طراحی بهینه یک نیروگاه خورشیدی جدا از شبکه به منظور کاهش هزینه های احداث و میزان بار تأمین نشده با استفاده از الگوریتم ALPSO

محورهای موضوعی : مهندسی برق قدرت

لیث خضیر عباس هلای ¹ , محمد مهدی رضایی ²

1 - دانشکده مهندسی برق، واحد اصفهان (خوراسگان)، دانشگاه آزاد اسلامی، خوراسگان، اصفهان، ایران
2 - دانشکده مهندسی برق، واحد خمینی شهر، دانشگاه آزاد اسلامی، خمینی شهر، اصفهان، ایران

تاریخ دریافت : 1401/08/18 تاریخ پذیرش : 1401/10/12 تاریخ انتشار : 1402/03/01

کلید واژه: اینورتر, بهینه‌سازی, فتوولتائیک, الگوریتم فرا ابتکاری, انرژی خورشیدی, باتری,

چکیده مقاله :

انرژی خورشیدی منحصربه فردترین و مقرون به صرفه منبع انرژی تجدیدپذیر در جهان است و می تواند به اشکال دیگر انرژی تبدیل گردد. لذا در این مقاله قرار است تا به صورت کلان و در چشم انداز دراز مدت به امکان سنجی فنی و اقتصادی نصب یک واحد نیروگاه خورشیدی از نوع منفصل از شبکه با پشتیبانی باتری برای تأمین بخشی از برق شهر بغداد در کشور عراق پرداخته شود. تابع هدف این مسئله شامل هزینه نصب و تعمیر و نگهداری صفحات خورشیدی، باتری ها و اینورتر است که با نرخ بهره مشخص در چشم انداز 20 ساله با استفاده از روش‌های فرا ابتکاری IPSO و ALPSOبرای حل شده است، همچنین فاکتور میزان بار از دست رفته، حد مجاز شارژ و دشار باتری ها از جمله قیود اصلی مسئله هستند. یکی از ویژگی هایی که این مقاله را از سایر مقالات متمایز می کند اجرای آن برای مورد عملی شهر بغداد است، همچنین بررسی سود احتمالی حاصل از فروش برق به شبکه بالادست و استفاده از الگوریتم جدید ALPSOهم از دیگر ویژگی ها و نوآوری های این مقاله به شمار می روند. این الگوریتم از یک فرآیند جستجوی تطبیقی سه مرحله ای استفاده می کند و باعث می شود تا قیود مسئله به خوبی رعایت شوند. نتایج نشان می دهند که روش های پیشنهادی باعث کاهش قابل ‌توجه بار از دست رفته (به خصوص در روش ALPSO)، کاهش هزینه تعمیر نگهداری و نصب می شوند و در کل باعث بهبود عملکرد سیستم می شوند.

چکیده انگلیسی:

Solar energy is the world's most unique and affordable renewable energy source and can be converted into many other forms. In this article, it will be discussed in a long-term perspective the technical and economic feasibility of installing stand-alone solar power plant units with battery support to supply part of Baghdad's electricity. The objective function of this problem includes the cost of installation and maintenance of solar panels, batteries and inverter, which is solved with a certain interest rate in a 20-year perspective using IPSO and ALPSO methods. Furthermore, the load loss supplied and the charging/discharging limit are among the constraints. This article is unique in that it is implemented in the context of Baghdad city, and it also investigates the possible profit from selling power to main grid. Other features and innovations include the implementation of the new ALPSO algorithm. In this algorithm, the constraints of the problem are respected through a three-step adaptive search process. The results show that the proposed methods significantly reduce the lost load (especially in the ALPSO method), reduce the cost of maintenance and installation, and generally improve the performance of the system.

منابع و مأخذ:

1. Y. Abou-Jieb and E. Hossain, Photovoltaic Systems: Fundamentals and Applications, Berlin/Heidelberg,Germany: Springer Cham, 2022, doi: 10.1007/978-3-030-89780-2


2. O. A. Al-Shahria, F.B. Ismaila, M. Hannanb, M. HossainLipuc, Q.A. Ali, R. Begumf, F.O.A. Nizar and E. Soujerih, "Solar photovoltaic energy optimization methods, challenges and issues: A comprehensive review," Journal of Cleaner Production, vol. 284, no. 125465, 2021, doi: 10.1016/j.jclepro.2020.125465


3. H. H. Istepanian, Iraq solar energy: from dawn to dusk, Jordan: Friedrich-Ebert-Stiftung Jordan et Iraq, 2020.


4. Y. X iaomo, W. Li, A. Maleki, M.A. Rosen, A.K. Birjandi and L. Tang, "Selection of optimal location and design of a stand-alone photovoltaic scheme using a modified hybrid methodology," Sustainable Energy Technologies and Assessments, vol. 45, no. 101071, doi: 101, 10.1016/j.seta.2021.101071


5. B. K. Das, R. Hassan, M. Shahed, H.K. Tushar, Z. Forhad, M. Hasan and P. Das, "Techno-economic and environmental assessment of a hybrid renewable energy system using multi-objective genetic algorithm: A case study for remote Island in Bangladesh," Energy Conversion and Management, vol. 230, no. 113823, 2021, doi: 10.1016/j.enconman.2020.113823


6. Y. Feng, S. Deb, G.-G. Wang and A. H.Alavi, "Monarch butterfly optimization: A comprehensive review," Expert Systems with Applications, vol. 168, no. 114418, 2021, doi: 10.1016/j.eswa.2020.114418


7. H. Liu, B. Wu, A. Maleki, F. Pourfayaz and R. Ghasempour, "Effects of reliability index on optimal configuration of hybrid solar/battery energy system by optimization approach: a case study," International Journal of Photoenergy, no. Performance Enhancement of Solar Energy Systems, 2021, doi: 10.1155/2021/9779996


8. Y. Liu, A. Heidari, X. Ye, G. Liang, H. Chen and C. He, "Boosting slime mould algorithm for parameter identification of photovoltaic models," Energy, vol. 234, no. 121164, 2021, doi: 10.1016/j.energy.2021.121164


9. S. A. Ferahtia, H. Rezk, M.A. Abdelkareem and A.G. Olabi, "Optimal techno-economic energy management strategy for building’s microgrids based bald eagle search optimization algorithm," Applied Energy, Vols. 306, Part B, no. 118069, 2022, doi: 10.1016/j.apenergy.2021.118069


10. S. Yu, A.A. Heidari, C. He, Z. Cai, M.M. Althobaiti, R.F. Mansour, G. Liang and H. Chen, "Parameter estimation of static solar photovoltaic models using Laplacian Nelder-Mead hunger games search," Solar Energy, vol. 242, no. August, pp. 79-104, 2022, doi: 10.1016/j.solener.2022.06.046


11. S. Song, P. Wang, A.A. Heidari, X. Zhao and H. Chen, "Adaptive Harris hawks optimization with persistent trigonometric differences for photovoltaic model parameter extraction," Engineering Applications of Artificial Intelligence, vol. 109, no. March, 2022, doi: 10.1016/j.engappai.2021.104608


12. B. A. Xu, S. Zhang, H. Chen and Q. Shao, "Extremal Nelder–Mead colony predation algorithm for parameter estimation of solar photovoltaic models," Energy Science & Engineering, 2022, doi: 10.1002/ese3.1273


13. Y. Xu, S. Huang, Z. Wang, Y. Ren, Z. Xie, J. Guo and Z. Zhu, "Optimization based on tabu search algorithm for optimal sizing of hybrid PV/energy storage system: Effects of tabu search parameters," Sustainable Energy Technologies and Assessments, Vols. 53, Part C, no. October, 2022, doi: 10.1016/j.seta.2022.102662


14. M. Belouda, M. Hajjaji, H. Sliti and A. Mami, "Bi-objective optimization of a standalone hybrid PV–Wind–battery system generation in a remote area in Tunisia," Sustainable Energy, Grids and Networks, vol. 16, no. December , pp. 315-326, 2018, doi: 10.1016/j.segan.2018.09.005


15. G. Yu, Z. Meng, H. Ma and L. Liu, "An adaptive Marine Predators Algorithm for optimizing a hybrid PV/DG/Battery System for a remote area in China," Energy Reports, vol. 7, no. November , pp. 398-412, 2021, doi: 10.1016/j.egyr.2021.01.005


16. A. F. Altun and M. Kilic, "Design and performance evaluation based on economics and environmental impact of a PV-wind-diesel and battery standalone power system for various climates in Turkey," Renewable Energy, vol. 157, no. September , pp. 424-443, 2020, doi: 10.1016/j.renene.2020.05.042


17. H. A. El-Sattar, H.M. Sultan, S. Kamel, T. Khurshaid and C. Rahmann, "Optimal design of stand-alone hybrid PV/wind/biomass/battery energy storage system in Abu-Monqar, Egypt," Journal of Energy Storage, Vols. 44, Part A, no. December , p. 103336, 2021, doi: 10.1016/j.est.2021.103336


18. K. Anoune, M. Ghazi, M. Bouya, A. Laknizi, M. Ghazouani, A.B. Abdellah and A. Astito, "Optimization and techno-economic analysis of photovoltaic-wind-battery based hybrid system," Journal of Energy Storage, vol. 32, no. December, p. 101878, 2020, doi: 10.1016/j.est.2020.101878


19. D. Emad, M. El-Hameed and A.A. El-Fergany, "Optimal techno-economic design of hybrid PV/wind system comprising battery energy storage: Case study for a remote area," Energy Conversion and Management, vol. 249, no. 1 December, p. 114847, 2021, doi: 10.1016/j.enconman.2021.114847


20. Y. Jiang, L. Kang and Y. Liu, "The coordinated optimal design of a PV-battery system with multiple types of PV arrays and batteries: A case study of power smoothing," Journal of Cleaner Production, vol. 310, no. 10 August, p. 127436, 2021, doi: 10.1016/j.jclepro.2021.127436


21. M. Hazem, Y. Amirat and M. Benbouzid, "Particle swarm optimization of a hybrid wind/tidal/PV/battery energy system. Application to a remote area in Bretagne, France," Energy Procedia, vol. 162, no. April , pp. 87-96, 2019, doi: 10.1016/j.egypro.2019.04.010


22. S. M. Mahmoudi, A. Maleki and D. Rezaei-Ochbelagh, "Optimization of a hybrid energy system with/without considering back-up system by a new technique based on fuzzy logic controller," Energy Conversion and Management, vol. 229, 2021, doi: 10.1016/j.enconman.2020.113723


23. M. Ismail, M. Moghavvemi and T. Mahlia, "Techno-economic analysis of an optimized photovoltaic and diesel generator hybrid power system for remote houses in a tropical climate," Energy Conversion and Management, vol. 69, pp. 163-173, 2013, doi: 10.1016/j.enconman.2013.02.005


24. A. L. Bukar, C.W. Tan and K.Y. Lau, "Optimal sizing of an autonomous photovoltaic/wind/battery/diesel generator microgrid using grasshopper optimization algorithm," Solar Energy, vol. 188, pp. 685-696, 2019, doi: 10.1016/j.solener.2019.06.050


25. J. Kennedy and R. Eberhart, "Particle swarm optimization," in Proceedings of ICNN'95-international conference on neural networks, Perth, WA, Australia, 1995, doi: 10.1109/ICNN.1995.488968


26. F. Wang, H. Zhang, K. Li, Z. Lin, J. Yang and X.-L. Shen, "A hybrid particle swarm optimization algorithm using adaptive learning strategy," Information Sciences, Vols. 436-437, pp. 162-177, 2018, doi: 10.1016/j.ins.2018.01.027