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
الموضوعات :
مهدی 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
الکلمات المفتاحية: منطق فازی, انرژی تجدید پذیر, سیستم انرژی ترکیبی, کنترل مدیریت انرژی,
ملخص المقالة :
: 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.
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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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[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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[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).
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[28] S. Sanajaoba Singh, E. Fernandez, “Modeling, size optimization and sensitivity analysis of a remote hybrid renewable energy system”, Energy, vol. 143, pp. 719–731, Jan. 2018 (doi: 10.1016/j.energy.2017.11.053).
[29] O. Abdalla, H. Rezk, E.M. Ahmed, “Wind driven optimization algorithm based global MPPT for PV system under non-uniform solar irradiance”, Solar Energy, vol. 180, pp. 429–444, Mar. 2019 (doi: 10.1016/j.solener.2019.01.056).
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