan, Operation of distributed energy resources in smart distribution networks. Academic Press, 2018.
  2. Jennings, “Cost to maintain an electric car [online].” Available:https://www.greencars.com/post/ what-does-it-cost-to-maintain-an-electric-car. Accessed: Jul. 2023.
  3. Neyestani, M. Y. Damavandi, G. Chicco, and J. P. Catalão, “Effects of pev traffic flows on the operation of parking lots and charging stations,” IEEE Trans. Smart Grid, vol. 9, no. 2, pp. 1521–1530, 2017.
  4. Sachan and N. Adnan, “Stochastic charging of electric vehicles in smart power distribution grids,” Sustainable Cities Soc., vol. 40, pp. 91–100, 2018.
  5. Neyestani, Sustainable Distribution Network Planning Considering Multi-Energy Systems and Plug-In Electric Vehicles Parking Lots. PhD thesis, Universidade da Beira Interior (Portugal), 2016.
  6. Rajakaruna, F. Shahnia, and A. Ghosh, Plug in electric vehicles in smart grids. Springer, 2015.
  7. Ford, “Electric vehicles and the electric utility company,” Energy Policy, vol. 22, no. 7, pp. 555–570, 1994.
  8. Kempton and S. E. Letendre, “Electric vehicles as a new power source for electric utilities,” Transp. Res. Part D Transp. Environ., vol. 2, no. 3, pp. 157–175, 1997.
  9. Stüdli, E. Crisostomi, R. Middleton, and R. Shorten, “Optimal real-time distributed v2g and g2v management of electric vehicles,” Int. J. Control, vol. 87, no. 6, pp. 1153–1162, 2014.
  10. Drude, L. C. P. Junior, and R. Rüther, “Photovoltaics (pv) and electric vehicle-to-grid (v2g) strategies for peak demand reduction in urban regions in brazil in a smart grid environment,” Renewable Energy, vol. 68, pp. 443–451, 2014.
  11. S. Hosseini, A. Badri, and M. Parvania, “The plug-in electric vehicles for power system applications: The vehicle to grid (v2g) concept,” in 2012 IEEE Int. Energy Conf. Exhibition (ENERGYCON), pp. 1101–1106, IEEE, 2012.
  12. C. Chukwu and S. M. Mahajan, “V2g electric power capacity estimation and ancillary service market evaluation,” in 2011 IEEE Power Energy Soc. Gen. Meet., pp. 1–8, IEEE, 2011.
  13. Honarmand, A. Zakariazadeh, and S. Jadid, “Optimal scheduling of electric vehicles in an intelligent parking lot considering vehicle-to-grid concept and battery condition,” Energy, vol. 65, pp. 572–579, 2014.
  14. Moon, S. Y. Park, C. Jeong, and J. Lee, “Forecasting electricity demand of electric vehicles by analyzing consumers charging patterns,” Transp. Res. Part D Transp. Environ., vol. 62, pp. 64–79, 2018.
  15. Mills and I. MacGill, “Assessing electric vehicle storage, flexibility, and distributed energy resource potential,” J. Energy Storage, vol. 17, pp. 357–366, 2018.
  16. R. Galiveeti, A. K. Goswami, and N. B. D. Choudhury, “Impact of plug-in electric vehicles and distributed generation on reliability of distribution systems,” Eng. Sci. Technol. Int. J., vol. 21, no. 1, pp. 50–59, 2018.
  17. K. Nunna, S. Battula, S. Doolla, and D. Srinivasan, “Energy management in smart distribution systems with vehicle-to-grid integrated microgrids,” IEEE Trans. Smart Grid, vol. 9, no. 5, pp. 4004–4016, 2016.
  18. Rahbari, M. Vafaeipour, N. Omar, M. A. Rosen, O. Hegazy, J.-M. Timmermans, S. Heibati, and P. Van Den Bossche, “An optimal versatile control approach for plug-in electric vehicles to integrate renewable energy sources and smart grids,” Energy, vol. 134, pp. 1053–1067, 2017.
  19. Neyestani, M. Y. Damavandi, T. D. Mendes, J. P. Catalão, and G. Chicco, “Effect of plug-in electric vehicles traffic behavior on multi-energy demand’s dependency,” in 2016 IEEE Int. Energy Conf. (ENERGYCON), pp. 1–6, IEEE, 2016.
  20. Wang, M. Shahidehpour, C. Jiang, and Z. Li, “Coordinated planning strategy for electric vehicle charging stations and coupled traffic-electric networks,” IEEE Trans. Power Syst., vol. 34, no. 1, pp. 268–279, 2018.
  21. Wang and J. S. Thompson, “Two-stage admission and scheduling mechanism for electric vehicle charging,” IEEE Trans. Smart Grid, vol. 10, no. 3, pp. 2650–2660, 2018.
  22. Turker and S. Bacha, “Optimal minimization of plug-in electric vehicle charging cost with vehicle-to-home and vehicle-to-grid concepts,” IEEE Trans. Veh. Technol., vol. 67, no. 11, pp. 10281–10292, 2018.
  23. AbouArkoub, M. Soliman, Z. Gao, S. Suh, and V. D. Perera, “An online smart microgrid energy monitoring and management system,” in 2018 IEEE Int. Conf. Smart Energy Grid Eng. (SEGE), pp. 58–61, IEEE, 2018.
  24. Wei, Y. Li, and L. Cai, “Electric vehicle charging scheme for a park-and-charge system considering battery degradation costs,” IEEE Trans. Intell. Veh., vol. 3, no. 3, pp. 361–373, 2018.
  25. Aghajani and I. Heydari, “Energy management in microgrids containing electric vehicles and renewable energy sources considering demand response,” J. Oper. Autom. Power Eng., vol. 9, no. 1, pp. 34–48, 2021.
  26. Azimi and A. Salami, “Optimal operation of integrated energy systems considering demand response program,” J. Oper. Autom. Power Eng., vol. 9, no. 1, pp. 60–67, 2021.
  27. Avvari and V. K. DM, “A novel hybrid multi-objective evolutionary algorithm for optimal power flow in wind, pv, and pev systems,” J. Oper. Autom. Power Eng., vol. 11, no. 2, pp. 130–143, 2023.
  28. Abapour, S. Nojavan, and M. Abapour, “Multi-objective short-term scheduling of active distribution networks for benefit maximization of discos and dg owners considering demand response programs and energy storage system,” J. Mod. Power Syst. Clean Energy, vol. 6, no. 1, pp. 95–106, 2018.
  29. Saadat et al., Power system analysis, vol. 2. McGraw-hill, 1999.
  30. Salehi and A. Abdolahi, “Optimal scheduling of active distribution networks with penetration of phev considering congestion and air pollution using dr program,” Sustainable Cities Soc., vol. 51, p. 101709, 2019.
  31. Rabiee, A. Soroudi, B. Mohammadi-Ivatloo, and M. Parniani, “Corrective voltage control scheme considering demand response and stochastic wind power,” IEEE Trans. Power Syst., vol. 29, no. 6, pp. 2965–2973, 2014.
  32. E. Baran and F. F. Wu, “Network reconfiguration in distribution systems for loss reduction and load balancing,” IEEE Trans. Power Delivery, vol. 4, no. 2, pp. 1401–1407, 1989.
  33. Yaprakdal, M. Baysal, and A. Anvari-Moghaddam, “Optimal operational scheduling of reconfigurable microgrids in presence of renewable energy sources,” Energ., vol. 12, no. 10, p. 1858, 2019.
  34. Mohammadi, Y. Noorollahi, and B. Mohammadi-Ivatloo, “Impacts of energy storage technologies and renewable energy sources on energy hub systems,” Oper. Plann. Anal. Energy Storage Syst. Smart Energy Hubs, pp. 23–52, 2018.
  35. Gen, Reliability and cost/worth evaluation of generating systems utilizing wind and solar energy. PhD thesis, University of Saskatchewan, 2005.
  36. S. Farsangi, S. Hadayeghparast, M. Mehdinejad, and H. Shayanfar, “A novel stochastic energy management of a microgrid with various types of distributed energy resources in presence of demand response programs,” Energy, vol. 160, pp. 257–274, 2018.
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