Chandrasekhar Yammani*1, Sydulu Maheswarapu1, Sailajakumari Matam1
J. Electrical Systems 7-4 (2011): 448-457 Regular paper Enhancement of voltage profile and loss minimization in Distribution Systems using optimal placement and sizing of power system modeled DGs
Distributed Generations (DGs) integration in distribution system is one of the options which give benefits like loss minimization, peak shaving, over load relieving and improved reliability. This paper presents a method for optimal placement and size of the DGs considering system loss minimization and voltage profile improvement as objective functions. The solar and wind systems are modeled as constant power factor model and variable reactive power model respectively. This work is tested on 37-bus and 69-bus Distribution Systems. The simulation technique based on Genetic Algorithm is studied. For all cases current injection based distribution load flow method is used.
Keywords: Distributed Generation, Optimization Algorithms, Genetic Algorithm, DG Modeling.
NOMENCLATURE -
OF IC IVD V1 Sl CSl Pgs m PDG Pload Ploss
: Objective function, : line flow Index, : Voltage Profile Index, : Reference bus voltage, : lth line flow, : Maximum flow capacity of line ‘l’, : Substation Bus Power, : Number of DGs, : Active power generation by DG, : Total system load, : Total system active power loss
-
Vi : Voltage of ith bus including DG, Vimin : Maximum bus voltage at bus i, Vimax : Maximum bus voltage at bus i, n : Number of busses, nline : Number of lines, PDG,i : Injected DG active power at ith bus. TPlossDG : Total Ploss with DG, TQlossDG : Total Qloss with DG, TPlossWODG : Total Ploss without DG, ILP and ILQ : Ploss and Qloss Indices, TQlossWODG : Total Qloss without DG,
1. INTRODUCTION The Distributed Generation (DG) is the electricity generated at consumer end and thereby reduces the problem associated with transmission and distribution losses, costs, saving of the fuel, reduction of sound pollution and green house gases, unreliability of the grid and the problem of remote and inaccessible regions [1]. Other benefits include peak shaving, better voltage profile, reliving of transmission and distribution congestion then improved network capacity, protection selectivity, network robustness, and islanding operations [2-3]. DG technologies are encouraged in the electrified villages where, very much dissatisfied with the quality of grid power. The more common DG technologies include Micro Turbines, Wind Turbines, Biomass, and Gasification of Biomass, Solar Photovoltaic’s and Hybrid systems. However, most of the plants are based on Wind Power, Hydel Power and Biomass and Biomass Gasification. The technology of Solar Photovoltaic’s is costly and Fuel cells are yet to be commercialized. *Corresponding author : Chandrasekhar Y, E-mail:
[email protected] 1 Department of Electrical engineering, National Institute of Technology, Warangal, 506004. INDIA. Copyright © JES 2011 on-line : journal/esrgroups.org/jes
J. Electrical Systems 7-4 (2011): 448-457
The impact of DG on power losses not only affected by DG location but also depends on the network topology as well as on DG size and type [1]. For different type of DGs separate mathematical modeling is required. For better understanding, efficient and robust operation, the load flow studies incorporating DGs, Distribution Automation and Demand Side Management, the mathematical modeling is very essential. Literature in [4-7] is available for different DG models in Distributed load flow. The placement, type and size of the DG should be optimal in order to maximize the benefits of it [8]. Influence of DG on transmission congestion also depends on location of DG in distribution system. Strategically located DG units may utilize the upstream transmission system less, if opportunely operated, and thereby help to relieve congestion in the transmission network. The review paper [1] presents different optimization techniques for optimal placement and size of the DG. Many analytical approaches [9-11] are available for same optimal DG problem, but they cannot be directly applied because of the size, complexity and the specific characteristics of distribution system [1]. Meta-heuristic approaches like heuristic iterative search approaches [12-16] are also available for optimal DER. In all these papers DGs are modeled as constant power injection source and multiple DGs are not considered. The paper [17] has discussed the size and location of the DG for ‘different load models’, but offered no details about the multiple DG placement and size. Further, the DG is modeled as unity Power factor source with fixed rating at 0.63p.u for all studies. In the present work, the renewable DGs Wind and Solar are modeled as constant p.f model and variable reactive power model in current injection based load flow. Further, the combination of the different DGs is also studied. The GA technique is used for optimization. The main objective is to optimize DG location and size, while minimizing system real, reactive losses and to improve voltage profile. 2. PROBLEM FORMULATION The main focus is to find the optimal place and size of the DG by minimizing Objective function (OF) [9]. OF = (0.4ILP + 0.2ILQ + 0.25IC + 0.15IVD)
(1)
Here, the first priority is given to renewable DGs because of the low maintenance and cost. After including one or more DGs the aim is to minimize OF. ⎛ TPlossDG ⎞ ILP = ⎜ ⎟ ⎝ TPlossWODG⎠ ⎛ TQlossDG ILQ = ⎜⎜ ⎝ TQlossWODG nline ⎛ S IC = Max ⎜⎜ l l =1 ⎝ CS l
(2) ⎞ ⎟⎟ ⎠
(3)
⎞ ⎟⎟ ⎠
n ⎛ V 1 − Vi IVD = Max ⎜⎜ i=2 ⎝ V1
(4)
⎞ ⎟ ⎟ ⎠
(5)
With Equality constraints Pgs +
m
∑P
DG =1
DG
= Pload + Ploss
(6)
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Equality constraints
V i min ≤ V i ≤ V i max
(7)
3. MODELLING OF DGS IN LOAD FLOW STUDIES In system with DGs, the gen eration of Photo voltaic systems, Fuel cells, Microturbines and some Wind turbine units are injected into the power grid via power electronic interfaces. In such cases, the model of a DG unit in load flows depends on the control method which is used in the converter control circuit. The DGs which have control over the voltage by regulating excitation voltage (Synchronous generator DGs) or the control circuit of the converter used to control ‘P’ and ‘V’ independently, then the DG unit may be model as PV type. Other DGs like Induction generator based units or converter used to control P and Q independently, then the DG shall be modeled as PQ type. Using these models for DGs, Current injection based load flow method is employed for Distribution system studies. 3.1 Current injection based load flow (CILF) The traditional load flow methods like Gauss-Siedel, Newton-Raphson and Fast Decoupled techniques are inefficient to solve Distribution networks due to the radial structure and wide range of resistance with low X/R ratios. Several methodologies have been proposed to solve the power flow problem in Distribution Systems like Vector based Distribution load flow, Primitive Impedance Distribution load flow and Forward & Backward Sweep Distribution load flow. But all the methods have limitations like, not applicable for meshed distribution systems and implementation become complex when control devices are present in the system. The CILF [18] can used for both radial and mesh systems and easy to implementation of control devices. 3.2 DG modeled as PQ node A DG unit can be modeled as three different ways in PQ node mode as illustrated below: 3.2.1 DG as a ‘negative PQ load’ model of PQ mode In this case the DG is simply modeled as a constant active (P) and reactive (Q) power generating source. The specified values of this DG model are real (PDG) and reactive (QDG) power output of the DG. It may me noted that Fuel cell type DGs can be modeled as negative PQ load model. The load at bus-i with DG unit is to be modified as
Pload ,i = Pload ,i − PDG ,i
(8)
Qload ,i = Qload ,i − Q DG ,i
(9)
3.2.2 DG as a ‘constant power factor’ model of PQ mode The DG is commonly modeled as constant power factor model [19]. Controllable DGs such as synchronous generator based DGs and power electronic based units are preferably modeled as constant power factor model. For example, the output power can be adjusted by controlling the exciting current and trigger angles for synchronous generator based DGs and power electronic based DGs, respectively [19]. For this model, the specified values are the real power and power factor of the DG. The reactive power of the DG can be calculated by (10) and then the equivalent current injection can be obtained by (11)
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QiDG = PiDG tan(cos −1 ( PFiDG ))
I iDG = I
r iDG
(ViDG ) +
jI
i iDG
⎛ + (ViDG ) = ⎜⎜ PiDG jQiDG ViDG ⎝
(10)
⎞ ⎟⎟ ⎠
∗
(11)
3.2.3 DG as ‘Variable Reactive Power’ model of PQ mode DGs employing Induction Generators as the power conversion devices will act mostly like variable Reactive Power generators. By using the Induction Generator based Wind Turbine as an example, the real power output can be calculated by Wind Turbine power curve. Then, its reactive power output can be formulated as a function comprising the real power output, bus voltage, generator impedance and so on. However, the reactive power calculation using this approach is cumbersome and difficult to calculate efficiently. From a steady-state view point, reactive power consumed by a Wind Turbine can be represented as a function of its Real Power [7], that is 2 Q ' iDG = −Q0 − Q1 PiDG − Q2 PiDG
(12)
Where Q’iDG is the Reactive Power function consumed by the Wind Turbine. The Q0, Q1 and Q2 are usually obtained experimentally. The reactive power consumed by the load cannot be fully provided by the distribution system, and therefore capacitor banks are installed for power factor correction where induction generator based DGs are employed. 3.3
Dg modeled as PV type
The DG as a PV node is commonly Constant voltage model. The specified values of this DG model are the real power output and bus voltage magnitude. For maintain constant voltage the, change in voltage ΔVi should maintain zero by injecting required reactive power. The energy sources of DGs can be categorized into stable and unstable energy sources, Fuel cell and Micro-gas turbine are some of the stable energy sources, Wind and Solar are most commonly used unstable energy sources. Different energy sources show special output characteristics when combining with different energy converters [19]. For example, the Induction Generator will act like a constant real power and variable reactive power generator, when it is used to convert wind energy to power grids. So it is modeled as a variable reactive power model in load flow analysis. However, if the static electronic converter is used to convert Solar to power grids, it will mostly act like a generator with a constant power factor in normal operating condition. Therefore it is modeled as constant power factor model. In this study, the maximum capacity of DG is taken as 0.63 p.u and the average maximum power generated by the Solar is 1.191p.u and Wind turbine is 0.471p.u.are considered. 4. GA IMPLEMENTATION FOR OPTIMAL PLACEMENT AND SIZE OF DG In this paper Genetic algorithm based optimization technique is used to find the optimal placement and size of the DG by minimizing the losses and voltage improvement. The simple GA contains chromosome size of 20 bits for every DG. In that 20 bits 8 are for placement of the DG and remaining 12 are for Size of the DG. The formation of chromosome is shown in fig.1. The fig.2 shows the flow chart of optimal place and size of the DG. Population size of 120 and Elitism operator of 0.1, Crossover probability of 0.7 and mutation probability 0.005 are used in this work. Maximum numbers of iterations are 500.
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1 0 1 0 1 1 1 1 0 0 0 1 0 1 0 0 1 1 1 0 1 1 1 1 1 0 1 0 0 0 1 0 1 0 1 0 1 0 0 1........ Placement of DG1
Size of DG1
Part of Chromosome for DG1
Placement of DG2
Size of DG2
Part of Chromosome for DG2
............. ….…..
Figure 1: Chromosome formation of DGS
Figure 2: Flow chart of optimal place and size of DGs
5. RESULTS AND VALIDATION For testing the proposed algorithm, the test data of 38 and 69 Bus Distribution Systems are considered. System data of 38 and 69 bus Distribution systems are available in papers [20] and [21] respectively. 5.1 Case-I: It deals with system studies on 38 bus system. This case study include various combination of different distribution generation units like: without DG, Solar, Wind, Fuel cell, Micro turbine, combination of Solar and Wind; Solar, Wind and Fuel cell; Solar, Wind, Fuel cell and Micro turbine. Here, the Solar is modeled as a constant power factor model, Wind is modeled as a Variable reactive power model, Fuel cell is modeled as a negative real power load model and Micro turbine is modeled as a constant voltage model in load flow studies. Table-I shows the optimal placement of DGs and its corresponding sizes. This paper compares the optimal placement of DG with the one presented in [17] which is summarized in table-I and it is observed that the optimal location of Fuel cell (Modeled as negative real power load model) is found to be same as that of the result presented in [17]. In addition, the study includes various combinations of different distribution generations, which are clearly tabulated and presented for 38 bus distribution bus system. Figure 3 shows the comparison charts for active and reactive power losses for different types and combination of DGs to 38 bus distribution system. It is found that without DG the losses are 20.2 kW and 13.4847 Kvar. There is significant reduction in losses in case of Solar Photovoltaic DG integration when compared to Wind turbine DG.
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This is because of the maximum available rating of Solar in this study is 2.52 times more than Wind turbine power rating. Table. I : Optimal Place and Size of the DG in 38 Bus system. S.No
1 2 3
various combination of different Distribution generations
Optimal DG BUS Number / -conventional from([9])#
- conventional from([9])#
NO DG 30 13
NO DG 1.191/0.61016 0.471/0.252478
Without DG Only Solar Only Wind Only with Fuel Cell Only Micro turbine
4 5 6
Solar +wind
7
Solar+ Wind+ Fuel cell
8
Solar+ Wind+ Fuel cell+ Micro turbine
DG Value(*100kW)/ reactive power(*100kVAR) required for Wind mills
Active power loss (kW)
Reactive power loss (kVAR)
0.202212 0.081327 0.176521
0.134847 0.056384 0.117465
14-14#
0.63-0.63#
0.142861
0.094558
30
0.63/2.515639
0.142159
0.100373
30 14 30 25 13
1.191/ 0.610167 (solar) 0.471/ 0.252478 (wind) 1.191/ 0.610167 (solar) 0.471/ 0.252478 (wind) 0.63(Fuel cell) 0.991449 / 0.572172 (solar ) 0.47002/ 0.252478 (wind) 0.630000 (Fuel cell) 0.63/ 1.051823 (Micro turbine)
0.069872
0.048245
0.044812
0.031072
0.022203
0.016688
30 24 15 7
The losses are reduced 89.1% with integration of all available four DG case. In this study the losses margin is very less in 3 DGs integration compared to 4 DGs integration. Which instigates that number of DGs should be limited to four taking into consideration the initial and maintenance cost.
Figure 3: Active and Reactive loss comparison for 38 bus system Table II gives the ILP, ILQ, IC, IVD and OF for all cases of 38 bus distributed systems. In all combination of DGs cases the IVD is nearer to zero. It means that the voltage profile is improved and voltage deviation observed is very small. With all DGs integration case it is observed that IVD is very small. It is observed that ILP, ILQ, IC and IVD all are reducing with increasing the number of DG integration case, where as the variation rate is small in case of three DGs and four DGs combination cases. Objective function is reduced 46.4% with combined integration of all four DGs compared to single DG (Fuel cell) case. Table. II : Performance Indices for 38 bus system.
ILP ILQ IC IVD OF
Only Solar
Only Wind
Only Fuel cell
Only Micro turbine
Solar + wind
0.402 0.418 0.9995 0.06237 0.5037
0.8729 0.871 0.9951 0.0784 0.7839
0.7065 0.7012 0.9951 0.0733 0.6826
0.703 0.744 1.132 0.053 0.721
0.3455 0.3577 0.995 0.0478 0.4656
Solar+ wind+ Fuel cell 0.2216 0.2304 0.9854 0.0308 0.3857
All 4 DGs 0.1098 0.1237 0.9854 0.01476 0.3172
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Figure 4: Voltage profile with combination of DGs in 38 bus system From figure 4, it is observed that the voltages are improved with optimal placement and appropriate size of the DGs. It is observed that, with the integration of the four available DGs the voltage profile is improved significantly. It is also observed that by increasing the number of DGs the voltage profile is improving. At low voltage (13,14,15,16,17,18, 30,31,32,33,36 and 38) busses the significance of DG integration is clearly observed . Whereas the improving rate of voltage is decreased by increasing the DG numbers. So it is clearly observed that for this case study the number of DGs is limited to four. 5.2 case-II: 69 bus system: Table III shows the optimal DG bus number and DG value for 69 bus distribution system. Figs 5 shows are the comparison between active and reactive power losses with different cases of inclusion of DG to 69 bus distribution system. Without DG the losses are 242.7782 kW and 109.2361Kvar with the integration of the solar the losses are reduced more compared to the wind and DG because of the high rating of the solar. The losses are reduced 18.33% with integration of the solar, wind, and 2 DGs of rating 0.63p.u. The losses are reduced very less compared to the 38 bus system, the 69 bus system load is high and system is complex. The DGs rating is very small when compared to system data. Higher rating of the DGs is preferable in this case. Table. III : Optimal Place and Size of the DG in 69 Bus system. DG Value
S.No
1 2 3 4 5 6
7
8
various combination of different Distribution generations Without DG Only Solar Only Wind Only Fuel Cell Only Micro turbine Solar +wind Solar+ Wind+ Fuel cell
Solar+ Wind+ Fuel cell+ Micro turbine
Optimal DG BUS Number
9
454
(*100kVAR) required consumed by solar Wind mills
Active power loss (100*kW)
Reactive power loss (100* kVAR)
NO DG
NO DG
2.427782
1.09236
54 54 52
1.191/0.61016 0.471/0.252478 0.63
2.157628 2.377182 2.321975
0.97276 1.06997 1.04553
2.118100
0.95618
2.019939
0.91293
2.017631
0.91018
1.929632
0.87229
0.63/12.515639
51 54 53
1.1907/ 0.610018 (solar) 0.4708/ 0.25244 (wind) 1.191/ 0.610167 (solar) 0.471/ 0.252478 (wind)
52
0.63(Fuel cell)
53
1.191 / 0.61016 (solar )
52
51 54 36
Solar Wind Fuel cell-I Micro turbine Fuel cell-II
(*100kW)/ reactive power
0.47002/ 0.252478 (wind) 0.630000 (Fuel cell) 0.63/ 1.051823 (Micro turbine)
54
1.191000/0.610167
52
0.470310/0.252246
50
0.630000
4
0.629692/35.270262
51
0.629846
J. Electrical Systems 7-4 (2011): 448-457
Figure 5: Active and Reactive loss comparison for 69 bus system
Figure 6: Voltage profile with combination of DGs in 69 bus system Table. IV : Performance Indices for 69 bus system.
ILP ILQ IC IVD OF
Only Solar
Only Wind
Only Fuel cell
0.888 0.890 0.961 0.086 0.787
0.979 0.979 0.987 0.092 0.8484
0.956 0.957 0.981 0.092 0.833
Only Micro turbine 0.999 0.998 0.823 0.094 0.819
Solar + wind 0.872 0.875 0.955 0.086 0.776
Solar+ wind+ Fuel cell 0.832 0.835 0.943 0.082 0.748
All 4 DGs 0.831 0.833 0.770 0.082 0.704
All 4 DGs and extra fuel cell 0.794 0.798 0.750 0.080 0.677
For the 69 bus system with 0.63 p.u DG rating the losses are reduced faintly when compared to 38 bus system. Table IV gives the ILP, ILQ, IC, IVD and OF for all cases for 69 bus distributed systems with DG rating of 0.63 p.u. In all combination of DGs the IVD is nearer to zero. It means that the voltage profile is improved and voltage deviation observed is very small. With all DGs integration case it is observed that IVD is very small. The ILP, ILQ, IC and IVD all are reducing with increasing the number of DG integration case. Figure 6 shows the voltage profiles of 69 bus systems with 0.63 p.u DG case. With the integration of the multiple DGs, The voltages profiles at the sensitive busses are significantly improved.
6. CONCLUSIONS In this paper a method for optimal placement and size of the DGs is proposed considering system loss minimization and voltage profile improvement as objective functions. The solar and wind systems are modeled as constant power factor model and variable reactive power model respectively. This work is tested on 38-bus and 69-bus distribution systems with different rating of the DGs and results are incorporated. For maintaining the required voltage reactive power requirement is more complex. The studies about reactive power are not discussed here. Further custom power devices addition is very useful for reactive power requirement.
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J. Electrical Systems 7-4 (2011): 448-457 APPENDIX
Table V and Table VI shows the system and load data for 38-bus and 69-bus systems. Table. V : System and load data of 38-bus system
From Bus 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 2 19
To bus 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Line Impedances in p.u R (p.u) X (p.u) 0.000574 0.000293 0.00307 0.001564 0.002279 0.001161 0.002373 0.001209 0.0051 0.004402 0.001166 0.003853 0.00443 0.001464 0.006413 0.004608 0.006501 0.004608 0.001224 0.000405 0.002331 0.000771 0.009141 0.007192 0.003372 0.004439 0.00368 0.003275 0.004647 0.003394 0.008026 0.010716 0.004558 0.003574 0.001021 0.000974 0.009366 0.00844
Loads on to-bus (p.u) P 0.1 0.09 0.12 0.06 0.06 0.2 0.2 0.06 0.06 0.45 0.06 0.06 0.12 0.6 0.06 0.06 0.09 0.09 0.09
Q 0.06 0.04 0.08 0.03 0.02 0.1 0.1 0.02 0.02 0.03 0.035 0.035 0.08 0.01 0.02 0.02 0.04 0.04 0.04
From Bus 20 21 3 23 24 6 26 27 28 29 30 31 32 8 9 12 18 25
To bus 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38
Line Impedances in p.u R (p.u) X (p.u) 0.00255 0.002979 0.004414 0.005836 0.002809 0.00192 0.005592 0.004415 0.005579 0.004366 0.001264 0.000644 0.00177 0.000901 0.006594 0.005814 0.005007 0.004362 0.00316 0.00161 0.006067 0.005996 0.001933 0.002253 0.002123 0.003301 0.012453 0.012453 0.012453 0.012453 0.012453 0.012453 0.003113 0.003113 0.003113 0.003113
Loads on to-bus (p.u) P 0.09 0.09 0.09 0.42 0.42 0.06 0.06 0.06 0.12 0.2 0.15 0.21 0.06 0 0 0 0 0
Q 0.04 0.04 0.05 0.2 0.2 0.025 0.25 0.02 0.07 0.6 0.07 0.1 0.04 0 0 0 0 0
Table. VI : System and load data of 69-bus system Loads on to-bus (p.u)
Line Impedances in p.u From Bus 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 3 28 29 30 31 32 33 34
To bus 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
Loads on to-bus (p.u)
Line Impedances in p.u
R (p.u)
X (p.u)
P
Q
0.000001 0.000001 0.000001 0.000016 0.000228 0.000238 0.000058 0.000031 0.000511 0.000117 0.000444 0.000643 0.000651 0.00066 0.000123 0.000234 0.000003 0.000204 0.000131 0.000213 0.000009 0.000099 0.000216 0.000467 0.000193 0.000108 0.000003 0.00004 0.000248 0.000044 0.000219 0.000523 0.001066 0.00092
0.000001 0.000001 0.000002 0.000018 0.000116 0.000121 0.000029 0.000016 0.000169 0.000039 0.000147 0.000212 0.000215 0.000218 0.000041 0.000077 0.000001 0.000068 0.000043 0.00007 0.000003 0.000033 0.000071 0.000154 0.000064 0.000036 0.000007 0.000098 0.000082 0.000014 0.000072 0.000176 0.000352 0.000304
0 0 0 0 0 0.026 0.404 0.75 0.3 0.28 1.45 1.45 0.08 0.08 0 0.455 0.6 0.6 0 0.01 1.14 0.053 0 0.28 0 0.14 0.14 0.26 0.26 0 0 0 0.14 0.195
0 0 0 0 0 0.022 0.3 0.54 0.22 0.19 1.04 1.04 0.055 0.055 0 0.3 0.35 0.35 0 0.006 0.81 0.035 0 0.2 0 0.1 0.1 0.186 0.186 0 0 0 0.1 0.14
From Bus 4 36 37 38 8 40 9 42 43 44 45 46 47 48 49 50 51 52 53 11 55 12 57 3 59 60 61 62 63 64 65 66 67 68
To bus 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69
R (p.u)
X (p.u)
P
Q
0.000002 0.000053 0.000181 0.000051 0.000058 0.000207 0.000109 0.000127 0.000177 0.000176 0.000992 0.000489 0.00019 0.000241 0.000317 0.000061 0.00009 0.000443 0.00065 0.000126 0.000003 0.000461 0.000003 0.000003 0.00004 0.000066 0.000019 0.000001 0.000454 0.000193 0.000026 0.000006 0.000068 0.000001
0.000005 0.00013 0.000442 0.000125 0.00003 0.00007 0.000055 0.000065 0.00009 0.000089 0.000333 0.000164 0.000063 0.000073 0.000161 0.000031 0.00046 0.000226 0.000331 0.000038 0.000001 0.000152 0.000001 0.000007 0.000098 0.000077 0.000022 0.000001 0.000531 0.000226 0.00003 0.000007 0.000086 0.000001
0.06 0 0.79 0.79 3.847 3.847 0.036 0.0435 0.264 0.24 0 0 0 1 0 12.44 0.32 0 2.27 0.59 0.18 0.18 0.28 0.28 0.26 0.26 0 0.24 0.24 0.012 0 0.06 0 0.3922
0.04 0 0.564 0.564 2.745 2.745 0.027 0.035 0.19 0.172 0 0 0 0.72 0 8.88 0.23 0 1.62 0.42 0.13 0.13 0.2 0.2 0.1855 0.1855 0 0.171 0.17 0.01 0 0.043 0 0.263
457
