مدل سازی برنامه های پاسخگویی بار در مدیریت چندگانه انرژی و ساختار در ریزشبکه های مجهز به تولیدکننده همزمان برق و حرارت

روش‌های هوشمند در صنعت برق

مقاله 6، دوره 14، شماره 53، خرداد 1402، صفحه 99-120   

نوع مقاله: مقاله پژوهشی

نویسندگان

مجید زارع؛ سید امین سعید^* ؛ حمیدرضا اکبری

گروه مهندسی برق- واحد یزد، دانشگاه آزاد اسلامی، یزد، ایران

چکیده

مدیریت انرژی در ریزشبکه­ها با اهداف متفاوتی نظیر کاهش هزینه­های بهره­برداری انجام می­شود. در این رویکرد، بهره­بردار ریزشبکه با تعیین میزان تولید انرژی توسط هر یک از منابع انرژی در دسترس، سعی در مدیریت انرژی به­منظور تامین انرژی مورد نیاز مصرف­کنندگان با حداقل هزینه ممکن دارد. این منابع می­توانند تولیدکننده انرژی الکتریکی، حرارتی یا ترکیبی باشند. در این مقاله مدیریت انرژی و ساختار در ریزشبکه با اهدافی نظیر کاهش هزینه­های بهره­برداری، کاهش آلودگی­های زیست­محیطی و بهبود شاخص­های فنی نظیر کاهش تلفات توان حقیقی صورت گرفته است. به این منظور از ابزارهایی مانند برنامه­های پاسخ­گویی بار، ذخیره­سازها، منابع تولید پراکنده مانند تولیدکننده­های همزمان برق و حرارت، منابع تجدیدپذیر مانند واحدهای بادی و فتوولتاییک و بازآرایی فیدرهای ریزشبکه بهره گرفته شده است. از الگوریتم ژنتیک که از معتبرترین الگوریتم­های فراابتکاری است برای حل مسئله بهینه­سازی مدنظر استفاده شده است. نتایج عددی کارایی مدل ارائه شده را نشان می­دهد.

کلیدواژه‌ها

برنامه پاسخ‌گویی بار؛ ریزشبکه؛ کاهش هزینه‌ها؛ مدیریت انرژی و ساختار

مراجع

[1] R.J. Bavandpour, H. Ghadiri, H. Khodadadi, “Optimal design of a hybrid solar-wind-battery system using the Grasshopper optimization algorithm for minimization of the loss of power supply probability”, Journal of Intelligent Procedures in Electrical Technology, vol. 13, no. 51, pp. 139-156, Dec. 2022  (in Persian) (dor: 20.1001.1.23223871.1401.13.51.9.2).

[2] H. Hasanshahi, M. Nafar, M. Simab, “Operation of micro-grid for provide clean energy constrained to system optimal reliability”, Journal of Intelligent Procedures in Electrical Technology, vol. 13, no. 50, pp. 141-156, Sep. 2022  (in Persian) (dor: 20.1001.1.23223871.1401.13.50.9.0).

[3] S. Gorji, S. Zamanian, M. Moazzami, “Techno-economic and environmental base approach for optimal energy management of microgrids using crow search algorithm”, Journal of Intelligent Procedures in Electrical Technology, vol. 11, no. 43, pp. 49-68, Sept. 2020  (in Persian) (dor: 20.1001.1.2322­3871.13­99.1­1.4­3.4.7).

[4] J. Pascual, D.A. Aviles, A. Ursua, P. Sanchis, L. Marroyo, “Energy management for an electro-thermal renewable-based residential microgrid with energy balance forecasting and demand-side management”, Applied Energy, vol. 295, pp. 1-22, Aug. 2021 (doi: 10.1016/j.apenergy.2021.117062).

[5] V.K. Garg, S. Sharma, “Overview on microgrid system”, Proceeding of the IEEE/PDGC, pp. 694-699, Solan, India, Dec. 2018 (doi: 10.1109/PDGC.2018.8745849).

[6] R. Velik, P. Nicolay, “Grid-price dependent energy management in microgrids using a modified simulated annealing triple-optimizer”, Applied Energy, vol. 130, pp. 384-395, Oct. 2014 (doi: 10.1016/j. apenerg­y.20­14.05.060).

[7] T. Niknam, R.A. Abarghooee, M.R. Narimani, “An efficient scenario based stochastic programming frame­work for multi-objective optimal microgrid operation”, Applied Energy, vol. 99, pp. 455-470, Nov. 2012 (doi: 10.1016/j.apenergy.2012.04.017).

[8] C. Wang, T. Tian, Z. Xu, S. Cheng, S. Liu, R. Chen, “Optimal management for grid-connected three/single phase hybrid multimicrogrids”, IEEE Trans. on Sustainable Energy, vol. 11, no. 3, pp. 1870-1882, July 2020 (doi: 10.1109/TSTE.2019.2945924).

[9] M. Zadsar, S.S. Sebtahmadi, M. Kazemi, S.M.M. Larimi, M.R. Haghifam, “Two stage risk based decision making for operation of smart grid by optimal dynamic multi microgrid”, International Journal of Electrical Power and Energy Systems, vol. 118, Article Number: 105791, June 2020 (doi: 10.1016/j.ijep­es.­20­19.10­57­9­1).

[10] M. Esmaeili, M. Sedighizadeh, M. Esmaili, “Multi-objective optimal reconfiguration and DG power allocation in distribution network using big bang-big crunch algorithm considering load uncertainty”, Energy, vol. 103, pp. 86-99, May 2016 (doi: 10.1016/j.energy.2016.02.152).

[11] M. Sedighizadeh, M. Esmaili, M. Esmaeili, “Allocation of the hybrid big bang-big crunch algorithm to optimal reconfiguration and distributed generation power allocation in distribution systems”, Energy, vol. 76, pp. 920-930, Nov. 2014 (doi: 10.1016/j.energy.2014.09.004).

[12] M. Sedighizadeh, G. Shaghaghi-shahr, M. Esmaili, M. R. Aghamohammadi, “Optimal distribution feeder reconfiguration and generation scheduling for microgrid day-ahead operation in the presence of electric vehicles considering uncertainties”, Journal of Energy Storage, vol. 21, pp. 58-71, Feb. 2019 (doi: 10.10­16/­j.­e­­st­.2018.11.009).

[13] R. Jabbari-Sabet, S.M. Moghaddas Tafreshi, S.S. Mirhosseini, “Microgrid operation and management using probabilistic reconfiguration and unit commitment”, International journal of Electrical Power and Energy Systems, vol. 75, pp. 328-336, Feb. 2016 (doi: 10.1016/j.ijepes.2015.09.012).

[14] I.B. Hamida, S.B. Salah, F. Msahli, M.F. Mimouni, “Optimal network reconfiguration and renewable DG integration considering time sequence variation in load and DGs”, Renewable Energy, vol. 121, pp. 66-80, Mar. 2018 (doi: 10.1016/j.renene.2017.12.106).

[15] F.S. Ghazijahani, J. Salehi, “Integrated DR and reconfiguration scheduling for optimal operation of microgrids using Hongs’ point estimate method”, International journal of Electrical Power and Energy Systems, vol. 99, pp. 481-492, July 2018 (doi: 10.1016/j.ijepes.2018.01.044).

[16] M.R. Kaveh, R.A. Hooshmand, S.M. Madani, “Simultaneous optimization of rephrasing, reconfiguration and DG placement in distribution networks using BF-SD algorithm”, Computational Intelligence in Electrical Engineering, vol. 8, no. 2, pp. 55-69, Aug. 2017 (doi: 10.1016/j.asoc.2017.09.041).

[17] J. Shukla, B. Das, V. Pant, “Stability constrained optimal distribution system reconfiguration considering uncertainties in correlated loads and distributed generations”, International Journal of Electrical Power and Energy Systems, vol. 99, pp. 121-133, July 2018 (doi: 10.1016/j.ijepes.2018.01.010).

[18] P. Harsh, D. Das “Energy management in microgrid using incentive-based demand response and reconfi­gured network considering uncertainties in renewable energy sources”, Sustainable Energy Technologies and Assessments, vol. 46, Article Number: 101225, Aug. 2021 (doi: 10.1016/j.seta.2021.101225).

[19] M. Kiani, J. Salahi, “Integrated approach of network reconfiguration with distributed generation and shunt capacitor placement for power loss minimization in radial distribution networks”, Applied Soft Computing, vol. 52, pp. 1262-1284, Mar. 2017 (doi: 10.1016/j.asoc.2016.07.031).

[20] M. Mohammadi, A.M. Rozbahani, S. Bahmanyar, “Power loss reduction of distribution systems using BFO based optimal reconfiguration  along with DG and shunt capacitor placement simultaneously in fuzzy fra­mew­ork”, Journal of Central South University, vol. 24, no. 1, pp. 90-103, Jan. 2017 (doi: 10.1007/s11771-017-3412-1).

[21] S.N. Ravadanegh, M.R.J. Oskuee, M. Karimi “Multi-objective planning model for simultaneous reconfigu­ration of power distribution network and allocation of renewable energy resources and capacitors with considering uncertainties”, Journal of Central South University, vol. 24, Issue. 8, pp. 1837-1849, Aug. 2017 (doi: 10.1007/s11771-017-3592-8).

[22] S.S. Fazlhashemi, M. Sedighizadeh, M.E. Khodayar “Day-ahead energy management and feeder reconfigu­ration for microgrids with CCHP and energy storage systems”, Journal of Energy Storage, vol. 29, Article Number: 101301, Jun. 2020 (doi: 10.1016/j.est.2020.101301).

[23] M. Kia, M. Shafiekhani, H. Arasteh, S.M. Hashemi, M. Shafie-khah, J.P.S. Catalão “Short-term operation of microgrids with thermal and electrical loads under different uncertainties using information gap decision theory”, Energy, vol. 208, Article Number: 118418, Jan. 2020 (doi: 10.1016/j.energy.2020.118418).

[24] Y. Liu, D. Cetenovic, H. Li, E. Gryazina, V. Terzija “An optimized multi-objective reactive power dispatch strategy based on improved genetic algorithm for wind power integrated systems”, International Journal of Electrical Power and Energy Systems, vol. 136, Article Number: 107764, Mar. 2022 (doi: 10.1016/j.i­jepe­s.20­21.107764).

[25] H. Zheng, W. Huang, J. Zhao, J. Liu, Y. Zhang, Z. Shi, C. Zhang “A novel falling model for wind speed pro­ba­bility distribution of wind farms”, Renewable Energy, vol. 184, pp. 91-99, Jan. 2022 (doi: 10.1016­/j.r­e­­nene.2021.11.073).

[26] L. Zhu, J. Zhang, “Uncertainty and sensitivity analysis of cooling and heating loads for building energy planning”, Journal of Building Engineering, vol. 45, Article Number: 103440, Jan. 2022 (doi: 10.1016/j.job­e.2­0­21.103440).

[27] Y. Kongjeen, K. Bhumkittipich, N. Mithulananthan, I.S. Amiri, P. Yupapin “A modified backward and forward sweep method for microgrid load flow analysis under different electric vehicle load mathematical models”, Electric Power Systems Research, vol. 168, pp. 46-54, Mar. 2019 (doi: 10.1016/j.epsr.2­018.1­0.­0­3­1).