برنامه‌ریزی توان تولیدی واحدهای حرارتی، بادی، خورشیدی با استفاده از الگوریتم سینوس کسینوس

نوع مقاله : مقاله برق

نویسندگان

1 دانشجوی دکتری، دانشکده فنی مهندسی، دانشگاه آزاد اسلامی واحد ساوه

2 استادیار، دانشکده فنی مهندسی، دانشگاه آزاد اسلامی واحد ساوه

چکیده

برنامه­ ریزی توان تولیدی واحدها جهت تامین تقاضای بار ساعتی، یکی از مسائل مهم در مدیریت تولید و بهره ­برداری از سیستم­های قدرت می­باشد. در این مقاله، مسئله توزیع بهینه بار با در نظر گرفتن تلفات شبکه انتقال، ملاحظات و محدودیت­های عملی نیروگاه­های حرارتی از قبیل نرخ افزایشی و کاهشی تولید، نواحی ممنوعه تولید، اثر شیر بخار با ترکیب منابع تجدیدپذیر شامل مزارع بادی و واحدهای خورشیدی مطرح شده است. منابع انرژی تجدیدپذیر به دلیل عدم استفاده از سوخت­های سوختی باعث کاهش آلودگی­های زیست محیطی شده اما این منابع، دارای عدم قطعیت و ماهیت تصادفی در تولید می­باشند. از طرف دیگر، منابع بادی و خورشیدی جزء منابع راه­اندازی سریع و منابع حرارتی جزء منابع حرارتی راه­اندازی کند محسوب می­شوند. در نظر گرفتن موارد ذکر شده به صورت توام، مساله توزیع بهینه بار را پیچیده می­نماید که در این مقاله برای تعیین میزان مشارکت منابع تولیدی مختلف در تامین بار، از روش جدیدی مبتنی بر الگوریتم سینوس کسینوس، استفاده شده است. به منظور بررسی کارآیی روش پیشنهادی، نتایج شبیه­سازی و مطالعات عددی روی یک سیستم نمونه شامل 6 واحد حرارتی، 5 واحد بادی و 13 واحد خورشیدی پیاده­سازی شده و با دیگر روش­های هوشمند مقایسه شده است. نتایج مطالعات عددی ضمن داشتن سرعت و دقت مناسب، برتری روش پیشنهادی را نسبت به سایر روشها نشان می­دهد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Planning the Production Power of Thermal, Wind and Solar Units Using the Sine Cosine Algorithm

نویسندگان [English]

  • Majid Khalili 1
  • Javad Nikoukar 2
1 PhD Student, Department of Electrical Engineering, Islamic Azad University, Saveh branch, Saveh, Iran
2 Assistant Professor, Department of Electrical Engineering, Islamic Azad University, Saveh branch, Saveh,
چکیده [English]

Dynamic production power planning to meet hourly load demand is one of the important issues in production management and operation of power systems. In this article, the problem of optimal load dispatch considering transmission network losses, considerations and practical limitations of thermal power plants such as increasing and decreasing ramp rates, prohibited production areas, steam valve effect with the combination of renewable resources including wind farms and solar units has been raised.
Renewable energy sources have reduced environmental pollution due to the non-use of fossil fuels, but these sources have uncertainty and random nature in production. On the other hand, wind and solar sources are considered to be part of fast start-up sources and thermal sources are considered to be part of slow start-up thermal sources. Considering the mentioned cases together complicates the problem of optimal load distribution, in this article, a new method based on the sine-cosine algorithm is used to determine the contribution of different production sources in the load supply.
To solve this problem, which has non-convex cost functions, a new method based on the sine-cosine algorithm has been used. In order to evaluation the effectiveness of the proposed method, simulation results and numerical studies on a sample system including 6 thermal units, 5 wind units and 13 solar units have been implemented and compared with other metaheuristic algorithms. The results of numerical studies show the superiority of the proposed method over other methods while having the appropriate speed and accuracy.
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کلیدواژه‌ها [English]

  • Optimal Dispatch
  • Sine Cosine Algorithm
  • Optimization
  • Renewable Energy Sources
  1. R. Singh, P.N. Narang, and H. Garg. "A novel TVAC-PSO based mutation strategies algorithm for generation scheduling of pumped storage hydrothermal system incorporating solar units." Energy 142. (2018): 822–837.
  2. R. Jabr, A. Coonick, and B.J. Cory. "A homogeneous linear programming algorithm for the security constrained economic dispatch problem." IEEE Transactions on Power Systems 15. no. 3 (2000): 930-936.
  3. A. Victoire, and A.E. Jeyakumar. "Hybrid PSO–SQP for economic dispatch with valve-point effect." Electric Power Systems Research 71. no. 1 (2004): 51-59.
  4. J. Kim. and K.K. Kim. "Dynamic programming for scalable just-in-time economic dispatch with non-convex constraints and anytime participation." International Journal of Electrical Power & Energy Systems 123. no. 5 (2020): 1-13.
  5. A.A. Haghrah, M. Nekoui, M.N. Heris, and B. Mohammadi‑ivatloo. "An improved real‑coded genetic algorithm with random walk based mutation for solving combined heat and power economic dispatch." Journal of Ambient Intelligence and Humanized Computing 12 (2020): 8561–8584.
  6. M. Gholamghasemi, E. Akbari, M.B. Asadpoor, and M. Ghasemi. "A new solution to the non-convex economic load dispatch problems using phasor particle swarm optimization." Applied Soft Computing Journal 79 (2019). 111–124.
  7. C. Panigrahi, R. Chakrabarti, and M. Basu. "Simulated Annealing Technique for Dynamic Economic Dispatch." Electric Power Components and Systems 34. no. 5 (2017): 577-586.
  8. S. Pothiya, I. Ngamroo, and W. Kongprawechnon. "Ant colony optimization for economic dispatch problem with non-smooth cost functions." International Journal of Electrical Power & Energy Systems 32. no. 5 (2010): 478-487.
  9. D. Zou, S. Li, X. Kong, and H. Ouyang. "Solving the dynamic economic dispatch by a memory-based global differential evolution and a repair technique of constraint handling." Energy 147 (2018): 59-80.
  10. S. Momen, J. Nikoukar, and M. Gandomkar. "Multi‑objective optimization of energy consumption in microgrids considering CHPs and renewables using improved shuffled frog leaping algorithm." Journal of Electrical Engineering & Technology. (2022): 1-17.
  11. A. Abid, T.N. Malik, F. Abid, and I.A. Sajjad. "Dynamic economic dispatch incorporating photovoltaic and wind generation using hybrid FPA with SQP." IETE Journal of Research 66, no. 2 (2020): 204-213.
  12. J. Nikoukar. "Unit commitment considering the emergency demand response programs and interruptible/curtailable loads." Turkish Journal of Electrical Engineering and Computer Sciences 26. no. 2 (2018): 1069-1080.
  13. M. El-hameed, and A. El-Fergany. "Water cycle algorithm-based economic dispatcher for sequential and simultaneous objectives including practical constraints." Applied Soft Computing Journal 58. (2017): 145–154.
  14. V.B. Vommi, and R. Vemula. "A very optimistic method of minimization for unconstrained methods". Information Science. (2018): 255-274.
  15. S. Padhi, B.P. Panigrahi, and D. Dash. "Assessment of Dynamic Economic and Emission Dispatch Problem using WOA in networked grids with photovoltaic power injection." Transactions of the Indian National Academy of Engineering 5, no. 4 (2020): 675-696.
  16. Z. Ullah, S. Wang, J. Radosavljevic, and J. Lai. " A solution to the optimal power flow problem considering WT and PV generation." IEEE Access. 7 (2019): 46763-46772.
  17. S. A. Mousavi Maleki, H. Hizam, and C. Gomes. "Estimation of hourly daily and monthly global solar radiation on inclined surfaces: Models Re-visited." Energies 10. no. 1 (2017): 1-28.
  18. S. Tan, X. Wang, and C. Jiang. "Optimal scheduling of hydro-pv-wind hybrid system considering CHP and BESS coordination." Applied Sciences 9. no. 5 (2019): 1-18.
  19. Y. Yin, T. Liu, and C. He. "Day-ahead stochastic coordinated scheduling for thermal-hydro-wind-photovoltaic systems." Energy 187 (2019): 1-12.
  20. M. Basu. "Dynamic economic dispatch incorporating renewable energy sources and pumped hydro energy storage." Soft Computing 24 (2019): 4829-4840.
  21. S. Pan, J. Jian, and L. Yang. "A hybrid MILP and IPM approach for dynamic economic dispatch with valve-point effect." Electric Power Energy System 97 (2018): 290-298.
  22. S. Pan, J. Jian, and L. Yang. "A full mixed-integer linear programming formulation for economic dispatch with valve-point effects, transmission loss and prohibited operating zones." Electric Power Systems Research 180 (2020): 1045-1056.
  23. C. Shilaja, and K. Ravi. "Optimization of emission/economic dispatch using Euclidean affine flower pollination algorithm (eFPA) and binary FPA (BFPA) in solar photo voltaic generation." Renewable Energy 107 (2017): 550-566.
  24. S.A. Mirjalilia. "SCA: A Sine cosine algorithm for solving optimization problems." Knowledge-Based Systems 96 (2016): 120-133.
  25. Z.L. Gaing. "Particle swarm optimization to solving the economic dispatch considering the generator constraints." IEEE Transactions on Power Systems 18. no. 3 (2003): 1187-1195.