پیاده سازی روش هوشمند مبتنی بر منطق فازی برای مدیریت منابع انرژی سیستم قدرت خانگی شامل انرژی خورشیدی و منبع ذخیره با استفاده از بردهای آردوینو
محورهای موضوعی : انرژی های تجدیدپذیرمهدی زنگنه 1 , ابراهیم آقاجری 2 , مهدی فروزانفر 3
1 - گروه مهندسی برق- واحد اهواز، دانشگاه آزاد اسلامی، اهواز، ایران
2 - گروه مهندسی برق- واحد اهواز، دانشگاه آزاد اسلامی، اهواز، ایران
3 - گروه مهندسی برق- واحد اهواز، دانشگاه آزاد اسلامی، اهواز، ایران
کلید واژه: منطق فازی, انرژی تجدید پذیر, سیستم انرژی ترکیبی, کنترل مدیریت انرژی,
چکیده مقاله :
: با افزایش نگرانی های زیست محیطی و کاهش سوخت های فسیلی، اهمیت تولید الکتریسیته توسط انرژی های تجدیدپذیر و جایگزینی آن با سوخت های مرسوم روز به روز بیشتر احساس می شود. از طرفی وابستگی انرژی های تجدیدپذیر به شرایط محیطی باعث شده استفاده از این انرژی ها با چالش هایی همراه باشد. یکی از این چالش ها مدیریت منابع انرژی بوده که در این تحقیق با طراحی یک کنترل کننده هوشمند فازی، مسئله ی مدیریت انرژی سیستم قدرت ترکیبی خانگی شامل انرژی خورشید، باتری و برق شبکه مورد توجه قرار گرفته و پیاده سازی آن در ابعاد آزمایشگاهی انجام شده است. در مطالعه ی پیش رو با به کار بردن برد آردوینو جهت مدیریت انرژی در سیستم قدرت ترکیبی، سعی بر آن بوده تا زمینه ی استفاده از این منابع در ابعاد واقعی و به صورت انبوه فراهم گردد همچنین مدل ریاضی اجزای سیستم قدرت ارائه شده، شبیه سازی سیستم با نرم افزار متلب انجام شده و توانایی مانیتورینگ زمان واقعی اطلاعات نیز به آن افزوده شده است. در انتها با اعمال شرایط آب و هوایی یک روز عادی بهاری تأثیر گذاری کنترل کننده هوشمند فازی مورد بررسی قرار گرفته است. نتایج نشان می دهد که استفاده از روش پیشنهادی نسبت به عدم استفاده از آن باعث کاهش 60 درصدی استفاده از برق شبکه می گردد.
With increasing environmental concerns and reducing fossil fuels, the significance of producing electricity via renewable energy resources and replacing it with conventional fuels is increasing day by day. At the same time, the dependency of renewable energies on environmental conditions makes it challenging. One of these challenges has been managing the energy resources of the hybrid power system. Hence, in this research, a fuzzy intelligent controller has been designed and implemented to manage the energy resources of a grid-tied hybrid power system including solar energy and battery storage in laboratory dimensions. In the present study, by using the Arduino board as an energy management unit in the hybrid power system, tried to provide a basis for the use of renewable energy resources in real dimensions and mass production. The mathematical modeling of the system's equipment is presented and the hybrid power system is simulated using MATLAB software. Moreover, the ability of real-time data monitoring has also been added to the system. Eventually, the capabilities of the proposed smart fuzzy logic controller have been assessed by applying a usual day in springtime. The outcomes indicate that the suggested hybrid power system and the controller can save energy about 60 percent.
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_||_[1] Y. Li, S. Q. Mohammed, G.S. Nariman, N. Aljojo, A. Rezvani, S. Dadfar, “Energy management of microgrid considering renewable energy sources and electric vehicles using the backtracking search optimization algorithm”, Journal of Energy Resources Technology, vol. 142, no. 5, Article Number: 52103, May 2020 (doi: 10.1115/1.4046098).
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[4] C. Mokhtara, B. Negrou, A. Bouferrouk, Y. Yao, N. Settou, M. Ramadan, “Integrated supply–demand energy management for optimal design of off-grid hybrid renewable energy systems for residential electrification in arid climates”, Energy Conversion and Management, vol. 221, Article Number: 113192, Oct 2020 (doi: 10.1016/j.enconman.2020.113192).
[5] S. Nojavan, M. Majidi, K. Zare, “Performance improvement of a battery/PV/fuel cell/grid hybrid energy system considering load uncertainty modeling using IGDT”, Energy Conversion and Management, vol. 147, pp. 29–39, Sept. 2017 (doi: 10.1016/j.enconman.2017.05.039).
[6] M. Majidi, S. Nojavan, K. Zare, “Optimal stochastic short-term thermal and electrical operation of fuel cell/photovoltaic/battery/grid hybrid energy system in the presence of demand response program”, Energy Conversion and Management, vol. 144, pp. 132–142, July 2017 (doi: 10.1016/j.enconman.2017.04.051).
[7] J. Pascual, J. Barricarte, P. Sanchis, L. Marroyo, “Energy management strategy for a renewable-based residential microgrid with generation and demand forecasting”, Appllied Energy, vol. 158, pp. 12–25, Nov. 2015 (doi: 10.1016/j.apenergy.2015.08.040).
[8] M. Patrone, D. Feroldi, “Passivity-based control design for a grid-connected hybrid generation system integrated with the energy management strategy", Journal of Process Control, vol. 74, pp. 99-109, Feb. 2019 (doi: 10.1016/j.jprocont.2017.11.012).
[9] A. Behzadi Forough, R. Roshandel, “Multi objective receding horizon optimization for optimal scheduling of hybrid renewable energy system”, Energy and Buildings, vol. 150, pp. 583–597, Sept. 2017 (doi: 10.1016/j.enbuild.2017.06.031).
[10] A. Chaib, D. Achour, M. Kesraoui, “Control of a solar PV/wind hybrid energy system”, Energy Procedia, vol. 95, pp. 89–97, Sept. 2016 (doi: 10.1016/j.egypro.2016.09.028).
[11] F.J. Vivas, A.D. Heras, F. Segura, J.M. Andújar, “A review of energy management strategies for renewable hybrid energy systems with hydrogen backup”, Renewable and Sustainable Energy Reviews, vol. 82, no, pp. 126–155, Feb. 2018 (doi: 10.1016/j.rser.2017.09.014).
[12] H. Zhang, A. Davigny, F. Colas, Y. Poste, B. Robyns, “Fuzzy logic based energy management strategy for commercial buildings integrating photovoltaic and storage systems”, Energy and Buildings, vol. 54, pp. 196–206, Nov. 2012 (doi: 10.1016/j.enbuild.2012.07.022).
[13] Z. Roumila, D. Rekioua, T. Rekioua, “Energy management based fuzzy logic controller of hybrid system wind/photovoltaic/diesel with storage battery”, International Journal of Hydrogen Energy, vol. 42, no. 30, pp. 19525–19535, July 2017 (doi: 10.1016/j.ijhydene.2017.06.006).
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[15] A. Derrouazin, M. Aillerie, N. Mekkakia-Maaza, J.P. Charles, “Multi input-output fuzzy logic smart controller for a residential hybrid solar-wind-storage energy system”, Energy Conversion and Management, vol. 148, pp. 238–250, Sept. 2017 (doi: 10.1016/j.enconman.2017.05.046).
[16] M. Tiar, A. Betka, S. Drid, S. Abdeddaim, “Optimal energy control of a PV-fuel cell hybrid system”, International Journal of Hydrogen Energy, vol. 42, no. 2, pp. 1456-1465, Jan. 2017 (doi: 10.1016/j.ijhydene.2016.06.113).
[17] C. Mokhtara, B. Negrou, A. Bouferrouk, Y. Yao, N. Settou, M. Ramadan, “Integrated supply–demand energy management for optimal design of off-grid hybrid renewable energy systems for residential electrification in arid climates”, Energy Conversion and Management, vol. 221, Article Number: 113192, Oct. 2020 (doi: 10.1016/j.enconman.2020.113192).
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[21] D. Wu, H. Zeng, C. Lu, B. Boulet, “Two-stage energy management for office buildings with workplace EV charging and renewable energy”, IEEE Trans. on Transportation Electrification, vol. 3, no. 1, pp. 225–237, Mar. 2017 (doi: 10.1109/TTE.2017.2659626).
[22] J. Yang, J. Liu, Z. Fang, W. Liu, “Electricity scheduling strategy for home energy management system with renewable energy and battery storage: a case study”, IET Renewable Power Generation, vol. 12, no. 6, pp. 639–648, Dec. 2018 (doi: 10.1049/iet-rpg.2017.0330).
[23] P.H. Divshali, B.J. Choi, H. Liang, “Multi-agent transactive energy management system considering high levels of renewable energy source and electric vehicles”, IET Generation, Transmission and Distribution, vol. 11, no. 15, pp. 3713–3721, June 2017 (doi: 10.1049/iet-gtd.2016.1916).
[24] S.M. Zahraee, M.K. Assadi, R. Saidur, “Application of artificial intelligence methods for hybrid energy system optimization”, Renewable and Sustainable Energy Reviews, vol. 66, pp. 617–630, Dec. 2016 (doi: 10.1016/j.rser.2016.08.028).
[25] M.J.B. Fulzele, “Simulation and optimization of hybrid PV-wind renewable energy system”, Proceeding of the IEEE/EEECOS, pp. 159–164, Tadepalligudem, June 2016 (doi: 10.1016/j.matpr.2017.11.151).
[26] M.A. Mohamed, A.M. Eltamaly, A.I. Alolah, “Swarm intelligence-based optimization of grid-dependent hybrid renewable energy systems”, Renewable and Sustainable Energy Reviews, vol. 77, no. 10, pp. 515–524, April 2017 (doi: 10.1016/j.rser.2017.04.048).
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