DOI: https://doi.org/10.20998/2074-272X.2018.5.10

PLANNING OF ENERGY CARRIERS BASED ON FINAL ENERGY CONSUMPTION USING DYNAMIC PROGRAMMING AND PARTICLE SWARM OPTIMIZATION

M. Dehghani, Z. Montazeri, A. Ehsanifar, A.R. Seifi, M.J. Ebadi, O. M. Grechko

Анотація


Цель. В настоящей статье предлагается новый подход к исследованию энергетических сетей для планирования энергоносителей. С этой целью разработан корректный план использования энергоносителей с учетом оптимального потребления энергии. Разработана соответствующая энергосистема с использованием информации о энергетическом баланса Ирана. Необходимо отметить, что моделирование энергосистемы выполняется в матричной форме. Распределение электрической энергии между электростанциями достигается за счет использования алгоритма оптимизации методом роя частиц. В настоящей работе, посвященной методу динамического программирования, предпринята попытка определить подходящую комбинацию энергоносителей.

Ключові слова


оптимизация методом роя частиц; конечное потребление энергии; планирование в энергетике; энергоносители; динамическое программирование

Повний текст:

PDF ENG (English)

Посилання


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3. Barbir F. Transition to renewable energy systems with hydrogen as an energy carrier. Energy, 2009, vol.34, no.3, pp. 308-312. doi: 10.1016/j.energy.2008.07.007.

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5. Ridjan I., Mathiesen B.V., Connolly D., Duić N. The feasibility of synthetic fuels in renewable energy systems. Energy, 2013, vol.57, pp. 76-84. doi: 10.1016/j.energy.2013.01.046.

6. Su W., Wang J., Roh J. Stochastic Energy Scheduling in Microgrids With Intermittent Renewable Energy Resources. IEEE Transactions on Smart Grid, 2014, vol.5, no.4, pp. 1876-1883. doi: 10.1109/tsg.2013.2280645.

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13. Dehghani M., Montazeri Z., Dehghani A., Seifi A.R. Spring search algorithm: A new meta-heuristic optimization algorithm inspired by Hooke's law. 2017 IEEE 4th International Conference on Knowledge-Based Engineering and Innovation (KBEI). doi: 10.1109/kbei.2017.8324975.

14. Wood A.J., Wollenberg B.F. Power generation, operation, and control. John Wiley & Sons, 2012.

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16. U.S. Energy Information Administration (EIA). Available at: http://www.eia.gov (accessed 21 July 2015).


Пристатейна бібліографія ГОСТ


1.     Krause T., Andersson G., Fröhlich K., Vaccaro A. Multiple-Energy Carriers: Modeling of Production, Delivery, and Consumption. Proceedings of the IEEE, 2011, vol.99, no.1, pp. 15-27. doi: 10.1109/jproc.2010.2083610.
2.     Cormio C., Dicorato M., Minoia A., Trovato M. A regional energy planning methodology including renewable energy sources and environmental constraints. Renewable and Sustainable Energy Reviews, 2003, vol.7, no.2, pp. 99-130. doi: 10.1016/s1364-0321(03)00004-2.
3.     Barbir F. Transition to renewable energy systems with hydrogen as an energy carrier. Energy, 2009, vol.34, no.3, pp. 308-312. doi: 10.1016/j.energy.2008.07.007.
4.     Amoo L.M., Fagbenle R.L. An integrated impact assessment of hydrogen as a future energy carrier in Nigeria's transportation, energy and power sectors. International Journal of Hydrogen Energy, 2014, vol.39, no.24, pp. 12409-12433. doi: 10.1016/j.ijhydene.2014.06.022.
5.     Ridjan I., Mathiesen B.V., Connolly D., Duić N. The feasibility of synthetic fuels in renewable energy systems. Energy, 2013, vol.57, pp. 76-84. doi: 10.1016/j.energy.2013.01.046.
6.     Su W., Wang J., Roh J. Stochastic Energy Scheduling in Microgrids With Intermittent Renewable Energy Resources. IEEE Transactions on Smart Grid, 2014, vol.5, no.4, pp. 1876-1883. doi: 10.1109/tsg.2013.2280645.
7.     Geng W., Ming Z., Lilin P., Ximei L., Bo L., Jinhui D. China׳s new energy development: Status, constraints and reforms. Renewable and Sustainable Energy Reviews, 2016, vol.53, pp. 885-896. doi: 10.1016/j.rser.2015.09.054.
8.     Trop P., Goricanec D. Comparisons between energy carriers' productions for exploiting renewable energy sources. Energy, 2016, vol.108, pp. 155-161. doi: 10.1016/j.energy.2015.07.033.
9.     Belier M. E.A.C.a.K.C.H. Interfuel substitution study-the role of electrification. Brookhaven National Laboratory informal Rept. BNL 19522 (ESAG-17), November, 1974.
10.  Kennedy J., Eberhart R. Particle swarm optimization. Proceeding of the 1995 IEEE International Conference on Neural Networks,Perth,Australia.IEEEServiceCenter,Piscataway, 1995. pp. 1942-1948.
11.  Available at: http://www.saba.org.ir/saba_content/media/image/2015/09/7811_orig.pdf (accessed 16 April 2016).
12.  Ebrahimi J., Hosseinian S.H., Gharehpetian G.B. Unit Commitment Problem Solution Using Shuffled Frog Leaping Algorithm. IEEE Transactions on Power Systems,2011, vol.26, no.2, pp. 573-581. doi: 10.1109/tpwrs.2010.2052639.
13.  Dehghani M., Montazeri Z., Dehghani A., Seifi A.R. Spring search algorithm: A new meta-heuristic optimization algorithm inspired by Hooke's law. 2017 IEEE 4th International Conference on Knowledge-Based Engineering and Innovation (KBEI). doi: 10.1109/kbei.2017.8324975.
14.  Wood A.J., Wollenberg B.F. Power generation, operation, and control. John Wiley & Sons, 2012.
15.  IEA Publications, rue dela Fédération, 75739 Paris Cedex 15, Printed in France by STEDI, September 2004.
16.  U.S. Energy Information Administration (EIA). Available at: http://www.eia.gov (accessed 21 July 2015).




Copyright (c) 2018 M. Dehghani, Z. Montazeri, A. Ehsanifar, A.R. Seifi, M.J. Ebadi, O. M. Grechko


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ISSN 2074–272X (Print)
ІSSN 2309–3404 (Online)