کنترل بهینه ریز شبکه به منظور بهبود پروفیل ولتاژ با در نظر گرفتن تولید همزمان برق و گرما
محورهای موضوعی : انرژی های تجدیدپذیر
1 - استادیار - گروه مهندسی برق، دانشکده فنی، واحد رفسنجان، دانشگاه آزاد اسلامی، رفسنجان، ایران
کلید واژه: منابع تولید پراکنده, پیل سوختی, فتوولتائیک, ریزشبکه, پروفیل ولتاژ,
چکیده مقاله :
معمولا در مطالعات کنترل بهینه ریز شبکه با تولید همزمان برق و گرما، بیشتر اهداف اقتصادی و آلایندگی مطرح میشود. در این مطالعات، بهینهسازی ادوات کنترلی و جبرانساز که با اهداف بهبود ابعاد فنی ریزشبکه میباشند، مدنظر قرار نمیگیرد. لذا در این مقاله این اهداف به صورت همزمان در نظر گرفته شده است. در این مقاله خازنها و راکتورهای موازی، جبرانسازهای استاتیکی توان راکتیو، تپ ترانسفورماتورها و تنظیمکنندههای ولتاژ و توان تولیدی منابع به عنوان متغییرهای کنترلی، با اهداف اقتصادی، کاهش تلفات و بهبود پروفیل ولتاژ استفاده شدهاند. شبکه مورد مطالعه دارای 22 باس میباشد که شامل منابع فتوولتائیک و پیل سوختی است. تمامی داده ها و اطلاعات شبکه، از یک سیستم حقیقی اقتباس شده است. در نتیجه سیستم مورد مطالعه میتواند کاملا بیانگر یک سیستم واقعی باشد. به منظور کاملتر کردن مطالعه سیستم، آلایندگی حاصل از تولید پیل سوختی در بهینهسازی لحاظ شده است. بازده پیل سوختی به صورت یک مدل استاندارد واقعی متغیر با بار در نظر گرفته شده است.
Usually, in the studies of optimal control for the micro-grid with combined heat and power (CHP), economic and environmental goals have been raised. In these studies, the optimization of control devices and compensators that are aimed at improving the technical aspects of micro-grid has not been considered. Therefore, in this paper, these goals are considered simultaneously. In this paper the shunt capacitor, shunt reactor, static var compensator, load ratio control transformer, step voltage regulator and power generation of DGs are investigated as control variables with the aim of considering costs, losses and voltage profile improvement. The case under study is a distribution test system including 22 buses, which consists of photovoltaic and fuel cell. All network data is extracted from a real system. As a result, the system can completely represent a real system. In order to complete the study of the system, the emissions of fuel cells are considered in the objective function. The efficiency and output electrical and thermal power of the fuel cell are considered related to part load ratio as a real standard model introduced in previous studies.
[1] T. Morstyn, B. Hredzak, V.G. Agelidis, "Control [1] T. Morstyn, B. Hredzak, V.G. Agelidis, "Control strategies for microgrids with distributed energy storage systems: An overview", IEEE Trans. on Smart Grid, Vol. 9, No. 4, pp. 3652-3666, July 2018.
[2] L.I. Dulău, D.Bică, “Optimization of generation cost in a microgrid”, Procedia Manufacturing, Vol. 22, No. 1, pp.703-708, 2018.
[3] C.C. Thompson, P.E. Konstantinos Oikonomou, A. H. Etemadi, V.J. Sorger, "Optimization of data center battery storage investments for microgrid cost savings, emissions reduction, and reliability enhancement", IEEE Trans. on Industry Applications, Vol. 52, No. 3, pp. 2053-2060, May-June 2016.
[4] A. Hussain, V. Bui, H. Kim, "A resilient and privacy-preserving energy management strategy for networked microgrids", IEEE Trans. on Smart Grid, Vol. 9, No. 3, pp. 2127-2139, May 2018.
[5] B. Lokeshgupta, S. Sivasubramani, "Multi-objective dynamic economic and emission dispatch with demand side management", International Journal of Electrical Power and Energy Systems, Vol. 97, pp. 334-343, April 2018.
[6] F.P. Mahdi, P. Vasant, V. Kallimani, J. Watada, P.Y.S. Fai, M. Abdullah-Al-Wadud, "A holistic review on optimization strategies for combined economic emission dispatch problem", Renewable and Sustainable Energy Reviews, Vol. 81, pp. 3006-3020, Jan. 2018.
[7] G. Liu, Y.L. Zhu, W. Jiang, "Wind-thermal dynamic economic emission dispatch with a hybrid multi-objective algorithm based on wind speed statistical analysis", IET Generation, Transmission and Distribution, Vol. 12, No. 17, pp. 3972-3984, Sep. 2018.
[8] M.L. Di Silvestre, G. Graditi, E. Riva Sanseverino, "A generalized framework for optimal sizing of distributed energy resources in micro-grids using an indicator-based swarm approach", IEEE Trans. on Industrial Informatics, Vol. 10, No. 1, pp. 152-162, Feb. 2014.
[9] H. Bakhtiari, R.A. Naghizadeh, "Multi-criteria optimal sizing of hybrid renewable energy systems including wind, photovoltaic, battery, and hydrogen storage with ɛ-constraint method", IET Renewable Power Generation, Vol. 12, No. 8, pp. 883-892, June 2018.
[10] M.H. Moradi, M. Eskandari, S.M. Hosseinian, "Operational strategy optimization in an optimal sized smart microgrid", IEEE Trans. on Smart Grid, Vol. 6, No. 3, pp. 1087-1095, May 2015.
[11] M.H. Moradi, M. Eskandari, H. Showkati, “Hybrid method for simultaneous optimization of DG capacity and operational strategy in microgrids utilizing renewable energy resources”, International Journal of Electrical Power and Energy Systems, Vol. 56, pp. 241–258, March 2014.
[12] P. Penkey, H. Samkari, B.K. Johnson, H.L. Hess, "Voltage control by using capacitor banks and tap changing transformers in a renewable microgrid", Proceeding of the IEEE/ISGT, Washington, DC, pp. 1-5, April 2017.
[13] M. Gheydi, A. Nouri, N. Ghadimi, "Planning in microgrids with conservation of voltage reduction", IEEE Systems Journal, Vol. 12, No. 3, pp. 2782-2790, Sept. 2018.
[14] A. Zeinalzadeh, Y. Mohammadi, M.H. Moradi, "Optimal multi objective placement and sizing of multiple DGs and shunt capacitor banks simultaneously considering load uncertainty via MOPSO approach", International Journal of Electrical Power & Energy Systems, Vol. 67, pp. 336-349, May 2015.
[15] J. Park, S. Nam, J. Park, "Control of a ULTC considering the dispatch schedule of capacitors in a distribution system", IEEE Trans. on Power Systems, Vol. 22, No. 2, pp. 755-761, May 2007.
[16] H. Ahmadi, J.R. Martí, "Distribution system optimization based on a linear power-flow formulation", IEEE Trans. on Power Delivery, Vol. 30, No. 1, pp. 25-33, Feb. 2015.
[17] A. Elmitwally, M. Elsaid, M. Elgamal, Z. Chen, "A fuzzy-multiagent self-healing scheme for a distribution system with distributed generations", IEEE Trans. on Power Systems, Vol. 30, No. 5, pp. 2612-2622, Sept. 2015.
[18] T. Senjyu, Y. Miyazato, A. Yona, N. Urasaki, T. Funabashi, “Optimal distribution voltage control and coordination with distributed generation”, IEEE Trans. on Power Delivery, Vol. 23, No. 2, pp. 1236–1242, April 2008.
[19] N. Daratha, B. Das, J. Sharma, “Coordination between OLTC and SVC for voltage regulation in unbalanced distribution system distributedgeneration”, IEEE Trans. on Power Systems, Vol. 29, No. 1, pp. 289–299, Jan. 2014.
[20] H. Karami, M.J. Sanjari, A. Tavakoli, G.B. Gharehpetian, “Optimal scheduling of residential energy system including combined heat and power system and storage device”, Electric Power Components and Systems,Vol. 41, No. 8, pp. 765 -781, 2013.
[21] H. Karami, M.J. Sanjari, S.H. Hosseinian, G.B. Gharehpetian ,“An optimal dispatch algorithm for managing residential distributed energy resources”, IEEE Trans. on Smart Grid., Vol. 5, No. 5, pp. 2360–2367, 2014.
[22] T. Niknam,H. Zeinoddini Meymand, "Impact of fuel cell power plants on multi-objective optimal operation management of distribution network", Fuel Cells, Vol. 12, No. 3, pp. 487 -505, April 2012.
[23] M. SailajaKumari, S. Maheswarapu, “Enhanced genetic algorithm based computation technique for multi-objective optimal power flow solution”, International Journal of Electrical Power and Energy Systems, Vol. 32, No. 6, pp. 736–742, July 2010.
_||_