Buildinga ContextualizedPower System
Network ModelBuildinga ContextualizedPower System
Network Model

Table of contents
Introduction....................................................................................................................................... pg. 3
Cognite Data Fusion® data model terminology.............................................................................. pg. 4
Functional power system network model....................................................................................... pg. 5
Physical equipment........................................................................................................................... pg. 8
Power transformers.................................................................................................................... pg. 8
SF6 gas-insulated systems........................................................................................................ pg. 10
Contextualization.............................................................................................................................. pg. 11
Power SDK.......................................................................................................................................... pg. 13
Summary............................................................................................................................................ pg. 16

About Cognite©COGNITE 2023 — COGNITE.COM

2

Cognite is a global industrial SaaS company that
supports the full-scale digital transformation ofasset-heavy industries around the world. Our core
Industrial DataOps platform, Cognite Data Fusion®,enables data and domain users to collaborate toquickly and safely develop, operationalize, andscale industrial AI solutions and applications.
Cognite Data Fusion® codifies industrial domain
knowledge into software that fits into your existing
ecosystem and enables scale from proofs ofconcepts to truly data-driven operations to deliver
both profitability and sustainability.
WHY COGNITE DATA FUSION® →BENEFITS FOR YOUR TEAMS →CONTACT SALES →Introduction

WHY COGNITE DATA FUSION® →Data is stored and used in silos and its structure
is in many ways a reflection of the organizational
setup where different parts of the organization
have developed a culture of working in isolation.
Combining data from different sources to conduct
more accurate analysis for power system operations or long-term planning of grid asset maintenance opens up a huge value potential and canlead to smarter investment decisions and better
grid utilization.
Statnett and Cognite, a global industrial AI Software-as-a-Service (SaaS) company, have from 2018
to 2020 been engaged in an R&D partnership. As
part of this R&D engagement, it was explored how
to construct a unified data model (starting with afunctional power system model) that can be populated with information from a multitude of data
sources and queried efficiently by domain experts
using a domain language that is familiar to them.
Such a data model would considerably extend whatis available in the CIM with information from a variety
of other data sources such as special regulations
redispatching events and net settlements, which
in turn would enable Statnett’s analysts to build
applications that improve the company’s operations, maintenance and analysis activities.
This paper describes the work conducted and is

BENEFITS FOR YOUR TEAMS →CONTACT SALES →organized as follows. Section 1 introduces the relevant data modelling terminology within Cognite
Data Fusion® and explains the main underlying
principles. Section 2 and 3 introduce aspects ofthe functional and physical representation of thepower system, respectively. Together, they lay out
the principles on which the unified data model in
Cognite Data Fusion® is built. Section 4 presents
our approach of contextualization, that is theprocess of (semi)automatically joining informationfrom multiple data sets together and extracting therelevant associations between information entities.
Section 5 then discusses our approach to expose
the richness and complexity of the data model toend users in a domain specific language. Section
6 offers a conclusion.

3

The Norwegian grid structure is complex andconducting analysis on grid asset health often
involves combining several data sources. Retrieving and querying data is today to a large degree
static and time consuming and this process is difficult to automate due to the current data platform’s
lack of flexibility hence, data is retrieved manually.
Nonstructured data makes it difficult to see relevant data in context, not just combining ERP and
CIM information, but also information from otherdata sources such as redispatch and metering data

for settlement.

©COGNITE 2023 — COGNITE.COM

Statnett is both the system operator of the Norwegian power system network as well as the ownerand maintainer of the high-voltage transmission
network. For Statnett as the system operator of theNorwegian power system, the natural way to represent data about the power system is not in a hierarchical tree structure, but rather in a graph that
supports advanced graph-related queries naturally. Statnett models its functional power system
network in adherence to the Common Information
Model (CIM) standard and is actively involved in
its ongoing development. For Statnett’s maintenance organization, however, data is organized in
an Enterprise Resource Planning (ERP) system witha hierarchical structure. While those components
form the backbone of the high-voltage transmission network in the real world, there is to date nodata model that fuses the physical equipment view
with the functional power system model.

Cognite Data Fusion® data model terminology

It is through the notion of Relationships that graphbased data models, such as power system models,can be represented in Cognite Data Fusion®.
Another key feature of Cognite Data Fusion® is that
Relationships are typed and timestamped. Types
allow for semantic distinction and are user-defined. The various types used in the power industry context will be addressed in more detail below.
Timestamping refers to the fact that each relationship has a valid_from and valid_to timestampthat allows for efficient versioning of any association over time. This allows, for instance, a powersystem network model to be versioned natively andenables queries for how the power system lookedat any given point in time and when it changed.
WHY COGNITE DATA FUSION® →implements This relationship type is used toexpress which physical equipment implemented
which functional role, e.g. PhysicalTransformer
implements FunctionalTransformer. This allows us
to track moving (physical) components in a (functional) power system network model.
Assets

Implements

Events

Implements

Files

Implements

belongsTo This relationship type is more genericand can be used in several ways:■ Associating data from events, time series,
sequences with functional or physical assets,
e.g. TimeSeriesA belongsTo AnalogB■ Associating assets to higher levels of aggregation, e.g. PowerTransformerA belongsTo SubstationB, or SubstationA belongsTo PriceAreaB
Figure 1: Cognite Data Fusion® data model

Data set

May have

Organize

Nodes
Time series

May have

Relationships

Implements
4

A resource type is an individual data entity witha unique identifier. As seen in Figure 1, resource
types include, among others, Assets, Time Series,
and Events which form the nodes in a labeled property graph. A data model in Cognite Data Fusion® iscontextualized through establishing associations
between related resources. These Relationships
are a resource type that is used to model associations between any two other resource types.

connectsTo This relationship type is used toexpress electrical connectivity in the power system
network context. It is understood that this typeis bidirectional, but directionality follows the CIM
implementation at Statnett, e.g. Terminal connectsTo ConnectivityNode.

Sequences

BENEFITS FOR YOUR TEAMS →May have

Implements

May have

May have
3D

Implements

LabelsProperties

CONTACT SALES →©COGNITE 2023 — COGNITE.COM

Cognite Data Fusion® provides a data model with
fixed resource types so that users and application developers can make assumptions about thestructure of the data and rapidly build applications
on top of the data model.

Functional power system network modelin Cognite Data Fusion® is at the core a mirrored
representation of the CIM model, particularly its
equipment profile, but extended with informationfrom additional data sources and enhanced querying capabilities that are provided through grid-specific extensions to the Cognite Python SDK which
is discussed in further detail below.
In the existing data model, the CIM model serves
as the master data source for all functional assets

5

It is important to note that Statnett’s power system
network model is a functional description of theNorwegian power system (including interconnections to and parts of neighboring countries) to theextent it needs to provide a model for system operations. In practice, this means that while it models
the entire Norwegian power system, it only does soto the degree necessary for operating the Norwegian power system. For example, some parts of theregional grid may be modeled as loads for simplification whereas other parts may be extensively
modeled, even for the same voltage level.

Figure 2: Example circuit withfull CIM mappings (see Figure
3.13 in McMorran, A.W. (2007).
An Introduction to IEC 61970301 & 61968-11: The Common
Information Model.)As Statnett adheres to the CIM standard whenrepresenting its power system network model, it
was important for any solution built in Cognite Data
Fusion® to be compliant with the CIM data modeling framework. As a consequence, the data model
WHY COGNITE DATA FUSION® →BENEFITS FOR YOUR TEAMS →and their attributes and metadata. This includes
mainly substations (and all components containedin a substation such as transformers, breakers,
disconnectors, etc.), and transmission lines. In
addition to asset information, we also extract electrical connectivity information where connectivity
between two instances of conducting equipment
is established through a Connectivity Node towhich Terminals connect (see Figure 2 below).

CONTACT SALES →©COGNITE 2023 — COGNITE.COM

A starting point for the data model in Cognite Data
Fusion® is the functional power system networkmodel which Statnett stores in a dedicated graphdatabase. As many other grid operators, Statnett
adheres to the CIM standard. The CIM is an open
data model standard developed with the intent tofacilitate the exchange of power system network
information between TSOs, DSOs, and regulators
and also with ENTSO-E, the European Network ofTransmission System Operators for Electricity. For
the purposes of this R&D project, we adhered toversion 15 of the CIM standard.

This node-breaker representation is mirrored in
Cognite Data Fusion®, where the following convention on how connectsTo relationships are used isshown in Figure 3.

Another important concept within a power systemmodel is the notion of a Power Transfer Corridor(PTC). A PTC is a set of conducting equipment(transmission lines or transformers) defined overa critical transmission corridor. Statnett maintains
a set of PTCs to ensure integrity of the grid/powersystem/transmission in interfaces between two
regions of the power system, or sets of conduc-tive equipment that are exposed to a significant
portion of the transmission exchange between
two parts of the system (READ MORE →). Monitoring the power flow over a PTC enables the transmission service operator (TSO) to ensure that theconsequences of contingencies (e.g. outages) inthe power system are limited. Safe operating conditions are ensured by keeping the power system
within its operating limits.
At present, the PTCs modeled in Cognite Data
Fusion® are defined in a separate system from
where Statnett stores and maintains its powersystem network model. In the source, the PTCs areserved as a nested data structure which contain its
constituent equipment, as well as pertinent metadata. In the metadata of the PTC we find information on the limiting factors, the type of monitoring
the power flow is under and whether the PTC is
market limiting. The groupings of components fromthe source are persisted in Cognite Data Fusion® asshown in Figure 5.

©COGNITE 2023 — COGNITE.COM

6

While we attempted to stay as true to the CIM
model as possible, there is an important differencein the way Cognite Data Fusion® handles temporalchanges to the power system network model. In
practice, Statnett stores snapshots of its powersystem network model in time which makes queries
for changes in the network model impossible. In
Cognite Data Fusion®, we leverage the fact that
relationships are a resource type that can be timestamped, which means any association between
nodes in the data model graph can be given a time
range in which said association is valid. This allows
for both the persistence of assets, time series,
and other information that is no longer present
in the current network model, but also allows foradvanced queries against the entire history of thepower system.

Operationally relevant time series data such asactive and reactive power, current, and voltage but
also oil pressure/temperature in transformers andcable systems is provided by a centralized SCADA/
EMS system at Statnett. The CIM model provides
asset classes for Analogs and Discretes that areused to link the relevant time series to. Each Analog
or Discrete is, like any other object in the CIM model,
identified through a unique master resource identifier (mRID). Time series from the SCADA system
contain the same mRID in their meta information
so that matching of time series to analog/discrete
assets in Cognite Data Fusion® is straightforward.
An example schema is given in Figure 4.

Figure 3: Cognite Data Fusion® node-breaker model

WHY COGNITE DATA FUSION® →BENEFITS FOR YOUR TEAMS →CONTACT SALES →In Figure 5, a ConductingEquipment is either a transmission line, or power transformer. The spsComponents container can also contain generating
units. The ConductingEquipment components aremodeled as the PTC view of the pertinent component. For instance, if a transmission line contributes 40% of its power flow to a PTC this informationis persisted in the ConductingEquipment node. The
ConductingEquipment node is a view of the equipment in CIM, because relationships in Cognite Data
Fusion® are not weighted.

◄ Figure 4: Example schema of how time series
from SCADA are linked to CIM analogs/discretes▲ Figure 5: Example schema for power transfer
corridors
WHY COGNITE DATA FUSION® →BENEFITS FOR YOUR TEAMS →CONTACT SALES →©COGNITE 2023 — COGNITE.COM

In addition to the architecture, we also persist thepower flow through a PTC in Cognite Data Fusion®
in time series.

7

We also note that each of the container nodes has
only one child for illustrative purposes, and thateach container holds a set of components.

Physical equipment

WHY COGNITE DATA FUSION® →To these physical assets other information is linked
through relationships that pertain to the physical equipment, such as work orders, oil sample
lab results, or field measurements on bushings.
Example schemas for different asset classes arepresented below.

For the physical equipment perspective, we
retrieve information from Statnett's ERP system.
To date, such transformer assets are not broken
down further into subcomponents in the source
system and constitute a monolithic structure. As
there is, however, a need to contextualize further
information (such as, e.g., field measurements onbushings) an additional structure of subcomponents is introduced. It is to these subcomponentsthat relevant data is linked, where appropriate. An
example schema is given in Figure 6.

Power transformers
Power Transformer are critical components in the
power system. Consequently, they are well instrumented and relevant data about their operationand maintenance history are available from multiple source systems.
For the functional perspective we follow theconventions laid out in the CIM standard and modelboth objects (PowerTransformer, PowertransformerEnd, Terminal, Analog, etc.) and their associations with Assets and Relationships in Cognite
Data Fusion®. Functional time series that are available from the centralized SCADA system are thenlinked to the respective Analog assets obtained
from the CIM representation of the power systemmodel.

BENEFITS FOR YOUR TEAMS →CONTACT SALES →8

Most of the asset-specific information about equipment that Statnett owns and maintains itself isprovided by the centralized ERP system. As suchit upholds a functional asset hierarchy which is
structured through the perspective of the maintenance organization. At the lowest level of detail, agiven functional asset has a child asset representing a physical piece of equipment, identified typically through its serial number. It is these physicalassets that are ingested into Cognite Data Fusion®
and linked to functional counterparts with relationships of type implements. As before, timestamps on
those relationships allow users to track equipment
as it moves across functional locations and enables
advanced queries for (functional) time series basedon an equipment-centric perspective. For example,
condition monitoring models may require the entire
history of load on a power transformer, irrespective of where in the grid it has been placed. Suchqueries involve several graph traversal steps in orderto retrieve the relevant time series data from potentially multiple functional transformers.

©COGNITE 2023 — COGNITE.COM

A complementary part of the power system networkmodel is information about the physical equipment that implements a given functional role, its
technical specification, and maintenance related
data. In principle, a functional equipment should
never contain information about physical attributes that would change once said physical equipment is altered, updated, or exchanged. In practice,
however, a clear distinction between functional
and physical attributes has not been upheld in thesource systems.

Whereas most power system components are
attributed with (univariate) time series datafrom sensors, more and more cable systems are
WHY COGNITE DATA FUSION® →BENEFITS FOR YOUR TEAMS →CONTACT SALES →◄ Figure 6: Example schema for power transformers cables
9

equipped with Distributed Temperature Sensing(DTS) technology that generate data that isdefined over a spatial domain. These spatial
profiles contain several thousand observations
for a single timestamp and are stored in Cognite
Data Fusion® as Sequences. An example schemais given in Figure 7.

▲ Figure 7: Example schema for cable systems©COGNITE 2023 — COGNITE.COM

From a functional perspective, cable systems
are a special instance of AC Line Segments, andmodeled as such in Statnett's CIM representation. Physically, however, there is one cable string
per phase, such that the resulting implements
relationships are many-to-one.

SF6 gas-insulated systems
Gas-insulated switchgears are becoming increasingly more common in situations where small
substation footprints are important, for example
in urban areas. These consist of compartmentsthat are filled with SF6 gas, an excellent insulator
but also a very potent greenhouse gas. Monitoringof SF6 leakage is therefore becoming paramountand requires access to relevant operational data.
While each breaker contained in these compartments is represented (physically) in the ERP systemand (functionally) in the power system model, an
additional structure is required that allows a given
breaker to be placed correctly in the respective
switchgear compartments and bays. Information about these bays and compartments are
retrieved from technical documentation provided
by the manufacturer and used to create the relevant assets and their associations in Cognite Data
Fusion®. An example schema is given in Figure 8.

©COGNITE 2023 — COGNITE.COM

10► Figure 8: Example schema for gas insulated
systemWHY COGNITE DATA FUSION® →BENEFITS FOR YOUR TEAMS →CONTACT SALES →Contextualization

In order to perform such analyses Statnett must
be able to map the generating unit or load affectedby a redispatch event to the correct power transWHY COGNITE DATA FUSION® →BENEFITS FOR YOUR TEAMS →To enable the analysts to fully leverage the unified
data model and solve use cases such as the cost
analysis for redispatching events, a "matchingtool" for contextualizing data from different data
sources was developed to complement the data
model. It provides a jupyter notebook-based GUI
as shown in Figure 9.

Figure 9: Jupyter notebook-based GUI for "matching tool".

11

Data originating from redispatch events provides agood example. Redispatching is a measure used by
TSOs to relieve physical congestions in the transmission grid or otherwise ensure system security. It can be applied both within a bidding zone
or between different bidding zones. It involves
altering generation or load patterns in order toalter the power flow. TSOs must remunerate activated internal or cross-zonal redispatching. If the
expected accumulated cost of relieving a bottleneck by redispatching exceeds the cost of expanding the capacity of the grid, the TSO should makethe appropriate investment. The required investment analyses are rather complex. This is because
many factors interact to determine the state of
the grid at any time, such as planned line outages
due to maintenance, unforeseen incidents causing
outages or inaccurate weather forecasts causing
generation from renewables to deviate from plan.

fer corridor, power line or transformer. In Statnetts
systems, when a “redispatch event” occurs, a setof inputs are added to the source system, among
others a free-text field that includes the affectedcomponent/asset. This results in a labor intensive and manual work process to match the freetext field to the affected components. Historicallythis has been a time consuming task performed in
Excel on a needs basis. This approach has resulted
in matched data living in silos and not available incontext to other data sources.

©COGNITE 2023 — COGNITE.COM

Statnett's analysts need to be able to assess andanalyze data from several data sources on top ofwhat is provided through CIM and the ERP system.
However, as many of these source systems historically have not been linked in a meaningful way, theanalysts have often found themselves manually
mapping data between these source systems.

CONTACT SALES →The actual matches are furthermore stored in
Cognite Data Fusion® as events, so that Statnett
can keep an audit trail of who matched what, what
rule was applied, how certain one is that the matchis correct etc. The algorithm tends to suggest
assets with long names, since they are not penalized for having many extra tokens. However, thecorrect assets still appear among the top suggestions that are presented to the end user, which iswhat the user needs.

This matching tool now allows Statnett to complement the CIM data model with additional contextualized data sources and for analysts to solve
additional use cases, s.a. the redispatching cost
analysis, on top of the Cognite Data Fusion® data
model. All redispatching events and meter data forsettlements have already been matched and verified by the end users, with significant time savings.

12

Example: "99 test-example T3" will result in thefollowing representation [99, test, example, T, 3].
The similarity between two strings is first determined by the number of common tokens. If thesame token is repeated 3 times in an asset and 2
times in an event, it counts as a minimum of (2, 3) =2 common tokens. With the same number of identical tokens between an event group and two different assets, one counts the number of letter tokens
that are within edit distance 1, then 2 and finally 3.

ing "redispatching" events to drive the development of this tool, while at the same time also using
other source systems and data (s.a. meter data
for settlements etc.) to validate our solution, we
ensured that our "matching tool" was generalizable and scalable to solve similar matching problems for other source data. Key to this development
was also to start with a simple solution solving the
problem end-to-end which the end users could
interact with through a simple UI. While the first
iteration of the logical functionality represented
the most basic functionality and smartness, we
gradually added more and more smartness to thelogic, allowing us to iterate continuously with theend user and optimize the workflow end to end.

©COGNITE 2023 — COGNITE.COM

The "matching tool" takes items from two datasets
as input, e.g. events and assets, and suggests a
mapping rule connecting the events (or group of
events based on identical metadata fields) andassets together based on similarity on the objects
data and metadata (text strings). The developed
algorithm determining the similarity between text
strings uses tokenization, where each string is
divided into tokens that consist of consecutive
letters, or consecutive digits.

By using the problem of contextualizing and matchWHY COGNITE DATA FUSION® →BENEFITS FOR YOUR TEAMS →CONTACT SALES →Power SDK

In Cognite Data Fusion®, physical items, such as
Substations and line segments, are stored as
Assets, and the connections between the assetsthat make up the data model are stored as Relationships. While it is natural to think of assets andrelationships as nodes and edges in a graph, that
does not represent how a power analyst thinks
about their data. For a power analyst, it is morenatural to think of the substations as nodes, andthe line segments between them as edges. For thisreason, building graph query capabilities into the
Cognite Data Fusion® back end would not provide
an analyst with a good way of exploring their data ina way that feels natural to them, regardless of thetechnical implementation (Gremlin, Cypher, etc).
The solution we came up with, was to construct a
WHY COGNITE DATA FUSION® →The choice to implement this in Python was made on
the basis that the ideal power analyst is proficient inusing Jupyter notebooks. The Power SDK adds functionality on top of the regular Cognite Python SDK,

which allows the users to query the data in ways thatare natural to them, rather than ways that reflect
how the data is stored in Cognite Data Fusion®.
Some of the most basic, but most useful features are
the addition of better typing on objects that are ofinterest to Statnett users. In the regular SDK, users
work with objects that are typed as Assets, which
are identified as substations, ac line segments or# get all substations (standard sdk)
client.assets.list(metadata = {'type':'Substation'})# get all substations (power-sdk)
client.substations.list()# list all transmission lines at voltage levels between 132 kV and 300 kV (standard sdk)
AssetList([aclfor acl in client.assets.list(metadata = {'type':'ACLineSegment', 'Equipment.
gridType': 'GridTypeKind.main'})
if int(float(acl.metadata['BaseVoltage_nominalVoltage'])) in range(132, 301)])
# list all transmission lines at voltage levels between 132 kV and 300 kV (power-sdk)
client.ac_line_segments.list(base_voltage=range(132, 301), grid_type='main')# list all hydro power plants in bidding area Elspot NO5 (standard sdk)
bidding_area_no5_extid = client.assets.list(name='Elspot NO5')[0].external_id
assets_in_bidding_area = client.relationships.list(target_resource_id=bidding_area_no5_
extid).to_pandas()['source'].map(lambda x: x['resourceId'])
client.assets.retrieve_multiple(external_ids=list(assets_in_bidding_area))# list all hydro power plants in bidding area Elspot NO5 (power-sdk)
client.hydro_generating_units.list(bidding_area='Elspot NO5')

Figure 10: Example component queries in the standard Cogite SDK as well as the Cognite Power SDK

BENEFITS FOR YOUR TEAMS →CONTACT SALES →13

Inevitably, however, such a data model increases
the complexity which is detrimental to its efficient
use and, as a consequence, an important part of
the R&D work has been to explore ways to efficiently
query such a data model from a domain expert view.

conceptual map from the way the analyst thinks
about the data, to the way it is represented in
Cognite Data Fusion®. This map is implemented in
the Power SDK.

©COGNITE 2023 — COGNITE.COM

In the previous sections we described our approachto constructing and populating a contextualized
power system model that is of relevance to a large
number of end users and can cater for different
perspectives into the power system and provide
richness through the availability of multiple data
sources in context.

# Define the power area by expanding 2 levels around the defined substations
area = client.power_area(substations_list).expand_area(level=2)# Draw power area on a map
area.draw_with_map()# Retrieve the interface (i.e. list of AC Line Segments) to adjacent gridinterface = area.interface(base_voltage=range(300,500))# Investigate power flow in the power area at a given datetime
area.draw_flow(date=dt.datetime(2020,5,17,12), position='spring')

to get a better understanding of the data. Example
queries to define a power area and investigate its
interface with adjacent parts of the grid as well as
historic power flow are given in Figure 11, whereas
an example plot of a user defined power area isshown on a map in Figure 12.

▲ Figure 11: Example queries for power area in the
Cognite Power SDK► Figure 12: Displaying power area on map with the
Cognite power-sdk
14

As part of the Power SDK work we have also implemented more sophisticated features that are
tailored for the power industry. One user story thatcame up was the wish to define geographical areas
in order to study the power consumption withinthem. We were able to solve this by iteratively working with Statnett domain experts, and the result is
the PowerArea class, which has methods for easilycollecting historical data for power flow acrossthe interface of a geographical region, and allows
analysts to gain quicker insight into when there
is surplus/demand of power within the area, howmuch of the available production is used within
the area, and how it is dispatched – uncovering,for example, situations where more redispatching
could be achieved because of a power surplus.

# Define a list of substations
substations_list = ['name1', 'name2', 'name3']©COGNITE 2023 — COGNITE.COM

transformers only by examining metadata on theasset. In the Power SDK, these variants are raised
as proper types, so that rather than working on an
Asset object which represents a substation, theuser can work on a Substation object. This subtle
difference has turned out to make a huge differencein how efficiently users are able to query their dataand showed how valuable a close cooperation withdomain experts is in building the right solution thatprovides immediate value. Examples of common
queries for power system components in the PowerSDK are given in Figure 10. To illustrate the ease ofuse, the equivalent queries are presented using thestandard Cognite Python SDK.

Further functionality includes methods to plot thesubstations and line segments in the area in order
WHY COGNITE DATA FUSION® →BENEFITS FOR YOUR TEAMS →CONTACT SALES →The Power Area is implemented by using the Python
library Networkx to construct a graph client side
where substations are nodes and AC line segments
are edges. We initially conceptualized the powerarea generation through a Cognite Data Fusion®
API endpoint, but it quickly became clear that therequirements of a power area were too domain-specific to be useful as a general endpoint for multiple industry verticals. For this reason, we made thedecision to implement the power area generationclient side through the Power SDK. This choice also
allowed the user to regenerate and alter power areas
much more rapidly. This turned out to be useful, as
one of the requested features was to expand power
areas incrementally by following AC line segments.

©COGNITE 2023 — COGNITE.COM

15

As the Power SDK serves as a map from the user's
mental model of the power grid to the Statnett data
model in Cognite Data Fusion®, the Power SDK willneed to be updated whenever the data model is
updated with breaking changes. Updating the PowerSDK can therefore be thought of as a natural part ofiterating on the data model.

WHY COGNITE DATA FUSION® →BENEFITS FOR YOUR TEAMS →CONTACT SALES →SummaryIn the previous sections we have described anddiscussed our approach on how to build, populate,and expose a rich, contextualized data model ofthe Norwegian power system. It was a comprehensive and iterative process that laid the foundationof design principles that further work on contextualization and Power SDK functionality bear fruit.
We liberated data from multiple data sources andallowed analysts at Statnett to find and retrieve
relevant information for their analysis tasks more
efficiently, leading to better data-driven decision-making.
Some immediate examples of where these benefits
can already be seen and could be further developed
is in Statnett's yearly transformer capacity study or,
more generally, the analysis of grid connections
where the Power SDK can help documenting andupdating the analysis more easily.

WHY COGNITE DATA FUSION® →BENEFITS FOR YOUR TEAMS →©COGNITE 2023 — COGNITE.COM

16

Furthermore, there is a vast value potential thatcan be explored in further development activities,
such as the integration with power flow simulatorsfor more advanced analysis tasks and the more
active use of the node breaker representation in
conjunction with breaker positions.

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