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,...
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Інститут технічних проблем магнетизму НАН України
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irk-123456789-1590822019-09-23T01:25:58Z Energy commitment: a planning of energy carrier based on energy consumption Dehghani, M. Montazeri, Z. Malik, O.P. Електричні станції, мережі і системи 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. 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. 2019 Article Energy commitment: a planning of energy carrier based on energy consumption / M. Dehghani, Z. Montazeri, O.P. Malik // Електротехніка і електромеханіка. — 2019. — № 4. — С. 69-72. — Бібліогр.: 25 назв. — англ. DOI: https://doi.org/10.20998/2074-272X.2019.4.10 2074-272X http://dspace.nbuv.gov.ua/handle/123456789/159082 621.3 en Електротехніка і електромеханіка Інститут технічних проблем магнетизму НАН України |
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Електричні станції, мережі і системи Електричні станції, мережі і системи |
| spellingShingle |
Електричні станції, мережі і системи Електричні станції, мережі і системи Dehghani, M. Montazeri, Z. Malik, O.P. Energy commitment: a planning of energy carrier based on energy consumption Електротехніка і електромеханіка |
| description |
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. |
| format |
Article |
| author |
Dehghani, M. Montazeri, Z. Malik, O.P. |
| author_facet |
Dehghani, M. Montazeri, Z. Malik, O.P. |
| author_sort |
Dehghani, M. |
| title |
Energy commitment: a planning of energy carrier based on energy consumption |
| title_short |
Energy commitment: a planning of energy carrier based on energy consumption |
| title_full |
Energy commitment: a planning of energy carrier based on energy consumption |
| title_fullStr |
Energy commitment: a planning of energy carrier based on energy consumption |
| title_full_unstemmed |
Energy commitment: a planning of energy carrier based on energy consumption |
| title_sort |
energy commitment: a planning of energy carrier based on energy consumption |
| publisher |
Інститут технічних проблем магнетизму НАН України |
| publishDate |
2019 |
| topic_facet |
Електричні станції, мережі і системи |
| url |
http://dspace.nbuv.gov.ua/handle/123456789/159082 |
| citation_txt |
Energy commitment: a planning of energy carrier based on energy consumption / M. Dehghani, Z. Montazeri, O.P. Malik // Електротехніка і електромеханіка. — 2019. — № 4. — С. 69-72. — Бібліогр.: 25 назв. — англ. |
| series |
Електротехніка і електромеханіка |
| work_keys_str_mv |
AT dehghanim energycommitmentaplanningofenergycarrierbasedonenergyconsumption AT montazeriz energycommitmentaplanningofenergycarrierbasedonenergyconsumption AT malikop energycommitmentaplanningofenergycarrierbasedonenergyconsumption |
| first_indexed |
2025-07-14T11:39:39Z |
| last_indexed |
2025-07-14T11:39:39Z |
| _version_ |
1837622289139499008 |
| fulltext |
Електричні станції, мережі і системи
ISSN 2074-272X. Електротехніка і Електромеханіка. 2019. №4 69
© M. Dehghani, Z. Montazeri, O.P. Malik
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:
,...
1
321 1321
i
s
sNs s
N
i
isN
sss
EFEF
EFEFEFF
(1)
where F is the objective function, Fi is the cost of i-th
source,
isE 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:
,...
1
21
N
i
iNf ECECECECEC (2)
where ECf 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.
70 ISSN 2074-272X. Електротехніка і Електромеханіка. 2019. №4
Firstly, the final energy consumption matrix based
on different sections is determined as
,......211
T
Ni ECECECECE (3)
where E1 is the final energy consumption matrix based on
different sections.
Now final energy consumption matrix based on
different energy carriers is determined as
,12,12 ETE (4)
where E2 is the final energy consumption matrix based on
different energy carriers and T1,2 is the transpose matrix of
different sections to different energy carriers.
Energy losses is modeled as
,23,23 ETE (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
,euu ETE (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
,,1 ufue ETE (7)
where
1e
E is the input fuel for different power plant
and Electrical manufacturer carriers is determined as
,
12 , ecfe ETE (8)
where
2eE is the electrical manufacturer carriers and Tf,c
is the conversion matrix of input fuel to energy carriers.
After simulation of electrical energy, final energy
consumption is calculated as
,
234 ee EEEE (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
,
1 ppp ETE (10)
where
1pE 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
,
145 pp EEEE (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 1
The output result of dynamic planning in ten unit energy grids
Strategy
Hour
S1 S2 S3 S 4 S5 S6
The initial state 2 2 2 2 2 2
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 3 3 3 3 3 3
6 4 4 4 4 4 4
7 4 4 4 4 4 4
8 9 9 9 9 9 9
9 9 9 9 9 9 9
10 9 9 9 9 9 9
11 10 10 10 10 10 10
12 10 10 10 10 10 10
13 10 10 10 10 10 10
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 9 9 9 9
21 4 4 4 4 9 9
22 3 3 4 4 6 9
23 3 3 4 4 6 7
24 2 3 4 5 6 7
Cost (USD) 8,555,398 8,554,182 8,554,502 8,557,153 8,557,192 8,557,932
ISSN 2074-272X. Електротехніка і Електромеханіка. 2019. №4 71
Table 2
The need of energy carriers in ten unit energy grids
Hour 1 2 3 4 5 6 7 8
Petroleum 3721.1 3721.1 3721.1 3721.1 3721.1 3721.1 3721.1 3721.1
Liquid gas –19.404 –12.28471.95407 16.1928123.3121837.55091 44.67028 51.78965
Fuel oil –647.68 –612.154–539.254–466.355–429.906–354.657 –365.265 –350.552
Gas oil –470.252–426.903–340.182–253.46 –210.1 –123.351 –61.1345 –11.7441
Kerosene –143.154–127.065–94.8888–62.7124–46.6241–14.4476 1.640607 17.72885
Gasoline –7.0615234.16357116.6137199.0639240.289 322.7392 363.9642 405.1893
Plane fuel 30.9534433.1644 37.5863242.0082444.2192 48.64113 50.85209 53.06305
Natural gas 2519.4152699.7963065.9593432.1233615.2043988.239 4190.728 4380.603
Coke gas 15.5181516.6265818.8434621.0603422.1687824.38566 25.4941 26.60254
Coal 34.2986736.7485741.6483846.5481948.9981 53.89791 56.34781 58.79772
Hour 9 10 11 12 13 14 15 16
Petroleum 3721.1 3721.1 3721.1 3721.1 3721.1 3721.1 3721.1 3721.1
Liquid gas 66.0283980.2671387.3865 94.5058680.2671366.02839 51.78965 30.43155
Fuel oil –275.591–198.861–158.969–135.511–198.861–275.868 –350.552 –459.901
Gas oil 75.0014 161.7678205.169 260.843 161.767874.99814 –11.7441 –141.826
Kerosene 49.9053382.0818 98.17004114.258382.0818 49.90533 17.72885 –30.5359
Gasoline 487.6395570.0897611.3148652.5398570.0897487.6395 405.1893 281.5141
Plane fuel 57.4849761.9068964.1178566.3288161.9068957.48497 53.06305 46.43017
Natural gas 4752.7985130.1685323.32 5531.0335130.1684751.988 4380.603 3831.358
Coke gas 28.8194131.0362932.1447333.2531731.0362928.81941 26.60254 23.27722
Coal 63.6975368.5973471.0472473.4971468.5973463.69753 58.79772 51.448
Hour 17 18 19 20 21 22 23 24
Petroleum 3721.1 3721.1 3721.1 3721.1 3721.1 3721.1 3721.1 3721.1
Liquid gas 23.3121837.5509151.7896580.2671366.0283937.55091 9.073439 –5.1653
Fuel oil –496.351–423.452–350.552–198.861–275.868–423.452 –548.095 –595.486
Gas oil –185.186–98.4652–11.7441161.767874.99813–98.4652 –277.548 –370.456
Kerosene –46.6241–14.447617.7288582.0818 49.90533–14.4476 –78.8006 –110.977
Gasoline 240.289 322.7392405.1893570.0897487.6395322.7392 157.8388 75.38865
Plane fuel 44.2192 48.6411353.0630561.9068957.4849748.64113 39.79728 35.37536
Natural gas 3648.2774014.44 4380.6035130.1684751.9884014.44 3278.051 2913.867
Coke gas 22.1687824.3856626.6025431.0362928.8194124.38566 19.9519 17.73502
Coal 48.9981 53.8979158.7977268.5973463.6975353.89791 44.09829 39.19848
Table 3
The electrical energy economical distribution within the energy grid
H
ou
r
U
ni
t 1
U
ni
t 2
U
ni
t 3
U
ni
t 4
U
ni
t 5
U
ni
t 6
U
ni
t 7
U
ni
t 8
U
ni
t 9
U
ni
t 1
0
1 420.9897 150 129.90540 0 0 0 0 0 0
2 455 165.9591130 0 0 0 0 0 0 0
3 455 266.087 130 0 0 0 0 0 0 0
4 455 366.2149130 0 0 0 0 0 0 0
5 455 416.2788130 0 0 0 0 0 0 0
6 455 455 130 61.406680 0 0 0 0 0
7 455 455 130 111.47060 0 0 0 0 0
8 454.5755 403.1555129.939520 25 78.9150125 10 54.949040
9 453.831 454.393 129.884740.5152425 79.9172725 38.19602 54.925220
10 454.8368 454.8779129.966 129.967525 79.9785575.69185 46.54565 54.990110
11 455 455 130 130 51.9821380 85 55 55 55
12 455 455 130 130 157.116480 85 55 55 55
13 455 455 130 130 25.8043580 85 55 55 31.11385
14 454.9096 455 130 130 25.1880380 25.09276 46.5999 55 0
15 446.8778 452.7482129.083420 25 42.3577225 10 50.467450
16 451.0978 260.4829129.572 20 25 75.6122625 10 54.577760
17 451.5645 209.902 129.481320 25 75.7485625 10 54.582480
18 455 401.2152130 20.1296325.0831580 25.04071 10.06585 55 0
19 455 455 130 130 25.1399780 25.03679 46.61355 55 0
20 453.5704 454.3342129.790670.7083525 79.8935325 10 53.365350
21 455 455 130 61.406680 0 0 0 0 0
22 455 316.1509130 0 0 0 0 0 0 0
23 455 216.023 130 0 0 0 0 0 0 0
24 455 216.023 130 0 0 0 0 0 0 0
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
72 ISSN 2074-272X. Електротехніка і Електромеханіка. 2019. №4
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.
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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
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