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Hajibeigy, M., Talavat, V., Galvani, S.. (1403). Optimal Allocation of Renewable Energy Sources to Enhance Distribution System Reliability with Confidence Interval Considerations. سامانه مدیریت نشریات علمی, (), -. doi: 10.22098/joape.2024.14178.2087
M. Hajibeigy; V. Talavat; S. Galvani. "Optimal Allocation of Renewable Energy Sources to Enhance Distribution System Reliability with Confidence Interval Considerations". سامانه مدیریت نشریات علمی, , , 1403, -. doi: 10.22098/joape.2024.14178.2087
Hajibeigy, M., Talavat, V., Galvani, S.. (1403). 'Optimal Allocation of Renewable Energy Sources to Enhance Distribution System Reliability with Confidence Interval Considerations', سامانه مدیریت نشریات علمی, (), pp. -. doi: 10.22098/joape.2024.14178.2087
Hajibeigy, M., Talavat, V., Galvani, S.. Optimal Allocation of Renewable Energy Sources to Enhance Distribution System Reliability with Confidence Interval Considerations. سامانه مدیریت نشریات علمی, 1403; (): -. doi: 10.22098/joape.2024.14178.2087

Optimal Allocation of Renewable Energy Sources to Enhance Distribution System Reliability with Confidence Interval Considerations

Journal of Operation and Automation in Power Engineering
مقالات آماده انتشار، اصلاح شده برای چاپ، انتشار آنلاین از تاریخ 23 اردیبهشت 1403    اصل مقاله (1.84 M)
نوع مقاله: Research paper
شناسه دیجیتال (DOI): 10.22098/joape.2024.14178.2087
نویسندگان
M. Hajibeigy؛ V. Talavat* ؛ S. Galvani
Department of Power Engineering, Faculty of Electrical and Computer Engineering, Urmia University, Urmia, Iran.
چکیده
Due to ever-increasing energy requirements, modern distribution systems are integrated with renewable energy sources (RESs), such as wind turbines and photovoltaics. They also bring economic, environmental, and technical advantages. However, they face the network operator with decision-making challenges due to their uncertain nature. Modern distribution systems usually operate at safety margins, and any contingency may lead to power supply losses. In this regard, any attempts to increase the planner/operator's awareness of the network situation will help improve the decision quality. This paper determines the optimal locations of the RESs to enhance the expected power not served as a reliability index. Besides, it reduces power losses and minimizes the 95\% confidence interval of power losses, as much as possible for having more awareness of network states. The K-medoids data clustering method is applied to handle the uncertainties of the RESs and demand loads. The MOPSO, NSGA II, and MOGWO algorithms are used to solve the proposed problem. The efficiency of the proposed approach is tested on the IEEE standard 33-bus and 118-bus distribution networks. The obtained results show that it is possible to reach a better confidence interval while keeping the losses and reliability index at a desired level. Considering solutions with identical losses and reliability index, the confidence interval of power losses using the MOPSO algorithm is 6.86% and 39.82% better rather than the NSGA II and MOGWO algorithms in the 33-bus distribution network and it is 30.23% and 129.63% better in the 118-bus distribution network.
کلیدواژه‌ها
Optimal allocation؛ renewable energy sources؛ distribution system؛ confidence interval
مراجع
  1. Y. Gilasi, S. H. Hosseini, and H. Ranjbar, “Resiliency-oriented optimal siting and sizing of distributed energy resources in distribution systems,” Electr. Power Syst. Res., vol. 208, p. 107875, 2022.
  2. A. Younesi, H. Shayeghi, P. Siano, and A. Safari, “A multiobjective resilience-economic stochastic scheduling method for microgrid,” Int. J. Electr. Power Energy Syst., vol. 131, p. 106974, 2021.
  3. P. Meera and S. Hemamalini, “Reliability assessment and enhancement of distribution networks integrated with renewable distributed generators: A review,” Sustainable Energy Technol. Assess., vol. 54, p. 102812, 2022.
  4. H. Ebrahimi, S. Galvani, V. Talavat, and M. Farhadi-Kangarlu, “A conditional value at risk based stochastic allocation of sop in distribution networks,” Electr. Power Syst. Res., vol. 228, p. 110111, 2024.
  5. Y. Luo, Q. Nie, D. Yang, and B. Zhou, “Robust optimal operation of active distribution network based on minimum confidence interval of distributed energy beta distribution,” J. Mod. Power Syst. Clean Energy, vol. 9, no. 2, pp. 423–430, 2020.
  6. T. E. Gümüs¸, S. Emiroglu, and M. A. Yalcin, “Optimal dg allocation and sizing in distribution systems with thevenin based impedance stability index,” Int. J. Electr. Power Energy Syst., vol. 144, p. 108555, 2023.
  7. K. Subbaramaiah, P. Sujatha, et al., “Optimal dg unit placement in distribution networks by multi-objective whale optimization algorithm & its techno-economic analysis,” Electr. Power Syst. Res., vol. 214, p. 108869, 2023.
  8. S. Abdul-Ameer, A. Al-Nussairi, R. Khalid, J. Abbas, and A. Al-Mansor, “Maximizing the generated power of wind farms by using optimization method,” J. Oper. Autom. Power Eng., vol. 11, no. Special Issue (Open), 2023.
  9. A. Selim, S. Kamel, and F. Jurado, “Efficient optimization technique for multiple dg allocation in distribution networks,” Appl. Soft Comput., vol. 86, p. 105938, 2020.
  10. L. A. Gallego Pareja, J. M. López-Lezama, and O. Gómez Carmona, “Optimal feeder reconfiguration and placement of voltage regulators in electrical distribution networks using a linear mathematical model,” Sustainability, vol. 15, no. 1, p. 854, 2023.
  11. M. Dixit, P. Kundu, and H. R. Jariwala, “Integration of distributed generation for assessment of distribution system reliability considering power loss, voltage stability and voltage deviation,” Energy Syst., vol. 10, pp. 489–515, 2019.
  12. R. Fathi, B. Tousi, and S. Galvani, “Allocation of renewable resources with radial distribution network reconfiguration using improved salp swarm algorithm,” Appl. Soft Comput., vol. 132, p. 109828, 2023.
  13. S. Rezaeian-Marjani, S. M. Jalalat, B. Tousi, S. Galvani, and V. Talavat, “A probabilistic approach for optimal operation of wind-integrated power systems including upfc,” IET Renewable Power Gener., vol. 17, no. 3, pp. 706–724, 2023.
  14. L. C. da Costa, F. S. Thomé, J. D. Garcia, and M. V. Pereira, “Reliability-constrained power system expansion planning: A stochastic risk-averse optimization approach,” IEEE Trans. Power Syst., vol. 36, no. 1, pp. 97–106, 2020.
  15. H. Ebrahimi, S. Rezaeian-Marjani, S. Galvani, and V. Talavat, “Probabilistic optimal planning in active distribution networks considering non-linear loads based on data clustering method,” IET Gener. Transm. Distrib., vol. 16, no. 4, pp. 686–702, 2022.
  16. L. A. Gallego, J. F. Franco, and L. G. Cordero, “A fast-specialized point estimate method for the probabilistic optimal power flow in distribution systems with renewable distributed generation,” Int. J. Electr. Power Energy Syst., vol. 131, p. 107049, 2021.
  17. J. S. Giraldo, J. C. López, J. A. Castrillon, M. J. Rider, and C. A. Castro, “Probabilistic opf model for unbalanced three-phase electrical distribution systems considering robust constraints,” IEEE Trans. Power Syst., vol. 34, no. 5, pp. 3443–3454, 2019.
  18. L. G. C. Bautista, J. Soares, J. F. F. Baquero, and Z. Vale, “Probabilistic algorithm based on 2m+ 1 point estimate method edgeworth considering voltage confidence intervals for optimal pv generation,” in 2022 17th Int. Conf. Probab. Methods Appl. Power Syst. (PMAPS), pp. 1–6, IEEE, 2022.
  19. S. Zhang, H. Cheng, K. Li, N. Tai, D. Wang, and F. Li, “Multi-objective distributed generation planning in distribution network considering correlations among uncertainties,” Appl. Energy, vol. 226, pp. 743–755, 2018.
  20. H.-S. Park and C.-H. Jun, “A simple and fast algorithm for k-medoids clustering,” Expert Syst. Appl., vol. 36, no. 2, pp. 3336–3341, 2009.
  21. C. Wang, C. Liu, F. Tang, D. Liu, and Y. Zhou, “A scenario-based analytical method for probabilistic load flow analysis,” Electr. Power Syst. Res., vol. 181, p. 106193, 2020.
  22. M. D. McKay, R. J. Beckman, and W. J. Conover, “A comparison of three methods for selecting values of input variables in the analysis of output from a computer code,” Technometrics, vol. 42, no. 1, pp. 55–61, 2000.
  23. M. Vahid-Pakdel and B. Mohammadi-Ivatloo, “Probabilistic assessment of wind turbine impact on distribution networks using linearized power flow formulation,” Electr. Power Syst. Res., vol. 162, pp. 109–117, 2018.
  24. M. Aien, M. Fotuhi-Firuzabad, and M. Rashidinejad, “Probabilistic optimal power flow in correlated hybrid wind–photovoltaic power systems,” IEEE Trans. Smart Grid, vol. 5, no. 1, pp. 130–138, 2014.
  25. M. Kumar and C. Samuel, “Statistical analysis of load demand distribution at banaras hindu university, india,” in 2016 Int. Conf. Adv. Comput. Commun. Inf. (ICACCI), pp. 2318–2323, IEEE, 2016.
  26. S. Rezaeian-Marjani, S. Galvani, V. Talavat, and M. FarhadiKangarlu, “Optimal allocation of d-statcom in distribution networks including correlated renewable energy sources,” Int. J. Electr. Power Energy Syst., vol. 122, p. 106178, 2020.
  27. R. Xu and D. Wunsch, “Survey of clustering algorithms,” IEEE Trans. Neural Networks, vol. 16, no. 3, pp. 645–678, 2005.
  28. L. Kaufman and P. J. Rousseeuw, Finding groups in data: an introduction to cluster analysis. John Wiley & Sons, 2009.
  29. R. 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.
  30. S. Galvani, A. Bagheri, M. Farhadi-Kangarlu, and N. Nikdel, “A multi-objective probabilistic approach for smart voltage control in wind-energy integrated networks considering correlated parameters,” Sustainable Cities Soc., vol. 78, p. 103651, 2022.
  31. Y. He, W. J. Ma, and J. P. Zhang, “The parameters selection of pso algorithm influencing on performance of fault diagnosis,” in MATEC Web Conf., vol. 63, p. 02019, EDP Sciences, 2016.
  32. C. A. C. Coello, G. T. Pulido, and M. S. Lechuga, “Handling multiple objectives with particle swarm optimization,” IEEE Trans. Evol. Comput., vol. 8, no. 3, pp. 256–279, 2004.
  33. K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: Nsga-ii,” IEEE Trans. Evol. Comput., vol. 6, no. 2, pp. 182–197, 2002.
  34. S. Mirjalili, S. Saremi, S. M. Mirjalili, and L. d. S. Coelho, “Multi-objective grey wolf optimizer: a novel algorithm for multi-criterion optimization,” Expert Syst. Appl., vol. 47, pp. 106–119, 2016.
  35. J. Juan and I. Ortega, “Reliability analysis for hydrothermal generating systems including the effect of maintenance scheduling,” IEEE Trans. Power Syst., vol. 12, no. 4, pp. 1561–1568, 1997.
  36. S. Galvani, V. Talavat, and S. Rezaeian Marjani, “Preventive/corrective security constrained optimal power flow using a multiobjective genetic algorithm,” IEEE Trans. Power Syst., vol. 46, no. 13, pp. 1462–1477, 2018.
  37. F. D. C. Kraaikamp and H. L. L. Meester, “A modern introduction to probability and statistics,” Springer: Berlin/Heidelberg, Germany, 2005.
  38. H. Ebrahimi, S. Galvani, V. Talavat, and M. Farhadi-Kangarlu, “Optimal parameters setting for soft open point to improve power quality indices in unbalanced distribution systems considering loads and renewable energy sources uncertainty,” Electr. Power Syst. Res., vol. 229, p. 110155, 2024.
  39. H. Ebrahimi, S. R. Marjani, and V. Talavat, “Optimal planning in active distribution networks considering nonlinear loads using the mopso algorithm in the topsis framework,” Int. Trans. Electr. Energy Syst., vol. 30, no. 3, p. e12244, 2020.
  40. H. Ebrahimi, S. Rezaeian-Marjani, M. Farhadi-Kangarlu, and S. Galvani, “Stochastic scheduling of energy storage systems in harmonic polluted active distribution networks,” IET Gener. Transm. Distrib., vol. 16, no. 23, pp. 4689–4709, 2022.
  41. M. Aien, M. Fotuhi-Firuzabad, and M. Rashidinejad, “Probabilistic optimal power flow in correlated hybrid wind–photovoltaic power systems,” IEEE Trans. Smart Grid, vol. 5, no. 1, pp. 130–138, 2014.
  42. G. Carpinelli, R. Rizzo, P. Caramia, and P. Varilone, “Taguchi’s method for probabilistic three-phase power flow of unbalanced distribution systems with correlated wind and photovoltaic generation systems,” Renewable Energy, vol. 117, pp. 227–241, 2018.
  43. M. 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.
  44. J. Zhou, B. Ayhan, C. Kwan, S. Liang, and W. Lee, “High performance arcing fault localization in distribution networks,” in 2011 IEEE Ind. Appl. Soc. Annu. Meet., pp. 1–5, IEEE, 2011.
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