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

Authors

  • M. Dehghani Shiraz University, Iran, Islamic Republic of
  • Z. Montazeri Islamic Azad University of Marvdasht, Iran, Islamic Republic of
  • A. Ehsanifar Shiraz University, Iran, Islamic Republic of
  • A.R. Seifi Shiraz University, Iran, Islamic Republic of https://orcid.org/0000-0002-6081-3543
  • M.J. Ebadi Chabahar Maritime University, Iran, Islamic Republic of
  • O. M. Grechko National Technical University «Kharkiv Polytechnic Institute», Ukraine https://orcid.org/0000-0001-7872-8585

DOI:

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

Keywords:

particle swarm optimization, final energy consumption, energy planning, energy carriers, dynamic programing

Abstract

Purpose. In the present article, a new approach of the energy grid studies is introduced to program energy carriers. In this view, a proper plan is designed on the use of energy carriers considering the energy optimum use. Indeed, the proper energy grid is designed by applying Iran energy balance sheet information. It is proper to mention that, the energy grid modelling is done in a matrix form. The electrical energy distribution among power stations is achieved by using the particle swarm optimization algorithm. In the present paper, concerning the dynamic programming method, it is tried to determine a suitable combination of energy carriers.

Author Biographies

M. Dehghani, Shiraz University

Department of Power and Control

Z. Montazeri, Islamic Azad University of Marvdasht

Department of Electrical Engineering

A. Ehsanifar, Shiraz University

Department of Power and Control

A.R. Seifi, Shiraz University

Department of Power and Control

M.J. Ebadi, Chabahar Maritime University

Faculty of Marine Science

O. M. Grechko, National Technical University «Kharkiv Polytechnic Institute»

Dept. of Electrical Apparatus

References

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).

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Published

2018-10-19

How to Cite

Dehghani, M., Montazeri, Z., Ehsanifar, A., Seifi, A., Ebadi, M., & Grechko, O. M. (2018). PLANNING OF ENERGY CARRIERS BASED ON FINAL ENERGY CONSUMPTION USING DYNAMIC PROGRAMMING AND PARTICLE SWARM OPTIMIZATION. Electrical Engineering & Electromechanics, (5), 62–71. https://doi.org/10.20998/2074-272X.2018.5.10

Issue

Section

Power Stations, Grids and Systems