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Alijanzadeh, Kourosh, Ghasemi-Marzbali, Ali. (1403). Optimal Load Distribution Based on Decision Theory with Information Gap in the Presence of Wind Farms Connected to the Power System. سامانه مدیریت نشریات علمی, (), -. doi: 10.22098/joape.2024.14914.2140
Kourosh Alijanzadeh; Ali Ghasemi-Marzbali. "Optimal Load Distribution Based on Decision Theory with Information Gap in the Presence of Wind Farms Connected to the Power System". سامانه مدیریت نشریات علمی, , , 1403, -. doi: 10.22098/joape.2024.14914.2140
Alijanzadeh, Kourosh, Ghasemi-Marzbali, Ali. (1403). 'Optimal Load Distribution Based on Decision Theory with Information Gap in the Presence of Wind Farms Connected to the Power System', سامانه مدیریت نشریات علمی, (), pp. -. doi: 10.22098/joape.2024.14914.2140
Alijanzadeh, Kourosh, Ghasemi-Marzbali, Ali. Optimal Load Distribution Based on Decision Theory with Information Gap in the Presence of Wind Farms Connected to the Power System. سامانه مدیریت نشریات علمی, 1403; (): -. doi: 10.22098/joape.2024.14914.2140

Optimal Load Distribution Based on Decision Theory with Information Gap in the Presence of Wind Farms Connected to the Power System

Journal of Operation and Automation in Power Engineering
مقالات آماده انتشار، اصلاح شده برای چاپ، انتشار آنلاین از تاریخ 30 آذر 1403    اصل مقاله (1.37 M)
نوع مقاله: Research paper
شناسه دیجیتال (DOI): 10.22098/joape.2024.14914.2140
نویسندگان
Kourosh Alijanzadeh؛ Ali Ghasemi-Marzbali*
Department of Electrical and Biomedical Engineering, Mazandaran University of Science and Technology, Babol, Iran.
چکیده
Optimal load distribution in power systems is crucial for minimizing overall costs while adhering to technical constraints. This process becomes increasingly complex with the integration of wind energy due to the inherent uncertainty in wind turbine production caused by variable wind conditions. This paper presents a novel approach to address these uncertainties within the context of the optimal power flow (OPF) problem by employing Information Gap Decision Theory (IGDT). Unlike traditional scenario-based methods, IGDT provides a computationally efficient and reliable framework for decision-making under uncertainty without extensive probabilistic data. The methodology uses the Weibull probability density function to model wind speed, allowing for realistic estimation of wind farm output power. The Evolutionary Particle Swarm Optimization (EPSO) algorithm, an advanced version of PSO, is utilized to solve the optimization problem, reducing the risk of convergence to local optima. Results are computed under two strategies: risk-averse and risk-taking, represented by immunity functions. These strategies highlight the impact of user demand on adjusting calculation parameters. Comparative analysis with scenario-based probabilistic optimization shows that the IGDT approach enhances system load cost evaluation by 0.12%. This study provides a robust framework for optimal power allocation under uncertainty, ensuring resilient and secure power generation.
کلیدواژه‌ها
Optimal load distribution؛ decision theory with information gap؛ uncertainty؛ wind farm
مراجع
  1. A. Ghasemi-Marzbali, M. Shafiei, and R. Ahmadiahangar, “Day-ahead economical planning of multi-vector energy district considering demand response program,” Appl. Energy, vol. 332, p. 120351, 2023.
  2. S. Halve, S. Raghuwanshi, and D. Sonje, “Radial distribution system network reconfiguration for reduction in real power loss and improvement in voltage profile, and reliability,” J. Oper. Autom. Power Eng., 2024.
  3. A. Altuma, R. Khalid, A. Alanssari, A. Hussien, Y. Mezaal, K. Al-Majdi, and T. Alawsi, “An application of the genetic algorithm optimization to voltage and reactive power control in the distribution systems,” J. Oper. Autom. Power Eng., vol. 11, no. Special Issue (Open), 2023.
  4. A. H. Shojaei, A. A. Ghadimi, M. R. Miveh, F. H. Gandoman, and A. Ahmadi, “Multiobjective reactive power planning considering the uncertainties of wind farms and loads using information gap decision theory,” Renewable Energy, vol. 163, pp. 1427–1443, 2021.
  5. E. Hassaballah, H. Keshta, K. Abdel-Latif, and A. Ali, “A novel strategy for real-time optimal scheduling of grid-tied microgrid considering load management and uncertainties,” Energy, vol. 299, p. 131419, 2024.
  6. M. Nooshyar and A. Ghasemi Marzbali, “Dynamic economic/emission dispatch with probability model of wind power with modified virus colony search algorithm,” Comput. Intell. Electr. Eng., vol. 13, no. 3, pp. 55–74, 2022.
  7. M. Zakaryaseraji and A. Ghasemi-Marzbali, “Evaluating congestion management of power system considering the demand response program and distributed generation,” Int. Trans. Electr. Energy Syst., vol. 2022, no. 1, p. 5818757, 2022.
  8. S. Seyedeh-Barhagh, M. Abapour, B. Mohammadi-Ivatloo, M. Shafie-Khah, and H. Laaksonen, “Optimal scheduling of a microgrid based on renewable resources and demand response program using stochastic and igdt-based approach,” J. Energy Storage, vol. 86, p. 111306, 2024.
  9. J. R. Birge and F. Louveaux, Introduction to stochastic programming. Springer Science & Business Media, 2011.
  10. N. Rezaei, Y. Pezhmani, A. R. Jordehi, and S. A. Mansouri, “A two-layer hybrid robust-stochastic model for energy management of isolated multi-energy microgrids with mobile storage systems and hydrogen refueling stations,” J. Energy Storage, vol. 90, p. 111905, 2024.
  11. X. Sun, W. Hui, X. Dong, X. Li, and S. S. Nahani, “Evaluating the reliability of microgrids consisting of renewable energy sources using stochastic scheduling based on the data-driven uncertainty set,” Eng. Appl. Artif. Intell., vol. 133, p. 108250, 2024.
  12. H. Karimi, “Stochastic scheduling of renewable-based energy systems considering power-to-hydrogen and hydrogen-topower units.,” Iran. J. Electr. Electron. Eng., vol. 20, no. 1, 2024.
  13. K. Qing, Y. Du, Q. Huang, C. Duan, and W. Hu, “Energy scheduling for microgrids with renewable energy sources considering an adjustable convex hull based uncertainty set,” Renewable Energy, vol. 220, p. 119611, 2024.
  14. A. Naderipour, S. A. Nowdeh, P. B. Saftjani, Z. Abdul-Malek, M. W. B. Mustafa, H. Kamyab, and I. F. Davoudkhani,
    “Deterministic and probabilistic multi-objective placement and sizing of wind renewable energy sources using improved spotted hyena optimizer,” J. Cleaner Prod., vol. 286, p. 124941, 2021.
  15. W. Zhu, J. Guo, and G. Zhao, “Multi-objective sizing optimization of hybrid renewable energy microgrid in a stand-alone marine context,” Electron., vol. 10, no. 2, p. 174, 2021.
  16. S. Si, W. Sun, and Y. Wang, “A decentralized dispatch model for multiple micro energy grids system considering renewable energy uncertainties and energy interactions,” J. Renewable Sustainable Energy, vol. 16, no. 1, 2024.
  17. M. Ebrahimi, M. Ebrahimi, A. Fallah, M. Shafie-Khah, and H. Laaksonen, “An iterative auction-based method for multi energy trading in a microgrid considering renewable energy uncertainties,” Electr. Power Syst. Res., vol. 232, p. 110381, 2024.
  18. M. Jabarnejad, “A genetic algorithm for ac optimal transmission switching,” in Proc. Genet. Evol. Comput. Conf., pp. 973–981, 2021.
  19. M. Ahmed, S. Mirsaeidi, M. A. Koondhar, N. Karami, E. M. Tag-Eldin, N. A. Ghamry, R. A. El-Sehiemy, Z. M. Alaas, I. Mahariq, and A. Sharaf, “Mitigating uncertainty problems of renewable energy resources through efficient integration of hybrid solar pv/wind systems into power networks,” IEEE Access, vol. 12, pp. 30311–30328, 2024.
  20. M. K. Ahmed, M. H. Osman, A. A. Shehata, and N. V. Korovkin, “A solution of optimal power flow problem in power system based on multi objective particle swarm algorithm,” in 2021 IEEE Conf. Russ. Young Res. Electr. Electron. Eng., pp. 1349–1353, IEEE, 2021.
  21. H. Xie, S. Gao, J. Zheng, and X. Huang, “A three-stage robust dispatch model considering the multi-uncertainties of electric vehicles and a multi-energy microgrid,” Int. J. Electr. Power Energy Syst., vol. 157, p. 109778, 2024.
  22. M. Abbasi, E. Abbasi, and B. Mohammadi-Ivatloo, “Single and multi-objective optimal power flow using a new differential-based harmony search algorithm,” J. Ambient Intell. Hum. Comput., vol. 12, no. 1, pp. 851–871, 2021.
  23. Y.-C. Chang, T.-Y. Lee, C.-L. Chen, and R.-M. Jan, “Optimal power flow of a wind-thermal generation system,” Int. J. Electr. Power Energy Syst., vol. 55, pp. 312–320, 2014.
  24. A. Rabiee, A. Soroudi, and A. Keane, “Information gap decision theory based opf with hvdc connected wind farms,” IEEE Trans. Power Syst., vol. 30, no. 6, pp. 3396–3406, 2014.
  25. A. Ma, J. Ji, and M. Khayatnezhad, “Risk-constrained non-probabilistic scheduling of coordinated power-to-gas conversion facility and natural gas storage in power and gas based energy systems,” Sustainable Energy Grids Networks, vol. 26, p. 100478, 2021.
  26. N. Ioakimidis, “Application of quantifier elimination to robust reliability under severe uncertainty conditions by using the info-gap decision theory (igdt),” 2021.
  27. N. Ioakimidis, “Robust reliability under uncertainty conditions by using modified info-gap models with two to four horizons of uncertainty and quantifier elimination,” 2021.
  28. F. Wang, H. Zhang, and A. Zhou, “A particle swarm optimization algorithm for mixed-variable optimization problems,” Swarm Evol. Comput., vol. 60, p. 100808, 2021.
  29. M. AlRashidi and M. El-Hawary, “Hybrid particle swarm optimization approach for solving the discrete opf problem considering the valve loading effects,” IEEE Trans. Power Syst., vol. 22, no. 4, pp. 2030–2038, 2007.
  30. “Vestas Wind Systems A/S, howpublished = https://www. vestas.com, note = Accessed: 2024-08-14.”
  31. J. Garcia-Gonzalez, R. M. R. de la Muela, L. M. Santos, and A. M. Gonzalez, “Stochastic joint optimization of wind generation and pumped-storage units in an electricity market,” IEEE Trans. Power Syst., vol. 23, no. 2, pp. 460–468, 2008.
  32. M. Azizi, S. Talatahari, and A. H. Gandomi, “Fire hawk optimizer: A novel metaheuristic algorithm,” Artif. Intell. Rev., vol. 56, no. 1, pp. 287–363, 2023.
  33. M. Abdel-Basset, R. Mohamed, M. Jameel, and M. Abouhawwash, “Spider wasp optimizer: a novel metaheuristic optimization algorithm,” Artif. Intell. Rev., vol. 56, no. 10, pp. 11675–11738, 2023.
  34. E.-S. M. El-Kenawy, N. Khodadadi, S. Mirjalili, A. A. Abdelhamid, M. M. Eid, and A. Ibrahim, “Greylag goose optimization: nature-inspired optimization algorithm,” Expert Syst. Appl., vol. 238, p. 122147, 2024.
  35. A. Ghasemi-Marzbali, “A novel nature-inspired meta-heuristic algorithm for optimization: bear smell search algorithm,” Soft Comput., vol. 24, no. 17, pp. 13003–13035, 2020.
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