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Farahbakhsh, H., Pourfar, I., Lashkar Ara, A.. (1402). Virtual power plant operation using an improved meta-heuristic optimization algorithm considering uncertainties. سامانه مدیریت نشریات علمی, (), -. doi: 10.22098/joape.2023.11310.1844
H. Farahbakhsh; I. Pourfar; A. Lashkar Ara. "Virtual power plant operation using an improved meta-heuristic optimization algorithm considering uncertainties". سامانه مدیریت نشریات علمی, , , 1402, -. doi: 10.22098/joape.2023.11310.1844
Farahbakhsh, H., Pourfar, I., Lashkar Ara, A.. (1402). 'Virtual power plant operation using an improved meta-heuristic optimization algorithm considering uncertainties', سامانه مدیریت نشریات علمی, (), pp. -. doi: 10.22098/joape.2023.11310.1844
Farahbakhsh, H., Pourfar, I., Lashkar Ara, A.. Virtual power plant operation using an improved meta-heuristic optimization algorithm considering uncertainties. سامانه مدیریت نشریات علمی, 1402; (): -. doi: 10.22098/joape.2023.11310.1844

Virtual power plant operation using an improved meta-heuristic optimization algorithm considering uncertainties

Journal of Operation and Automation in Power Engineering
مقالات آماده انتشار، اصلاح شده برای چاپ، انتشار آنلاین از تاریخ 28 خرداد 1402    اصل مقاله (1.08 M)
نوع مقاله: Research paper
شناسه دیجیتال (DOI): 10.22098/joape.2023.11310.1844
نویسندگان
H. Farahbakhsh1؛ I. Pourfar* 2؛ A. Lashkar Ara1
1Department of Electrical Engineering, Dezful Branch, Islamic Azad University, Dezful, Iran
2Department of Electrical Engineering, Jundi-Shapur University of Technology, Dezful, Iran
چکیده
In this paper, virtual power plant (VPP) planning is done using distributed generation sources to create a safe platform for electricity exchange and to increase the profitability and sustainability of electricity. In the proposed model, the effect of micro-grid interaction with the electricity market in the presence of distributed generation resources and storage is investigated. To solve this problem, an improved artificial bee colony algorithm using the accept-reject method (AR-ABC) is used. The AR method is employed to limit the initial search space as well as for the scenario reduction process. Also, uncertainties related to loads and renewable sources are formulated in a sample micro-grid including micro-turbine (MT), fuel cell (FC), wind turbine (WT), photovoltaic cells (PV) and batteries for storage; the results are compared with those of other methods, which shows this method works better than others. The software simulations of this research are done in the MATLAB software environment.
کلیدواژه‌ها
Accept-reject method؛ Electricity market؛ Renewable energy sources؛ Uncertainty؛ Virtual power plant
مراجع
  1. Löschenbrand, “Modeling competition of virtual power plants via deep learning,” Energy, vol. 214, p. 118870, 2021.
  2. -T. Lin, G. Chen, and C. Li, “Risk-averse energy trading among peer-to-peer based virtual power plants: A stochastic game approach,” Int. J. Electr. Power Energy Syst., vol. 132, p. 107145, 2021.
  3. Baringo, L. Baringo, and J. M. Arroyo, “Holistic planning of a virtual power plant with a nonconvex operational model: A risk-constrained stochastic approach,” Int. J. Electr. Power Energy Syst., vol. 132, p. 107081, 2021.
  4. Azimi, R.-A. Hooshmand, and S. Soleymani, “Optimal integration of demand response programs and electric vehicles in coordinated energy management of industrial virtual power plants,” J. Energy Storage, vol. 41, p. 102951, 2021.
  5. Aguilar, C. Bordons, and A. Arce, “Chance constraints and machine learning integration for uncertainty management in virtual power plants operating in simultaneous energy markets,” Int. J. Electr. Power Energy Syst., vol. 133, p. 107304, 2021.
  6. Liu, R. J. Yang, X. Yu, C. Sun, P. S. Wong, and H. Zhao, “Virtual power plants for a sustainable urban future,” Sustain. Cities Soc., vol. 65, p. 102640, 2021.
  7. Sreenivasulu and P. Balakrishna, “Optimal dispatch of renewable and virtual power plants in smart grid environment through bilateral transactions,” Electr. Power Compon. Syst., vol. 49, no. 4-5, pp. 488–503, 2021.
  8. Guo, P. Liu, and X. Shu, “Optimal dispatching of electricthermal interconnected virtual power plant considering market trading mechanism,” J. Clean. Prod., vol. 279, p. 123446, 2021.
  9. A. F. Asl, L. Bagherzadeh, S. Pirouzi, M. Norouzi, and M. Lehtonen, “A new two-layer model for energy management in the smart distribution network containing flexi-renewable virtual power plant,” Electr. Power Syst. Res., vol. 194, p. 107085, 2021.
  10. S. Gougheri, H. Jahangir, M. A. Golkar, A. Ahmadian, and M. A. Golkar, “Optimal participation of a virtual power plant in electricity market considering renewable energy: A deep learning-based approach,” Sustain. Energy Grids Netw., vol. 26, p. 100448, 2021.
  11. M. Othman, Y. G. Hegazy, and A. Y. Abdelaziz, “Optimal operation of virtual power plant in unbalanced distribution networks,” Electr. Power Compon. Syst., vol. 44, no. 14, pp. 1620–1630, 2016.
  12. Seyyed Mahdavi, J. Saebi, and A. Ghasemi, “Risk-based approach for self-scheduling of virtual power plants in competitive power markets,” J. Oper. Autom. Power Eng., vol. 11, no. 2, pp. 94–104, 2023.
  13. B. Shahmars, J. Salehi, and N. T. Kalantari, “Bi-level unit commitment considering virtual power plants and demand response programs using information gap decision theory,” J. Oper. Autom. Power Eng., vol. 9, no. 2, pp. 88–102, 2021.
  14. Kong, J. Xiao, D. Liu, J. Wu, C. Wang, and Y. Shen, “Robust stochastic optimal dispatching method of multienergy virtual power plant considering multiple uncertainties,” Appl. Energy, vol. 279, p. 115707, 2020.
  15. Rahimiyan and L. Baringo, “Real-time energy management of a smart virtual power plant,” IET Gener. Transm. Distrib., vol. 13, no. 11, pp. 2015–2023, 2019.
  16. Yang, B. Wei, and Z. Qin, “Sequence-based differential evolution for solving economic dispatch considering virtual power plant,” IET Gener. Transm. Distrib., vol. 13, no. 15, pp. 3202–3215, 2019.
  17. Baringo, L. Baringo, and J. M. Arroyo, “Day-ahead self-scheduling of a virtual power plant in energy and reserve electricity markets under uncertainty,” IEEE Trans. Power Syst., vol. 34, no. 3, pp. 1881–1894, 2018.
  18. Wang, “Improving the rejection sampling method in quasimonte carlo methods,” J. Comput. Appl. Math., vol. 114, no. 2, pp. 231–246, 2000.
  19. Martino, D. Luengo, and J. Míguez, Independent random sampling methods. Springer, 2018.
  20. Andrieu, N. De Freitas, A. Doucet, and M. I. Jordan, “An introduction to mcmc for machine learning,” Mach. Learn., vol. 50, pp. 5–43, 2003.
  21. Casella, J. Ferrándiz, D. Peña, D. R. Insua, J. M. Bernardo, P. García-López, A. González, J. Berger, A. Dawid, T. J. Diciccio, et al., “Statistical inference and monte carlo algorithms,” Test, vol. 5, pp. 249–344, 1996.
  22. Karaboga and B. Akay, “A comparative study of artificial bee colony algorithm,” Appl. Math. Comput., vol. 214, no. 1, pp. 108–132, 2009.
  23. Xu, S. Liu, and D. Li, “Prediction of water temperature in prawn cultures based on a mechanism model optimized by an improved artificial bee colony,” Comput. Electron. Agric., vol. 140, pp. 397–408, 2017.
  24. Wang, W. Wang, S. Xiao, Z. Cui, M. Xu, and X. Zhou, “Improving artificial bee colony algorithm using a new neighborhood selection mechanism,” Inf. Sci., vol. 527, pp. 227–240, 2020.
  25. -J. Yu, Z.-H. Zhan, and J. Zhang, “Artificial bee colony algorithm with an adaptive greedy position update strategy,” Soft Comput., vol. 22, pp. 437–451, 2018.
  26. Farahbakhsh, I. Pourfar, and A. Lashkar Ara, “A modified artificial bee colony algorithm using accept–reject method: Theory and application in virtual power plant planning,” IETE J. Res., pp. 1–16, 2021.
  27. Awerbuch and A. Preston, The virtual utility: Accounting, technology & competitive aspects of the emerging industry, vol. 26. Springer Science & Business Media, 2012.
  28. Liu, Y. Liu, and Z. Jing, “Impact of industrial virtual power plant on renewable energy integration,” Global Energy Interconnection, vol. 3, no. 6, pp. 545–552, 2020.
  29. Giuntoli and D. Poli, “Optimized thermal and electrical scheduling of a large scale virtual power plant in the presence of energy storages,” IEEE Trans. Smart Grid, vol. 4, no. 2, pp. 942–955, 2013.
  30. A. Moghaddam, A. Seifi, T. Niknam, and M. R. A. Pahlavani, “Multi-objective operation management of a renewable mg (micro-grid) with back-up micro-turbine/fuel cell/battery hybrid power source,” Energy, vol. 36, no. 11, pp. 6490–6507, 2011.
  31. J. Kasaei, M. Gandomkar, and J. Nikoukar, “Optimal management of renewable energy sources by virtual power plant,” Renew. Energ., vol. 114, pp. 1180–1188, 2017.
  32. Kefayat, A. L. Ara, and S. N. Niaki, “A hybrid of ant colony optimization and artificial bee colony algorithm for probabilistic optimal placement and sizing of distributed energy resources,” Energy Convers. Manag., vol. 92, pp. 149– 161, 2015.
  33. Mazidi, N. Rezaei, and A. Ghaderi, “Simultaneous power and heat scheduling of microgrids considering operational uncertainties: A new stochastic p-robust optimization approach,” Energy, vol. 185, pp. 239–253, 2019.
  34. Atwa, E. El-Saadany, M. Salama, and R. Seethapathy, “Optimal renewable resources mix for distribution system energy loss minimization,” IEEE Trans. Power Syst., vol. 25, no. 1, pp. 360–370, 2009.
  35. Rakipour and H. Barati, “Probabilistic optimization in operation of energy hub with participation of renewable energy resources and demand response,” Energy, vol. 173, pp. 384–399, 2019.
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