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25th International Conference on Electricity Distribution Madrid, 3-6 June 2019
Paper n° 1957
CIRED2019 1/5
DYNAMIC LINE RATING OPERATIONAL PLANNING: ISSUES AND CHALLENGES
Seyede Fatemeh HAJEFOROSH Math H. J. BOLLEN Lars ABRAHAMSSON
Luleå University of Technology Luleå University of Technology Luleå University of Technology
Sweden Sweden Sweden
fatemeh.hajeforosh@ltu.se math.bollen@ltu.se lars.abrahamsson@ltu.se
ABSTRACT
Network operational planning aims to provide smart
and cost effective solutions to postpone conventional
transmission and sub-transmission expansion. One
emerging measure of improving the efficiency of
power lines utilization is Dynamic Line Rating
(DLR). DLR has to deal with different uncertainties
ranging from production and consumption to
meteorological variabilities. This paper presents the
application of DLR from operational planning
viewpoints and reviews relevant works. It also
addresses DLR protection and challenges that the
power system has to cope with.
INTRODUCTION
Progressive developments in the smart power grid
technology have opened up the possibility to have
increased secure transfer capacity for transmission
and distribution networks without building new lines
[1]. One approach towards this is dynamic line rating
(DLR), which determines the actual current-carrying
capacity based on continuous measurements rather
than conservative assumptions on weather
conditions. There are two approaches to determine
DLR: indirect and direct. Indirect DLR estimation is
a prediction-based method using data from local
weather stations or generating the data by using
numerical weather modeling. Direct methods on the
other hand are based on monitoring line
characteristic such as conductor temperature, line
sag, the tension through the line and clearance to the
ground [2].
One uncertainty for applying DLR to the system is
the uncertain behavior of weather conditions and
current flows. During recent years several
mathematical methods have been proposed to predict
the thermal rating of the conductor and to keep the
maximum allowable current below safety limits [3].
Another uncertainty is the integration of renewable
energy resources (RES) to the grid, specifically wind
power plants that have been addressed in some
research works [4-6]. The objective of these
publications is the estimation of the amount of wind
power generation in the presence of DLR and
investigating possible ways to apply DLR to the
control and management systems. In addition to the
aforementioned uncertainties, various sorts of errors
and/or failures may take place, such that line
capacity will not be estimated correctly.
The purpose of this paper is to illustrate different
applications of DLR for operational planning along
transmission and sub-transmission (regional)
networks [7-8]. Besides, a study of DLR protection
has been conducted to get an overview regarding the
risk of unfavorable interruptions.
OPERATIONAL PLANNING OF DLR
The increasing demand for electricity, the need for
restructuring the generation to renewables, and
obtaining an uninterrupted power system make the
electric power industry working on possible smart
solutions to increase the capacity of the existing
lines. DLR technology is a short-term solution that
helps to improve power system operational planning
by providing a higher current capacity along
transmission lines and reducing operational costs in
the case of thermal limitations.
Line Rating and Conductor’s Temperature
The term DLR was first introduced in the early
1980’s for estimating the real time rating of
overhead aluminium conductor steel-reinforced
(ACSR) conductors. The initial aim was maximizing
the load and evaluating the thermal rating for
different environmental conditions [9]. IEEE
standards discuss both dynamic and steady state
models for calculating heat transfer and heat balance
of the overhead conductor temperature [10]. The
steady-state relation between heat gain and loss,
(1)
In which,, and are convection heat loss,
radiation heat loss, solar heat gain and joule heating
respectively.
For any variations in parameters as ambient
temperature,
, wind speed,
, wind direction, ,
and conductor temperature,
, equation (1) turns
into,
(2)
Where refers to conductor mass per unit
length times the specific heat of the conductor,
is the AC resistance of the conductor at
temperature
, and is the conductor current and
the conductor temperature derivative with
respect to time.
In order to have an accurate thermal estimation, it is
important to seek for independent variables for
25th International Conference on Electricity Distribution Madrid, 3-6 June 2019
Paper n° 1957
CIRED2019 2/5
modeling the weather. However there are some
variables, such as ambient and conductor
temperatures as well as wind speed and direction,
that reoccurs as independently modelled in the
literature studied in this paper. In equation (2) some
variables and parameters needed for full accuracy
are omitted for simplicity.
Time-scale classification of DLR
From decision making perspective, here in this
study, we categorize DLR studies into two main time
horizons. Long-term studies that refers to
(investment) planning and design issues on the one
side; and short-term planning, such as weekly, daily,
hourly and real-time operation concerning modeling
of environmental data uncertainties [11] on the other
side.
Long-term planning
The main objective of the long-term planning is the
extension and/or expansion of the current
transmission networks. The classical approach is to
define some (bad, but yet probable case as the) worst
case weather conditions, static line rating (SLR), for
which planning is done. Since the on-average
weather changes for different seasons, seasonal
rating was introduced to increase the capacity of the
line compared to the worst case scenario. Expected
load growth and structural grid changes such as the
integration of RES motivates a more efficient and
flexible usage of the line capacities. Therefore, DLR
as a part of the smart grid technology enables the
utilities to postpone expansion through revealing the
hidden current capacity.
Short-term operational planning
DLR is well suited for the short-term operational
planning such as scheduled maintenance of overhead
lines from a week ahead or up-rating the loaded lines
if weather conditions allow. Updating the thermal
rating of the critical lines based on the changes of the
weather conditions is the main emphasis of the short-
term studies. It is especially valuable in an
emergency situation when higher capacity is needed.
In order to keep track of changes in the
meteorological data, weather forecast models are
introduced for making informed decisions prior to
the operation. This is especially important when it
comes to managing the short-term overload capacity
of transmission lines. Generally, ampacity
estimation can be predicted in different time periods
based on the importance of the line and the limitation
of clearance to the ground for each line.
There are different techniques to handle
uncertainties in the DLR calculation, of which
probabilistic, hybrid possibilistic–probabilistic
(fuzzy-based) and interval-based techniques are
three common ones [11]. While each technique
describes different method to achieve an accurate
model, all of them work to show the effect of input
parameters on the output of the model. In a brief, for
the probabilistic technique, model input parameters
are treated as random variables with a known
probability density function (PDF). Combination of
both random and probabilistic parameters forming
the second technique as fuzzy-based DLR based on
fuzzy set theory. And finally, an interval-based
technique is to some extent similar to a probabilistic
modeling with a uniform PDF, assuming that a
specific variable obtains its value from a known
interval [11].
The application of probabilistic DLR estimation is
widespread through different research works.
Modeling the climate changes through a Markov
Chain Monte Carlo algorithm is carried out in [6, 12-
13] not only to predict the conductor temperature
each hour but also to increase the reliability of the
DLR implementation. Modelling weather variables
as multivariate correlated Gaussian random
variables are used in [14] to characterize the weather
data uncertainties each hour. The simulation is
carried out by a Monte Carlo technique, then based
on the minimum ampacity obtained at different
spans, they estimate the maximum current capacity
of the entire transmission line.
A probabilistic forecast method to model and predict
the ampacity of overhead lines up to 27 hours ahead
is introduced in [15] based on combining numerical
weather prediction and a machine learning algorithm
to calculate the ampacity of two lines located in
Northern Ireland. The benefits of this method is in a
daily operation to overcome some thermal
constraints while providing safe and reliable
operating conditions.
Comparing probabilistic to fuzzy (hybrid
possibilistic–probabilistic) techniques indicates that
fuzzy DLR has a better performance in terms of
measuring inaccuracies. Besides, they are less
computationally burdensome estimating the line
thermal rating [16]. Despite advantages of hybrid
possibilistic–probabilistic solutions, there are still
very few papers working in this area [13, 16- 17]. In
[13] the authors suggest an hourly prediction method
by extending their Markov model to a fuzzy-based
reliability model. They use an interactive method
resolution (IMR) technique to solve the optimization
problem for the load curtailment model. A fuzzy-
based solution to calculate the DLR is introduced in
[17]. The method presented is capable of calculating
the DLR by predicting some specific weather
variables an hour ahead. Ambient temperature, solar
hour angle, as well as the wind speed and direction
are the input variables that were characterized as
fuzzy numbers. It was [17] concluded that if there is
a good weather forecast, fuzzy evaluation can
effectively model the measurement inaccuracies and
condition changes.
25th International Conference on Electricity Distribution Madrid, 3-6 June 2019
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During operation
System operation includes all the activities for
predicting and estimating the thermal rating from
several minutes prior to the real-time operation to
on-line monitoring and measurement during
operation [18]. Weather monitoring system is done
through two common ways based of the network
topology. One way is by using weather stations to
measure data in a specific point of the line and the
other is by getting access to on-line data sources
from satellites. In addition to weather monitoring
system, direct monitoring of the line’s characteristic
can offer more accurate estimation for the line’s
ampacity associated with the weather measuring
system [4, 19-20]. In this regard [21] suggests a
probabilistic state estimation method to predict and
estimate the conductor’s average temperature. This
method uses an Extended Kalman Filter algorithm
based on a dynamic heat transfer. Weather
conditions, current intensity, conductor parameters
and direct measurements of the line’s characteristic
are considered as input parameters. The reason
behind direct measurements is to increase the
accuracy of the DLR calculation in critical spans.
Curtailment in Combination with DLR
Installing additional generation units such as wind
power plants will bring extra capacity to the system.
At the same time, however, it will increase the risk
of lines’ thermal overloading. One practical way to
relieve a critically overloaded line is the curtailment
of consumption or production to ensure the
availability of overhead transmission lines [22-23].
Since, there is a positive correlation between wind
farms’ outputs, wind speed and cooling of the
conductor it is important to find a way to increase the
capacity while keeping the system under the safety
margins. Combining curtailment and DLR provides
this opportunity to maintain both criteria at the same
time. Such scheme allows larger amount of
production to be connected to the grid with
deduction in economic risks compared to using only
the curtailment method. Moreover, it can relieve the
possible congestion brought by RES integration. An
approach presented in [24] is a flexible load
shedding model to compromise between benefits of
obtaining an allowed higher, more flexible current
rating of the line, and the increased risks for system
instability caused by weather data uncertainty. In
fact, this combination will help to minimize the rate
of produced energy curtailment per year and to
increase the flexibility of the overall system.
DLR AND PROTECTION OPERATION
Increasing the demand for more electricity and
migrating the electricity generation from fossil fuels
to RES are some uncertainties that have been added
to the electrical power networks. One way to
mitigate the amount of curtailed energy in case of a
risky situation is improving the protection system
along the overhead lines. It also incorporates in
reducing possible economical risks brought by
unnecessary curtailment. In this regard, overload and
overcurrent are two classical protection systems that
prevent the conductor exceeds its thermal limits. One
approach to improve the typical system is combining
the DLR technology with the protection operation.
Table 1 indicates different states for the DLR using
terminology from protection operation.
Table1: Different states for DLR
States
Action is
Needed
Action is
Taken
Protection
Operation
RMS<AA<EA
No
No
Correct Action
AA<RMS<EA
Yes
No
Failure to Operate
EA<AA<RMS
Yes
Yes
Correct
Disconnection
RMS<EA<AA
No
No
Correct Action
EA<RMS<AA
No
Yes
Maloperation
AA<EA<RMS
Yes
Yes
Correct
Disconnection
In Table 1 the term AA stands for the Actual
Ampacity, EA for Estimated Ampacity and RMS for
the RMS current that momentary flows through the
line.
Clearly, failing to operate is one of the critical
conditions that indicates that the DLR has been over-
estimated. It can be defined as a situation in which
the line is overloaded, but this overloading cannot be
detected by the protection system. Under-estimation
of the DLR is the other condition that cause wrong
operation of protective devices. This can emanate
from measurement errors or failure of
communication sensors and can lead to unnecessary
disconnections of production or loads, and/or costly
usage of fast ramping production units, to change the
power flows in the grid.
Thermal Overload protection
In order to prevent overheating of conductors in case
of any faults or heavy loads, overload protection is
installed in the power lines [25]. The principle of
this protection relies on the quantity of the current
compared with the predefined, offline, threshold.
But as the conductor temperature is dependent on the
current, line characteristics, and ambient variables, it
would be more efficient to make the threshold
variable and online. Thus, the model would be able
to estimate the temperature of the conductor in a
specific time interval and compare it with the
maximally tolerable conductor temperature rather
than comparing current intensity with the worst case
current. Fig. 1. illustrates an overall scheme of the
overload protection combined with DLR, at which
CB refers to the circuit breaker.
25th International Conference on Electricity Distribution Madrid, 3-6 June 2019
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Fig. 1. A general protection scheme combined with DLR
The benefit of such modification is the better
utilization of the power system considering the
realistic weather condition and at the same time
protecting the network in case of true risky weather
conditions. In [26] research has been done modeling
the weather as stochastic variables with Monte Carlo
integration and linear filtering to estimate the
distribution of the conductor temperature. Results
indicate that the used method provides a fast
approximation of conductor temperature which is
necessary for a reliable operating decision.
UNCERTAINTY AND RELIABILITY
One of the DLR challenges is assessing the accuracy
and validity of the models for calculating the line’s
rating. In order to reduce feasible risks brought by
DLR, one can apply different DLR methods under
similar conditions, e.g. in a same line, to find the
most reliable method for each case depending on its
application [27].
Bad data, difficulty in modeling weather variables
and device inaccuracies are some other uncertainties
from the measurement viewpoint that degrades the
estimation accuracy. Besides, any modelling flaws,
as well as deviations in geographical and
conductor’s parameters data may lead to inaccurate
DLR estimations. Therefore, it is of great importance
to analyze the risk of each DLR implementation to
see how reliable it would be. Increasing the DLR
reliability by optimizing the system operation is
discussed in [28]. Authors in [29] estimate the
reliability of DLR in heavily loaded networks to
reduce the loss of load expectation considerably.
When weather variables change suddenly, operators
need to have a fast reliable response to keep the
system in a safe mode. One way of hedging for
sudden weather changes is considering the average
value of rating over a time horizon [30]. Calculating
rating of components far from their observation
point and using pre-defined critical spans are some
other great uncertainties that should be investigated
considering the reliability of DLR.
DISCUSSION
Dynamic thermal line rating associated with the
protection system can increase the productivity of
the line and the system, provided that accurate and
reliable meteorological input models are applied.
There are several challenges for the utilization of the
DLR in power grids. Adjusting the rating of other
electrical components such as transformers and
protective relays with DLR technology is one of
these issues [31]. Location-dependency of weather
parameters is one other problem because the weather
can vary through different spans or even through
specified distances within each span. Thus, there is a
need to identify critical spans and spots for
monitoring and measurement [32]. Forecasting the
weather data in a shorter time prior to the real-time
operation and increasing the accuracy of the spatial
resolution needed for the DLR forecasting are some
issues good to consider in the future studies. Another
subject in this regard is the emergence of
technological advances that makes the system
smarter and complicated. One of the features of a
smart grid is usage of smart sensors and
measurement systems like PMUs in the system [33].
As a result it is important to take the reliability of
these systems into account while working on the
DLR estimation.
CONCLUSION
This paper studies the application of DLR with focus
on the operational planning viewpoint and stochastic
aspects. Besides providing a thorough literature
study, the paper discusses protection operation and
schematic improvements.
In the end, increased renewable energy and load
growth might lead to investments in new lines being
necessary. Methods like DLR can help to release the
SLR thermal constraints and increase the capacity of
lines without large-scale investments, or at least
postponing them.
Among different methods introduced for DLR
operation, a generalized protection, combined with
curtailment can have a significant effect on the short-
term operational planning and help improving the
reliability for network operators.
ACKNOWLEDGMENT
This work has been funded by Skellefteå Kraft,
Energiforsk and the Swedish Energy Agency.
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