Applying digitalizationtrends in grid control15th INTERNATIONAL WORKSHOP ONELECTRIC POWER CONTROL CENTERSMay 12 – 15, 2019 // Reykjavik, Iceland
Rolf Apel, Siemens Smart Infrastructures
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Agenda
1 Digitalization Trends
3 Data Analytics and Machine Learning
2 Digitalization in Power Grids
4 Digital Twin and IoT architecture
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Data management and energy systems –In the age of digitalization they merge
Internet Mobiletelephone
Computer Industry4.0
>50%
new “things”get connected every day
Global data volume
Internetof Things
~1960 ~1970 ~1990~1980 ~2000 2030~2010 2020~1945
Nuclear PhotovoltaicGas WindEnergysystems
Decentralenergysystem
of the world’s datawas created last year
… but less than 0.5%was analyzed or used
by 2020
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IoT allows tremendous speed in business model innovation
snapchat
Time until used by 1/4 of American population
1873 1926 1975 1991 2016
26 years
7 years months
46 years
16 years
Unrestricted © Siemens AG 2019Page 5 Rolf Apel , SI TI COE 15. May 2019
Agenda
1 Digitalization Trends
3 Data Analytics and Machine Learning
2 Digitalization in Power Grids
4 Digital Twin and IoT architecture
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Digitalization in Substation already in the 4th Generation
1st generation –Standard cabling
2nd generation – Point- to-pointconnections since 1985 …
3rd generation – DigitalStation Bus since 2004 …
Mimic board
Fault recorderProtection
RTU
Parallel wiring
Parallelwiring
Control Center
HMI
Parallel wiring
Substationcontroller
Other bays
Serialconnection
Substationcontroller
Control Center
HMI
Station Bus
Parallel wiring
Switch
SwitchBay …Bay …Bay …
Digital Substation 4.0
Control Center
IEC
618
50
Substation controller
Parallel wiring CB ControllerCT/VTNCIT
3rd
Party
Sampled Values
Processbus
Station bus
IEC 61850 Apps and Data Analytics
IoT Interface
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IEC 61850
IEC 60870-5-104
SIPROTEC 5 SIPROTEC 4 SIPROTECCompact
SICAM A8000 SICAM PQQ200/Q100
SICAM PAS SICAM SCC
SPECTRUM 5/7
Con
trol
Cen
ter L
evel
Stat
ion
Leve
lFi
eld
Lev
el
IEC 61850, Modbus, IEC 60870-5-103, …
SICAM A8000IoT Gateway
…3rd
Party
EnergyIPpowered by MindSphere
OPC UA PubSub
Benefits• Easy access to data of field
level devices e.g. for service• Access to expanded / entire
data set from field level forenhanced analytics by artificialintelligence or experts
Connectivity and interoperability in Grid Control
Data Exchange
IEC 60968/70 (CIM)
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Big Data in Grid Control through bothAutomation Pyramid and Sensor streams
Mindsphere/ EnergyIP
Stored Data
Conversion of formats by library
Edge processed Data
Streamed sensorData
SCADAand
Control Centers
Control andprotection devices
Many other sensors (especially low-cost)
Electrical System Sensors
Data concentration/conversionby Intelligent Edge Devices (IED),e.g. SIPROTEC, SICAM
MindLib
AD conv.+Gateway
Data converted and streamedto MindsphereGateway separate fromautomation, no interference
Unrestricted © Siemens AG 2019Page 9 Rolf Apel , SI TI COE 15. May 2019
Agenda
1 Digitalization Trends
3 Data Analytics and Machine Learning
2 Digitalization in Power Grids
4 Digital Twin and IoT architecture
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Why Big Data?Because resolution matters!
15 min resolution
“Low” resolution – useful to knowoverall energy consumption.
1 min resolution
“High” resolution – useful tounderstand behavior patterns andto implement algorithms.
Near real-time
High volume of data gathered –detailed information fromprocessed data.
Create the ability to visualizethe behavior of the equipmentin regard to energyconsumption
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Deep Learning Example:Online Decision Support for Power Grids
Growing share of renewable
• Wide area monitoring combined withdecision support
• Disturbance identification andcompensation
Wide-AreaDisturbanceClassification
• Increase quality of supporting informationin case of faults
• Localization of faults even in difficult cases
FaultLocalization andClassification
Growing share of renewable energy and distributed power generation call forenhanced capabilities of intelligent devices.
Operation Center: Disturbance Classification Infield: Fault location using neural networks
Inter-preter
Sourcecode
Model Training Model Generation Model DeploymentStream Data Recognize Contingencies
CounterMeasures
Embedded Analytics Framework (LEAF)
contingencyTIME
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Local artificial intelligence in distribution grids
The secondary substation as thebrain of the digitalized LV-network• Intelligent control increases grid
capacity for distributed generationand electric vehicles
• Autonomous operation improvesresilience of distribution networks
• No / minimal number of datainterfaces to other OT/IT systemreduces the complexity
• Minimal communication to otherOT/IT systems during operationreduces costs and vulnerability
• Self-learning and self-configurationreduces implementation efforts
ANN: Artificial Neural Networks
MicroSCADA
Medium Voltage Low Voltage
ANN-based MicroSCADA in secondary substationsCould berealized withMindSphere
Unrestricted © Siemens AG 2019Page 13 Rolf Apel , SI TI COE 15. May 2019
Agenda
1 Digitalization Trends
3 Data Analytics and Machine Learning
2 Digitalization in Power Grids
4 Digital Twin and IoT architecture
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Contextual relation example for a power grid(Only a small partial subset)
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Interoperability of Digital Twins for Energy Systems
Transmission Distribution Industry,infrastructure,buildings
Cross-sector couplings
Generation
DynamicModel
PowerTransmission & Distribution
AssetManagement
Protection
SensorData
Digital Twin Graph
System Planning/Operation:Simulation
and Optimization
PredictiveOperation and Maintenance
Data analytics/Machine learning
1011
0111
0111
Electrical Digital Twin• Digital representation of the network and its resources
• Prognosis of the System behavior
• Connection of specific software tools into an overall system
• »Enabler« for new methods, efficient work flows undcooperation
• Smart, automated and logic linkage of various sourcesà lean data model
• Data validation and improvement – independentfrom the used software tool
• »Single Source of Truth« –A central system as an intermediary between application andstorage. Yet:
• Flexible and decentral storage –efficient »back end« storage inproblem-specific Data bases
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Digitalization - The energy system will be anelement of an economy-wide IoT infrastructure
Cloud-based operating system for IoTe.g. MindSphere
Maintenance,monitoring & serviceAutomation & controlPlanning, simulation &
engineering
Productivityand time-to-market
Flexibilityand resilience
Availabilityand efficiency
Copyright: Tafyr
Generation Transmission / Distribution & Smart Grid Consumption / Prosumption
Use cases, applications
Connected power assets and … … connected edge devices
1) DER: Distributed energy resources like smart meters, inverters for photovoltaics, e-mobility assets, storage systems, microgrids, …
Griddiagnostics
Digital twin Grid simulation Smartmetering
Energy efficiencyand analytics
MonitoringDER1)
123 ~
Virtual powerplant
Grid planning Grid control Digitalsubstation
Assetmanagement
ü
Key areas tostep upEnhanced electrification
Automation
Digitalization
• Sensing
• Connectivity / IoT
• Monitoring
• Controlling
• Managing
• Digital twin
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