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A Sensitivity-Based Three-Phase Weather-Dependent Power Flow Approach for Networks with Local Controllers—PART II: Case Studies

TL;DR: Simulation results confirm that the proposed sensitivity-based approach produces more accurate results than the existing approaches since it considers the actual switching sequence of LVCs as well as the weather and magnetic effects on the network.
Abstract: Power flow is an integral part of distribution system planning, monitoring, operation, and analysis. This two-part paper proposes a sensitivity-based three-phase weather-dependent power flow approach for accurately simulating distribution networks with local voltage controllers (LVC). This part II, firstly, presents simulation results of the proposed approach in an 8-Bus and 7-Bus network, which are validated using dynamic simulation. Secondly, simulation results for the IEEE 8500-node network are also presented. An extensive comparison is conducted between the proposed sensitivity-based approach and the other existing power flow approaches with respect to result accuracy and convergence speed. Moreover, the influence of weather and magnetic effects on the power flow results and the LVC states is also investigated. Simulation results confirm that the proposed sensitivity-based approach produces more accurate results than the existing approaches since it considers the actual switching sequence of LVCs as well as the weather and magnetic effects on the network. Moreover, the proposed algorithm exhibits accelerated convergence due to the usage of the sensitivity parameters, which makes it an important tool for distribution system analysis.

Summary (2 min read)

I. INTRODUCTION

  • HE first part of this paper presents the theoretical development of the proposed sensitivity-based threephase weather-dependent power flow approach for distribution networks with LVCs.
  • The rest of this Part II is structured as follows: Section II validates the proposed algorithm against dynamic simulation via MATLAB ® Simulink.
  • Moreover, a comparison between the proposed algorithm and two existing approaches is presented in the same section.

II. VALIDATION AND PERFORMANCE OF THE PROPOSED SENSITIVITY-BASED ALGORITHM

  • First and foremost, the proposed algorithm is validated against dynamic simulation in MATLAB ® Simulink using an 8-bus balanced and a 7-bus unbalanced network.
  • In addition, the proposed approach is compared against the power flow methods of [1] * and [3] , with respect to result accuracy and convergence speed.
  • It should be noted that for the purpose of validation against the dynamic simulation, the weather-dependent impacts were neglected in this section.

A) 8-Bus Balanced Network

  • EQUATION * It is noted that the method proposed in [2] presents almost identical results with the method of [1] .
  • SVR 1 executes the first switching action after an intentional time delay (10 sec), followed by subsequent switching actions with a mechanical time delay (2 sec), until the voltage lies within the bandwidth.
  • In Fig. 4 , the tap change profile versus iteration number is presented for the proposed algorithm with the consideration of the sensitivity parameters (refer Section VI of part I).
  • "This work has been submitted to IEEE for possible publication.
  • The produced power flow results are not accurate compared to the dynamic simulation, as explained above.

B) 7-Bus Unbalanced Network

  • This network was selected for simulation since it includes all kind of LVCs (OLTC, SVR, Capacitor, DG), it is unbalanced and also its simple topology facilitates the comparison and interpretation of the simulation results.
  • Data about the network, the controller of OLTC, SVR, capacitors and DG are presented in Table IV .
  • Table VI summarizes the calculated LVC's states of each phase in the 7-Bus network using dynamic simulation, the proposed algorithm without and with sensitivity parameters, as well as the methods of [1] and [3] .
  • It is observed that the proposed algorithm presents identical results with those of Simulink, confirming its accuracy.
  • Copyright may be transferred without notice, after which this version may no longer be accessible." sensitivity parameters combines the high accuracy with the fast convergence, as no other power flow method so far.

III. INFLUENCE OF WEATHER ON THE LVC STATES AND POWER FLOW

  • The authors conduct a case study on the large IEEE 8500-Node network to highlight the important impacts of weather on the LVC's state and power flow results, which cannot be investigated via conventional approaches.
  • Moreover, the accuracy of the proposed sensitivity-based algorithm in a large-scale network is investigated.

A) Network description

  • Distance of the buses from the substation for the IEEE 8500-Node network, also known as 12.
  • Penguin is the largest single-layer ACSR conductor and can successfully withstand the full load of the network in both investigated environmental conditions, without thermal violation.
  • With the original lines of IEEE 8500-node network, this would not be possible since all lines consist of constant impedances with unknown conductor specific details.
  • On the other hand, the self-reactance is more related to the current than the "This work has been submitted to IEEE for possible publication.

B) Impacts of Weather on LVC's state estimation

  • Simulation results and analysis are presented in this section.
  • It should be noted that the proposed algorithm produces the same power flow results for the two cases, with and without sensitivity parameters.
  • In "This work has been submitted to IEEE for possible publication.
  • In Fig. 17 , both algorithms [1] and [3] yield different results, which do not reflect the impact of weather and magnetic effects at all.

C) Accuracy of the Sensitivity-Based Algorithm

  • The authors investigate how accurate the proposed sensitivity-based algorithm is, compared with the proposed algorithm without sensitivities, when applied in the large IEEE 8500-node network.
  • Six scenarios were simulated for this purpose in the IEEE 8500-node network, explained below.
  • In all scenarios, the winter condition was assumed for calculating the line impedances.
  • In most of the simulations presented in this manuscript, there has been full agreement between the proposed algorithm without and with sensitivities.
  • Nevertheless, this difference is negligible in comparison to the major reduction in the number of iterations required to obtain the power flow solution when sensitivities are adopted, as observed in the last column of Table XVI .

IV. CONCLUSION

  • This part II presents simulation results of the proposed sensitivity-based three-phase weather-dependent power flow method presented in Part I.
  • The proposed algorithm accurately calculates the power flow and estimates the LVC states in distribution networks, by considering the actual switching sequence of LVCs, based on their intentional and mechanical time delays.
  • The proposed algorithm is validated through dynamic simulation in MATLAB ® Simulink.
  • "This work has been submitted to IEEE for possible publication.
  • Copyright may be transferred without notice, after which this version may no longer be accessible.".

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“This work has been submitted to IEEE for possible publication. Copyright may be transferred without notice, after which
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A Sensitivity-Based Three-Phase Weather-
Dependent Power Flow Approach for Networks
with Local ControllersPART II: Case Studies
Evangelos E. Pompodakis
*
, Arif Ahmed
, and Minas C. Alexiadis
*
Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Greece
TUMCREATE, 1 CREATE Way, #10-02 CREATE Tower, Singapore 138602
Email:
*
bobodakis@hotmail.com,
arif.ahmed@tum-create.edu.sg,
minalex@auth.com
AbstractPower flow is an integral part of distribution
system planning, monitoring, operation, and analysis. This two-
part paper proposes a sensitivity-based three-phase weather-
dependent power flow approach for accurately simulating
distribution networks with local voltage controllers (LVC). This
part II, firstly, presents simulation results of the proposed
approach in an 8-Bus and 7-Bus network, which are validated
using dynamic simulation. Secondly, simulation results for the
IEEE 8500-node network are also presented. An extensive
comparison is conducted between the proposed sensitivity-based
approach and the other existing power flow approaches with
respect to result accuracy and convergence speed. Moreover, the
influence of weather and magnetic effects on the power flow
results and the LVC states is also investigated. Simulation
results confirm that the proposed sensitivity-based approach
produces more accurate results than the existing approaches
since it considers the actual switching sequence of LVCs as well
as the weather and magnetic effects on the network. Moreover,
the proposed algorithm exhibits accelerated convergence due to
the usage of the sensitivity parameters, which makes it an
important tool for distribution system analysis.
Index TermsDistributed power generation, Magnetic effects,
Power flow, Time delays, Local voltage controllers, Weather
effects.
I. INTRODUCTION
HE first part of this paper presents the theoretical
development of the proposed sensitivity-based three-
phase weather-dependent power flow approach for
distribution networks with LVCs. The proposed approach has
four distinct characteristics: a) it considers the three-phase
unbalanced nature of distribution systems, b) the operating
state of LVCs is calculated using sensitivity parameters to
accelerate the convergence speed of the algorithm, c) it
considers the exact switching sequence of LVCs based on
their reaction time delays, and d) the influence of weather
variations on the power flow is also taken into consideration.
This second part complements the theoretical development
of part I by presenting simulation results in a balanced 8-Bus
and an unbalanced 7-Bus network as well as in the large IEEE
8500-node network.
The rest of this Part II is structured as follows: Section II
validates the proposed algorithm against dynamic simulation
This research is co-financed by Greece and the European Union (European
Social Fund- ESF) through the Operational Programme «Human Resources
Development, Education and Lifelong Learning» in the context of the project
“Strengthening Human Resources Research Potential via Doctorate
Research 2nd Cycle” (MIS-5000432), implemented by the State
Scholarships Foundation (ΙΚΥ).
via MATLAB
®
Simulink. Moreover, a comparison between
the proposed algorithm and two existing approaches is
presented in the same section. In Section III, a simulation case
study of the proposed algorithm is presented for the large-
scale IEEE 8500-node network under two distinct weather
conditions. The simulation results presented highlight the
importance of considering the weather and magnetic effects
in power flow analysis since they can significantly affect the
power flow results and LVC state estimation. Finally,
Section IV concludes the paper.
II. VALIDATION AND PERFORMANCE OF THE PROPOSED
SENSITIVITY-BASED ALGORITHM
First and foremost, the proposed algorithm is validated
against dynamic simulation in MATLAB
®
Simulink using an
8-bus balanced and a 7-bus unbalanced network. In addition,
the proposed approach is compared against the power flow
methods of [1]
*
and [3], with respect to result accuracy and
convergence speed. All algorithms were coded and
implemented in MATLAB
®
. It should be noted that for the
purpose of validation against the dynamic simulation, the
weather-dependent impacts were neglected in this section.
A) 8-Bus Balanced Network
Fig. 1: 8-Bus network consisting of 3 SVRs and 1 DG.
Fig. 1 depicts the 8-Bus network consisting of 3 SVRs and
1 DG operating in 󰇛󰇜 droop control mode [4]. The 󰇛󰇜
droop equation is given by (1) [4]:
󰇛󰇜
󰇛󰇜
(1)
* It is noted that the method proposed in [2] presents almost identical
results with the method of [1]. Therefore, the method of [2] is not
simulated in this paper due to its strong similarity with [1] with
respect to the produced results.
T

“This work has been submitted to IEEE for possible publication. Copyright may be transferred without notice, after which
this version may no longer be accessible.”
where
,
󰇛󰇜
,
󰇛󰇜
,
are the positive sequence reactive
power, the reference voltage, the droop gain, and the positive
sequence voltage of DG i, respectively. The topology of the
network is similar to the one investigated in [5]. Data about
the network, the controllers of SVRs, and the DG are
provided in Table I. The delays of the controllers are set based
on their distance from the substation.
The slack bus in Fig 1 is assumed to be a substation. The
SVR near the substation has the fastest reaction time and as
the distance from the substation increases, the reaction time
increases [6]. All SVRs are in wye configuration and
initialized to the 0 position for the purposes of simulation.
Each phase of an SVR is modelled with its own local
controller, which is independently controlled.
The DG at bus 8 operates in 󰇛󰇜 mode and generates
balanced phase-to-neutral voltages [7]. The network supplies
four balanced three-phase loads. All the loads are modelled
as constant impedance loads, as shown in Table II, due to the
inaccuracy that Simulink presents in the modeling of constant
power loads.
TABLE I
PARAMETERS OF 8-BUS NETWORK
Distance of the lines
10 km
Voltage of slack bus
7200 V
Frequency of the network
50 Hz
Resistance of lines
0.4 Ω/km
Self-reactance of the lines
0.3 Ω/km
Mutual-reactance of the lines
0.1 Ω/km
Reference voltage of SVRs
7500 V
Bandwidth of SVRs
70 V
Intentional delay of SVR1
10 s
Mechanical delay of SVR1
2 s
Intentional delay of SVR2
20 s
Mechanical delay of SVR2
3 s
Intentional delay of SVR3
30 s
Mechanical delay of SVR3
4 s
Active power of DG
1 MW
Reference voltage of DG (

)
7500 V
Droop gain of DG (
󰇛󰇜
 󰇝󰇞)
0.5

V/Var
TABLE II
LOADS OF 8-BUS NETWORK
Load
Bus 3

 

 

 
Load
Bus 5

 

 

 
Load
Bus 7

 

 

 
Load
Bus 8

 

 

 
The tap positions and switching sequence for all SVRs of
the 8-bus network obtained via the dynamic simulation of
Simulink are presented in Fig. 2. It should be noted that since
all the loads are balanced, the three phases of the SVRs
undergo similar tap changes in this case. SVR 1 executes the
first switching action after an intentional time delay (10 sec),
followed by subsequent switching actions with a mechanical
time delay (2 sec), until the voltage lies within the bandwidth.
Similarly, for SVR 2 and 3. Since SVR 1 has the lowest
intentional and mechanical delay, it undergoes the highest
number of tap changes. The final tap positions obtained via
dynamic simulation for SVR 1, SVR 2 and SVR 3 is 11, 3,
and 1, respectively.
Fig. 2: Switching sequence of SVRs in the 8-Bus network versus time as
calculated by MATLAB
®
Simulink.
In Figs. 3-6, the results of the SVR tap change versus
iteration number are presented for the proposed algorithm as
well as for the algorithms of [1] and [3]. Fig. 3 presents the
tap change profile of all the SVRs versus the iteration number
for the proposed algorithm without consideration of
sensitivity parameters (refer Section IV of part I). In Fig. 4,
the tap change profile versus iteration number is presented for
the proposed algorithm with the consideration of the
sensitivity parameters (refer Section VI of part I). The
proposed algorithm, for both with and without sensitivity
parameter, considers the actual reaction delays of LVCs.
However, as observed in Fig. 4, the consideration of
sensitivity parameters yields accelerated convergence. It is
reminded that the proposed algorithm, both with and without
sensitivity parameters, considers the same switching
algorithm, thus, it presents identical results in both cases.
Their main difference is that the usage of sensitivity
parameters makes the execution of a complete power flow
after each switching action unnecessary, reducing the total
iteration number required for the final power flow solution.
More details are provided in Part I.
Fig. 3: Switching sequence of the three SVRs for the 8-Bus network versus
iteration number, as calculated by the proposed method without sensitivity.

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this version may no longer be accessible.”
Fig. 4: Switching sequence of the three SVRs for the 8-Bus network versus
iteration number, as calculated by the proposed method with sensitivity.
It is observed that the proposed power flow algorithm
yields correct tap positions when compared to the dynamic
simulation, both with and without sensitivities. This is due to
the consideration of the actual switching sequence of LVCs,
as presented in Part I Section IV. On the other hand, the
LVC’s states estimated by the power flow algorithms of [1]
and [3] do not conform to the dynamic simulation, as shown
in Figs. 5 and 6.
Fig. 5: Switching sequence of the three SVRs for the 8-Bus network versus
iteration number, as calculated by the algorithm of reference [1].
Fig. 6: Switching sequence of the three SVRs for the 8-Bus network versus
iteration number, as calculated by the algorithm of reference [3].
Algorithm [1] is only able to correctly estimate the tap
position of SVR 2 as shown in Fig 5. It is reminded that the
authors in [1] divide the LVCs in delay groups based on their
reaction delays. In this example, SVR 1, SVR 2 and SVR 3
belong to the first, second and third delay group, respectively.
Initially, the SVR of the first delay group reacts, by varying
its taps until its voltage lies inside the bandwidth.
Subsequently, the SVR of the second delay group undertakes
switching actions, and so on until all SVR voltages lie inside
their bandwidths.
The algorithm of [3] reaches final tap position in only a few
iterations (Fig. 6), because the algorithm completely neglects
the LVC’s reaction delays in its formulation, and thus, it
updates all the LVC states simultaneously.
A detailed depiction of the convergence characteristic of
the investigated algorithms is presented in Fig. 7. Figure 7
presents the maximum mismatch voltage error between two
consecutive iterations in per-unit versus the iteration number.
As observed, the proposed algorithm converges quickly to the
correct solution when sensitivity parameters are considered.
Although algorithm of [3] appears to have the fastest
converge, the produced power flow results are not accurate
compared to the dynamic simulation, as explained above. It
is again highlighted here that although a difference in
convergence of the proposed algorithm exists with and
without the usage of the sensitivity parameters, the final
results in both cases match those of the dynamic simulation.
Fig. 7: Maximum mismatch voltage error (pu) between two consecutive
iterations versus iteration number for all investigated algorithms.
In Fig. 8, the evolution of the voltage of bus 2 and 5 versus
iteration is presented for the investigated methods. The
proposed algorithm with and without the sensitivity yields the
same final voltages but with different convergence speed as
observed in Fig. 8. On the other hand, the algorithms of [1]
and [3] yield different voltages, which are not in agreement
with the proposed algorithm. The final voltages of the
network for the investigated methods and Simulink are
provided in Table III at the end of the paper. The proposed
approach with and without sensitivities present almost
identical results with those of Simulink, in contrast to the
methods of [1] and [3] that show significant deviations.
Fig. 8: Bus voltage evolution versus iteration for bus 2 and 5.
In Fig. 9, the total three-phase reactive power of the DG
connected at bus 8 is presented. It operates in inductive mode
according to droop equation (1), to mitigate the voltage rise
caused by the high amount of generated active power [8]. It
is observed that the proposed algorithm indicates a
consumption of 542 kVar, which is exactly the same as in the
dynamic simulation. Algorithms [1] and [3] yield different
results. The deviation of the reactive power in algorithms [1]

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this version may no longer be accessible.”
and [3] is caused due to the inaccurate state estimation of
SVRs, which inevitably leads to an imprecise reactive power
calculation.
Fig. 9: Total reactive power consumption of the DG at bus 8.
B) 7-Bus Unbalanced Network
An unbalanced 7-bus network consisting of an OLTC, a
voltage-controlled capacitor, an SVR, and a DG is considered
for further simulation and validation, as shown in Fig. 10.
This network was selected for simulation since it includes all
kind of LVCs (OLTC, SVR, Capacitor, DG), it is unbalanced
and also its simple topology facilitates the comparison and
interpretation of the simulation results. Moreover, its small
size allows the execution of simulations in the time domain
environment of MATLAB
®
Simulink.
Fig. 10: 7-Bus unbalanced network consisting of one OLTC, one SVR, one
three-phase capacitor and one DG.
The DG operates in 󰇛󰇜 droop control mode (see Eq. (1)).
Data about the network, the controller of OLTC, SVR,
capacitors and DG are presented in Table IV. The SVR is
connected in wye configuration, while OLTC in Y
g
-Y
g
connection [11, Section II.C]. Each phase of OLTC, SVR,
and capacitor has its own local controller and is
independently controlled. The DG generates balanced phase-
to-neutral voltages [7]. The network supplies three balanced
and one unbalanced three-phase constant impedance load, as
shown in Table V.
Table VI summarizes the calculated LVC’s states of each
phase in the 7-Bus network using dynamic simulation, the
proposed algorithm without and with sensitivity parameters,
as well as the methods of [1] and [3]. It is observed that the
proposed algorithm presents identical results with those of
Simulink, confirming its accuracy. On the other hand, the
calculated states of the approaches in [1], [3] deviate from
those of Simulink. Moreover, Table VII at the end of the
paper depicts indicatively the voltages of each phase for the
last three buses of the network for the investigated
approaches. The remaining buses are not depicted due to
space limitation. As shown, the proposed method with and
without sensitivity parameter yields near identical results
with those of Simulink, while the other investigated power
flow methods present significant deviations.
TABLE IV
PARAMETERS OF 7-BUS NETWORK
Distance of the lines
10 km
Voltage of slack bus
7200 V
Frequency of the network
50 Hz
Resistance of lines
0.4 km
Self-reactance of the lines
0.3 /km
Mutual-reactance of the lines
0.1 /km
Reference voltage of SVR and OLTC
7500 V
Bandwidth of SVR and OLTC
70 V
Reference voltage of CAP
7500 V
Capacitance of each phase


F
Bandwidth of Capacitors
350V
Intentional delay of OLTC
10 s
Mechanical delay of OLTC
2 s
Intentional delay of CAP
20 s
Intentional delay of SVR
30 s
Mechanical delay of SVR
4 s
Active power of DG
1 MW
Reference voltage of DG
7500 V
Droop gain of DG (
󰇛󰇜
 󰇝󰇞)
 

V/Var
TABLE V
LOADS OF 7-BUS NETWORK

 

 

 

 

 

 

 

 

 

 

 

 
TABLE VI
LVC’S STATES CALCULATED BY THE INVESTIGATED APPROACHES FOR
THE 7-BUS NETWORK
OLTC 1 Taps
(phase a, b, c)
CAP 1
(phase a, b, c)
SVR 1 Taps
(phase a, b,
c)
DG Reactive
Power
Simulink
(10, 12, 10)
(ON, ON, OFF)
(2, 4, 3)
690 kVar
(inductive)
without
sensitivity
(10, 12, 10)
(ON, ON, OFF)
(2, 4, 3)
690 kVar
(inductive)
with
sensitivity
(10, 12, 10)
(ON, ON, OFF)
(2, 4, 3)
690 kVar
(inductive)
Algorithm
[1]
(11, 10, 10)
(OFF, ON, OFF)
(3, 2, 2)
666 kVar
(inductive)
Algorithm
[3]
(11, 11, 10)
(OFF, ON, OFF)
(3, 3, 2)
625 kVar
(inductive)
Finally, the number of iterations required by the algorithms
to converge with an accuracy of 10
-4
pu are presented in
Tables VIII and IX, for the 8-Bus and 7-Bus networks,
respectively. As shown, the algorithm of [3] presents the
fastest convergence since it calculates all LVC’s state
simultaneously ignoring their reaction delays. The proposed
algorithm without the sensitivity parameters has the slowest
convergence due to the successive power flow executions
after each switching action. The proposed method with

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sensitivity parameters combines the high accuracy with the
fast convergence, as no other power flow method so far.
TABLE VIII
REQUIRED ITERATIONS FOR THE INVESTIGATED APPROACHES IN THE 8-BUS
NETWORK
without
sensitivity
with
sensitivity
Algorithm
[1]
Algorithm
[3]
Required
iterations
74
27
61
9
TABLE IX
REQUIRED ITERATIONS FOR THE INVESTIGATED APPROACHES IN THE 7-BUS
NETWORK
without
sensitivity
with
sensitivity
Algorithm
[1]
Algorithm
[3]
Required
iterations
96
29
76
8
III. INFLUENCE OF WEATHER ON THE LVC STATES AND
POWER FLOW
We conduct a case study on the large IEEE 8500-Node
network to highlight the important impacts of weather on the
LVC’s state and power flow results, which cannot be
investigated via conventional approaches. Moreover, the
accuracy of the proposed sensitivity-based algorithm in a
large-scale network is investigated.
A) Network description
Fig. 11 depicts the IEEE 8500-Node Network, which has
been modified by including 4 DGs operating in several
modes. The network originally includes 4 SVRs and 4
capacitors [9], as shown in Fig. 11.
Fig. 11: IEEE 8500-Node network consisting of 4 SVRs and 4 three-phase
capacitors [9].
To get a sense on the position of the buses in the network,
the distance of each bus from the substation is depicted in Fig.
12.
Fig. 12: Distance of the buses from the substation for the IEEE 8500-Node
network.
The 4 additional DGs are connected to the buses 100, 350,
835, 1600. Data about the power profile and voltage/current
profile of DGs are provided in Table X, while data about the
reference voltages, the droop gains, the active powers, etc. are
given in Table XI. For DG 1, which is a synchronous
generator (SG), the negative- and zero-sequence impedance
are
 . DG 1 and DG 4 operate in constant
voltage mode (treated as PV buses) and generate constant
active power and positive-sequence voltage. DG 2 and DG 3
operate in droop control mode generating balanced currents
(refer to Section III.D of Part I for more details about the
operational modes of DGs).
Moreover, data of the LVCs are provided in Table XI. Each
phase of the LVCs is independently controlled. The
capacitors are voltage controlled and the SVRs are in wye
configuration. The time delays of SVRs and capacitors are set
based on their distance from the substation [6], while DGs
react instantaneously. LVCs near to substation have faster
reaction times as shown in Table XI. Each controller
regulates the voltage of the connection point of LVC and no
line drop compensator or any other remote voltage control are
applied.
Finally, all the lines of the network were replaced with the
Penguin ACSR [10]. It is a single-layer conductor with a
cross-sectional area 125.1 
composed of a 6 Aluminum
and 1 steel wire. Penguin is the largest single-layer ACSR
conductor and can successfully withstand the full load of the
network in both investigated environmental conditions,
without thermal violation. This modification was necessary
in order to simulate the influence of weather and magnetic
effects into the power flow results. With the original lines of
IEEE 8500-node network, this would not be possible since all
lines consist of constant impedances with unknown
conductor specific details.
In Figs 13 and 14, the resistance and self-reactance of
Penguin ACSR conductor are presented, respectively, as a
function of conductor temperature and current. The
methodology to calculate the resistance and self-reactance of
Figs. 13 and 14 is presented in [7].
It is observed in Fig. 13 that the resistance of Penguin
ACSR is strongly related to both current and conductor
temperature (conductor temperature is a function of both
current and weather conditions [7]). On the other hand, the
self-reactance is more related to the current than the

Citations
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TL;DR: The proposed step voltage regulator model can constitute an efficient simulation tool in applications where subsequent tap variations are required and can reduce the computation time of power flow around one minute for every tap variation in the large IEEE 8500-node network.

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References
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01 Jun 2017
TL;DR: In this article, the authors provide information about the logistics of this network, including real-time applications of the collected data as well as information on the quality control protocols, the construction of the station data and metadata repository and the means through which the data are made available to users.
Abstract: During the last 10 years, the Institute for Environmental Research and Sustainable Development of the National Observatory of Athens has developed and operates a network of automated weather stations across Greece. The motivation behind the network development is the monitoring of weather conditions in Greece with the aim to support not only the research needs (weather monitoring and analysis, weather forecast skill evaluation) but also the needs of various communities of the production sector (agriculture, constructions, leisure and tourism, etc.). By the end of 2016, 335 weather stations are in operation, providing real-time data at 10-min intervals. This paper provides information about the logistics of this network, including real-time applications of the collected data as well as information on the quality control protocols, the construction of the station data and metadata repository and the means through which the data are made available to users.

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Frequently Asked Questions (1)
Q1. What are the contributions in "A sensitivity-based three-phase weather- dependent power flow approach for networks with local controllers—part ii: case studies" ?

This twopart paper proposes a sensitivity-based three-phase weatherdependent power flow approach for accurately simulating distribution networks with local voltage controllers ( LVC ). This part II, firstly, presents simulation results of the proposed approach in an 8-Bus and 7-Bus network, which are validated using dynamic simulation.