Planning of energy carriers based on final energy consumption using dynamic programming and particle swarm optimization
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...
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| Cite this: | 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 // Електротехніка і електромеханіка. — 2018. — № 5. — С. 62-71. — Бібліогр.: 16 назв. — англ. |
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Dehghani, M. Montazeri, Z. Ehsanifar, A. Seifi, A.R. Ebadi, M.J. Grechko, O.M. 2019-02-16T12:31:18Z 2019-02-16T12:31:18Z 2018 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 // Електротехніка і електромеханіка. — 2018. — № 5. — С. 62-71. — Бібліогр.: 16 назв. — англ. 2074-272X DOI: https://doi.org/10.20998/2074-272X.2018.5.10 https://nasplib.isofts.kiev.ua/handle/123456789/147961 621.3 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. 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 en Інститут технічних проблем магнетизму НАН України Електротехніка і електромеханіка Електричні станції, мережі і системи Planning of energy carriers based on final energy consumption using dynamic programming and particle swarm optimization Article published earlier |
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Planning of energy carriers based on final energy consumption using dynamic programming and particle swarm optimization |
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Planning of energy carriers based on final energy consumption using dynamic programming and particle swarm optimization Dehghani, M. Montazeri, Z. Ehsanifar, A. Seifi, A.R. Ebadi, M.J. Grechko, O.M. Електричні станції, мережі і системи |
| title_short |
Planning of energy carriers based on final energy consumption using dynamic programming and particle swarm optimization |
| title_full |
Planning of energy carriers based on final energy consumption using dynamic programming and particle swarm optimization |
| title_fullStr |
Planning of energy carriers based on final energy consumption using dynamic programming and particle swarm optimization |
| title_full_unstemmed |
Planning of energy carriers based on final energy consumption using dynamic programming and particle swarm optimization |
| title_sort |
planning of energy carriers based on final energy consumption using dynamic programming and particle swarm optimization |
| author |
Dehghani, M. Montazeri, Z. Ehsanifar, A. Seifi, A.R. Ebadi, M.J. Grechko, O.M. |
| author_facet |
Dehghani, M. Montazeri, Z. Ehsanifar, A. Seifi, A.R. Ebadi, M.J. Grechko, O.M. |
| topic |
Електричні станції, мережі і системи |
| topic_facet |
Електричні станції, мережі і системи |
| publishDate |
2018 |
| language |
English |
| container_title |
Електротехніка і електромеханіка |
| publisher |
Інститут технічних проблем магнетизму НАН України |
| format |
Article |
| description |
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.
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
|
| issn |
2074-272X |
| url |
https://nasplib.isofts.kiev.ua/handle/123456789/147961 |
| citation_txt |
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 // Електротехніка і електромеханіка. — 2018. — № 5. — С. 62-71. — Бібліогр.: 16 назв. — англ. |
| work_keys_str_mv |
AT dehghanim planningofenergycarriersbasedonfinalenergyconsumptionusingdynamicprogrammingandparticleswarmoptimization AT montazeriz planningofenergycarriersbasedonfinalenergyconsumptionusingdynamicprogrammingandparticleswarmoptimization AT ehsanifara planningofenergycarriersbasedonfinalenergyconsumptionusingdynamicprogrammingandparticleswarmoptimization AT seifiar planningofenergycarriersbasedonfinalenergyconsumptionusingdynamicprogrammingandparticleswarmoptimization AT ebadimj planningofenergycarriersbasedonfinalenergyconsumptionusingdynamicprogrammingandparticleswarmoptimization AT grechkoom planningofenergycarriersbasedonfinalenergyconsumptionusingdynamicprogrammingandparticleswarmoptimization |
| first_indexed |
2025-11-26T01:42:58Z |
| last_indexed |
2025-11-26T01:42:58Z |
| _version_ |
1850605617489641472 |
| fulltext |
Електричні станції, мережі і системи
62 ISSN 2074-272X. Електротехніка і Електромеханіка. 2018. №5
© M. Dehghani, Z. Montazeri, A. Ehsanifar, A.R. Seifi, M.J. Ebadi, O.M. Grechko
UDC 621.3 doi: 10.20998/2074-272X.2018.5.10
M. Dehghani, Z. Montazeri, A. Ehsanifar, A.R. Seifi, M.J. Ebadi, O.M. Grechko
PLANNING OF ENERGY CARRIERS BASED ON FINAL ENERGY CONSUMPTION
USING DYNAMIC PROGRAMMING AND PARTICLE SWARM OPTIMIZATION
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. References 16, tables 17, figures 1.
Key words: particle swarm optimization, final energy consumption, energy planning, energy carriers, dynamic programing.
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.
Библ. 16, табл. 17, рис. 1.
Ключевые слова: оптимизация методом роя частиц, конечное потребление энергии, планирование в энергетике,
энергоносители, динамическое программирование.
Introduction. One of the suitable criterions in
determining the development level and the life quality of
a typical country is the energy application. Both the
durance of energy presentation and the long term access
ability to sources require energy comprehensive planning.
One of the key issues of energy planning is energy
carriers.
Despite the present applied method, the energy
planning program needs the initial comprehensive study
of the energy system. It is possible to offer a general
framework to model different systems holding different
energy carriers like electrical, thermal, gas, etc. energies.
The mentioned modelling framework is based on the
energy-based approach. The energy-based main idea is
defining a converter matrix having the ability of
describing the generation, delivery and consumption
within systems carrying some types of energies [1]. Based
on the energy current optimization model, Cormio has
proposed a linear-based planning optimization model in a
region in south of Italy. This plan includes energy
optimization details of the energy initial sources, thermal
and electrical energies generation, transition and the
consumption section. The energy system optimization
model is introduced in [2] from the final energy
consumption level to the initial energy carriers that is
from down to up.
The global energy system is mainly based on applying
fossil fuels like coal, oil and natural gas. Although
renewable energy sources are under focused, their reliable
ability is low. Considering the lack of fossil sources,
transition to renewable energy sources by applying
hydrogen as the energy carrier is introduced [3]. This
economic transition includes uncertainty and it is
simultaneously introduced by the greenhouse gases effects.
By applying long-term planning, this energy substitution is
investigated and it is highly tried to supply proper hydrogen
or the energy carrier assessment in the future [4].
While renewable energies are introduced as the
energy initial carriers, the transportation industry is highly
dependent to oil energy carrier. Indeed, there is no simple
renewable solution to answer the transition section
demand. Today, biofuels along with electricity is
introduced as a main planning choice in replacing the
transportation fossil fuels [5].
Concerning the micro grid concept, the random
energy planning is introduced by taking the renewable
energy sources uncertainty and its oscillation entity.
Renewable energy sources which are known as initial
energy carriers are integral parts of a micro grid. The
oscillatory entity of these sources makes a micro grid
exploiting complex [6].
The common initial energy sources (the fossil fuels)
are limited and they need to be programmed considering
the renewable initial energy carriers. Considering the
planning present limitations, four dimensions known as
system, application, generation and technology terms can
be discussed. Indeed, the generation and exploiting initial
energy sources can be studied by considering the new
energy industry properties [7]. Accordingly, different
energy carriers are studied regarding their application
efficiency and abilities. Thus, energy carriers exploiting is
optimally done [8].
Different studies have been proposed by researchers
within the field of energy planning and management.
Therefore, in none of these studies, an hourly exploiting
of these energy carriers to supply the final energy
consumption is not investigated. In the present article, the
ultimate effort is done to exploit energy carriers by
neglecting energy carriers' independency. To implement
this planning, the proper energy grid is designed.
In the following, in section two, the present problem
is introduced. Then, in section three, the energy grid
modelling is analyzed. The particle swarm algorithm is
introduced in section four. Designing the proper energy
ISSN 2074-272X. Електротехніка і Електромеханіка. 2018. №5 63
grid to be used in energy studies is done in section five.
Section six simulates planning. Finally, discussion and
conclusion are studied in section seven.
Problem presentation. In planning energy initial
carriers, the lowest energy level that is the final energy
application is considered as the first level; then, different
energy losses and their converting are analyzed step by
step to determine the quantity of initial energy carriers in
order to supply the final energy consumption.
An important portion of the final energy use is
related to the electrical energy. In each hour of planning,
different modes of power stations can supply the
consumption of electrical energy. For each mode, the best
economic distribution among power stations must be
determined. Therefore, in each hour considering different
modes of power stations' combination, there are different
modes of energy carriers. Indeed, we are facing the power
station commitment problem. The only difference is that
instead of having different combinations of power
stations, we face with energy carriers different
combinations. Considering the study period and the grid
information, the proper combination is chosen by taking
the study period length into account.
The energy grid modelling. After compiling and
expanding the notion of the referent energy system in the
Brochain national laboratory, the energy system simulator
is developed. The matrix formulation main concept is to
cut the energy system vertically [9].
The energy grid matrix model starts from the lowest
energy level or the final energy consumption. Then, it
reaches the highest energy level or the initial energy carriers.
At first, the final energy consumption matrix is
defined as V1 matrix based on different sections. In this
case, there is
V2 = T1,2 V1, (1)
where V2 is the final energy consumption based on
different carriers and T1,2 is the consumption part to
carriers converter part.
Considering the energy consumption, distribution
and transition losses, the final energy consumption is
defined as
V3 = T2,3 V2, (2)
where V3 is the final energy consumption based on
different carriers considering losses, T2,3, is the transition,
distribution and consumption efficiency matrix.
To model the final electrical energy consumption,
the electrical supply shares of different power stations are
calculated by applying (3); then, the power stations input
fuels are measured by (4)
12 2,1 eee VTV , (3)
23 3,2 eee VTV , (4)
where Ve1 is the total generated electrical energy,
2,1e
T
stands for the separation matrix of the electrical energy
generation at different power stations, Ve2 is the electrical
energy generation of different power stations, Ve3 is
different power stations input fuel and
3,2eT is the power
stations efficiency matrix.
Besides, to compute the electrical energy generator
carriers (5) is used
34 4,3 eee VTV , (5)
where Ve4 is the electrical energy generator vectors and
4,3eT is the power stations’ input fuel separated from
different vectors input fuel matrix.
After simulating the electrical energy generation
process, the need for different vectors is computed by
considering the electrical energy generation
V4 = V3 + Ve4 – Ve, (6)
where V4 stands for the need for different vectors
considering the consumption, distribution and transition
losses of electrical energy generation, and Ve is the
generated electrical energy.
Some of these carriers are derived from refining
process. Therefore, it is necessary to simulate the
petroleum refinery; thus, (7) is used
12 ppp VTV , (7)
where
1pV is the refineries maximum capacity, Tp is the
share of each generated products of the petroleum
refinement, and
2pV shows the carriers generated by
refinement.
By using (8), the need for carriers can be computed
considering refinement
pp VVVV
245 , (8)
where Vp is the refined petroleum and V5 shows the need
for carriers after considering the electrical energy
generation losses and refinement.
Finally, the quantities of carriers' import and export
are determined by applying
V6 = V5 – P, (9)
where P is the national generation quantity of the initial
energy carriers; V6 is the initial energy carriers' import
and export. Noticeably, the positive sign represents
import and the negative sign shows the export.
In (3), in order to determine different power stations
shares of the electrical energy generation, it is necessary
to establish the economic distribution. To fulfill this aim,
the particle swarm optimization is used.
The particle swarm optimization. The particle
swarm optimization (PSO) was first introduced by Candy
and Aberheart [10]. After then, it was used in different
scientific and applied fields. PSO is a population based
optimization algorithm in which each person is
considered as a particle. These particles positions within
the search space determine the problem solution. Particles
can search the best position in cooperation with each
other. Particles' movements can be determined by
applying (10) and (11)
tvtxtx iii 1 , (10)
,
1
22
11
txtgbestrc
txtpbestrcwvtv
i
iiii
(11)
where xi(t) is the position, vi(t) is the i-th particle velocity
at t moment, pbesti(t) is the best position found by the i-th
particle, and gbest(t) is the best found position by the
whole population till t moment, w is the inertial
coefficient, c1 and c2 are the controlling parameters of
each particle and the whole population best effect on the
particles velocity and r1 and r2 are random numbers
within (1-0).
64 ISSN 2074-272X. Електротехніка і Електромеханіка. 2018. №5
Designing the energy grid suitable for studies.
Since the present study is novel, information related to the
proper energy grid is not accessible. Indeed, in this study,
the energy grid comprising the 24-hour final energy
consumption information is needed. Both Iran energy
balance sheet information [11] and the standard electrical
grid used in the power station commitment problem
studies are used.
The idea of designing the proper energy grid is
proposed based on the concept of the electrical energy
vital role. Indeed, some part of the final energy
consumption is related to the final electrical energy
consumption. In the energy balance sheet, there is no
information of the final energy use. However, it is clear
that the final energy consumption of different energies is
not independent of one another and the final energy
consumption of different energies is symmetric.
Considering the energy balance sheet, the final
energy consumption for a year is in Table 1.
Table 1
Different sections of the final energy consumption [11]
Sectors of energy Row
399.9 mboe Residential, commercial, general E1 1
188.2 mboe Industrial E2 2
254.3 mboe Transportation E3 3
33.4 mboe Agriculture E4 4
2.5 mboe Other E5 5
85.3 mboe Non-energy E6 6
9636.6 mboeTotal of final energy consumption Etf 7
79.7 mboe Final electrical energy consumption Eef 8
The final electrical energy consumption in the above
table is shown by Eef. It is known that considering the
electrical energy losses from generation till consumption
(consumption, distribution and transition losses) of power
stations must generate more electrical energies in order to
supply this quantity.
Concerning the final energy consumption, the
electrical final energy consumption in different power
stations is calculated as below
n
i
iief EaE
1
, (12)
665544332211 EaEaEaEaEaEaEef , (13)
where Eef is the final electrical energy consumption, n is
the number of different energy consumption power
stations, ai is the electrical final energy consumption
coefficient in the relation which is related to i-th final
energy consumption, and Ei is the i-th section final energy
consumption.
Considering losses of consumption, distribution and
transition of electrical energy, its consumption is
calculated by applying
ef
e
e EE
1
, (14)
where Ee is the electrical energy consumption; e is the
energy grid efficiency concerning losses of consumption,
distribution and transition of electrical energy.
In the next phase of designing, it is possible to
approximately compute the final energy per hour by
applying information related to the power station
commitment problem
V
E
load
V
e
n
h 1 , (15)
where hV1 is the designed final energy consumption,
loadn is the grid electrical energy quantity in h hour and V
is the balance sheet based final energy consumption for
the Ee electrical consumption quantity or Eef electrical
final energy consumption quantity.
Therefore, the 24-hour information of the final energy
consumption is computed. Although, this final characteristic
is approximately calculated and it might differ from the real
value, this information answers our energy study.
The energy grid information and designing by applying
ten power stations. In order to plan energies of initial energy
carriers, a ten power station system is proposed. The
electrical grid is derived from [12] reference. Information
related to the mentioned system is designed based on the
afore-said process. These data are attached to the same
paper. The maximum power station capacities equals to
3721.1 boe. It is necessary to mention that quantities related
to the power station capacity are chosen approximately and
in accordance with the energy balance sheet.
Simulation. Regarding the energy grid modelling,
the simulation trend can be represented as the followings:
1) defining parameters and converting matrices;
2) applying 3 to 10 steps for each hour of under
studied 24 hour span;
3) determining the final energy consumption;
4) determining the final energy consumption based on
different carriers;
5) determining the final energy consumption
considering the energy, distribution, transition and
consumption of energies;
6) determining possible combinations of power station
generators in order to supply the electrical energy;
7) the economic distribution of the electrical energy
among power station generators by means of the
optimization algorithm for all possible combinations;
8) the contribution of each carrier from the refining of
crude oil;
9) determine the need to provide energy to the final
energy consumption for each of the possible
combinations;
10) determining the import and export of energy
carriers regarding the national energy carriers presentation
for each possible combination;
11) determining the total request, import and export
values of the energy carriers in the whole under studied span
(24 hours) by means of the dynamic planning method.
The objective function. One important stage in
planning energy carriers is to distribute electrical energy
economically. The objective function of the electrical
energy economical distribution is introduced in (16). This
objective function can be solved using optimization
algorithms [13]
i
C
N
i
U
N
i
i
FIU
i
FIUobj CSCEF
DU
i
FIU
11
, (16)
FIU
k
IU
N
k
ji
N
j
i
FIU NiEeE
j
UDU
:1&
1
,
1
, (17)
ISSN 2074-272X. Електротехніка і Електромеханіка. 2018. №5 65
DUFIU NNji ETFe , , (18)
OU
U
IU EE
1
, (19)
where Fobj is the objective function, NFIU is the number of
different input fuels of power station generators, i
FIUE is
the sum of input energy to power stations of the i-th fuel
type, i
FIUC is the i-th type input fuel type cost of power
stations, NDU is the number of different fuel generators,
iUS stands for the i-th power station on or off position,
i
CC is the i-th power station constant costs, j
UN shows
the number of j-th power station generators within the
under studied energy grid, ei,j represents the i-th fuel share
coefficient from the j-th power station energy input, ETF
is the power station input energy matrix converting to
fuels appropriate with different power stations, EIU is the
power station input energy matrix, U shows power
stations efficiency vector and EOU stands for power
stations output electrical energy.
In the optimization algorithm, EOU is the power
stations generated electrical energy which is chosen as the
problem variables. Optimization limitations are defined as
below:
1) the load balance
;
1
N
i
i tDtP (20)
2) the upper and lower unit generations
iii PPP maxmin (21)
where N represents the number of units, Pi(t) shows the
i-th unit generated power at the t time, D(t) is the value of
electrical power request at t time, iPmin is the lower limit,
Pi manifests generation, and iPmax shows the i-th unit
upper limit.
The dynamic planning application. After distributing
the electrical energy in each hour of planning that is done
in appropriation with each possible energy division
among power stations, the planning trend continues'; thus,
energy carriers combinations parallel with power stations
combinations are concluded. By applying the dynamic
planning method, the proper strategy of energy carriers
planning is determined along with the study.
At K hour with I combination, the retrospective
algorithm of computing the minimum cost is defined as
LKF
IKLKSIKP
IKF
cost
costcost
L
cost ,1
,:,1,
min, , (22)
where Fcost(K,I) is the minimum total cost to arrive at the
(K,I) mode, Pcost(K,I) is the (K,I) mode cost and Scost(K–1,
L: K, I) shows the transition cost from (K–1, L) to (K,I)
mode. The (K,I) mode is the I combination at K hour [14].
The energy grid simulation with ten power stations.
The final energy consumption based planning of energy
carriers designed with ten power stations is implemented.
The dynamic planning is done by saving paths equal with
the number of each study hour maximum modes and its
results are shown in Table 2.
Table 3 holds the need for energy carriers in order to
provide final energy consumption. The need for energy
carriers of the total study period is determined in Table 4.
The economical distribution of electrical energy among
units is represented in Table 5. The optimization
algorithms access trend to the economical distribution of
the electrical energy is depicted in Fig .1. Besides,
considering the quantity of energy carriers national
representation, the value of carriers import and export
quantities are listed in Table 6.
Table 2
The output of dynamic planning in ten unit energy grids by means of PSO
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 (dollar) 8555398 8554182 8554502 8557153 8557192 8557932
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Table 3
The need for energy carriers in ten unit energy grids by means of PSO
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 –12.2847 1.95407 16.19281 23.31218 37.55091 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 –7.06152 34.16357 116.6137 199.0639 240.289 322.7392 363.9642 405.1893
Plane fuel 30.95344 33.1644 37.58632 42.00824 44.2192 48.64113 50.85209 53.06305
Natural gas 2519.415 2699.796 3065.959 3432.123 3615.204 3988.239 4190.728 4380.603
Coke gas 15.51815 16.62658 18.84346 21.06034 22.16878 24.38566 25.4941 26.60254
Coal 34.29867 36.74857 41.64838 46.54819 48.9981 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 66.02839 80.26713 87.3865 94.50586 80.26713 66.02839 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 161.7678 205.169 260.843 161.7678 74.99814 –11.7441 –141.826
Kerosene 49.90533 82.0818 98.17004 114.2583 82.0818 49.90533 17.72885 –30.5359
Gasoline 487.6395 570.0897 611.3148 652.5398 570.0897 487.6395 405.1893 281.5141
Plane fuel 57.48497 61.90689 64.11785 66.32881 61.90689 57.48497 53.06305 46.43017
Natural gas 4752.798 5130.168 5323.32 5531.033 5130.168 4751.988 4380.603 3831.358
Coke gas 28.81941 31.03629 32.14473 33.25317 31.03629 28.81941 26.60254 23.27722
Coal 63.69753 68.59734 71.04724 73.49714 68.59734 63.69753 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 23.31218 37.55091 51.78965 80.26713 66.02839 37.55091 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 –11.7441 161.7678 74.99813 –98.4652 –277.548 –370.456
Kerosene –46.6241 –14.4476 17.72885 82.0818 49.90533 –14.4476 –78.8006 –110.977
Gasoline 240.289 322.7392 405.1893 570.0897 487.6395 322.7392 157.8388 75.38865
Plane fuel 44.2192 48.64113 53.06305 61.90689 57.48497 48.64113 39.79728 35.37536
Natural gas 3648.277 4014.44 4380.603 5130.168 4751.988 4014.44 3278.051 2913.867
Coke gas 22.16878 24.38566 26.60254 31.03629 28.81941 24.38566 19.9519 17.73502
Coal 48.9981 53.89791 58.79772 68.59734 63.69753 53.89791 44.09829 39.19848
Table 4
The need for different energy carriers within the total study period of the energy grid
Row Carrier Energy
1 Petroleum 89306.4
2 Liquid gas 1000.893
3 Fuel oil –9132.05
4 Gas oil –1916.25
5 Kerosene –121.508
6 Gasoline 8322.891
7 Plane fuel 1198.34
8 Natural gas 98855.34
9 Coke gas 600.7739
10 Coal 1327.848
Table 5
The electrical energy economical distribution within the energy grid by utilizing PSO
H
ou
r
un
it
1
un
it
2
un
it
3
un
it
4
un
it
5
un
it
6
un
it
7
un
it
8
un
it
9
un
it
10
O
F
1 420.9897 150 129.90540 0 0 0 0 0 0 66339.36
2 455 165.9591 130 0 0 0 0 0 0 0 71199.98
3 455 266.087130 0 0 0 0 0 0 0 81353.53
4 455 366.2149 130 0 0 0 0 0 0 0 91507.08
5 455 416.2788 130 0 0 0 0 0 0 0 96583.85
6 455 455 130 61.406680 0 0 0 0 0 107287.4
7 455 455 130 111.47060 0 0 0 0 0 113108.7
8 454.5755 403.1555 129.939520 25 78.9150125 10 54.94904 0 118165.9
9 453.831 454.393129.884740.5152425 79.9172725 38.1960254.92522 0 128802.2
10 454.8368 454.8779 129.966129.967525 79.9785575.6918546.5456554.99011 0 139852.8
11 455 455 130 130 51.9821380 85 55 55 55 145735.7
12 455 455 130 130 157.116480 85 55 55 55 152855.1
13 455 455 130 130 25.8043580 85 55 55 31.11385 139852.8
14 454.9096 455 130 130 25.1880380 25.0927646.5999 55 0 128737.4
15 446.8778 452.7482 129.083420 25 42.3577225 10 50.46745 0 118165.9
16 451.0978 260.4829 129.57220 25 75.6122625 10 54.57776 0 102935.5
17 451.5645 209.902129.481320 25 75.7485625 10 54.58248 0 97858.76
18 455 401.2152 130 20.1296325.0831580 25.0407110.0658555 0 108012.3
19 455 455 130 130 25.1399780 25.0367946.6135555 0 118165.9
20 453.5704 454.3342 129.790670.7083525 79.8935325 10 53.36535 0 139852.8
21 455 455 130 61.406680 0 0 0 0 0 128737.4
22 455 316.1509 130 0 0 0 0 0 0 0 108012.3
23 455 216.023130 0 0 0 0 0 0 0 87907.97
24 455 216.023130 0 0 0 0 0 0 0 78494.37
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Table 6
Import and export of carriers
Row Carrier Export Import
1 Petroleum 163006 0
2 Liquid gas 0 1000.893
3 Fuel oil 9132.05 0
4 Gas oil 1916.25 0
5 Kerosene 121.508 0
6 Gasoline 0 8322.891
7 Plane fuel 0 1198.34
8 Natural gas 0 2221.738
9 Coke gas 37.6261 0
10 Coal 0 367.8484
Iteration (a)
Hours=1 State=3
Iteration (b)
Hours=2 State=3
Iteration (c)
Hours=3 State=3
Iteration (d)
Hours=4 State=3
Iteration (e)
Hours=5 State=3
Iteration (f)
Hours=6 State=4
Iteration (g)
Hours=7 State=6
Iteration (h)
Hours=8 State=9
Iteration (i)
Hours=9 State=9
Iteration (j)
Hours=10 State=9
Iteration (k)
Hours=11 State=9
Iteration (l)
Hours=12 State=9
68 ISSN 2074-272X. Електротехніка і Електромеханіка. 2018. №5
Iteration (m)
Hours=13 State=9
Iteration (n)
Hours=14 State=9
Iteration (o)
Hours=15 State=9
Iteration (p)
Hours=16 State=9
Iteration (q)
Hours=17 State=9
Iteration (r)
Hours=18 State=9
Iteration (s)
Hours=19 State=9
Iteration (t)
Hours=20 State=9
Iteration (u)
Hours=21 State=9
Iteration (v)
Hours=22 State=9
Iteration (w)
Hours=23 State=9
Iteration (x)
Hours=24 State=9
Fig. 1. The access trend to the electrical energy economical distribution\ within the energy grid by applying PSO
Discussion and conclusion. In the present article, a
new approach in energy studies was introduced. In this
view, the maximum effort was made to arrive at the
suitable planning of energy carriers based on the final
energy consumption. This planning was done such that it
showed energy carriers beside each other as a system and
neglected their planning independent view.
The energy grid modeling started from the lowest
energy level of the final energy consumption and went to
the highest level of the energy initial carriers step by step
in a matrix shape. In this modelling, some factors like the
energy grid losses, the electrical energy distribution
among units, and the petroleum refinement were taken
into account. After a matrix form energy grid modelling,
the energy grid was designed based on the 24 hour
information of Iran energy balance sheet and the standard
electrical grid since there was no available authentic
information of energy grid.
In the proposed planning, the dynamic planning
method and the particle swarm optimization algorithm
were used. Indeed, particle swarm optimization algorithm
was used along with the electrical energy economical
ISSN 2074-272X. Електротехніка і Електромеханіка. 2018. №5 69
distribution; hence, the dynamic planning program was
utilized in order to access the proper strategy of mixing
energy carriers along with the study period.
The proposed planning done on the authentic-based
designed energy grid was implemented and its results
were represented.
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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
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(KBEI). doi: 10.1109/kbei.2017.8324975.
14. Wood A.J., Wollenberg B.F. Power generation, operation,
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15. IEA Publications, rue de la Fédération, 75739 Paris Cedex
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Received 14.06.2018
M. Dehghani1, Candidate of Power Engineering, M.Sc.,
Z. Montazeri2, Candidate of Power Engineering, M.Sc. Student,
A. Ehsanifar1, Candidate of Power Engineering, M.Sc.,
A.R. Seifi1, Doctor of Power Engineering, Professor,
M.J. Ebadi3, Doctor of Applied Mathematics, Assistant
Professor,
O.M. Grechko4, Candidate of Technical Science, Associate
Professor,
1 Department of Power and Control,
Shiraz University,
Shiraz, I.R. Iran,
e-mail: adanbax@gmail.com, ali.ehsanifar2020@gmail.com,
seifi@shirazu.ac.ir
2 Department of Electrical Engineering,
Islamic Azad University of Marvdasht,
Marvdasht, I.R. Iran,
e-mail: Z.montazeri2002@gmail.com
3 Faculty of Marine Science,
Chabahar Maritime University,
Chabahar, Iran,
e-mail: ebadi@cmu.ac.ir
4 National Technical University «Kharkiv Polytechnic Institute»,
2, Kyrpychova Str., Kharkiv, 61002, Ukraine,
e-mail: a.m.grechko@gmail.com
Appendix (Tables A.1–A.11)
Table A.1. Unit Information
Capacity of
unit (MW)
R
ow
Power plant
Min Max
E
ff
ic
ie
nc
y
C
on
st
an
t
co
st
P
ri
or
it
y
1 Thermal 150 455 0.368 1 1
2 Thermal 150 455 0.345 2 2
3 Combined Cycle 20 130 0.455 3 3
4 Thermal 20 130 0.317 4 4
5 Gas 25 162 0.3 5 5
6 Combined Cycle 20 80 0.47 6 6
7 Thermal 25 85 0.35 7 7
8 Thermal 10 55 0.35 8 8
9 Combined Cycle 10 55 0.5 9 9
10 Gas 10 55 0.25 10 10
Table A.2. The time information of energy networks
R
ow
Power plant Cold start Initial conditions
1 Thermal 8 8 5 8
2 Thermal 8 8 5 8
3 Combined Cycle 5 5 4 –5
4 Thermal 5 5 4 –5
5 Gas 6 6 4 –6
6 Combined Cycle 3 3 2 –3
7 Thermal 3 3 2 –3
8 Thermal 1 1 0 –1
9 Combined Cycle 1 1 0 –1
10 Gas 1 1 0 –1
70 ISSN 2074-272X. Електротехніка і Електромеханіка. 2018. №5
Table A.3. The cost of setting up units
Row Power plant Hot start Cold start
1 Thermal unit 4500 9000
2 Thermal unit 5000 10000
3 Combined Cycle unit 550 1100
4 Thermal unit 560 1120
5 Gas unit 900 1800
6 Combined Cycle unit 170 340
7 Thermal unit 260 520
8 Thermal unit 30 60
9 Combined Cycle unit 30 60
10 Gas unit 30 60
Table A.4. Matrix Tp
Petroleum 0
Liquid gas 0.032
Fuel oil 0.293
Gas oil 0.293
Kerosene 0.099
Gasoline 0.157
Plane fuel 0
Other products 0.058
Natural gas 0
Coke gas 0
Coal 0
Non-commercial fuels 0
Electricity(power) 0
Table A.5. Conversion matrix input energy to power plants
Power plant Thermal unit Combined Cycle unit Gas unit
Fuel oil 0.254 0 0
Gas oil 0.003 0.082 0.166
Natural gas 0.743 0.918 0.834
Table A.6. Domestic supplies of energy carriers
Row Energy carrier Energy (boe)
1 Petroleum 10513
2 Liquid gas 0
3 Fuel oil 0
4 Gas oil 0
5 Kerosene 0
6 Gasoline 0
7 Plane fuel 0
8 Other products 0
9 Natural gas 4026.4
10 Coke gas 26.6
11 Coal 40
12 Non-commercial fuels 161
13 Electricity (power) 0
Table A.7. Heating value[15] and energy rates[16]
Energy carrier Heating value Energy rates
Petroleum 38.5 MJ/lit 48 dollar/boe
Liquid gas 46.15 MJ/kg 374 dollar/tone
Fuel oil 42.18 MJ/kg 180 dollar/tone
Gas oil 43.38 MJ/kg 350 dollar/tone
Kerosene 43.32 MJ/kg 500 dollar/tone
Gasoline 44.75 MJ/kg 450 dollar/tone
Plane fuel 45.03 MJ/kg 555 dollar/tone
Natural gas 39 MJ/m3 237 dollar/1000m3
Coke gas 16. 9 MJ/kg 157 dollar/tone
Coal 26.75 MJ/kg 61 dollar/tone
Table A.8. Electrical load demand
Hour 1 2 3 4
Load 700 750 850 950
Hour 5 6 7 8
Load 1000 1100 1150 1200
Hour 9 10 11 12
Load 1300 1400 1450 1500
Hour 13 14 15 16
Load 1400 1300 1200 1050
Hour 17 18 19 20
Load 1000 1100 1200 1400
Hour 21 22 23 24
Load 1300 1100 900 800
Table A.9. Final energy consumption
Hour 1 2 3 4 5 6 7 8
Residential, commercial, general 1570.19 1682.347 1906.66 2130.973 2243.129 2467.442 2579.599 2691.755
Industrial 738.9593 791.7421 897.3078 1002.873 1055.656 1161.222 1214.005 1266.787
Transportation 998.4982 1069.819 1212.462 1355.105 1426.426 1569.069 1640.39 1711.711
Agriculture 131.1437 140.5111 159.2459 177.9807 187.3481 206.0829 215.4503 224.8177
Other 9.816144 10.5173 11.9196 13.32191 14.02306 15.42537 16.12652 16.82768
Non-energy 334.9268 358.8502 406.6969 454.5436 478.4669 526.3136 550.2369 574.1603
Hour 9 10 11 12 13 14 15 16
Residential, commercial, general 2916.068 3140.381 3252.537 3364.694 3140.381 2916.068 2691.755 2355.286
Industrial 1372.353 1477.919 1530.701 1583.484 1477.919 1372.353 1266.787 1108.439
Transportation 1854.354 1996.996 2068.318 2139.639 1996.996 1854.354 1711.711 1497.747
Agriculture 243.5526 262.2874 271.6548 281.0222 262.2874 243.5526 224.8177 196.7155
Other 18.22998 19.63229 20.33344 21.03459 19.63229 18.22998 16.82768 14.72422
Non-energy 622.007 669.8537 693.777 717.7004 669.8537 622.007 574.1603 502.3903
Hour 17 18 19 20 21 22 23 24
Residential, commercial, general 2243.129 2467.442 2691.755 3140.381 2916.068 2467.442 2018.816 1794.503
Industrial 1055.656 1161.222 1266.787 1477.919 1372.353 1161.222 950.0906 844.5249
Transportation 1426.426 1569.069 1711.711 1996.996 1854.354 1569.069 1283.783 1141.141
Agriculture 187.3481 206.0829 224.8177 262.2874 243.5526 206.0829 168.6133 149.8785
Other 14.02306 15.42537 16.82768 19.63229 18.22998 15.42537 12.62076 11.21845
Non-energy 478.4669 526.3136 574.1603 669.8537 622.007 526.3136 430.6202 382.7735
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Table A.10. Matrix T12
Residential and commercial Industrial Transportation Agriculture Other Non-energy
Petroleum 0 0 0 0 0 0
Liquid gas 0.051 0.013 0.01 0 0 0
Fuel oil 0.023 0.212 0.014 0 0 0
Gas oil 0.055 0.087 0.363 0.689 0 0
Kerosene 0.141 0.002 0 0.018 0 0
Gasoline 0.002 0.002 0.573 0.003 0 0
Plane fuel 0 0 0.031 0 0 0
Other products 0 0 0 0 0 0.402
Natural gas 0.564 0.521 0.007 0 0 0.497
Coke gas 0 0.021 0 0 0 0
Coal 0.0003 0 0 0 0 0.101
Non-commercial fuels 0.064 0 0 0 0 0
Electricity(power) 0.102 0.142 0.0004 0.29 1 0
Table A.11. Matrix T23
Petroleum 1 0 0 0 0 0 0 0 0 0 0 0 0
Liquid gas 0 1 0 0 0 0 0 0 0 0 0 0 0
Fuel oil 0 0 1 0 0 0 0 0 0 0 0 0 0
Gas oil 0 0 0 1 0 0 0 0 0 0 0 0 0
Kerosene 0 0 0 0 1 0 0 0 0 0 0 0 0
Gasoline 0 0 0 0 0 1 0 0 0 0 0 0 0
Plane fuel 0 0 0 0 0 0 1 0 0 0 0 0 0
Other products 0 0 0 0 0 0 0 1 0 0 0 0 0
Natural gas 0 0 0 0 0 0 0 0 1.1601 0 0 0 0
Coke gas 0 0 0 0 0 0 0 0 0 1 0 0 0
Coal 0 0 0 0 0 0 0 0 0 0 1 0 0
Non-commercial fuels 0 0 0 0 0 0 0 0 0 0 0 1 0
Electricity(power) 0 0 0 0 0 0 0 0 0 0 0 0 1.3158
|