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 تجدیدپذیر

مهدی زنگنه ¹ (گروه مهندسی برق- واحد اهواز، دانشگاه آزاد اسلامی، اهواز، ایران)
ابراهیم آقاجری ² (گروه مهندسی برق- واحد اهواز، دانشگاه آزاد اسلامی، اهواز، ایران)
مهدی فروزانفر ³ (گروه مهندسی برق- واحد اهواز، دانشگاه آزاد اسلامی، اهواز، ایران)

تاریخ دریافت : 1400/02/04 تاریخ پذیرش : 1400/05/26 تاریخ انتشار : 1401/03/01

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.

چکیده انگلیسی:

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.

منابع و مأخذ:

[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).

[2] L. Xiong, P. Li, Z. Wang, J. Wang, “Multi-agent based multi objective renewable energy management for diversified community power consumers”, Applied Energy, vol. 259, Article Number: 114140, Feb 2020 (doi: 10.1016/j.apenergy.2019.114140).

[3] A. Hasankhani, S.M. Hakimi, “Stochastic energy management of smart microgrid with intermittent renewable energy resources in electricity market”, Energy, vol. 219, Article Number: 119668, Mar 2021 (doi: 10.1016/j.energy.2020.119668).

[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).

[14] O. Erdinç, O. Elma, M. Uzunoglu, U.S. Selamoğullari, B. Vural, E. Ugur, A.R. Boynueğri, S. Dusmez, “Experimental performance assessment of an online energy management strategy for varying renewable power production suppression”, International Journal of Hydrogen Energy, vol. 37, no. 6, pp. 4737–4748, April 2012 (doi: 10.1016/j.ijhydene.2011.12.042).

[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).

[18] A. Ahmad, A. Khan, N. Javaid, H.M. Hussain, W. Abdul, A, Almogren, A. Alamri, I.A. Niaz, “An optimized home energy management system with integrated renewable energy and storage resources”, Energies, vol. 10, no. 4, Article Number: 549, April 2017 (doi: 10.3390/en10040549).

[19] N.T. Mbungu, R.C. Bansal, R. Naidoo, V. Miranda, M. Bipath, “An optimal energy management system for a commercial building with renewable energy generation under real-time electricity prices”, Sustainable Cities and Society, vol. 41, pp. 392–404, Aug. 2018 (doi: 10.1016/j.scs.2018.05.049).

[20] A.M. Abdelshafy, J. Jurasz, H. Hassan, A.M. Mohamed, “Optimized energy management strategy for grid connected double storage (pumped storage-battery) system powered by renewable energy resources”, Energy, vol. 192, Article Number: 116615, Feb 2020 (doi: 10.1016/j.energy.2019.116615).

[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).

[27] Y. Sawle, S.C. Gupta, A.K. Bohre, “Socio-techno-economic design of hybrid renewable energy system using optimization techniques”, Renewable Energy, vol. 119, pp. 459–472, April 2018 (doi: 10.1016/j.ren­ene.20­17.1­1.0­58).

[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).

[30] S. Motahhir, A. El-Hammoumi, A. El-Ghzizal, “Photovoltaic system with quantitative comparative between an improved MPPT and existing INC and P&O methods under fast varying of solar irradiation”, Energy Reports, vol. 4, pp. 341–350, Nov. 2018 (doi: 10.1016/j.egyr.2018.04.003).

[31] K. Amara, A. Fekik, D. Hocine; M.L. Bakir, E, Bourennane, T.A. Malek, A. Malek, “Improved performance of a PV solar panel with adaptive neuro fuzzy inference system ANFIS based MPPT”, Proceeding of the IEEE/ICRERA, pp. 1098–1101, Paris, France, Oct. 2018 (doi: 10.1109/ICRERA.2018.8566818).

[32] N. Kumar, I. Hussain, B. Singh, B.K. Panigrahi, “Normal harmonic search algorithm-based MPPT for solar PV system and integrated with grid using reduced sensor approach and PNKLMS algorithm”, IEEE Trans. on Industry Applications, vol. 54, no. 6, pp. 6343–6352, July 2018 (doi: 10.1109/TIA.2018.2853744).

[33] K. Bedoud, M. Ali-rachedi, T. Bahi, R. Lakel, “Adaptive Fuzzy Gain Scheduling of PI Controller for control of the Wind Energy Conversion Systems”, Energy Procedia, vol. 74, pp. 211–225, Aug. 2015 (doi: 10.1016/j.eg­ypr­o.2015.07.580).

[34] X. Yin, Y. Lin, W. Li, Y. Gu, H. Liu, P. Lei, “A novel fuzzy integral sliding mode current control strategy for maximizing wind power extraction and eliminating voltage harmonics”, Energy, vol. 85, pp. 677–686, June 2015 (doi: 10.1016/j.energy.2015.04.005).

[35] A. Ghani, A. Tahour, N. Essounbouli, F. Nollet, M. Abid, “A fuzzy-PI control to extract an optimal power from wind turbine”, Energy Conversion and Management, vol. 65, pp. 688–696, Jan. 2013 (doi: 10.1016/j.en­con­man.2011.11.034).

[36] X. Yin, Y. Lin, W. Li, Y. Gu, P. Lei, H. Liu, “Sliding mode voltage control strategy for capturing maximum wind energy based on fuzzy logic control”, International Journal of Electrical Power and Energy Systems, vol. 70, pp. 45–51, Sept. 2015 (doi: 10.1016/j.ijepes.2015.01.029).

[37] P. Molaro, S. Monai, “Solar atlas revised”, Astronomy and Astrophysics, vol. 544, p. A125, Agus. 2012 (doi: 10.1051/0004-6361/201118675).

[38] J. A. Gow, C.D. Manning, “Development of a photovoltaic array model for use in power-electronics simulation studies”, IEE Proceedings- Electric Power Applications, vol. 146, no. 2, pp. 193-200, Mar. 2002 (doi: 10.1049/ip-epa:19990116).

[39] “Grand View Research.” https://www.grandviewresearch.com/industry-analysis/photovoltaic-pv-materi­als-m­arket.

[40] Amin, R. T. Bambang, A. S. Rohman, C. J. Dronkers, R. Ortega, A. Sasongko, “Energy management of fuel cell/battery/supercapacitor hybrid power sources using model predictive control”, IEEE Trans. on Industrial Informatics, vol. 10, no. 4, pp. 1992–2002, July 2014 (doi: 10.1109/TII.2014.2333873).

[41] O. Tremblay, L.A. Dessaint, “Experimental validation of a battery dynamic model for EV applications”, World Electric Vehicle Journal, vol. 2, pp. 930–939, June 2009 (doi: 10.3390/wevj3020289).

[42] L.H. Saw, K. Somasundaram, Y. Ye, A.A.O. Tay, “Electro-thermal analysis of lithium iron phosphate battery for electric vehicles”, Journal of Power Sources, vol. 249, pp. 231–238, 2014 (doi: 10.1016/j.jpowsour.2013­.10­.052).

[43] D. Doerffel, S.A. Sharkh, “A critical review of using the Peukert equation for determining the remaining capacity of lead-acid and lithium-ion batteries”, Journal of Power Sources, vol. 155, no. 2, pp. 395–400, April 2006 (doi: 10.1016/j.jpowsour.2005.04.030).

[44] P. Bjoy, C. Chen, “Lithium-ion polymer rechargeable battery specification sheet”, pp. 1–19, 2014, [Online]. Available: https://www.mouser.com/catalog/additional/RushUp_Battery.pdf.

[45] M. Zangeneh, E. Aghajari, M. Forouzanfar, “A survey: fuzzify parameters and membership function in electrical applications”, International Journal of Dynamics and Control, vol. 8, no. 3, pp. 1040–1051, April 2020 (doi: 10.1007/s40435-020-00622-1).

[46] M. Zangeneh, E. Aghajari, M. Forouzanfar, “Fuzzy membership function selection in electrical systems: A Review”, Journal of Novel Researches on Electrical Power, vol. 8, no. 3, pp. 13–25, Dec, 2019 (in Persian).

[47] A. Fereidouni, M.A.S. Masoum, M. Moghbel, “A new adaptive configuration of PID type fuzzy logic controller”, ISA Transaction, vol. 56, pp. 1–19, May 2014 (doi: 10.1016/j.isatra.2014.11.010).

[48] M. Zangeneh, E. Aghajari, S.M. Seyed-Moosavi, “A PSO-based automatic design of fuzzy inference system for speed control of DC motors”, Journal of Novel Researches on Electrical Power, vol. 9, no. 3, pp. 9–19, Nov 2020 (in Persian).

[49] M. Zangeneh, E. Aghajari, M. Forouzanfar, “A review on optimization of fuzzy controller parameters in robotic applications”, IETE Journal of Research, pp. 1–10, July 2020 (doi: 10.1080/03772063.2020.1787878).

[50] L.A. Zadeh, L. Fellow, “Fuzzy Logic= Computing with Words”, IEEE Trans. on Fuzzy Systems, vol. 4, no. 2, pp. 103–111, May 1996 (doi: 10.1109/91.493904).

[51] M.P. Hajiabbas, B. Mohammadi-Ivatloo, “Optimization of power system problems: methods, algorithms and MATLAB codes” , vol. 262. Springer Nature, 2020.