CC BY
85
Power Stations, Grids and Systems
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
© M. Dehghani, Z. Montazeri, A. Ehsanifar, A.R. Seifi, M.J. Ebadi, O.M. Grechko
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 = Tu x Vu (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 x 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)
Vg4 = . xVe
e3
(5)
Ve2 = xVe
e2
Ve3 = Te__ XVe
el
e2
(3)
(4)
where Ve4 is the electrical energy generator vectors and Te34 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
(7)
Vp = Tp x V p P2 p Pi
where Ve1 is the total generated electrical energy, Te1,2
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 Te2,3 is the power
stations efficiency matrix.
Besides, to compute the electrical energy generator carriers (5) is used
where Vpi is the refineries maximum capacity, TP is the
share of each generated products of the petroleum refinement, and V P2 shows the carriers generated by
refinement.
By using (8), the need for carriers can be computed considering refinement
V5 = V4 - Vp2 + Vp, (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)
x, (t +1)= x, (t) + v, (t , (10)
v, (t +1) = wvj + cr (pbestj (t( - xj (t() + + C2^2 tgbest(t)- x, (t)), where x,(t) is the position, v,(t) is the i-th particle velocity at t moment, pbest(i) 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).
(11)
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]
Row Sectors of energy
1 E1 Residential, commercial, general 399.9 mboe
2 E2 Industrial 188.2 mboe
3 E3 Transportation 254.3 mboe
4 E4 Agriculture 33.4 mboe
5 E5 Other 2.5 mboe
6 E6 Non-energy 85.3 mboe
7 Ef Total of final energy consumption 9636.6 mboe
8 Eef Final electrical energy consumption 79.7 mboe
E
ef
=XaEi
(12)
i=1
Ee =■
-E.
Ve
ef ,
(14)
Vh =-
loadn
Ee
-V ,
(15)
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
Eef = aiEi + a2E2 + a3E3 + a4E4 + a5E5 + a6E6 , (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 Et is the i-th section final energy consumption.
Considering losses of consumption, distribution and transition of electrical energy, its consumption is calculated by applying
1
where Ee is the electrical energy consumption; r 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
where V1 is the designed final energy consumption, load" 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 to10 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]
Fo
obj
= X EFIUCFIU + X SUCC ,
i=1
i=1
Ndu nu
E'fiu = X X e, jEU & i = 1: Nfiu j=1 k=1
(16)
(17)
eU j e
[etf]
nfu xn
FIU DU
1
(18) (19)
EIU --EOU ,
Vu
where Fobj is the objective function, NFIU is the number of different input fuels of power station generators, E'FIU is the sum of input energy to power stations of the i-th fuel type, C'FIU is the i-th type input fuel type cost of power stations, NDU is the number of different fuel generators, SU, stands for the i-th power station on or off position,
CCi is the i-th power station constant costs, NUj shows
the number of j-th power station generators within the under studied energy grid, e,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, Vu 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
Fc,
tK,I)-^
(22)
N
I P K)-D{f )
i-1
2) the upper and lower unit generations
P< P < P
1 min — 1 — 1 max
(20)
(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, Pmin is the lower limit,
Pi manifests generation, and Pmi ax 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 rPcost fK,I)+ Scost K -1,L : K,I)+"
_+ Fcost K -1, L) _
where Fcost(K,I) is the minimum total cost to arrive at the (K,I) mode, Pcos(K,I) is the (KI) mode cost and Scos(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
S6 S5 S 4 S3 S2 S1
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
10 10 10 10 10 10 11
10 10 10 10 10 10 12
10 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
8557932 8557192 8557153 8554502 8554182 8555398 Cost (dollar)
Table 3
The need for energy carriers in ten unit energy grids by means of PSO
8 7 6 5 4 3 2 1 Hour
3721.1 3721.1 3721.1 3721.1 3721.1 3721.1 3721.1 3721.1 Petroleum
51.78965 44.67028 37.55091 23.31218 16.19281 1.95407 -12.2847 -19.404 Liquid gas
-350.552 -365.265 -354.657 -429.906 -466.355 -539.254 -612.154 -647.68 Fuel oil
-11.7441 -61.1345 -123.351 -210.1 -253.46 -340.182 -426.903 -470.252 Gas oil
17.72885 1.640607 -14.4476 -46.6241 -62.7124 -94.8888 -127.065 -143.154 Kerosene
405.1893 363.9642 322.7392 240.289 199.0639 116.6137 34.16357 -7.06152 Gasoline
53.06305 50.85209 48.64113 44.2192 42.00824 37.58632 33.1644 30.95344 Plane fuel
4380.603 4190.728 3988.239 3615.204 3432.123 3065.959 2699.796 2519.415 Natural gas
26.60254 25.4941 24.38566 22.16878 21.06034 18.84346 16.62658 15.51815 Coke gas
58.79772 56.34781 53.89791 48.9981 46.54819 41.64838 36.74857 34.29867 Coal
16 15 14 13 12 11 10 9 Hour
3721.1 3721.1 3721.1 3721.1 3721.1 3721.1 3721.1 3721.1 Petroleum
30.43155 51.78965 66.02839 80.26713 94.50586 87.3865 80.26713 66.02839 Liquid gas
-459.901 -350.552 -275.868 -198.861 -135.511 -158.969 -198.861 -275.591 Fuel oil
-141.826 -11.7441 74.99814 161.7678 260.843 205.169 161.7678 75.0014 Gas oil
-30.5359 17.72885 49.90533 82.0818 114.2583 98.17004 82.0818 49.90533 Kerosene
281.5141 405.1893 487.6395 570.0897 652.5398 611.3148 570.0897 487.6395 Gasoline
46.43017 53.06305 57.48497 61.90689 66.32881 64.11785 61.90689 57.48497 Plane fuel
3831.358 4380.603 4751.988 5130.168 5531.033 5323.32 5130.168 4752.798 Natural gas
23.27722 26.60254 28.81941 31.03629 33.25317 32.14473 31.03629 28.81941 Coke gas
51.448 58.79772 63.69753 68.59734 73.49714 71.04724 68.59734 63.69753 Coal
24 23 22 21 20 19 18 17 Hour
3721.1 3721.1 3721.1 3721.1 3721.1 3721.1 3721.1 3721.1 Petroleum
-5.1653 9.073439 37.55091 66.02839 80.26713 51.78965 37.55091 23.31218 Liquid gas
-595.486 -548.095 -423.452 -275.868 -198.861 -350.552 -423.452 -496.351 Fuel oil
-370.456 -277.548 -98.4652 74.99813 161.7678 -11.7441 -98.4652 -185.186 Gas oil
-110.977 -78.8006 -14.4476 49.90533 82.0818 17.72885 -14.4476 -46.6241 Kerosene
75.38865 157.8388 322.7392 487.6395 570.0897 405.1893 322.7392 240.289 Gasoline
35.37536 39.79728 48.64113 57.48497 61.90689 53.06305 48.64113 44.2192 Plane fuel
2913.867 3278.051 4014.44 4751.988 5130.168 4380.603 4014.44 3648.277 Natural gas
17.73502 19.9519 24.38566 28.81941 31.03629 26.60254 24.38566 22.16878 Coke gas
39.19848 44.09829 53.89791 63.69753 68.59734 58.79772 53.89791 48.9981 Coal
Table 4
The need for different energy carriers within the total study period of the energy grid
Energy Carrier Row
89306.4 Petroleum 1
1000.893 Liquid gas 2
-9132.05 Fuel oil 3
-1916.25 Gas oil 4
-121.508 Kerosene 5
8322.891 Gasoline 6
1198.34 Plane fuel 7
98855.34 Natural gas 8
600.7739 Coke gas 9
1327.848 Coal 10
Table 5
9 8 7 6 5 4 3 2 1 r
O hS
66339.36 0 0 0 0 0 0 0 129.9054 150 420.9897 1
71199.98 0 0 0 0 0 0 0 130 165.9591 455 2
81353.53 0 0 0 0 0 0 0 130 266.087 455 3
91507.08 0 0 0 0 0 0 0 130 366.2149 455 4
96583.85 0 0 0 0 0 0 0 130 416.2788 455 5
107287.4 0 0 0 0 0 0 61.40668 130 455 455 6
113108.7 0 0 0 0 0 0 111.4706 130 455 455 7
118165.9 0 54.94904 10 25 78.91501 25 20 129.9395 403.1555 454.5755 8
128802.2 0 54.92522 38.19602 25 79.91727 25 40.51524 129.8847 454.393 453.831 9
139852.8 0 54.99011 46.54565 75.69185 79.97855 25 129.9675 129.966 454.8779 454.8368 10
145735.7 55 55 55 85 80 51.98213 130 130 455 455 11
152855.1 55 55 55 85 80 157.1164 130 130 455 455 12
139852.8 31.11385 55 55 85 80 25.80435 130 130 455 455 13
128737.4 0 55 46.5999 25.09276 80 25.18803 130 130 455 454.9096 14
118165.9 0 50.46745 10 25 42.35772 25 20 129.0834 452.7482 446.8778 15
102935.5 0 54.57776 10 25 75.61226 25 20 129.572 260.4829 451.0978 16
97858.76 0 54.58248 10 25 75.74856 25 20 129.4813 209.902 451.5645 17
108012.3 0 55 10.06585 25.04071 80 25.08315 20.12963 130 401.2152 455 18
118165.9 0 55 46.61355 25.03679 80 25.13997 130 130 455 455 19
139852.8 0 53.36535 10 25 79.89353 25 70.70835 129.7906 454.3342 453.5704 20
128737.4 0 0 0 0 0 0 61.40668 130 455 455 21
108012.3 0 0 0 0 0 0 0 130 316.1509 455 22
87907.97 0 0 0 0 0 0 0 130 216.023 455 23
78494.37 0 0 0 0 0 0 0 130 216.023 455 24
Table 6
Import and export of carriers_
Import Export Carrier Row
0 163006 Petroleum 1
1000.893 0 Liquid gas 2
0 9132.05 Fuel oil 3
0 1916.25 Gas oil 4
0 121.508 Kerosene 5
8322.891 0 Gasoline 6
1198.34 0 Plane fuel 7
2221.738 0 Natural gas 8
0 37.6261 Coke gas 9
367.8484 0 Coal 10
x 10
Hours=1 State=3
x 10
Hours=2 State=3
x 10
Hours=3 State=3
x 10
Hours=4 State=3
1 10 20 30 40 50
Iteration (d)
S.18
8.16
=
№
8.14
! 10 20 30 40 50
Iteration (a)
1 10 20 30 40 50
Iteration (b)
X 10
Hours=5 State=3
1 10 20 30 40 50
Iteration (c)
x 10 Hours=6 State=4
9.67
a 9.66
9.65
1.075
u
- 1.074
№
1.073
1.072
1 10 20 30 40 50
Iteration (e)
1 10 20 30 40 50
Iteration f)
x [ Q- Hours=7 State=6
X105 Hours=8 State=9
1.17
tt 1.16
e 1 is
№
1.14
1.13
S;
1.22 1.21
= I 9 1.19
1.34 ¡2 1-32
u
u
s —
1.3
1 10 20 30 40 50
Iteration (g)
1 10 20 30 40 50
Iteration (h)
1 10 20 30 40 50
Iteration (i)
1.44
1.43
ft
- 1.42
№
1.41
1.4
xlO5 Hours=10 State=9
X 10
Hours=11 State=9
1.5
1.49
| 1.48 №
1.47 1.46
xlO5 Hours=12 State=9
1 10 20 30 40 50
Iteration (j)
1 10 20 30 40 50
Iteration (k)
1 10 20 30 40 50
Iteration (l)
Hours=13 State=9
Hours=14 State=9
xlO5 Hours=15 State=9
1 10 20 30 40 5C
Iteration (m)
x!05 Hours=16 State=9
1 10 20 30 40 50 1 10 20 30 40 50
Iteration (n) Iteration (o)
x 105 Hours=17 State=9 x105 Hours=18 State=9
1 10 20 30 40 50
Iteration (p)
1.08
1 10 20 30 40 50 1 10 20 30 40 50
Iteration (q) Iteration (r)
X 10J
Hours=19 State=9
X 10
Hours=20 State=9
x 10
Hours=21 State=9
1 10 20 30 40 50 Iteration (s)
1 10 20 30 40 50
Iteration (f)
10 20 30 40 50
Iteration (u)
x ] q5 Hours=22 State=9
X 10
Hours=23 State=9
x^04 Hours=24 State=9
1 10 20 30 40 50
Iteration (v)
1 10 20 30 40 50 Iteration (w)
1 10 20 30 40 50
Iteration (x)
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
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.
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., Duic 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. 18761883. 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. IEEE Service Center, 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 de la 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).
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: [email protected], [email protected], [email protected]. ir
2 Department of Electrical Engineering, Islamic Azad University of Marvdasht, Marvdasht, I.R. Iran,
e-mail: [email protected]
3 Faculty of Marine Science, Chabahar Maritime University, Chabahar, Iran,
e-mail: [email protected]
4 National Technical University «Kharkiv Polytechnic Institute», 2, Kyrpychova Str., Kharkiv, 61002, Ukraine,
e-mail: [email protected]
Table A.1. Unit Information
Capacity of unit (MW) Constant cost
s o m Power plant Min Max Ü3 W s^ O 'C Ph
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
*
o ai Power plant MUT MDT 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
Appendix (Tables A.1-A.11)
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
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
How to cite this article:
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.
