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Behnamfar, Mohammad reza, Barati, Hassan, Abasi, Mahyar, Alaleh, Mohammad, Torabi, Ghoalm Ali. (1403). 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, (), -. 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, , , 1403, -. doi: 10.22098/joape.2024.12419.1934
Behnamfar, Mohammad reza, Barati, Hassan, Abasi, Mahyar, Alaleh, Mohammad, Torabi, Ghoalm Ali. (1403). '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, (), pp. -. 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, 1403; (): -. 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
مقالات آماده انتشار، اصلاح شده برای چاپ، انتشار آنلاین از تاریخ 25 بهمن 1403    اصل مقاله (3.92 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
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آمار
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