Abstract Representation of Power System
Networks as a Function of Regularity Properties
ESREL 2014
A.B. Svendsen, T. Tollefsen, R.F. Pedersen & K.P. Petursson
Goodtech Power, Bergen, Norway
D. Patel, O.D. Lampe
Christian Michelsen Research AS, Bergen, Norway
Content
• Part 1: Online risk simulator in operation at
Statnett
• Part 2: Visualization
Part 1:
Promaps Online
Probability method applied to power system,
Online calculations
Introduction
• In 2013 the first online risk calculator was put in operation for Norway’s
Transmission system operator, Statnett.
• The model repressing the Norwegian power system, includes a part of the
Swedish and Finland power system.
• Model size: >7000 branches, > 3000 nodes and data update every 10
minutes.
• Promaps Online gives new insight regarding risk in power system, that is
updated every 10 minutes
• With all this new data, new ways for representing the power systems
property regarding the changing risk, is needed.
Background
It started with a storm called Narve hitting Mid- and Northern
Norway in 2006
A need presented itself; to be able to calculate the risk level
during operation and for the following hours…
– To be aware of the current situation
– Planning of measures in areas that cannot handle the consequence of
power outage
The scope of work
In 2009 Statnett R&D started collaborating with Troll Power
(now Goodtech) on developing an online risk simulator of
power systems
The requirements were to be able to calculate:
– The risk level in the entire Norwegian power system every 5 minutes
– Receive power system data from EMS Scada
– Present the risk level with a few colour parameters along with a
dynamic colour indicating risk
– «a glance at the screen»
Status Promaps Norway model
Norway model
Regions
Sub regions
The challenges
• It became obvious that it was needed a better
way to get system insight, fast.
• We need to understand underlying property
of the power systems, which are changing
every 10 minutes.
• In the current way it is too much information
to process to get a grasp on this property.
The inspiration
Part 2
Visualization
• Current visualization techniques with
geographically fixed nodes and straight lines
have issues:
– Limited information can be shown
– To show the entire grid, multiple large screens
must be used, making it hard to get an overview
– Visualizations are static and do not show the most
relevant information
• We present three graph visualization
techniques
– edge bundling,
– focus and context visualization and
– non-geographic graph layout with clustering.
Edge bundling
(Cui, 2008)
Our result
Focus and context visualization
• Standard visualization
of power system.
• SMS levels on nodes
colored with scale
green-yellow-red.
• Percentage of max flow
on edges with scale
blue-yellow-red
• Notice red area to the
left
Focus and context visualization
• A focus and context map
where important area is
zoomed in on displacing the
nodes and edges around but
maintaining an overall
correct geographic
placement of the nodes.
• One can imagine a dynamic
system automatically
creating multiple zoom-ins
on areas of importance.
Focus area
Non-geographic Graph Layout with
Clustering
Cluster correspondences
Supernodes and superbranches
reorganized
and given
quantitaive
values
Conclusions
•
After installing the new simulation tool for reliability studies at Statnett SF, the need
for a customized presentation method has become prominent.
•
The most promising approach has been the graph layout with clustering. This
approach gives the opportunity to, by a single look at the screen, identify the most
critical cuts, and get an overview of the risk level in each area along with the state of
the power system as a whole. By zooming in on a cluster, more information regarding
the risk related to each individual branch and bus is shown.
•
The focus and context approach is particularly suitable if the operators want a
complete overview of the power system, while still being able to identify individual
critical components. It offers a great way of visualizing changes in a network over
time, as different parts of the system will be prominent, depending on the current
risk level in each area.
•
Edge bundling is suited to use in combination with the focus and context approach,
as branches in areas of little interest can be bundled together, in order to simplify the
presentation.