بهینه‌سازی سیستم هیبرید بادی- خورشیدی- باتری جدا از شبکه با در نظر گرفتن قابلیت اطمینان با استفاده از الگوریتم بهینه‌سازی ملخ
محورهای موضوعی : انرژی های تجدیدپذیر
روناک جهانشاهی باوندپور
1
,
حمید قدیری
2
,
حامد خدادادی
3
1 - دانشکده مهندسی برق- موسسه آموزش عالی دارالفنون، قزوین، ایران
2 - دانشکده مهندسی برق، پزشکی و مکاترونیک- واحد قزوین، دانشگاه آزاد اسلامی، قزوین، ایران
3 - دانشکده مهندسی برق و کامپیوتر- دانشگاه آزاد اسلامی واحد خمینی شهر، اصفهان، ایران
کلید واژه: بهینه‌سازی, توربین بادی, باتری, احتمال عدم تامین بار, الگوریتم بهینه‌سازی ملخ,
چکیده مقاله :
انرژی‌های تجدیدپذیر در سال‌های اخیر به دلیل محدودیت و احتمال اتمام منابع سوخت‌های فسیلی و مسائل زیست محیطی مرتبط به شدت توسعه یافته است. مهمترین چالش در این نوع سیستم‌ها، دستیابی به اندازه بهینه برای داشتن یک سیستم مقرون به صرفه بر اساس ذخیره انرژی خورشیدی و بادی است. در این مقاله بهینه‌سازی سیستم هیبرید بادی-خورشیدی با سیستم ذخیره باتری برای تامین یک بار مشخص ساعتی با هدف حداقل‌سازی هزینه‌های سالیانه سیستم و احتمال تلفات عرضه توان مورد توجه قرار گرفته است. هزینه‌های سالیانه سیستم شامل هزینه‌های سرمایه‌گذاری اولیه، هزینه نگهداری و هزینه تعویض تجهیزات می‌باشد. هدف بهینه‌سازی، تعیین بهینه تعداد پنل‌های خورشیدی، توربین‌های بادی، تعداد باتری‌ها، ارتفاع برج بادی و زاویه پنل خورشیدی نسبت به تابش خورشید است. به این منظور الگوریتم جدید بهینه‌سازی ملخ مورد استفاده قرار گرفته است. همچنین در این مطالعه، اثر تغییرات راندمان اینورتر، تغییرات تقاضای بار و اثر تغییرات ماکزیمم احتمال تلفات عرضه توان بر طراحی سیستم مورد ارزیابی قرار گرفته است. نتایج شبیه‌سازی نشان می‌دهد که کاهش راندمان، افزایش بار و حداکثر قابلیت اطمینان در سیستم در قالب کاهش احتمال تلفات عرضه توان موجب افزایش هزینه‌های سالیانه انرژی سیستم می‌گردد. به‌علاوه، نتایج حاصله موید برتری روش بهینه‌سازی ملخ نسبت به روش اجتماع ذرات در دستیابی به تابع هدف بهتر و هزینه کمتر می‌باشد.
Renewable energy has been developed in recent years due to the limited sources of fossil fuels, their possibility of depletion, and the related environmental issues. The main challenges of these type of systems is reaching to the optimum size in order to have an affordable system based on storing the solar and wind energy. In this paper, optimization of a solar-wind hybrid system is presented with a saving battery system for supplying a specific hourly load annually to minimize annual system expenses and the probability of Loss of Power Supply Probability (LPSP). Annual expenses of the system include initial investment, maintenance, and replacement costs. The purpose of optimization is to determine the numbers of solar panels, wind turbines, batteries, the height of the wind tower, and the angle of the solar panel toward solar radiation. For this issue, a new method named Grasshopper Optimization Algorithm (GOA) is employed. Also, the effects of changes in inverter efficiency, load demand, and of maximum probability of LPSP on system designing are evaluated. Simulation results show that the efficiency reduction, load increase, and increasing the load and maximum reliability in the system in the form of reducing of LPSP lead to an increase in annual energy costs of systems. Furthermore, the results indicate the superiority of the GOA method toward particle swarm optimization (PSO) in reaching better target function and less cost.
[1] M.A. Kashem, A.D. Le, M. Negnevitsky, G. Ledwich, “Distributed generation for minimization of power losses in distribution systems”, Proceeding of the IEEE/PES, pp. 1-8, Montreal, QC, Canada, June 2006 (doi: 10.1109/PES.2006.1709179).
[2] P. Buduma, S. J. Pinto, G. Panda, "Loss of utility detection and seamless operation of distributed generation system", IEEE Trans. on Industry Applications, vol. 56, no. 3, pp. 3149-3158, May/June 2020 (doi: 10.1109/TIA.2020.2976800).
[3] N. Dkhili, J. Eynard, S. Thil, S. Grieu, "Asurvey of modelling and smart management tools for power grids with prolific distributed generation", Sustainable Energy, Grids and Networks, vol. 21, Article Number: 100284, March 2020 (doi: 10.1016/j.segan.2019.100284).
[4] M. Salari, F. Haghighatdar-Fesharaki, “Optimal placement and sizing of distributed generations and capacitors for reliability improvement and power loss minimization in distribution networks”, Journal of Intelligent Procedures in Electrical Technology, vol. 11, no. 43, pp. 83-94, Dec. 2020 (doi: 20.1001.1.23223871.1399.11.43.6.9) (in Persian).
[5] R. Borjali-Navesi, D. Nazarpour-Akbari, R. Ghanizadeh, P. Alemi, "Switchable capacitor bank coordination and dynamic network reconfiguration for improving operation of distribution network integrated with renewable energy resources", Journal of Intelligent Procedures in Electrical Technology, vol. 12, no. 48, pp. 43-59, March 2022 (dor: 20.1001.1.23223871.1400.12.48.2.2) (in Persian).
[6] O. Ekren, B. Y. Ekren, “Size optimization of a PV/wind hybrid energy conversion system with battery storage using simulated annealing”, Applied Energy, vol. 87, no. 2, pp. 592-598, Feb. 2010 (doi: 10.1016/j.apenergy.2009.05.022).
[7] A. Hepbasli, “A key review on exergetic analysis and assessment of renewable energy resources for a sustainable future”, Renewable and Sustainable Energy Reviews, vol. 12, no. 3, pp. 593-661, April 2008 (doi: 10.1016/j.rser.2006.10.001).
[8] H. Ghadiri, “Real-time stability assessment of power system using ANN without requiring expert experience”, Majlesi Journal of Electrical Engineering, vol. 14, no. 2, pp. 43-49, June 2020.
[9] M.H. Jahangir, R. Cheraghi, “Economic and environmental assessment of solar-wind-biomass hybrid renewable energy system supplying rural settlement load”, Sustainable Energy Technologies and Assessments, vol. 42, Article Number: 100895, Dec. 2020 (doi: 10.1016/j.seta.2020.100895).
[10] Z. Liu, S. Wang, M. Lim, M. Kraft, X. Wang, “Game theory-based renewable multi-energy system design and subsidy strategy optimization”, Advances in Applied Energy, vol. 2, Article Number: 100024, May 2021 (doi: 10.1016/j.adapen.2021.100024).
[11] H. Mehrjerdi, “Modeling, integration, and optimal selection of the turbine technology in the hybrid wind-photovoltaic renewable energy system design”, Energy Conversion Management, vol. 205, Article Number: 112350, Feb. 2020 (doi: 10.1016/j.enconman.2019.112350).
[12] A. Ghaffari, A. Askarzadeh, “Design optimization of a hybrid system subject to reliability level and renewable energy penetration”, Energy, vol. 193, p. 116754, Feb. 2020 (doi: 10.1016/j.energy.2019.116754).
[13] A.H. Mamaghani, S.A.A. Escandon, B. Najafi, A. Shirazi, F. Rinaldi, “Techno-economic feasibility of photovoltaic, wind, diesel and hybrid electrification systems for off-grid rural electrification in Colombia”, Renewable Energy, vol. 97, pp. 293-305, Nov. 2016 (doi: 10.1016/j.renene.2016.05.086).
[14] S. Sanajaoba, E. Fernandez, “Maiden application of cuckoo search algorithm for optimal sizing of a remote hybrid renewable energy System”, Renewable Energy, vol. 96, pp. 1-10, Oct. 2016 (doi: 10.1016/j.renene.2016.04.069).
[15] R.D. Lopez, I.R.C. Monreal, J. Yusta, “Stochastic-heuristic methodology for the optimisation of components and control variables of PV-wind-diesel-battery stand-alone systems”, Renewable Energy, vol. 99, pp. 919-935, Dec. 2016 (doi: 10.1016/j.renene.2016.07.069).
[16] S. Olówósejéjé, P. Leahy, A.P. Morrison, “Optimising photovoltaic-centric hybrid power systems for energy autonomy”, Energy Reports, vol. 7, pp. 1943-1953, Nov. 2021 (doi: 10.1016/j.egyr.2021.03.039).
[17] A.H. Schleifer, C.A. Murphy, W.J. Cole, P.L. Denholm, “The evolving energy and capacity values of utility-scale PV-plus-battery hybrid system architectures”, Advances in Applied Energy, vol. 2, Article Number: 100015, May 2021 (doi: 10.1016/j.adapen.2021.100015).
[18] L. Amabile, D.B. Pietri, G.E. Hajje, S. Labbé, N. Petit, “Optimizing the self-consumption of residential photovoltaic energy and quantification of the impact of production forecast uncertainties”, Advances in Applied Energy, vol. 2, Article Number: 100020, May 2021 (doi: 10.1016/j.adapen.2021.100020).
[19] S. Saremi, S. Mirjalili, A. Lewis, “Grasshopper optimisation algorithm: Theory and application”, Advances in Engineering Software, vol. 105, pp. 30-47, March 2017 (doi: 10.1016/j.advengsoft.2017.01.004).
[20] M. Mohammadhosseini, H. Ghadiri, “A Combination of genetic algorithm and particle swarm optimization for power systems planning subject to energy storage”, Journal of Computer and Robotics, vol. 12, no. 1, pp. 65-76, Jan, 2019 (doi: 10.1016/j.ijepes.2011.08.023).
[21] D.P. Rini, S.M. Shamsuddin, S.S. Yuhaniz. “Particle swarm optimization: technique, system and challenges”, International Journal of Computer Applications, vol. 14, no. 1, pp. 19-26, Jan. 2011 (doi: :10.5120/ijais-3651).
[22] H. B. Novin, H. Ghadiri, “Particle swarm optimization base explicit model predictive controller for limiting shaft torque”, Proceeding of the IEEE/CFIS, pp. 35-40, Qazvin, Iran, March 2017 (doi: 10.1109/CFIS.2017.8003593).
[23] M. Steczek, W. Jefimowski, A. Szeląg. “Application of grasshopper optimization algorithm for selective harmonics elimination in low-frequency voltage source inverter”, Energies, vol. 13, no. 23, p. 6426, Dec. 2020 (doi: 10.3390/en13236426).
[24] Z. Shi, R. Wang, T. Zhang, “Multi-objective optimal design of hybrid renewable energy systems using preference-inspired coevolutionary approach”, Solar Energy, vol. 118, pp. 96-106, Aug. 2015 (doi: 10.1016/j.solener.2015.03.052).
[25] M.A. Mohamed, A.M. Eltamaly, “Modeling of hybrid renewable energy system”, In Modeling and Simulation of Smart Grid Integrated with Hybrid Renewable Energy Systems, New York, Springer, 2018.
[26] R. Samani, H. Khodadadi, “A particle swarm optimization approach for sliding mode control of electromechanical valve actuator in camless internal combustion engines”, Proceeding of the IEEE/EEEIC, pp. 1-4, Milan, Italy, June 2017 (doi: 10.1109/EEEIC.2017.7977560).
[27] M.R. Ahmadpour, H. Ghadiri, S.R. Hajian, “Model predictive control optimisation using the metaheuristic optimisation for blood pressure control”, IET Systems Biology, vol. 15, no. 2, pp. 41-52, Feb. 2021 (doi: 10.1049/syb2.12012).
[28] R.T. Marler, J.S. Arora, “The weighted sum method for multi-objective optimization: new insights”, Structural and multidisciplinary optimization, vol. 41, no. 6, pp. 853-862, Dec. 2010 (doi: 10.1007/s00158-009-0460-7).
[29] H. Borhanazad, S. Mekhilef, V.G. Ganapathy, M. Modiri-Delshad, A. Mirtaheri, “Optimization of micro-grid system using MOPSO”, Renewable Energy, vol. 71, pp. 295-306, Nov. 2014 (doi: 10.1016/j.renene.2014.05.006).
[30] S. Diaf, D. Diaf, M. Belhamel, M. Haddadi, A. Louche, “A methodology for optimal sizing of autonomous hybrid PV/wind system”, Energy policy, vol. 35, no. 11, pp. 5708-5718, Nov. 2007 (doi: 10.1016/j.enpol.2007.06.020).
_||_