No. E-16-EEM-1119
Planning of energy carriers based on final energy
consumption using dynamic programing and particle
swarm optimization
Mohammad Dehghani, Saeed Dehbozorgi, and Alireza Seifi
Shiraz University
Electrical and Computer Engineering
Shiraz, Iran
Abstract— In this paper, a new approach in studies of energy
network in order to the planning of energy carriers is introduced.
In this attitude, a proper planning according to the final energy
consumption is done on the use of energy carriers. Suitable energy
network is designed using data from Iran's energy balance sheet.
Matrix modeling of energy network is done. Distribution of
electrical energy between units is determined using particle swarm
optimization. The right combination of energy carriers is obtain
using dynamic programming.
Keywords— particle swarm optimization, final energy
consumption, energy planning, energy carriers, dynamic
programing.
I.
INTRODUCTION
Energy consumption is one of the criteria for determining
the level of development and quality of life in a country.
Continuity of energy supply and ensuring long-term access to
resources have required a comprehensive energy plan. Energy
carriers are one of the key topics in the field of energy planning.
Regardless of the method used, energy planning requires
detailed study of the energy system. A general framework for
modeling energy systems include multiple carriers, such as
electricity, heating, gas and other items can offered. The
modeling framework is dependent on Energy Center strategy.
The main notion of Energy Center is determined a
transformation matrix with the ability to describe the interaction
of production, delivery and consumption in multi-energy carrier
systems [1]. Cormio proposed a linear programming
optimization model predicated on flow of energy adopted
optimization model within an area in the south of Italy. This
model include information of energy extraction from primary
resources of energy, electricity and heating generation,
transmission and last consumption. In this paper, an
optimization model for energy system provided which is
bottom-up and supports planning policies to promote the use of
renewable energy sources [2].
The global energy system based mostly on the use of fossil
fuels, coal, oil, and natural gas. Due to the shortage of fossil
fuels, the transition to renewable energy systems with hydrogen
as an energy carrier, is expressed in [3]. This transition
economies, includes uncertainty. An appropriate assessment
has been carried out from Hydrogen as an energy carrier in the
future using long-term planning [4]. Although renewable
energy is considered as primary carrier of energy. Today, one
of the key options planned to replace fossil fuels in the transport
sector is biofuels with electricity offer [5].
Based on the micro grid concept, energy planning randomly
according to uncertainty and fluctuating character of renewable
energy resources arises. Renewable energy sources (which is a
type of primary energy carriers) are an important component of
a micro grid. The fluctuating nature of the resources
complicates the operation of the micro grid [6].
Conventional primary energy sources (fossil fuels) are
limited and must be a proper planning done due to the presence
of renewable primary energy carriers. Due to restrictions in the
planning, four aspects: system, consumption, production and
technology reforms are debatable. In fact, modified production
and utilization of primary energy sources considering the
features of the new energy industry are expressible [7].
Accordingly, different energy carriers compare in terms of
efficiency and the ability to exploit. Thus, energy carriers can
be optimally utilized [8].
Many studies have been done on planning and energy
management. However, in none of these investigations not
provided comprehensive planning in the operation of energy
carriers.
In this article, a comprehensive planning is introduced in the
operation of energy network. This planning will cover the
following:
1. The relationship between energy carriers.
2. Final energy consumption in various sectors.
Planning of energy carriers based on final energy consumption using dynamic programing and particle swarm optimization
31th Power System Conference - 2016 Tehran, Iran
3. Unit commitment according to efficiency and fuel type of
plants.
Where , is the final energy consumption by different
is the transmission,
carriers considering losses, and
,
distribution and energy consumption efficiency matrix.
In order to model the final consumption of electrical
energy, the share of various power plants of electric energy
supply, determines by (3). Then the input fuel to power plants
will be counted by (4).
(3)
,
4. The efficiency of energy networks.
5. Refining of energy carriers.
6. Import and export of energy carriers.
7. The different strategies to the operation of energy
resources.
This paper is organized as follows: Section 2 discusses the
expression of the problem. In Section 3, the modeling of energy
network is explained. PSO is explained in Section 4. In section
5 design of energy network is introduced. In addition, the
numerical results of the simulation are presented in Section 6.
Finally, the discussion and conclusion are given in Section 7 of
paper.
II.
,
(4)
is the total electrical energy
In the above equations,
generated, , is the separation of power generation matrix by
different power plants,
is the electrical energy produced by
different power plants,
is the matrix efficiency of power
,
denotes input fuel of different power plants.
plants and
As well as to calculate electrical energy generator carriers,
(5) used as follows:
(5)
,
EXPRESSION OF PROBLEM
In planning of primary energy carriers based on final energy
consumption, from the lowest energy level (the final energy
consumption) has started and then step by step review the
energy losses of stages of various transformations until finally
the amount of primary energy carriers in order to provide the
final energy consumption be determined.
Where
is the electrical energy generator carriers and
is
the
separation
of input fuel to power plants to different
,
carrier’s matrix.
After modeling the electrical energy production process,
the need for different carriers with regard to the production of
electrical energy is calculated by the (6) as follows:
An important part of the final energy consumption related
to electrical energy, per hour of programming several modes of
combination of power plants capable of supplying electrical
energy consumption. for each of these modes is the best
economic distribution between power plants should be
determined, So at any time according to different modes of
power plants, we will have several modes of energy. In fact,
with a challenge like Unit Commitment would be facing with
the difference that instead of being with a different mix of
power plants are facing, with the combination of different
energy carriers will encounter due to the period of the study and
the information network, determined the proper composition in
accordance with the period of the study.
III.
(6)
Where is the need for different carriers taking into
account transmission, distribution, energy consumption and the
production of electrical energy losses, and
is the electrical
energy generated.
Some of these carriers, are derived from the refining
process. Therefore, it is necessary to also be simulated crude oil
refining. For this purpose (7) used as follows:
(7)
In this equation,
is the maximum capacity of refineries,
is the share of each products produced from crude oil
refining and
denotes the carriers that produced by refining.
By (8), the need for carriers, is calculated by considering
the refining process:
MOLDELLING OF ENERGY NETWORK
After design and development of the concept of the
reference energy system in Brookhaven National Laboratory,
power system simulator was developed. The basic idea of
matrix formulation is to create vertical incisions in the energy
system[9].
(8)
Energy network matrix model starts from the lowest
energy level of final energy consumption and reaches the
highest level of primary energy carriers energy, step by step. In
the first, defines the matrix of final energy consumption
according to various sectors as as . So:
(1)
,
Where
is crude oil refined,
is the need for carriers,
after taking into account the loss of electrical power generation
and refinement.
At last, the import and export of carriers, determined
using (9) as follows:
(9)
In this equation, P is the domestic production of primary
energy carriers and is import and export of energy carriers.
In matrix , positive sign means import and negative sign
means export of primary energy carriers.
In (3), in order to determine the contribution of various
power plants of electrical energy supply, economic distribution
In Equation (1), is the final energy consumption by
different carriers and , is the transformation matrix to convert
consumption sectors to carrier.
Taking into account transmission and distribution losses
and energy consumption, (2) is defines as follows:
(2)
,
2
Planning of energy carriers based on final energy consumption using dynamic programing and particle swarm optimization
31th Power System Conference - 2016 Tehran, Iran
of power is required to be done. For this purpose, particle
swarm optimization is used.
IV.
In the above, final electric energy consumption by the
,the number of different sectors of final energy consumption by
n, correlation coefficient of the final electric energy
consumption and final energy consumption related to sector i
by and final energy consumption related to sector i by has
been determined.
Considering the losses of transmission, distribution and
consumption of electrical energy, electrical energy
consumption is obtained by using (14)
1
(14)
ȵ
Here, is the electrical energy consumption, and ȵ is the
efficiency of the energy grid considering the losses of
transmission, distribution and consumption of electrical energy.
Using the information related to the issue of Unit
Commitment and features a 24-hour load can be approximate
calculated final energy consumption data for each hour.
PARTICLE SWARM OPTIMIZATION (PSO)
The particle swarm optimization (PSO) was initially
produced by Kennedy and Eberhart [10]. It has been effectively
used in several scientific and applied fields since that time. PSO
is an optimization algorithm predicated on the population where
individual is recognized as a particle and every population
involves a number of these particles.
In PSO the solution space is regarded as a search space and
every position in this search space is a problem-based solution.
In this population, particles, employed in collaboration, look for
the best position in the search space.
In addition, every particle moves according to its velocity.
At each iteration, the activity of each particle is computed using
the next formulas:
(10)
1
1
(15)
(11)
is the Final energy consumption designed,
Where
is the amount of electric energy networks in hours h, and V is
the final energy consumption for electrical energy
consumption.
where
is the position of the particle i at the t moment,
is the velocity of particle i at the t moment, ω is the inertia
weight that represents a ratio of the previous velocity,
is the best position that has been found by the particle
i so far, gbest(t) is the best position that has been found by the
whole population so far, and are the velocity constants,
and and are random variable ranging from [0-1].
V.
A.
design of the energy network with ten units
of power plants
In order to the schedule of the primary energy carriers, a
system with 10 power plant units is recommended. Profile of
electrical networks has been taken from of reference[12].
Information for this system in the Appendix has been
determined.
DESIGN OF ENERGY NETWORK
Because the subject of this study is new, relevant
information to the energy network is not available, In fact in
this study we need to a power grid with 24hour final energy
consumption information. In order to design this data, use the
Iran's energy balance sheet information[11] as well as electrical
network standard used in the research of the unit Commitment.
According to energy balance, final energy consumption for
a year as follows:
VI. SIMULATION
According to the energy network modeling, process
simulation is expressed as follows:
1) The definition of parameters and matrices.
2) Do steps 3 to 10, for each hour during the
studied period of 24 hours.
3) Determination of final energy consumption.
4) Determination of final energy consumption
by different carriers.
5) Determination of final energy consumption
with regard to distribution losses, transformation and
energy sectors.
6) Determine the possible combinations of
power plants to supply electrical energy
consumption.
7) Economic distribution of electrical energy
between power plants using optimization algorithms
for all possible combinations.
8) The contribution of each carrier from the
refining of crude oil.
Table 1. Various sectors of final energy consumption
row
1
2
3
4
5
6
7
8
E1
E2
E3
E4
E5
E6
Etf
Eef
sectors of energy
Residential, commercial, general
industrial
Transportation
Agriculture
other
Non-energy
Total of final energy consumption
Final electrical energy consumption
399.9 mboe
188.2 mboe
254.3 mboe
33.4 mboe
2.5 mboe
85.3 mboe
9636.6 mboe
79.7 mboe
In the above table, the final electric energy consumption is
marked with the Eef. The final electric energy consumption,
according to the various sectors of final energy consumption
can be calculated as follows:
(12)
(13)
3
Planning of energy carriers based on final energy consumption using dynamic programing and particle swarm optimization
31th Power System Conference - 2016 Tehran, Iran
strategy planning of energy, will be determined during the study
period.
Recursive algorithm to calculate the least cost, in time K by
combining I are as follows:
,
,
1, : ,
(22)
min
1, 9) Determine the need to provide energy to the
final energy consumption for each of the possible
combinations.
10) Determine import and export of energy
carriers according to the domestic supply of energy
carriers for each of the possible combinations.
11) Determine the total demand and the import
and export of energy carriers in the period studied (24
hour period) using dynamic programming.
, is the least total cost to arrive at state
, is the production cost for state ( K , I ), and
1, : , is the transition cost from state (K - 1, L)
to state ( K , I ). State ( K , 1) is the combination i in hour K
[13].
Where
(K,I),
A. Economic distribution of electrical energy
An important step in energy planning, economic
distribution of electrical energy. Objective function distribution
of electrical energy economic in (16) has been introduced:
,
&
,
C. Result
Dynamic programming with the storage paths equal to the
maximum number of modes per hour of study has been done
and the results in Table 2 below. The need of primary energy in
order to provide final energy consumption has been determined
in Table 3. The need of energy carriers has been set in the entire
study period in Table 4. Economic distribution of power
between the units has been set in the Table 5. According to the
domestic supply of energy, the import and export of carriers in
Table 6 have been set. The process of achieving of PSO to the
economic distribution of electrical power shows in fig. 1.
(16)
(17)
1:
∈
(18)
1
ȵ
(19)
Where
is the Objective function,
is the number of
input fuel to power plants,
is the total energy input to
power stations of fuel type i,
is the cost of fuel type i,
is the number of different units,
is the On or off status of
Table 2. The output of dynamic programming
hour
The initial state
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
Cost
dollar
8495171
4
S1
2
3
3
3
3
3
4
4
6
7
8
8
8
8
8
8
8
8
8
8
8
8
8
6
4
8484829
B. Application of dynamic programming
After the distribution of electrical energy per hour of
programming, corresponding to each of the Power sharing
modes between units planning process continues and thus
obtained the combination of energy, corresponding to the
combination of electric power. Using dynamic programming,
S2
2
3
3
3
3
3
4
6
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
7
5
8484438
(21)
Where N is the number of units,
is the production
capacity at time t by unit i,
is the electric power demand
is the Low and is the production and
is
at time t,
the Hi of unit i.
S3
2
3
3
3
3
3
4
6
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
7
6
8484088
Unit generation limits
Strategy
S4
2
3
3
3
3
3
4
6
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
7
8484128
2)
S5
2
3
3
3
3
3
4
6
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
8
8484051
(20)
S6
2
3
3
3
3
3
4
6
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
8484737
is the fixed price of unit i,
is the number of
the plant i,
unit type j, , is the coefficient of share of fuel type i of the
unit's energy input type j, ETF is the transformation matrix of
input energy to fuel power plants tailored to the types of power
plants,
is the matrix of plant's energy input, ȵ is the Vector
power efficiency of units, and
is the Electric power plant
output.
The constraints for the problem are:
1) System power balance
S7
2
3
3
3
3
3
4
6
9
9
9
9
9
9
9
9
9
9
9
9
9
9
10
10
10
Planning of energy carriers based on final energy consumption using dynamic programing and particle swarm optimization
31th Power System Conference - 2016 Tehran, Iran
Table 3. The need of energy
8
3721.1
51.78965
-350.552
-11.7441
17.72885
405.1893
53.06305
4380.603
26.60254
58.79772
16
3721.1
30.43155
-459.901
-141.826
-30.5359
281.5141
46.43017
3831.358
23.27722
51.448
24
3721.1
-5.1653
-595.486
-370.456
-110.977
75.38865
35.37536
2913.867
17.73502
39.19848
7
3721.1
44.67028
-365.265
-61.1345
1.640607
363.9642
50.85209
4190.728
25.4941
56.34781
15
3721.1
51.78965
-350.552
-11.7441
17.72885
405.1893
53.06305
4380.603
26.60254
58.79772
23
3721.1
9.073439
-548.095
-277.548
-78.8006
157.8388
39.79728
3278.051
19.9519
44.09829
6
3721.1
37.55091
-354.657
-123.351
-14.4476
322.7392
48.64113
3988.239
24.38566
53.89791
14
3721.1
66.02839
-275.868
74.99814
49.90533
487.6395
57.48497
4751.988
28.81941
63.69753
22
3721.1
37.55091
-423.452
-98.4652
-14.4476
322.7392
48.64113
4014.44
24.38566
53.89791
5
3721.1
23.31218
-429.906
-210.1
-46.6241
240.289
44.2192
3615.204
22.16878
48.9981
13
3721.1
80.26713
-198.861
161.7678
82.0818
570.0897
61.90689
5130.168
31.03629
68.59734
21
3721.1
66.02839
-275.868
74.99813
49.90533
487.6395
57.48497
4751.988
28.81941
63.69753
4
3721.1
16.19281
-466.355
-253.46
-62.7124
199.0639
42.00824
3432.123
21.06034
46.54819
12
3721.1
94.50586
-135.511
260.843
114.2583
652.5398
66.32881
5531.033
33.25317
73.49714
20
3721.1
80.26713
-198.861
161.7678
82.0818
570.0897
61.90689
5130.168
31.03629
68.59734
3
3721.1
1.95407
-539.254
-340.182
-94.8888
116.6137
37.58632
3065.959
18.84346
41.64838
11
3721.1
87.3865
-158.969
205.169
98.17004
611.3148
64.11785
5323.32
32.14473
71.04724
19
3721.1
51.78965
-350.552
-11.7441
17.72885
405.1893
53.06305
4380.603
26.60254
58.79772
2
3721.1
-12.2847
-612.154
-426.903
-127.065
34.16357
33.1644
2699.796
16.62658
36.74857
10
3721.1
80.26713
-198.861
161.7678
82.0818
570.0897
61.90689
5130.168
31.03629
68.59734
18
3721.1
37.55091
-423.452
-98.4652
-14.4476
322.7392
48.64113
4014.44
24.38566
53.89791
1
3721.1
-19.404
-647.68
-470.252
-143.154
-7.06152
30.95344
2519.415
15.51815
34.29867
9
3721.1
66.02839
-275.591
75.0014
49.90533
487.6395
57.48497
4752.798
28.81941
63.69753
17
3721.1
23.31218
-496.351
-185.186
-46.6241
240.289
44.2192
3648.277
22.16878
48.9981
hour
Petroleum
liquid gas
Fuel oil
Gas oil
Kerosene
Gasoline
plane fuel
natural gas
Coke gas
coal
hour
Petroleum
liquid gas
Fuel oil
Gas oil
Kerosene
Gasoline
plane fuel
natural gas
Coke gas
coal
hour
Petroleum
liquid gas
Fuel oil
Gas oil
Kerosene
Gasoline
plane fuel
natural gas
Coke gas
coal
Table 5. Economic distribution
OF
66339.36
71199.98
81353.53
91507.08
96583.85
107287.4
113108.7
118165.9
128802.2
139852.8
145735.7
152855.1
139852.8
128737.4
118165.9
102935.5
97858.76
108012.3
118165.9
139852.8
128737.4
108012.3
87907.97
78494.37
unit 10
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
unit 9
0
0
0
0
0
0
0
55
54.99999
55
55
55
55
55
55
55
55
55
55
54.99999
55
55
55
10
unit 8
0
0
0
0
0
0
0
10
46.6471
46.79025
36.85425
55
46.79031
10.00003
10
10
10
10
10
46.79023
10.00005
10
10
10
unit 7
0
0
0
0
0
0
0
25
25.00008
25.00003
85
85
25
25.00059
25
25
25
25
25
25.00009
25
25
25
25
unit 6
0
0
0
0
0
0
80
80
79.99998
80
80
80
80
80
80
80
80
80
80
80
80
80
31.15087
20
5
unit 5
0
0
0
0
0
0
25
25
25
25.00004
25
56.91819
25
25.00003
25
25
25
25
25
25
25
25
25
25
unit 4
0
0
0
0
0
61.40668
20
20
30.01528
130
130
130
130
66.66199
20
20
20
20
20
130
66.66239
20
20
20
unit 3
130
130
130
130
130
130
130
130
130
130
130
130
130
130
130
130
130
130
130
130
130
130
130
130
unit 2
150
165.9591
266.087
366.2149
416.2788
455
441.4706
401.5346
455
455
455
455
455
454.9998
401.5346
251.3427
201.2788
301.4067
401.5346
455
455
301.4067
150
150
unit 1
420.8952
455
455
455
455
455
455
455
455
455
455
455
455
455
455
455
455
455
455
455
455
455
455
411.023
‫ساعت‬
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
Planning of energy carriers based on final energy consumption using dynamic programing and particle swarm optimization
31th Power System Conference - 2016 Tehran, Iran
Table 6. import and export of
carriers
Import
0
1000.893
0
0
0
8322.891
1198.34
2221.738
0
367.8484
Export
163006
0
9132.05
1916.25
121.508
0
0
0
37.6261
0
carrier
Petroleum
liquid gas
Fuel oil
Gas oil
Kerosene
Gasoline
plane fuel
natural gas
Coke gas
coal
Table 4. The need for different energy carriers in the whole
study period
row
1
2
3
4
5
6
7
8
9
10
energy
89306.4
1000.893
-9132.05
-1916.25
-121.508
8322.891
1198.34
98855.34
600.7739
1327.848
carrier
Petroleum
liquid gas
Fuel oil
Gas oil
Kerosene
Gasoline
plane fuel
natural gas
Coke gas
coal
(a)
(b)
(c)
(d)
(e)
(f)
(g)
(h)
(i)
row
1
2
3
4
5
6
7
8
9
10
Fig. 1. The process of achieving to economic distribution of electrical energy in the power grid using PSO
6
Planning of energy carriers based on final energy consumption using dynamic programing and particle swarm optimization
31th Power System Conference - 2016 Tehran, Iran
Table A.4. Matrix Tp
VII. DISCUSSION AND CONCLUSION
Petroleum
liquid gas
Fuel oil
Gas oil
Kerosene
Gasoline
plane fuel
In this paper, a new approach was introduced in Energy
Studies, this approach has been tried on the basis of final energy
consumption a proper planning be done on energy carriers.
Power network modeling in the form of a matrix is done step
by step from the lowest energy level (final energy consumption)
to the highest level (primary energy carriers).
Other products
natural gas
Coke gas
coal
Non-commercial fuels
Electricity(power)
During the study period were used PSO to distribute
electrical energy between units and DP in order to achieve
proper strategy of combining energy carriers. The proposed
plan implemented on the designed energy network with
appropriate information and the results are presented.
Table A.5. Conversion matrix input energy to fuel power
plants
Appendix A
See Tables A.1–A.11
Table A.1. Unit Information
Min
Max
Constant
cost
Priority
150
150
20
20
25
20
25
10
10
10
455
455
130
130
162
80
85
55
55
55
0.368
0.345
0.455
0.317
0.3
0.47
0.35
0.35
0.5
0.25
1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
9
10
Power plant
Thermal
Thermal
Combined Cycle
Thermal
Gas
Combined Cycle
Thermal
Thermal
Combined Cycle
Gas
Power plant
efficiency
row
1
2
3
4
5
6
7
8
9
10
Capacity of
unit (MW)
Fuel oil
Gas oil
natural gas
row
1
2
3
4
5
6
7
8
9
10
Thermal
Thermal
Combined Cycle
Thermal
Gas
Combined Cycle
Thermal
Thermal
Combined Cycle
Gas
8
8
5
5
6
3
3
1
1
1
8
8
5
5
6
3
3
1
1
1
Cold
start
Initial
conditions
5
5
4
4
4
2
2
0
0
0
8
8
-5
-5
-6
-3
-3
-1
-1
-1
row
1
2
3
4
5
6
7
8
9
10
11
12
13
Power plant
Thermal unit
Thermal unit
Combined Cycle unit
Thermal unit
Gas unit
Combined Cycle unit
Thermal unit
Thermal unit
Combined Cycle unit
Gas unit
Hot start
4500
5000
550
560
900
170
260
30
30
30
Combined Cycle
unit
0
0.082
0.918
Gas
unit
0
0.166
0.834
Energy carrier
Petroleum
liquid gas
Fuel oil
Gas oil
Kerosene
Gasoline
plane fuel
Other products
natural gas
Coke gas
coal
Non-commercial fuels
Electricity(power)
Energy (boe)
10513
0
0
0
0
0
0
0
4026.4
26.6
40
161
0
Table A.7. heating value[14] and energy rates[15]
Energy carrier
Table A.3. The cost of setting up units
row
1
2
3
4
5
6
7
8
9
10
Thermal
unit
0.254
0.003
0.743
Table A.6. Domestic supplies of energy carriers
Table A.2. The time information of energy networks
Power plant
0
0.032
0.293
0.293
0.099
0.157
0
0.058
0
0
0
0
0
Cold start
9000
10000
1100
1120
1800
340
520
60
60
60
7
heating value
energy rates
Petroleum
38.5
48 dollar/boe
liquid gas
46.15
374 dollar/tone
Fuel oil
42.18
180 dollar/tone
Gas oil
43.38
350 dollar/tone
Kerosene
43.32
500 dollar/tone
Gasoline
44.75
450 dollar/tone
plane fuel
45.03
555 dollar/tone
natural gas
39
237 dollar/1e3m3
Coke gas
16.9
157 dollar/tone
coal
26.75
61 dollar/tone
Planning of energy carriers based on final energy consumption using dynamic programing and particle swarm optimization
31th Power System Conference - 2016 Tehran, Iran
Table A.8. Electrical load demand
hour
load
hour
load
hour
load
hour
load
hour
load
hour
load
1
700
5
1000
9
1300
13
1400
17
1000
21
1300
2
750
6
1100
10
1400
14
1300
18
1100
22
1100
3
850
7
1150
11
1450
15
1200
19
1200
23
900
4
950
8
1200
12
1500
16
1050
20
1400
24
800
Table A.9. Final energy consumption
hour
Residential, commercial, general
industrial
Transportation
Agriculture
other
Non-energy
hour
Residential, commercial, general
industrial
Transportation
Agriculture
other
Non-energy
hour
Residential, commercial, general
industrial
Transportation
Agriculture
other
Non-energy
1
1570.19
738.9593
998.4982
131.1437
9.816144
334.9268
9
2916.068
1372.353
1854.354
243.5526
18.22998
622.007
17
2243.129
1055.656
1426.426
187.3481
14.02306
478.4669
2
1682.347
791.7421
1069.819
140.5111
10.5173
358.8502
10
3140.381
1477.919
1996.996
262.2874
19.63229
669.8537
18
2467.442
1161.222
1569.069
206.0829
15.42537
526.3136
3
1906.66
897.3078
1212.462
159.2459
11.9196
406.6969
11
3252.537
1530.701
2068.318
271.6548
20.33344
693.777
19
2691.755
1266.787
1711.711
224.8177
16.82768
574.1603
4
2130.973
1002.873
1355.105
177.9807
13.32191
454.5436
12
3364.694
1583.484
2139.639
281.0222
21.03459
717.7004
20
3140.381
1477.919
1996.996
262.2874
19.63229
669.8537
5
2243.129
1055.656
1426.426
187.3481
14.02306
478.4669
13
3140.381
1477.919
1996.996
262.2874
19.63229
669.8537
21
2916.068
1372.353
1854.354
243.5526
18.22998
622.007
6
2467.442
1161.222
1569.069
206.0829
15.42537
526.3136
14
2916.068
1372.353
1854.354
243.5526
18.22998
622.007
22
2467.442
1161.222
1569.069
206.0829
15.42537
526.3136
7
2579.599
1214.005
1640.39
215.4503
16.12652
550.2369
15
2691.755
1266.787
1711.711
224.8177
16.82768
574.1603
23
2018.816
950.0906
1283.783
168.6133
12.62076
430.6202
8
2691.755
1266.787
1711.711
224.8177
16.82768
574.1603
16
2355.286
1108.439
1497.747
196.7155
14.72422
502.3903
24
1794.503
844.5249
1141.141
149.8785
11.21845
382.7735
Table A.10. Matrix T12
Petroleum
liquid gas
Fuel oil
Gas oil
Kerosene
Gasoline
plane fuel
Other products
natural gas
Coke gas
coal
Non-commercial fuels
Electricity(power)
Residential and
commercial
industrial
Transportation
Agriculture
other
Non-energy
0
0.051
0.023
0.055
0.141
0.002
0
0
0.564
0
0.0003
0.064
0.102
0
0.013
0.212
0.087
0.002
0.002
0
0
0.521
0.021
0
0
0.142
0
0.01
0.014
0.363
0
0.573
0.031
0
0.007
0
0
0
0.0004
0
0
0
0.689
0.018
0.003
0
0
0
0
0
0
0.29
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0.402
0.497
0
0.101
0
0
8
Planning of energy carriers based on final energy consumption using dynamic programing and particle swarm optimization
31th Power System Conference - 2016 Tehran, Iran
Table A.11. Matrix T23
Petroleum
liquid gas
Fuel oil
Gas oil
Kerosene
Gasoline
plane fuel
Other products
natural gas
Coke gas
coal
Non-commercial fuels
Electricity(power)
1
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
[2]
[3]
[4]
[5]
[6]
[7]
[8]
[9]
[10]
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
1.1601
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
1.3158
[11]
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0
0
0
0
0
0
1
0
0
0
0
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