CC BY
59
Power Stations, Grids and Systems
UDC 621.3
doi: 10.20998/2074-272X.2019.4.10
M. Dehghani, Z. Montazeri, O.P. Malik
ENERGY COMMITMENT: A PLANNING OF ENERGY CARRIER BASED ON ENERGY CONSUMPTION
Purpose. Energy consumption is one of the criteria for determining the quality of life in a country. Continued supply of energy and the possibility of long-term access to resources require a comprehensive plan. One of the key issues in the field of energy planning is energy carriers. In this paper, a new theory is introduced to energy network studies for planning of energy carriers called Energy Commitment. In this theory, an appropriate planning is applied for energy carriers based the final energy consumption. Energy carriers are available either naturally or after the energy conversion process. Energy commitment is modeled on an energy network with the presence of electrical energy, gas energy, transportation section, agriculture section, industrial section, residential section, commercial section, and general section. References 25, tables 3. Key words: energy, energy commitment, energy carrier, energy consumption, unit commitment.
Цель. Потребление энергии является одним из критериев определения качества жизни в стране. Непрерывные поставки энергии и возможность долгосрочного доступа к ресурсам требуют комплексного плана. Одним из ключевых вопросов в области энергетического планирования являются энергоносители. В данной статье в исследования энергетических сетей для планирования энергоносителей вводится новая теория под названием Energy Commitment («энергетическое обязательство»). В этой теории для энергоносителей применяется соответствующее планирование на основе конечного потребления энергии. Энергоносители доступны либо естественным путем, либо после процесса преобразования энергии. Energy Commitment моделируется в энергетической сети с учетом электрической энергии, энергии газа, транспортной отрасли народного хозяйства, сельскохозяйственной отрасли, промышленного сектора экономики, жилищно-коммунального хозяйства, реального сектора экономики и прочих видов экономической активности. Библ. 25, табл. 3. Ключевые слова: энергия, энергетическое обязательство, энергоноситель, энергопотребление, единичное обязательство.
Introduction. Energy consumption is one of the criteria for determining the level of development and quality of life in a country [1]. If energy used properly and reasonably, it can in any country make progress in the science, technology and welfare of its people. Otherwise, it will cause irreparable economic losses and a massive economic downturn [2]. The energy consumption trend has been very fast and critical in recent years. Continued supply of energy and the possibility of long-term access to resources require a comprehensive energy planning, which is why energy planning is indisputable economic, national and strategic imperatives. One of the key issues in the field of energy planning is energy resources.
Many studies is done on the power system such as: transformers [3], battery energy storage [4], distributed generation [5], energy [6]. One of the most important studies of electric power network is the issue of Unit Commitment (UC) [7]. UC is to determine the most appropriate electrical power generation pattern at power plants, firstly, to meet technical requirements, and then to be the most economical [8]. UC has been studied using various methods. The priority list method and dynamic programing are the first methods in UC [9]. In the Lagrange method, equal and unequal constraints were added to the objective function [10]. In [11] UC problem is investigated the in presence of FACTS devices and energy storage. In [12] UC problem is studied under cyber-attacks. In addition, evolutionary methods have been used for solving UC in recent years. In [13] a method is proposed based on the classical genetic algorithm. Integer-coded genetic algorithm in [14] is proposed. Researchers have also used other methods to solve the UC problem such as: Particle Swarm Optimization (PSO) [15], Teaching Learning Based Optimization (TLBO) [16], Gravitational Search Algorithm (GSA) [17] , Water Cycle Algorithm (WCA) [18] and Grey Wolf Optimization (GWO) [19], Whale
Optimization Algorithm (WOA) [20]. Other algorithms are also suggested for UC solving [21-24].
Energy Commitment (EC) is to determine the most appropriate pattern for using energy resources to meet energy demand, firstly, to meet technical requirements, and secondly, to be the most economical. In other words, energy sources should be used as much as needed, if the energy sources are in line with the demand peak it will cost a lot. Therefore, EC reduces energy supply costs.
This problem can be articulated mathematically, so that a function called F is defined as the objective function, which is equal to the total cost of supplying energy demand. In this case, the problem is to minimize F. Note that losses are discarded and there is no explicit mention of any exploitation restrictions in the issue. So: F = Fi E)+ F2 (E,2 )+ F3 (Eii3 )+
+... + fns L )=ZF- (e, )
(1)
1=1
where F is the objective function, Fi is the cost of i-th source, Es. is the i-th kind of energy demand and Ns is
the number of energy carriers.
The above issue is an optimization problem that can be examined using appropriate methods.
Problem Formulation. Energy grid modelling. The energy network consists of the following sections: transportation, agriculture, industrial, residential, commercial and general.
In the energy grid, energy demand is calculated as a sum of sub networks of the grid:
N
ECf = EC1 + EC2 +... + ECN = ^ EC,
(2)
1=1
where ECj is the final energy consumption, N is the number of different sections of energy consumption and ECi is the energy consumption of i-th section.
© M. Dehghani, Z. Montazeri, O.P. Malik
Firstly, the final energy consumption matrix based on different sections is determined as
Ei = [ec1 EC2 ... EC, ... ECn F
-N J , (3)
where E\ is the final energy consumption matrix based on different sections.
Now final energy consumption matrix based on different energy carriers is determined as
E2 = Tu x Eb (4)
where E2 is the final energy consumption matrix based on different energy carriers and T12 is the transpose matrix of different sections to different energy carriers.
Energy losses is modeled as
E3 = T2,3 x E2, (5)
where E3 is the final energy consumption based on different energy carriers considering losses and T2,3 is the efficiency matrix.
At this stage, electrical energy is converted into energy carriers. The electrical energy of different power plants is determined as
Eu = Tu x Ee, (6)
where Eu is the electrical energy of different power plants, Tu is the separation matrix of electricity generation by different power plants and Ee is the total electricity demand.
Input fuel for different power plants is determined as
Eej = Tu, f x Eu , (7)
where Ee1 is the input fuel for different power plant and Electrical manufacturer carriers is determined as
Ee2 = Tfc x Eex, (8)
where Ee2 is the electrical manufacturer carriers and TfcC is the conversion matrix of input fuel to energy carriers.
After simulation of electrical energy, final energy consumption is calculated as
E4 = E3 + Ee2 - Ee, (9)
where E4 is the final energy consumption after conversion of electrical energy.
At this stage, the process of refining crude oil is simulated as
EPI = Tp x Ep, (10)
where Epi is the energy carriers produced by refining, Tp
is the separation matrix of produced products from refining crude oil and Ep is the maximum capacity of refineries.
After simulation of process of refining crude oil, final energy consumption is calculated as
E5 = E4 + Ep - E
Pi'
(11)
where E5 is the final energy consumption after refining crude oil. Actually E5 determines energy carriers in order to supply of energy demand.
Test energy grid. EC is applied to energy grid with 10 power units. Electrical network information is adapted from [25].
Simulation. After modeling the energy network, EC is simulated on energy grid.
The simulation results of EC on the energy grid studied are presented in Tables 1-3.
In Table 1, dynamic scheduling results are presented with equal paths to the maximum number of states per hour of the study. The second path, (S2) is identified as an appropriate strategy. The cost of EC in this path is equal by 8,554,182 USD. The need for energy carriers to provide final energy consumption is specified in Table 2. The result of economic distribution of electrical energy is presented in Table 3.
Table i
The output result of dynamic planning in ten unit energy grids
Strategy Hour
S6 S5 S 4 S3 S2 Si
2 2 2 2 2 2 The initial state
3 3 3 3 3 3 1
3 3 3 3 3 3 2
3 3 3 3 3 3 3
3 3 3 3 3 3 4
3 3 3 3 3 3 5
4 4 4 4 4 4 6
4 4 4 4 4 4 7
9 9 9 9 9 9 8
9 9 9 9 9 9 9
9 9 9 9 9 9 10
i0 10 10 10 10 10 11
i0 10 10 10 10 10 12
i0 10 10 10 10 10 13
9 9 9 9 9 9 14
9 9 9 9 9 9 15
9 9 9 9 9 9 16
9 9 9 9 9 9 17
9 9 9 9 9 9 18
9 9 9 9 9 9 19
9 9 9 9 9 9 20
9 9 4 4 4 4 21
9 6 4 4 3 3 22
7 6 4 4 3 3 23
7 6 5 4 3 2 24
8,557,932 8,557,192 8,557,153 8,554,502 8,554,182 8,555,398 Cost (USD)
Table 2
The need of energy carriers in ten unit energy grids
8 7 6 5 4 3 2 i Hour
372i.i 372i.i 372i.i 372i.i 372i.i 372i.i 372i.i 372i. i Petroleum
51.78965 44.67028 37.55091 23.31218 I6.i928i i.95407 -i2.2847 -i9.404 Liquid gas
-350.552 -365.265 -354.657 -429.906 -466.355 -539.254 -6i2.i54 -647.68 Fuel oil
-ii.744i -6i.i345 -123.351 -2i0.i -253.46 -340.182 -426.903 -470.252 Gas oil
17.72885 i.640607 -14.4476 -46.6241 -62.7124 -94.8888 -i27.065 -i43.i54 Kerosene
405.1893 363.9642 322.7392 240.289 199.0639 Il6.6i37 34.i6357 -7.06i52 Gasoline
53.06305 50.85209 48.64ii3 44.2192 42.00824 37.58632 33.i644 30.95344 Plane fuel
4380.603 4190.728 3988.239 3615.204 3432.123 3065.959 2699.796 25i9.4i 5 Natural gas
26.60254 25.4941 24.38566 22.16878 21.06034 18.84346 i6.62658 i5.5i8i5 Coke gas
58.79772 56.34781 53.89791 48.9981 46.54819 41.64838 36.74857 34.29867 Coal
16 15 14 13 12 ii i0 9 Hour
372i.i 372i.i 372i.i 372i.i 372i.i 372i.i 372i.i 372i. i Petroleum
30.43155 51.78965 66.02839 80.26713 94.50586 87.3865 80.267i3 66.02839 Liquid gas
-459.901 -350.552 -275.868 -198.861 -I35.5ii -i58.969 -i98.86i -275.59i Fuel oil
-141.826 -ii.744i 74.99814 161.7678 260.843 205.i69 i6i.7678 75.00i4 Gas oil
-30.5359 17.72885 49.90533 82.0818 114.2583 98.i7004 82.08i8 49.90533 Kerosene
28i.5i4i 405.1893 487.6395 570.0897 652.5398 6ii.3i48 570.0897 487.6395 Gasoline
46.43017 53.06305 57.48497 61.90689 66.32881 64.ii785 6i.90689 57.48497 Plane fuel
3831.358 4380.603 4751.988 5130.168 5531.033 5323.32 5i30.i68 4752.798 Natural gas
23.27722 26.60254 28.8i94i 31.03629 33.25317 32.i4473 3i.03629 28.8i94i Coke gas
51.448 58.79772 63.69753 68.59734 73.49714 7i.04724 68.59734 63.69753 Coal
24 23 22 2i 20 i9 i8 i7 Hour
372i.i 372i.i 372i.i 372i.i 372i.i 372i.i 372i.i 372i. i Petroleum
-5.1653 9.073439 37.55091 66.02839 80.26713 5i.78965 37.5509i 23.3i2i8 Liquid gas
-595.486 -548.095 -423.452 -275.868 -198.861 -350.552 -423.452 -496.35i Fuel oil
-370.456 -277.548 -98.4652 74.99813 161.7678 -ii.744i -98.4652 -i85.i86 Gas oil
-110.977 -78.8006 -14.4476 49.90533 82.0818 i7.72885 -i4.4476 -46.624i Kerosene
75.38865 157.8388 322.7392 487.6395 570.0897 405.i893 322.7392 240.289 Gasoline
35.37536 39.79728 48.64ii3 57.48497 61.90689 53.06305 48.64ii3 44.2i92 Plane fuel
2913.867 3278.051 4014.44 4751.988 5130.168 4380.603 40i4.44 3648.277 Natural gas
17.73502 19.9519 24.38566 28.8i94i 31.03629 26.60254 24.38566 22.i6878 Coke gas
39.19848 44.09829 53.89791 63.69753 68.59734 58.79772 53.8979i 48.998i Coal
Table 3
The electrical energy economical distribution within the energy grid
o 00 ^t- m (N
3
e e C C C C e C e
^3 ^3 ^3 ^3 ^3 ^3 ^3
0 0 0 0 0 0 0 i29.9054 i50 420.9897 i
0 0 0 0 0 0 0 i30 i65.959i 455 2
0 0 0 0 0 0 0 i30 266.087 455 3
0 0 0 0 0 0 0 i30 366.2i49 455 4
0 0 0 0 0 0 0 i30 4i6.2788 455 5
0 0 0 0 0 0 6i.40668 i30 455 455 6
0 0 0 0 0 0 ii i.4706 i30 455 455 7
0 54.94904 i0 25 78.9i50i 25 20 i29.9395 403.i555 454.5755 8
0 54.92522 38.i9602 25 79.9i727 25 40.5i524 i29.8847 454.393 453.83i 9
0 54.990ii 46.54565 75.69i85 79.97855 25 i29.9675 i29.966 454.8779 454.8368 i0
55 55 55 85 80 5i.982i3 i30 i30 455 455 ii
55 55 55 85 80 i57.ii 64 i30 i30 455 455 i2
3i.ii385 55 55 85 80 25.80435 i30 i30 455 455 i3
0 55 46.5999 25.09276 80 25.i8803 i30 i30 455 454.9096 i4
0 50.46745 i0 25 42.35772 25 20 i29.0834 452.7482 446.8778 i5
0 54.57776 i0 25 75.6i226 25 20 i29.572 260.4829 45i.0978 i6
0 54.58248 i0 25 75.74856 25 20 i29.48i3 209.902 45i.5645 i7
0 55 i0.06585 25.0407i 80 25.083i5 20.i2963 i30 40i.2i52 455 i8
0 55 46.6i355 25.03679 80 25.i3997 i30 i30 455 455 i9
0 53.36535 i0 25 79.89353 25 70.70835 i29.7906 454.3342 453.5704 20
0 0 0 0 0 0 6i.40668 i30 455 455 2i
0 0 0 0 0 0 0 i30 3i6.i509 455 22
0 0 0 0 0 0 0 i30 2i6.023 455 23
0 0 0 0 0 0 0 i30 2i6.023 455 24
Conclusions.
Energy Commitment (EC) was introduced as a planning of energy carrier based on energy consumption. EC is to determine the most appropriate pattern for using energy resources to meet energy demand, firstly, to meet
technical requirements, and secondly, to be the most economical.
The energy grid including different sections was modeled in matrix form. EC was simulated on the one energy grid with ten power plants and result was
presented. Different combinations of power plants are available to provide final energy consumption. Due to the different fuel inputs to each power plant, there are different combinations of energy carriers. The proper combination of energy carriers is determined to provide final energy consumption using the dynamic programming method.
REFERENCES
1. Dehghani M., Montazeri Z., Ehsanifar A., Seifi A.R., Ebadi M.J., Grechko O.M. Planning of energy carriers based on final energy consumption using dynamic programming and particle swarm optimization. Electrical engineering & electromechanics,
2018, no.5, pp. 62-71. doi: 10.20998/2074-272X.2018.5.10.
2. Montazeri Z., Niknam T. Energy carriers management based on energy consumption. 2017 IEEE 4th International Conference on Knowledge-Based Engineering and Innovation (KBEI), Dec. 2017. doi: 10.1109/kbei.2017.8325036.
3. Ehsanifar A., Dehghani M., Allahbakhshi M. Calculating the leakage inductance for transformer inter-turn fault detection using finite element method. 2017 Iranian Conference on Electrical Engineering (ICEE), May 2017. doi: 10.1109/iraniancee.2017.7985256.
4. Dehbozorgi S., Ehsanifar A., Montazeri Z., Dehghani M., Seifi A. Line loss reduction and voltage profile improvement in radial distribution networks using battery energy storage system. 2017 IEEE 4th International Conference on Knowledge-Based Engineering and Innovation (KBEI), Dec. 2017. doi: 10.1109/kbei.2017.8324976.
5. Dehghani M., Mardaneh M., Montazeri Z., Ehsanifar A., Ebadi M.J., Grechko O.M. Spring search algorithm for simultaneous placement of distributed generation and capacitors. Electrical engineering & electromechanics, 2018, no.6, pp. 6873. doi: 10.20998/2074-272X.2018.6.10.
6. Montazeri Z., Niknam T. Optimal utilization of electrical energy from power plants based on final energy consumption using gravitational search algorithm. Electrical engineering & electromechanics, 2018, no.4, pp. 70-73. doi: 10.20998/2074-272X.2018.4.12.
7.Shi J., Oren S.S. Stochastic Unit Commitment With Topology Control Recourse for Power Systems With Large-Scale Renewable Integration. IEEE Transactions on Power Systems, 2018, vol.33, no.3, pp. 3315-3324. doi: 10.1109/tpwrs.2017.2772168.
8. Gupta A., Anderson C.L. Statistical Bus Ranking for Flexible Robust Unit Commitment. IEEE Transactions on Power Systems,
2019, vol.34, no.1, pp. 236-245. doi: 10.1109/tpwrs.2018.2864131.
9. Yamin H.Y. Review on methods of generation scheduling in electric power systems. Electric Power Systems Research, 2004, vol.69, no.2-3, pp. 227-248. doi: 10.1016/j.epsr.2003.10.002.
10. Geoffrion A.M. Lagrangian Relaxation for Integer Programming. 50 Years of Integer Programming 1958-2008. Nov. 2009, pp. 243-281, doi:10.1007/978-3-540-68279-0_9.
11. Luburic Z., Pandzic H. FACTS devices and energy storage in unit commitment. International Journal of Electrical Power & Energy Systems, 2019, vol.104, pp. 311-325 doi: 10.1016/j.ijepes.2018.07.013.
12. Shayan H., Amraee T. Network Constrained Unit Commitment Under Cyber Attacks Driven Overloads. IEEE Transactions on Smart Grid, pp. 1-1, 2019. doi: 10.1109/tsg.2019.2904873.
13. Swarup K.S., Yamashiro S. Unit commitment solution methodology using genetic algorithm. IEEE Transactions on Power Systems, 2002, vol.17, no.1, pp. 87-91. doi: 10.1109/59.982197.
14. Damousis I.G., Bakirtzis A.G., Dokopoulos P.S. A Solution to the Unit-Commitment Problem Using Integer-Coded Genetic Algorithm. IEEE Transactions on Power Systems, 2004, vol.19, no.2, pp. 1165-1172. doi: 10.1109/tpwrs.2003.821625.
15. Anand H., Narang N., Dhillon J.S. Multi-objective combined heat and power unit commitment using particle swarm optimization. Energy, 2019, vol.172, pp. 794-807. doi: 10.1016/j.energy.2019.01.155.
16. Krishna P.V.R., Sao S. An Improved TLBO Algorithm to Solve Profit Based Unit Commitment Problem under Deregulated Environment. Procedia Technology, 2016, vol.25, pp. 652-659. doi: 10.1016/j.protcy.2016.08.157.
17. Barani F., Mirhosseini M., Nezamabadi-pour H., Farsangi M.M. Unit commitment by an improved binary quantum GSA. Applied Soft Computing, 2017, vol.60, pp. 180-189. doi: 10.1016/j.asoc.2017.06.051.
18. El-Azab H.-A.I., Swief R.A.-W., El-Amary N.H., Temraz H.K. Decarbonized Unit Commitment Applying Water Cycle Algorithm Integrating Plug-In Electric Vehicles. 2018 Twentieth International Middle East Power Systems Conference (MEPCON), Dec. 2018. pp. 455-462. doi: 10.1109/mepcon.2018.8635152.
19. Srikanth K., Panwar L.K., Panigrahi B., Herrera-Viedma E., Sangaiah A.K., Wang G.-G. Meta-heuristic framework: Quantum inspired binary grey wolf optimizer for unit commitment problem. Computers & Electrical Engineering, 2018, vol.70, pp. 243-260. doi: 10.1016/j.compeleceng.2017.07.023.
20. Kumar V., Kumar D. Binary whale optimization algorithm and its application to unit commitment problem. Neural Computing and Applications, Oct. 2018, pp. 1-29, doi: 10.1007/s00521-018-3796-3.
21. Dehghani M., Montazeri Z., Dehghani A., Nouri N., Seifi A. BSSA: Binary spring search algorithm. 2017 IEEE 4th International Conference on Knowledge-Based Engineering and Innovation (KBEI), Dec. 2017. doi: 10.1109/kbei.2017.8324977.
22. Dehghani M., Montazeri Z., Dehghani A., Seifi A. 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), Dec. 2017. doi: 10.1109/kbei.2017.8324975.
23. Dehghani M., Montazeri Z., Malik O.P., Ehsanifar A., Dehghani A. OSA: Orientation Search Algorithm. International Journal of Industrial Electronics, Control and Optimization, 2019, vol.2, pp. 99-112.
24. Dehghani M., Mardaneh M., Malik O. FOA: Following Optimization Algorithm for solving power engineering optimization problems. Journal of Operation and Automation in Power Engineering, 2019. (Article in press). doi: 10.22098/JOAPE.2019.5522.1414.
25. 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.
Received 19.04.2019
M. Dehghani1, Candidate of Power Engineering, PhD Student, Z. Montazeri1, Candidate of Power Engineering, PhD Student, O.P. Malik2, Doctor of Power Engineering, Professor,
1 Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran,
e-mail: adanbax@gmail.com, Z.Montazeri@sutech.ac.ir
2 Department of Electrical Engineering, University of Calgary, Calgary Alberta Canada e-mail: maliko@ucalgary.ca
How to cite this article:
Dehghani M., Montazeri Z., Malik O.P. Energy commitment: a planning of energy carrier based on energy consumption. Electrical engineering & electromechanics, 2019, no.4, pp. 69-72. doi: 10.20998/2074-272X.2019.4.10.
