Rev. Téc. Ing. Univ. Zulia. Vol. 39, Nº 2, 14 - 24, 2016
Reliability Evaluation of Distribution Network with Different
Distributed Generators
Binbin Fan, Limei Zhang*, Yongfu Liu, Jing Lv, Wenzhi Li
College of Information Science and Technology, Agricultural University of Hebei, Baoding 071000, China
Wei Tang
College of Information and Electrical Engineering, China Agricultural University, Haidian District, Beijing
100083, China
Abstract
Different types of distributed generations(DG)have different impacts on the reliability of distribution network.
In this paper, a new method of reliability evaluation for distribution network with multi-type of DG is proposed
by analyzing the reliability models of different DG. Based on the analysis of the randomness of DG, the
multi-state model for DG output power is established. The proposed evaluation method based on the breadth
first search strategy can achieve a reasonable division of the island, and the network topology analysis is
implemented to achieve the simplification of the distribution system network structure. The improved minimal
path analysis is given to evaluate the reliability of distribution network with multi-type of DG. The RBTS Bus6
IEEE system is applied to demonstrate the feasibility and practicality of the proposed method. Numerical
simulations results show that the proposed method is simple, practical and reliable, and can quickly and
efficiently evaluate the reliability of distribution network with different DG.
Key words: Distributed Generators, Distribution Network, Minimal Path Analysis, Reliability Evaluation.
1. INTRODUCTION
Distributed generator (DG) which is distributed in the vicinity of the users is small size, economic, efficient
and reliable power source (Liang and Hu, 2003). DGs not only can be used as a standby power source or
integration of combined heat and power (CHP) units but also can regulate peak power and can realize
independent power generation (Zhang and Tang, 2010). Because DG can make full use of abundant, clean and
environmental local renewable resources, it has become a feasible measure to develop green power and achieve
energy saving and emission reduction (KANG and GUO, 2010). In our country, it is one of the main application
styles that DG connects to distribution network. However, a large number of DG connected, the distribution
system transform from the original single power source network to multi-source power network. The power
flow is no longer a one-way and two-way power flow will appear, which make the reliability evaluation models
and methods of distribution network must be a big change (Liang and Hu, 2003; Li and Liu, 2012).
At present, reliability evaluations of distribution network with different distributed generators mainly
include analytical method (Wan and Ren, 2003) and simulation method (Wan and Ren, 2004). Reference (Qian
and Yuan, 2008) considered the uncertainty of the components and original load parameters of distribution
system, analyzed the influence of DG on distribution system reliability by using the method of interval
calculation, and reduced the computation time through simplified network. In (Al-Muhaini and Heydt, 2013),
authors make a comprehensive evaluation of the reliability index of distribution network with DG by
establishing Markov model and state transfer matrix. In (Al-Muhaini and Heydt, 2013), the topology structure of
distribution network was simplified, then minimal path and minimum cut sets were solved by prime coding and
Peri network, finally reliability index were calculated. According to the influence of DG on distribution network
reliability and the fault recovery method of distribution network partition, (Tian and Yuan, 2013) proposed the
probability distribution model and algorithm of reliability with DG, effectively overcome the deficiency that the
traditional expectation index is insufficient. In (Chowdhury and Agarwal, 2003; Mitra and Vallem, 2012), the
sequential Monte Carlo simulation method was used to evaluate reliability on distributed network with DG or
micro grid. This simulation method had included the uncertain impact of DG units, but the computation time
would be longer than the other.
At the moment, there have been considerable works with respect to reliability of distribution network under
the certainty environment, but related research considering the randomness and uncertainty of renewable energy
DG is lack. In addition, most of the researches have been carried out from single DG type, but they have a lack
of adaptability to the reliability evaluation with various types of DG in distribution network. Therefore, this
paper establishes the multi-state model for DG output power based on the analysis of the randomness of DG.
The proposed evaluation method can achieve a reasonable division of the island based on the breadth first search
strategy, and the network topology analysis is implemented to achieve the simplification of the distribution
system network structure. The improved minimal path analysis is given to evaluate the reliability of distribution
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Rev. Téc. Ing. Univ. Zulia. Vol. 39, Nº 2, 14 - 24, 2016
network with multi-type of DG. The feasibility and practicability of the proposed method are verified by using
the RBTS Bus6 IEEE system as an example.
2. MULTI-STATE MODEL OF DIFFERENT DG
Wind power and photovoltaic power generation has been widely studied and applied because of its clean,
environmental, renewable, and other characteristics. However, the randomness and uncertainty of the output
power of the DG has an important influence on the reliability of distributed network (Wei and Wu, 2007; Chen
and Wen, 2015). Therefore, based on the historical data of wind power generation and the probability
distribution of photovoltaic power generation, the multi-state model of two kinds of DG output is established by
using statistical analysis and Monte Carlo simulation method to analyze the influence of different DG on the
reliability of distribution network.
2.1. Multi-State Model for DG Output of Wind Turbine
At present, the researches of wind power generation in distributed network have accumulated a huge mass
of data. Based on the Weibull distribution of wind speed and the existing wind speed data, the output of wind
power is mainly calculated by following equation (1) (Guo and Xu, 2012; Karki and Hu, 2006).
0
0 ≤ 𝑉 < 𝑉1
𝑎 + 𝑏𝑉 + 𝑐𝑉 2 𝑉1 ≤ 𝑉 < 𝑉2
𝑃𝑤 =
(1)
𝑃𝑟 𝑉2 ≤ 𝑉 < 𝑉3
0
𝑉 ≥ 𝑉3
where, Pw is the output power of wind power generation.Pr isthe rated output power of wind power generator.
𝑉1 is cut-in wind speed, 𝑉2 israted wind speed, 𝑉3 is cut-out wind speed; a, b and c are parameters.
In this paper, the wind speed data come from the historical measured data in a certain area, the data is
measured every ten minutes. The multi-state model of DG output of wind power generation is obtained by
statistical analysis, and the concrete steps are as follows:
Step 1:When the wind speed is less than cut-in wind speed or greater than the cut-out wind speed, this part
of the range is described a state, mean time the output power is 0; When the wind speed is greater than rated
wind speed and less than the cut-out wind speed, this part of the range is described another state, meantime the
output power is 1 MW; Finally, when the wind speed is greater than cut-in wind speed and less than the rated
wind speed, this part of the range is divided into n states. So wind speed coordinate axis is in total divided into
n+2 states.
Step2: To statistical hours of wind speed of wind turbine belongs to each state within a year.
Step 3:The average of the wind speed and power of each state is respectively calculated, they will be as
output model of each state.
Step 4:The calculated hours per state divide the total numbers of hours within one year (8760h), and the
probability of each state will be obtained.
It is assumed that the cut-in wind speed of wind turbine is 3m/s, the rated wind speed is 12 m/s, the cut-out
wind speed is 25 m/s. The rated power of the wind turbine is 1MW. Let n be 3, the multi-state model of wind
turbine DG output is shown in Table1.
Table 1. Multi-state model of wind turbine
wind speed(m/s)
output power(MW)
probability
<3 or >25
0
0.3970
3~6
0.0277
0.1403
6~9
0.2399
0.0985
9~12
0.6753
0.0726
>12 and<25
1
0.2916
2.2. Multi-State Model for DG Output of Photovoltaic Generation
The output of photovoltaic generation (PV) is closely related to the sun lighting, and the light intensity is
generally subject to beta distribution (Guo and Xu, 2012), which can be simulated the light intensity of a certain
region within a year (8760h) by Homer software. By the light intensity data, the output power of photovoltaic
distributed generation (Park and Wu, 2009) can be expressed as
𝑃𝑛 𝐺bi 2
𝑃𝑝 =
𝐺𝑠𝑡𝑑 𝑅𝑐
𝑃𝑛 𝐺bi
𝐺𝑠𝑡𝑑
0 ≤ 𝐺bi < 𝑅𝑐
𝑅𝑐 ≤ 𝐺bi < 𝐺𝑠𝑡𝑑 (2)
𝑃𝑛 𝐺bi ≥ 𝐺𝑠𝑡𝑑
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Rev. Téc. Ing. Univ. Zulia. Vol. 39, Nº 2, 14 - 24, 2016
where, Pp is the output power of the PV array;Pn is the rated output power of the PV array;Gbi is the light
intensity coefficient of the i-th hours; Gstd is a given light intensity under the standard environment, usually take
1000W/m2; R c is a specific light intensity, usually take 150 W/m2.
Fully considered the uncertainty of the sun lighting, the relationship between the multi-state model and the
probability of photovoltaic generation system is established according to the size of output power of the PV
generator. Specific steps are as follows:
Step 1: To simulate and calculate power output of photovoltaic generation DG within a year (8760h), the
output power per hour is sorted by descending order.
Step 2: Take out of the maximum and minimum of the output power of the PV DG within a year, the
output power is divided into n equal interval, each interval as a state of the DG output.
Step 3: To statistical hours of output power of PV generation belongs to each state within a year.
Step 4:To calculate the average of the output power of each state will be as output model of each state.
Step 5: The calculated hours per state divide the total numbers of hours within one year (8760h), and the
probability of each state will be obtained.
Let n be 5, the multi-state model of photovoltaic power generation system is shown in Table 2.
Table 2. Multi-state model of photovoltaic power generation system
output power
probability
0.01
0.5358
0.3016
0.041
0.4982
0.0341
0.7086
0.035
0.9902
0.3541
3.RELIABILITY INDEX OF DISTRIBUTION NETWORK WITH DG
DG to connect to distribution network has important impacts on the reliability of load point and distribution
system. Therefore, in this paper, authors make use of load point reliability index and system reliability index to
study the effect of different DG on distribution system reliability through previously established multi-state
model.
3.1.Reliability Index of Load Point
Reliability index of load point includes (Guo, 2003): interruption frequencyλl , average interruption
duration every fault, average interruption durationUl . Based on established multi-state model, probability
formula with every DG is expressed as formula (3-5).
λ𝑙 = 𝑠𝑗=1(𝑃𝑗 𝑛𝑖=1 𝜆𝑖 )
(3)
𝛾𝑙 = 𝑈𝑙 λ𝑙
𝑠
𝑗 =1 𝑃𝑗
𝑈𝑙 =
(4)
𝑛
𝑖=1 𝜆𝑖
𝛾𝑖 (5)
where, Pj is the probability of DG output power to𝑗; 𝑠is the model number of all output power states; λi is
fault rate of component𝑖; γi is average repair time of component𝑖; 𝑛is the sum of all components.
3.2.Reliability Index of Distribution System
The reliability index of the distribution system (Ghajar and Billinton, 2006; Balijepalli and Venkata,
2005)includes: the system average interruption frequency index (SAIFI), the system average interruption
duration index(SAIDI), the average service availability index (ASAI), the energy not supplied index (ENSI),
customer average interruption frequency index (CAIFI), customer average interruption duration index (CAIDI),
calculated formulas are expressed as formula (6-10).
SAIFI = 𝑙 𝜆𝑙 𝑁𝑙 𝑙 𝑁𝑙
(6)
SAIDI =
ASAI = (8760
𝑙
𝑁𝑙 −
𝑙 𝑁𝑙
CAIFI =
𝑙
𝑁𝑙 𝑈𝑙
𝑈𝑙 ) 8760
𝑙
𝑁𝑙 (7)
𝑙
𝑁𝑙 (8)
ENSI =
𝐿𝑎(𝑙) 𝑈𝑙 (9)
𝜆𝑙 𝑁𝑙
𝑗 ∈∅ 𝑁𝑙 (10)
𝑙
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Rev. Téc. Ing. Univ. Zulia. Vol. 39, Nº 2, 14 - 24, 2016
CAIDI =
𝑙 𝑁𝑙
𝑈𝑙
𝑙 𝜆𝑙 𝑁𝑙 (11)
where, Nl is number of users of load point l; λl is fault rate of load point l; Ul is average interruption time of
load point l; La(l) is average load of load pointl; ∅ is a set of load points affected by interruption power.
4.RELIABILITY EVALUATION METHODS OF DISTRIBUTION NETWORK WITH DG
In this paper, the proposed evaluation method based on the breadth first search can achieve a reasonable the
island division, and the network topology and position of the circuit breaker are judged to simplify the network
structure of distribution system, finally reliability index is calculated.
4.1.Isolated Island Division Based on Breadth Search
Isolated island is a new operation mode of the after DG is connected to distribution network, that is, DG
can independently to supply power to a region. The isolated island can flexibly transform between
grid-connected mode and islanded mode according to the actual situation. The operation mode can not only
improve the comprehensive utilization of the energy efficiency, and ensure the power supply reliability of the
system (Liu and Mu, 2015).
Taking into account that the distributed generation is small scale, is located in the vicinity of the customer,
so that the transmission distance should be considered in the view of reducing the network loss when isolated
island is divided. At the same time, in this paper, in order to ensure the reliable supply of important power load,
the weight coefficient is used to reflect the importance degree of load in the process of isolated island division.
The high weight coefficient is given to the important loads, we give priority to consider that load of larger
weight coefficient is divided into the isolated island. Isolated island formation process: firstly, breadth first
search strategy is carried out from bus where DG lies in. The load is hierarchically put into the range of isolated
island according to the transmission distance of DG. Secondly, the load of last layer of the same distance is
successively divided into range of isolated island according to the size of load weight when the total load in the
isolated islands close to the output power of the DG. At the same time, the rated power of the DG must meet the
need of load in the isolated island, and the formed isolated island region is connected.
A4
LP6
A3
A2
DG
LP2
A1
LP1
Figure1.
LP3
LP4
LP5
Isolated Island Division
The process of isolated island division is shown in Figure 1. Load point LP1 connected to DG is divided
into first layer of isolated island according to the connection relations among the load point. Then, the load LP2
and LP3 connected to LP1 are divided into second layer of isolated island; In turn, load LP4 is included into
third layer of isolated island; If LP5 and LP6 were added into fourth layer of isolated island, total load would
exceed the rated power of DG. In order to meet need of rated power of DG, LP5 is divided into the fourth layer
of isolated island according to weight coefficient of the load point which is shown in Table 3.
Load point
Table 3. Load point data
Weight coefficient
Load/MW
LP1
0.3
0.2
LP2
0.5
0.1
LP3
0.7
0.3
LP4
0.3
0.2
LP5
0.5
0.2
LP6
0.3
0.2
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Rev. Téc. Ing. Univ. Zulia. Vol. 39, Nº 2, 14 - 24, 2016
Table 4. Isolated Island division results
Numbers of layer
Node No.
A1
DG,LP1
A2
LP2,LP3
A3
LP4
A4
LP5
4.2. Reliability Calculation Based on the Minimal Path Method
The reliability evaluation of distribution system based on the minimal path method can not only take full
account of the influence of the branch protection, section breaker, disconnecting switch and planned
maintenance, but also can effectively deal with the situation of non-standby power supply and non-standby
transformer (Guo, 2007). According to the actual configuration of the system, the algorithm can point out the
weakest link in the system and can be applied to the distribution system with different connection modes.
(1) Principle of minimum path method
The graph is a set of nodes and arcs. A path between two nodes is a set that is composed of a lot of directed
arcs or undirected arcs. If a path of removing any an arc is no longer a path, which is called the minimal path
(Liu and Zhang, 2008).
Start
Input the connection relations
of node in distribution network
Stack initialization
Input initial node,
marked as "accessed"
Push initial vertex
into stack
Pop up the current
element of stack vertex
Elements of stack vertex have
the adjacent points not to access
No
Yes
Output vertex W
Vertex W be marked
as "accessed"
Push vertex W into stack
No
Vertex W is the goal vertex
Yes
Output minimum path of stack in
sequence from starting vertex to
the goal vertex
End
Figure 2. Flowchart of minimum path algorithm
The basic principle of the minimum path method as follows: each load point is required to find the minimal
path. The influence component fault of non-minimum path on the reliability of the load point will be converted
into the corresponding node of minimal path according to the topological relation of distribution network, and
the reliability calculation of the corresponding load point only consider component and node of the minimal
path (Zhang and Chen, 1995; Bie and Wang, 1997). At present, the method of solving the minimal path has the
incidence matrix method, the Boolean determinant method and the search method (Guo, 2003). Because the first
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Rev. Téc. Ing. Univ. Zulia. Vol. 39, Nº 2, 14 - 24, 2016
two methods are lack of adaptability to the programming of large scale nodes, this paper adopts the minimal
path search method based on depth search. The minimal path search algorithm flow chart is shown in Figure 2.
(2) Distribution network simplification based on topology structure
According to calculating minimal path, the distribution network is simplified to reduce the calculation and
improve the efficiency of the calculation. The paper assumes the circuit breaker is 100% reliable, and therefore,
the distribution network is divided into 4 regions based on the circuit breaker position. The fault rate and fault
time of the load will not be affected by the regions that are unrelated with minimal path of the load point to be
calculated, so the unrelated regions can be ignored. In addition, the 100% reliable fuse is equipped at the
forepart of the other load branches in the study area, so load malfunction can’t affect the reliability of the load
point and the branch is removed from the distribution network. Taking IEEE RBTS Bus6 (Billinton and
Jonnavithula, 1995) as an example, as shown in Figure 3, the reliability of load LP15 is calculated, and the
distribution network is simplified as shown in figure 4.
1
LP2
LP1
2
3
LP3
4
LP4
5
7
LP6
6
9
8
DG
24
LP5
20
22
19
LP7
10
23
11
LP18
LP17
21
LP16
LP14
LP8
13
12
LP9
LP15
14
DG
26
28
30
LP23
25
15
29
LP22
27
LP21
LP19
LP10
16
LP11
17
LP12
LP20
18
LP13
Figure 3. Connection diagram of distribution network with DG
1
2
3
4
5
7
DG
24
6
9
8
20
22
19
10
23
11
21
13
12
LP15
14
25
15
Figure 4.Distribution network after simplification
(3) Improved minimal path method
The minimal path method can consider the issue of some components in solving the reliability of
distribution system, but for relatively complex system composed of main feeder and branch line, it is complex
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Rev. Téc. Ing. Univ. Zulia. Vol. 39, Nº 2, 14 - 24, 2016
for solving and simplifying minimal path and time-consuming calculation (Guo, 2007). Therefore, the
minimal path method is improved to calculate the reliability of distribution network with DG in this paper.
Distribution network reliability assessment algorithm flow chart is shown in figure 5. According to the
simplified distribution network, all the components in the system can be divided into the minimal path
component and the non-minimal path component. Load point can be divided into two types of load points to
calculate the reliability index based on whether in the area of the isolated island.
Start
Input the initial data
Determine the scope of
the isolated island
Solve minimum path of
the load points
Simplify network
Yes
Load point is within
the scope of the isolated island
No
All the equipment can be
divided into: minimum path
within or outside isolated
island, non-minimum path
All the equipment can be
divided into: minimum path
and non-minimum path
Calculate reliability
index of load points
Calculate reliability
index of load points
No
Load point search is completed
Yes
Calculate reliability
index of system
Output the result
End
Figure 5. Flow chart of reliability evaluation algorithm for distribution network
The first category is the load point of the isolated island, the minimal path from bus to the load point is
calculated, and then the minimal path from DG to the load point will be calculated. Because this kind of load
point have two most paths which can switch the power supply lines, only when the components simultaneously
fault in two minimal paths, the load point fault can be caused. The two second-order faults in minimal paths are
converted, the conversion formula of interruption frequency l (times / year), average interruption duration in
every fault l (hours/ times) and average interruption duration Ul (hours / year) are shown in (12), (13) and
(14).
𝜆𝑙 = 𝜆𝐷 𝜆𝑠 (𝛾𝐷 + 𝛾𝑠 ) (12)
𝛾𝑙 = 𝛾𝐷 𝛾𝑠 (𝛾𝐷 + 𝛾𝑠 )
(13)
𝑈𝑙 = 𝜆𝑙 𝛾𝑙
(14)
where,λD , γD is respectively fault rate and average interruption duration every fault of minimal of path from
DG to load point;s, s is respectively fault rate and average interruption duration every fault of minimal of path
from bus to load point.
Assuming that isolation switch or circuit breaker is equipped between components of non-minimum path
and minimal path, the interruption time of load point is taken as the operation time of isolating devices.
The second kind of load point is outside of isolated island, if the faults occurred in the components of
minimal path, the faults of load point would be caused
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5. ANALYSIS OF EXAMPLES
5.1. Simulation System Parameters
In this section, simulation system adopts the RBTS Bus6 IEEE to verify the validity of purposed method.
The RBTS Bus6 IEEE system have 23 load points, 21 isolated switches, 23 fuses, 23 power distribution
transformers and 4 circuit breakers. It is assumed that operation of circuit breaker and fuse are 100% reliable,
operation time of the isolating switch is 0.5H. The improved connected diagram of distribution network is
shown in Figure 3. Other components data (Allan and Billinton, 1991) are shown in Table 5- Table 7.
Table 5.Reliability indices of equipment
Name
Fault rate
Average repair time/h
feeder
0.05 time/ (km*year)
4
transformer
0.015 time /transformer
30
DG
5 time/year
50
Table 6. Line data
Long/km
Feeder type
Line No.
1
0.60
7, 13
2
0.75
27
3
0.80
9, 21
4
0.90
4, 10
5
1.60
3, 5, 8, 15, 20, 28
6
2.50
2, 6, 18, 23, 26
7
2.80
1, 12, 16, 22, 25, 30
8
3.20
11, 17, 19, 24, 29
9
3.50
14
Table 7. Load Data
Customer
Load Type
numbers of
load node
third
126
Number of
Load node
Load node No.
1
2
1
5
first
132
0.2070
2
1, 6
second
147
0.1659
2
15, 20
second
1
0.1861
2
4, 18
third
1
0.2431
2
7, 23
first
1
0.2101
2
9, 21
first
1
0.2831
3
3, 13, 17
third
1
0.2501
4
10, 12, 16, 22
second
76
0.1585
4
8, 11, 14, 19
Second
79
0.1554
Load Value/MW
0.1808
5.2. Parameters of DG
In order to verify the influence of different types of DG on the reliability of distribution network, in this
paper, the reliability simulation without DG is not only carried out, also reliability simulation under the other
four kinds of DG scenarios are implemented.
(1) Without DG
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Rev. Téc. Ing. Univ. Zulia. Vol. 39, Nº 2, 14 - 24, 2016
RBTS Bus6 IEEE system does not add any DG.
(2) Constant power DG
In the RBTS Bus6 system, the 23-node of the main feeders is connected with the constant power DG of
1MW, the output power is kept as 1MW; the 29-node is connected with the DG of the 1MW, and the output
power is kept as 1MW.
(3) Wind generation DG
In the RBTS Bus6 IEEE system, the 23-node of the main feeders is connected with the wind generation DG,
the rated power is 1MW; the 29-node is connected with the constant power DG of the 1MW and the output
power is kept as 1MW.
(4) Photovoltaic power generation DG
In the RBTS Bus6 IEEE system, the 23-node of the main feeder is connected with the photovoltaic power
generation DG, the rated power is 1MW; the 29-node is connected with the constant power DG of the 1MW and
the output power is kept as 1MW.
(5) Mixed DG
In the RBTS Bus6 system, the 23-node of the main feeder is connected wind generation DG, the rated
power is 1MW; 29-node is connected with photovoltaic power generation DG, the rated power is 1MW.
4.3 Simulation results and analysis
The reliability indices of load point with different DG are shown in Tab. 8, Tab. 9 and Tab. 10. The
changes of the system reliability indices in five cases are shown in Figure 6. Figure 6 has the two longitudinal
coordinates to use MATLAB software to simulate, system reliability index such as SAIFI, SAIDI, CAIDI and
ASAI may reference left vertical axis, ENSI system reliability index may reference right vertical axis.
Table8.Load point reliability indices without DG / with constant power DG
μ/(h*a-1)
λ/(time*a-1)
γ/(h*time-1)
Load
Constant
Constant
Constant
point
Without DG
Without DG
Without DG
power DG
power DG
power DG
1
1.29
1.29
1.2229
1.2229
1.5775
1.5775
12
1.715
1.715
3.9723
3.9723
6.8125
3.8125
15
1.995
2.012
2.4762
1.2404
4.94
2.5022
17
1.955
1.9772
2.9194
1.6537
5.7075
3.2697
20
1.9725
2.0118
3.5234
1.3088
6.95
2.6330
22
1.935
1.9743
3.9483
1.6832
7.64
3.3230
Table 9.Load point reliability indices with wind DG / with PV DG
λ/(time*a-1)
γ/(h*次-1)
Load
Wind
PV
Wind
PV
point
generation
generation
generation
generation
1
1.29
1.29
1.2229
1.2229
μ/(h*a-1)
Wind
PV
generation
generation
1.5775
1.5775
12
1.715
1.715
3.9723
3.9723
6.8125
6.8125
15
2.0029
2.0046
2.0368
1.9427
4.0712
3.8849
17
1.9866
1.9864
1.1251
1.1398
2.347
2.2634
20
2.0118
2.0118
1.3088
1.3088
2.6330
2.6330
22
1.9743
1.9743
1.6832
1.6832
3.3230
3.3230
Table10.Load point reliability indices with mixed DG
Load
λ/(time*a-1)
γ/(h*time-1)
point
1
1.29
1.2229
μ/(h*a-1)
1.5775
12
1.715
3.9723
6.8125
15
2.0029
2.0368
4.0712
17
1.9866
1.1251
2.2347
20
1.9879
2.6550
5.2535
22
1.9825
1.2206
2.4194
22
Rev. Téc. Ing. Univ. Zulia. Vol. 39, Nº 2, 14 - 24, 2016
4.3
23
SAIFI
SAIDI
CAIDI
ASAI
ENSI
3.8
22
3.3
21
2.8
20
2.3
19
1.8
18
1.3
17
0.8
Without DG
Mixed DG
PV DG
Wind DG
16
Constant power DG
Figure 6.Changes of system reliability index with DG
1) Comparison of reliability of load point index with DG and without DG, it can be seen that the reliability
index of the load point within the isolated island is only affected by the connected to DG in distribution network.
The fault rate of the load point in the isolated island is slightly increased, but the time of the load point in the
isolated island is obviously reduced.
2) Compared with the load point index of different position, it can be seen that improvement of reliability
index of the load point is more obvious within isolated island when the load point distance of connected to the
DG is shorter. The improvement of reliability index of LP17 and LP22 in the simulation is best.
3) Compared with changes of system reliability index with different DG, it can be seen that, due to the
uncertainty of wind generation and PV power generation, the fault time of load within isolated island with wind
generation DG and PV DG is also significantly reduced, but it is worse than the reliability index of constant
power DG. The improvement of constant power of DG on reliability of distribution network is the largest
6. CONCLUSIONS
In this paper, a new method for reliability evaluation of distribution network with different types of DG is
presented. Through the analysis of the randomness of the output power of DG, the multi-state model for output
power DG is established. Finally, the reliability assessment of distribution network with different types of DG is
calculated by using the minimal path method. Simulation results show that the reliability index of distribution
network has certain improvement after distribution network is connected with intermittent energy such as wind
and solar, etc.
Acknowledgements
This work was supported by Natural Science Foundation of Hebei Province (No.F2015204090), Science
Research Project of Hebei Higher Education (No. QN2016063), National Natural Science Foundation of China
(No. 51377162) and Science and Technology Project of Baoding (No. 11ZG007).
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