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Behnamfar, Mohammad reza, Barati, Hassan, Abasi, Mahyar, Alaleh, Mohammad, Torabi, Ghoalm Ali. (1404). Multi-Objective Optimization for Day-Ahead HT-WP-PV-PSH with LS-EVs Systems Self-Scheduling Unit Commitment Using HHO-PSO Algorithm. سامانه مدیریت نشریات علمی, 14(1), 1-19. doi: 10.22098/joape.2024.12419.1934
Mohammad reza Behnamfar; Hassan - Barati; Mahyar Abasi; Mohammad Alaleh; Ghoalm Ali Torabi. "Multi-Objective Optimization for Day-Ahead HT-WP-PV-PSH with LS-EVs Systems Self-Scheduling Unit Commitment Using HHO-PSO Algorithm". سامانه مدیریت نشریات علمی, 14, 1, 1404, 1-19. doi: 10.22098/joape.2024.12419.1934
Behnamfar, Mohammad reza, Barati, Hassan, Abasi, Mahyar, Alaleh, Mohammad, Torabi, Ghoalm Ali. (1404). 'Multi-Objective Optimization for Day-Ahead HT-WP-PV-PSH with LS-EVs Systems Self-Scheduling Unit Commitment Using HHO-PSO Algorithm', سامانه مدیریت نشریات علمی, 14(1), pp. 1-19. doi: 10.22098/joape.2024.12419.1934
Behnamfar, Mohammad reza, Barati, Hassan, Abasi, Mahyar, Alaleh, Mohammad, Torabi, Ghoalm Ali. Multi-Objective Optimization for Day-Ahead HT-WP-PV-PSH with LS-EVs Systems Self-Scheduling Unit Commitment Using HHO-PSO Algorithm. سامانه مدیریت نشریات علمی, 1404; 14(1): 1-19. doi: 10.22098/joape.2024.12419.1934

Multi-Objective Optimization for Day-Ahead HT-WP-PV-PSH with LS-EVs Systems Self-Scheduling Unit Commitment Using HHO-PSO Algorithm

Journal of Operation and Automation in Power Engineering
مقاله 1، دوره 14، شماره 1، فروردین 2026، صفحه 1-19    اصل مقاله (3.89 M)
نوع مقاله: Research paper
شناسه دیجیتال (DOI): 10.22098/joape.2024.12419.1934
نویسندگان
Mohammad reza Behnamfar* 1؛ Hassan - Barati2؛ Mahyar Abasi3، 4؛ Mohammad Alaleh1؛ Ghoalm Ali Torabi1
1Khuzestan Regional Electric Company (KZREC), Ahvaz, Iran.
2Department of Electrical Engineering, Dezful Branch, Islamic Azad University, Dezful, Iran.
3Department of Electrical Engineering, Faculty of Engineering, Arak University, Arak, Iran.
4Research Institute of Renewable Energy, Arak University, Arak, Iran.
چکیده
A stochastic multi-objective structure is introduced for integrating hydro-thermal, wind power, photovoltaic (PV), pumped storage hydro (PSH), and large-scale electric vehicle (LS-EV) systems using a day-ahead self-scheduling mechanism. The paper incorporates an improved Harris Hawks Optimizer combined with Particle Swarm Optimization, termed HHO-PSO. Uncertain parameters of the problem, such as energy prices, spinning reserve, non-spinning reserve prices, and renewable output, are also considered. Additionally, the lattice Monte Carlo simulation and roulette wheel mechanism are utilized. By adopting an objective function that optimizes multiple goals, the paper proposes an approach to assist generation companies (GenCos) in maximizing profit (PFM) and minimizing emissions (EMM). However, to make the modeling of the multi/single-objective day-ahead hydro-thermal self-scheduling problem with WP, PV, PSH, and LS-EVs practical, additional factors must be considered in the problem formulation. According to the findings, the HHO-PSO algorithm provides satisfactory values for profit maximization and emission minimization in the day-ahead operation of power systems across all considered cases. The paper applies the proposed method to a 118-bus test network, demonstrating its accuracy and capability.
کلیدواژه‌ها
Self-scheduling unit commitment؛ uncertainty؛ HHO-PSO algorithm؛ PSH and LS-EVs units؛ modelling
مراجع
  1. A. J. Wood, B. F. Wollenberg, and G. B. Sheblé, Power generation, operation, and control. John Wiley & Sons, 2013.
  2. S. Bisanovic, M. Hajro, and M. Dlakic, “Hydrothermal self-scheduling problem in a day-ahead electricity market,” Electr. Power Syst. Res., vol. 78, no. 9, pp. 1579–1596, 2008.
  3. I. Farhat and M. El-Hawary, “Optimization methods applied for solving the short-term hydrothermal coordination problem,” Electr. Power Syst. Res., vol. 79, no. 9, pp. 1308– 1320, 2009.
  4. A. J. Conejo, J. M. Arroyo, J. Contreras, and F. A. Villamor, “Self-scheduling of a hydro producer in a pool-based electricity market,” IEEE Trans. Power Syst., vol. 17, no. 4, pp. 1265–1272, 2002.
  5. A. M. Foley, P. G. Leahy, K. Li, E. McKeogh, and A. P. Morrison, “A long-term analysis of pumped hydro storage to firm wind power,” Appl. Energy, vol. 137, pp. 638–648, 2015.
  6. J. Dhillon, S. Parti, and D. Kothari, “Fuzzy decisionmaking in stochastic multiobjective short-term hydrothermal scheduling,” IEE Proc.-Gener. Transm. Distrib., vol. 149, no. 2, pp. 191–200, 2002.
  7. M. R. Norouzi, A. Ahmadi, A. M. Sharaf, and A. E. Nezhad, “Short-term environmental/economic hydrothermal scheduling,” Electr. Power Syst. Res., vol. 116, pp. 117–127, 2014.
  8. A. Ahmadi, J. Aghaei, H. A. Shayanfar, and A. Rabiee, “Mixed integer programming of multiobjective hydro-thermal self scheduling,” Appl. Soft Comput., vol. 12, no. 8, pp. 2137–2146, 2012.
  9. M. Izadbakhsh, M. Gandomkar, A. Rezvani, and A. Ahmadi, “Short-term resource scheduling of a renewable energy based micro grid,” Renewable Energy, vol. 75, pp. 598–606, 2015.
  10. J. Catalão, H. Pousinho, and J. Contreras, “Optimal hydro scheduling and offering strategies considering price uncertainty and risk management,” Energy, vol. 37, no. 1, pp. 237–244, 2012.
  11. F. Partovi, M. Nikzad, B. Mozafari, and A. M. Ranjbar, “A stochastic security approach to energy and spinning reserve scheduling considering demand response program,” Energy, vol. 36, no. 5, pp. 3130–3137, 2011.
  12. C.-L. Tseng and W. Zhu, “Optimal self-scheduling and bidding strategy of a thermal unit subject to ramp constraints and price uncertainty,” IET Gener. Transm. Distrib., vol. 4, no. 2, pp. 125–137, 2010.
  13. M. Li, Y. Li, and G. Huang, “An interval-fuzzy twostage stochastic programming model for planning carbon dioxide trading under uncertainty,” Energy, vol. 36, no. 9, pp. 5677–5689, 2011.
  14. A. Ahmadi, M. Charwand, and J. Aghaei, “Risk-constrained optimal strategy for retailer forward contract portfolio,” Int. J. Electr. Power Energy Syst., vol. 53, pp. 704–713, 2013.
  15. K. Meng, H. G. Wang, Z. Dong, and K. P. Wong, “Quantuminspired particle swarm optimization for valve-point economic load dispatch,” IEEE Trans. Power Syst., vol. 25, no. 1, pp. 215–222, 2009.
  16. J. Aghaei, A. Ahmadi, A. Rabiee, V. G. Agelidis, K. M. Muttaqi, and H. A. Shayanfar, “Uncertainty management in multiobjective hydro-thermal self-scheduling under emission considerations,” Appl. Soft Comput., vol. 37, pp. 737–750, 2015.
  17. T. Li and M. Shahidehpour, “Dynamic ramping in unit commitment,” IEEE Trans. Power Syst., vol. 22, no. 3, pp. 1379–1381, 2007.
  18. Y. Li, Q. Wu, M. Li, and J. Zhan, “Mean-variance model for power system economic dispatch with wind power integrated,” Energy, vol. 72, pp. 510–520, 2014.
  19. X.    Yuan,    B.    Ji,    S.    Zhang,    H.    Tian,    and    Z.    Chen, “An improved artificial physical optimization algorithm for dynamic dispatch of generators with valve-point effects and wind power,” Energy Convers. Manage., vol. 82, pp. 92–105, 2014.
  20. H. M. Dubey, M. Pandit, and B. Panigrahi, “Hybrid flower pollination algorithm with time-varying fuzzy selection mechanism for wind integrated multi-objective dynamic economic dispatch,” Renewable Energy, vol. 83, pp. 188–202, 2015.
  21. X. Yuan, H. Tian, Y. Yuan, Y. Huang, and R. M. Ikram, “An extended nsga-iii for solution multi-objective hydro-thermalwind scheduling considering wind power cost,” Energy Convers. Manage., vol. 96, pp. 568–578, 2015.
  22. J. Zhou, P. Lu, Y. Li, C. Wang, L. Yuan, and L. Mo, “Short-term hydro-thermal-wind complementary scheduling considering uncertainty of wind power using an enhanced multi-objective bee colony optimization algorithm,” Energy Convers. Manage., vol. 123, pp. 116–129, 2016.
  23. X. Yuan, H. Tian, Y. Yuan, Y. Huang, and R. M. Ikram, “An extended nsga-iii for solution multi-objective hydro-thermalwind scheduling considering wind power cost,” Energy Convers. Manage., vol. 96, pp. 568–578, 2015.
  24. S. Mirjalili, “The ant lion optimizer,” Adv. Eng. Software, vol. 83, pp. 80–98, 2015.
  25. H. M. Dubey, M. Pandit, and B. Panigrahi, “Hydro-thermalwind scheduling employing novel ant lion optimization technique with composite ranking index,” Renewable Energy, vol. 99, pp. 18–34, 2016.
  26. A. Wijesinghe and L. L. Lai, “Small hydro power plant analysis and development,” in 2011 4th Int. Conf. Electr. Util. Deregulation Restructuring Power Technol., pp. 25–30, IEEE, 2011.
  27. M. Baneshi and F. Hadianfard, “Techno-economic feasibility of hybrid diesel/pv/wind/battery electricity generation systems for non-residential large electricity consumers under southern iran climate conditions,” Energy Convers. Manage., vol. 127, pp. 233–244, 2016.
  28. F.-F. Li and J. Qiu, “Multi-objective optimization for integrated hydro–photovoltaic power system,” Appl. Energy, vol. 167, pp. 377–384, 2016.
  29. Z. Ding, H. Hou, G. Yu, E. Hu, L. Duan, and J. Zhao, “Performance analysis of a wind-solar hybrid power generation system,” Energy Convers. Manage., vol. 181, pp. 223–234, 2019.
  30. X. Wang, J. Chang, X. Meng, and Y. Wang, “Hydrothermal-wind-photovoltaic coordinated operation considering the comprehensive utilization of reservoirs,” Energy Convers. Manage., vol. 198, p. 111824, 2019.
  31. X. Wang, J. Chang, X. Meng, and Y. Wang, “Short-term hydro-thermal-wind-photovoltaic complementary operation of interconnected power systems,” Appl. Energy, vol. 229, pp. 945–962, 2018.
  32. A. Zakaria, F. B. Ismail, M. H. Lipu, and M. A. Hannan, “Uncertainty models for stochastic optimization in renewable energy applications,” Renewable Energy, vol. 145, pp. 1543– 1571, 2020.
  33. L. Wu, M. Shahidehpour, and T. Li, “Stochastic securityconstrained unit commitment,” IEEE Trans. Power Syst., vol. 22, no. 2, pp. 800–811, 2007.
  34. X. Wang, Y. Mei, Y. Kong, Y. Lin, and H. Wang, “Improved multi-objective model and analysis of the coordinated operation of a hydro-wind-photovoltaic system,” Energy, vol. 134, pp. 813–839, 2017.
  35. A. Panda, U. Mishra, M.-L. Tseng, and M. H. Ali, “Hybrid power systems with emission minimization: Multi-objective optimal operation,” J. Cleaner Prod., vol. 268, p. 121418, 2020.
  36. S. Mandal, B. K. Das, and N. Hoque, “Optimum sizing of a stand-alone hybrid energy system for rural electrification in bangladesh,” J. Cleaner Prod., vol. 200, pp. 12–27, 2018.
  37. J.-Y. Lee, K. B. Aviso, and R. R. Tan, “Multi-objective optimisation of hybrid power systems under uncertainties,” Energy, vol. 175, pp. 1271–1282, 2019.
  38. E. Rakhshani, H. Mehrjerdi, and A. Iqbal, “Hybrid winddiesel-battery system planning considering multiple different wind turbine technologies installation,” J. Cleaner Prod., vol. 247, p. 119654, 2020.
  39. S. Yao, S. Zhang, and X. Zhang, “Renewable energy, carbon emission and economic growth: A revised environmental kuznets curve perspective,” J. Cleaner Prod., vol. 235, pp. 1338–1352, 2019.
  40. M. Simab, M. S. Javadi, and A. E. Nezhad, “Multi-objective programming of pumped-hydro-thermal scheduling problem using normal boundary intersection and vikor,” Energy, vol. 143, pp. 854–866, 2018.
  41. P. Aliasghari, B. Mohammadi-Ivatloo, M. Alipour, M. Abapour, and K. Zare, “Optimal scheduling of plug-in electric vehicles and renewable micro-grid in energy and reserve markets considering demand response program,” J. Cleaner Prod., vol. 186, pp. 293–303, 2018.
  42. O. Abedinia, M. Zareinejad, M. H. Doranehgard, G. Fathi, and N. Ghadimi, “Optimal offering and bidding strategies of renewable energy based large consumer using a novel hybrid robust-stochastic approach,” J. Cleaner Prod., vol. 215, pp. 878–889, 2019.
  43. L. Ju, Q. Tan, R. Zhao, S. Gu, W. Wang, et al., “Multi-objective electro-thermal coupling scheduling model for a hybrid energy system comprising wind power plant, conventional gas turbine, and regenerative electric boiler, considering uncertainty and demand response,” J. Cleaner Prod., vol. 237, p. 117774, 2019.
  44. H. Khaloie, A. Abdollahi, M. Shafie-Khah, P. Siano, S. Nojavan, A. Anvari-Moghaddam, and J. P. Catalão, “Co-optimized bidding strategy of an integrated windthermal-photovoltaic system in deregulated electricity market under uncertainties,” J. Cleaner Prod., vol. 242, p. 118434, 2020.
  45. M. A. Ramli, H. Bouchekara, and A. S. Alghamdi, “Optimal sizing of pv/wind/diesel hybrid microgrid system using multi-objective self-adaptive differential evolution algorithm,” Renewable Energy, vol. 121, pp. 400–411, 2018.
  46. Y. Shen and H. Yang, “Multi-objective optimization of integrated solar-driven co2 capture system for an industrial building,” Sustainability, vol. 15, no. 1, p. 526, 2022.
  47. X. Wu, B. Liao, Y. Su, and S. Li, “Multi-objective and multi-algorithm operation optimization of integrated energy system considering ground source energy and solar energy,” Int. J. Electr. Power Energy Syst., vol. 144, p. 108529, 2023.
  48. J. A. Concha-Carrasco, M. A. Vega-Rodríguez, and C. J. Pérez, “A multi-objective artificial bee colony approach
    for profit-aware recommender systems,” Inf. Sci., vol. 625, pp. 476–488, 2023.
  49. R. Mena, M. Godoy, C. Catalán, P. Viveros, and E. Zio, “Multi-objective two-stage stochastic unit commitment model for wind-integrated power systems: A compromise programming approach,” Int. J. Electr. Power Energy Syst., vol. 152, p. 109214, 2023.
  50. Y. Ding, Q. Tan, Z. Shan, J. Han, and Y. Zhang, “A two-stage dispatching optimization strategy for hybrid renewable energy system with low-carbon and sustainability in ancillary service market,” Renewable Energy, vol. 207, pp. 647–659, 2023.
  51. S. Adhvaryyu, S. Prabhakar, and P. K. Adhvaryyu, “Multi objective short term hydro-thermal-chp scheduling using social spider algorithm,” Results Eng., vol. 16, p. 100586, 2022.
  52. C. Paul, P. K. Roy, and V. Mukherjee, “Wind and solar based multi-objective hydro-thermal scheduling using chaoticoppositional whale optimization algorithm,” Electr. Power Compon. Syst., vol. 51, no. 6, pp. 568–592, 2023.
  53. M. Behnamfar, H. Barati, and M. Karami, “Stochastic short-term hydro-thermal scheduling based on mixed integer programming with volatile wind power generation,” J. Oper. Autom. Power Eng., vol. 8, no. 3, pp. 195–208, 2020.
  54. M. Behnamfar, H. Barati, and M. Karami, “Antlion optimization algorithm for optimal self-scheduling unit commitment in power system under uncertainties,” J. Oper. Autom. Power Eng., vol. 9, no. 3, pp. 226–241, 2021.
  55. M. Behnamfar and M. Abasi, “Uncertainty management in short-term self-scheduling unit commitment using harris hawks optimization algorithm,” J. Oper. Autom. Power Eng., vol. 12, no. 4, pp. 280–295, 2024.
  56. V. Vahidinasab and S. Jadid, “Stochastic multiobjective self-scheduling of a power producer in joint energy and reserves markets,” Electr. Power Syst. Res., vol. 80, no. 7, pp. 760–769, 2010.
  57. T. Li and M. Shahidehpour, “Price-based unit commitment: A case of lagrangian relaxation versus mixed integer programming,” IEEE Trans. Power Syst., vol. 20, no. 4, pp. 2015–2025, 2005.
  58. L. Wu, M. Shahidehpour, and T. Li, “Cost of reliability analysis based on stochastic unit commitment,” IEEE Trans. Power Syst., vol. 23, no. 3, pp. 1364–1374, 2008.
  59. N. Amjady, J. Aghaei, and H. A. Shayanfar, “Stochastic multiobjective market clearing of joint energy and reserves auctions ensuring power system security,” IEEE Trans. Power Syst., vol. 24, no. 4, pp. 1841–1854, 2009.
  60. I. G. Damousis, A. G. Bakirtzis, and P. S. Dokopoulos, “A solution to the unit-commitment problem using integer-coded genetic algorithm,” IEEE Trans. Power Syst., vol. 19, no. 2, pp. 1165–1172, 2004.
  61. Z. Michalewicz, Genetic algorithms+ data structures= evolution programs. Springer Science & Business Media, 2013.
  62. 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.
  63. Y. Zhang, J. Le, X. Liao, F. Zheng, K. Liu, and X. An, “Multi-objective hydro-thermal-wind coordination scheduling integrated with large-scale electric vehicles using imopso,” Renewable Energy, vol. 128, pp. 91–107, 2018.
  64. A. A. Heidari, S. Mirjalili, H. Faris, I. Aljarah, M. Mafarja, and H. Chen, “Harris hawks optimization: Algorithm and applications,” Future Gener. Comput. Syst., vol. 97, pp. 849– 872, 2019.
  65. M. R. Elkadeem, M. Abd Elaziz, Z. Ullah, S. Wang, and S. W. Sharshir, “Optimal planning of renewable energy-integrated distribution system considering uncertainties,” IEEE Access, vol. 7, pp. 164887–164907, 2019.
آمار
تعداد مشاهده مقاله: 175
تعداد دریافت فایل اصل مقاله: 159


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