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Thank you
The role of AI to accelerate the
energy transition from
research perspective
Dr. Jochen Cremer
Assistant Professor
j.l.cremer@tudelft.nl
Dutch Power Workshop
28-05-2024
Topic
Mission & objective
Education
Research
Key innovations
Team
Dynamic Sustainable Energy Systems
• combine groundbreaking ML with the reliable theory
of the physical energy system
• make energy systems sustainable, reliable, effective
▪ EE4C12 ML for Electrical Engineering
▪ SC42150 Statistical Signal Processing
▪ SC42110 Dynamic Programming and Stochastic
Control
▪ MOOC Digitalization of Intelligent and Integrated
Energy Systems
▪ Crash course of “Data-science”
• Supervised learning for real-time grid
assessment
• Distributed learning for power system
congestion management
• Data-driven grid models for electricity load
and weather forecasts
• Characterizing healthy/normal trajectories
of complex dynamical systems using
dictionary learning
• From fast Fourier transform to fast
reinforcement learning
▪ AI-based algorithms for grid operation
▪ Real-time security assessment and
anomaly detection
▪ Real-time learning algorithms for
control and security of complex
dynamical systems
Haiwei Xie
Mert Karaçelebi
Ali Rajaei
Demetris
Chrysostomou
Peyman Mohajerin
Esfahani
Shabnam
Khodakaramzadeh
Mohammad
Boveiri
Viktor
Zobernig
Olayiwola
Arowolo
Basel Morsy
Jochen Stiasny
Team
https://www.tudelft.nl/ai/delft-ai-energy-lab
DAIEnergyLab
DAI-Energy-Lab@tudelft.nl
Jochen Cremer
Delft AI Energy Lab
Benjamin
Habib Runyao
Yu
Perine
Cunat
Challenge: Highly complex systems
AI: Decentralised multi-agent
algorithms
Challenge: Standard software slow
AI: Real-time-fast algorithms
3
Why AI for Electrical Power Systems (EPS)?
Houston, Texas 07 Feb 2021 Houston, Texas 16 Feb 2021
• Damages from the blackouts were estimated at $195 billion
• Seconds away from a total power blackout in Texas
4
Software for control room: Predict Blackouts
5
design an AI-based algorithm that can
predict power blackouts
Define
requirements
Design Test & analyse Product
Our goal today:
6
• Data
• AI method & model
• Engineers
What do we need for an AI-based algorithm
to predict power blackouts?
7
• Temporal
• Discrete
• Continuous
Different types of energy data
8
• Example: Load profile, renewable generation
Load Profile Wind generation
https://dataviewer.pjm.com/
Temporal data
10
• What pattern do you see in the electricity consumption data?
https://williamkoehrsen.medium.com/building-energy-data-analysis-part-two-7861b0c6a2d6
Quiz
11
• day of week
• business day vs weekend
• time of day, season
Patterns in energy consumption
12
Sometimes outliers. What happened?
https://blog.scienceandindustrymuseum.org.uk/electricity-half-time-peak/
North West England
13
• Power blackouts happen
very rarely!
Define
requirements
Design Test & analyse Product
Houston, Texas 07 Feb 2021 Houston, Texas 16 Feb 2021
Is this also a problem for our design?
•Data
•AI method & model
•Engineers
14
What do we need to design an AI-algorithm to
predict power blackouts?
that includes power blackouts
Decision Trees as a model?
𝑋𝑋1
insecure
insecure secure
secure
𝑋𝑋
2
Two-dimensional example
Decision trees:
• Limited expressive power
• Fantastic interpretability
15
Metrics for classification
Predicted and
actual security limits
are different!
16
Metrics for classification
TP FN
FP TN
Predicted Class
Positive Negative
Negative
Positive
True
Class
Predicted and
actual security limits
are different!
Confusion Matrix
17
Metrics for classification
TP FN
FP TN
Predicted Class
Positive Negative
Negative
Positive
True
Class
Predicted and
actual security limits
are different!
Two types of accurate predictions:
TN: Is secure and we think it is secure (GOOD)
TP: Is insecure and we think it is insecure (VERY GOOD!)
18
Metrics for classification
TP FN
FP TN
Predicted Class
Positive Negative
Negative
Positive
True
Class
This can have a severe effect!
Two types of wrong predictions:
FP: Is secure but we think it is insecure (BAD)
FN: Is insecure but we think it is secure (VERY BAD!)
Predicted and
actual security limits
are different!
Two types of accurate predictions:
TN: Is secure and we think it is secure (GOOD)
TP: Is insecure and we think it is insecure (VERY GOOD!)
19
Metrics for classification
TP FN
FP TN
TP FN
FP TN
Ratio of correct predictions
Accuracy =
𝑁𝑁𝑇𝑇𝑇𝑇+𝑁𝑁𝑇𝑇𝑇𝑇
𝑁𝑁𝐹𝐹𝐹𝐹+𝑁𝑁𝑇𝑇𝑇𝑇+𝑁𝑁𝐹𝐹𝐹𝐹+𝑁𝑁𝑇𝑇𝑇𝑇
Predicted Class
True
Class
20
Metrics for classification
Accuracy =
𝑁𝑁𝑇𝑇𝑇𝑇+𝑁𝑁𝑇𝑇𝑇𝑇
𝑁𝑁𝐹𝐹𝐹𝐹+𝑁𝑁𝑇𝑇𝑇𝑇+𝑁𝑁𝐹𝐹𝐹𝐹+𝑁𝑁𝑇𝑇𝑇𝑇
TP FN
FP TN
TP FN
FP TN
Ratio of correctly found insecure cases to
predicted insecure predictions
Precision =
𝑁𝑁𝑇𝑇𝑇𝑇
𝑁𝑁𝑇𝑇𝑇𝑇+𝑁𝑁𝐹𝐹𝐹𝐹
Predicted Class
True
Class
21
Metrics for classification
Accuracy =
𝑁𝑁𝑇𝑇𝑇𝑇+𝑁𝑁𝑇𝑇𝑇𝑇
𝑁𝑁𝐹𝐹𝐹𝐹+𝑁𝑁𝑇𝑇𝑇𝑇+𝑁𝑁𝐹𝐹𝐹𝐹+𝑁𝑁𝑇𝑇𝑇𝑇
Precision =
𝑁𝑁𝑇𝑇𝑇𝑇
𝑁𝑁𝑇𝑇𝑇𝑇+𝑁𝑁𝐹𝐹𝐹𝐹
Recall =
𝑁𝑁𝑇𝑇𝑇𝑇
𝑁𝑁𝑇𝑇𝑇𝑇+𝑁𝑁𝐹𝐹𝐹𝐹
TP FN
FP TN
Ratio of correctly found insecure
cases to all insecure cases
Predicted Class
True
Class
22
Duality: Precision vs Recall
When do we observe the
highest performance?
Precision
Recall
23
Blackout predictions: Precision or Recall?
Houston, Texas 07 Feb 2021 Houston, Texas 16 Feb 2021
24
Designed algorithm for predicting blackouts
Data with power
blackouts
25
Recall =
𝑁𝑁𝑇𝑇𝑇𝑇
𝑁𝑁𝑇𝑇𝑇𝑇+𝑁𝑁𝐹𝐹𝐹𝐹
Decision Trees Ratio of correctly found insecure
cases to all insecure cases
How can we move from ideas to implementation?
26
• Interviewed 110 engineers in power systems (with focus on transmission
system operators)
[1] Jochen L. Cremer and Adrian Kelly and Ricardo J. Bessa and Milos Subasic and Panagiotis N. Papadopoulos and Samuel Young and Amar Sagar and
Antoine Marot, “A Pioneering Roadmap for ML-Driven Algorithmic Advancements in Electrical Networks”, https://arxiv.org/pdf/2405.17184, 2024
[2] CIGRE C2.42 Working Group, “The impact of the growing use of ML-AI in the operation and control of power networks from an operational Perspective”
Findings
27
• AI-innovations are often rooted in open-source developments
• High testing quality and safety is needed
• Example PowSyBl
N. Omont, Artelys, “Continental-wide coordination of the power grid powered by PowSyBl,” in Linux Foundation Energy Summit,
Paris, 2023.
Need to adapt to changing innovation environment
28
Academic and Applied Research Centres
Open-Source Development
AI Testing and Experimentation Facility
Operators
Operators
 Collaboration between knowledge institutes and private and/or public parties
 Shared fundamental research agenda determined together
 At least 5 PhD students, a lab manager, and scientific directors
 Plus custom made possibilities (postdocs, external PhDs, etc.)
 Five year collaboration based on open knowledge development
 External focus
 PhD students work part of their time at the location of the partner
 Knowledge transfer based on the needs (e.g., meetups, internships)
ICAI Labs
29
AI for Energy Grids Lab
Flin Verhaasdonk
Decentral AI & Control
Charlotte Cambier van Nooten
Graph Neural Networks
Eva De Winkel
Explainable AI
Shaohong Shi
Risk-based invesments and operation
Mission & objective
• enhance the transport capability of distribution grids
• increase the share of renewable energy
• universal access to affordable, reliable and modern
energy services
• combine groundbreaking ML with the reliable theory of
the physical grid
• open-source code and data
 ICAI is a national network in The Netherlands aimed at technology and talent development between knowledge
institutes, industry, and government in the area of artificial intelligence.
 The Netherlands has the talent, the world-class research and the longstanding tradition in AI education to be one
of the world’s top ranked countries in terms of innovation power.
 ICAI brings these forces together in a unique national initiative.
ICAI Innovation Center for AI
31
AI innovation ecosystem
32
Thank you for your attention
Jochen Cremer
j.l.cremer@tudelft.nl
Reference:
Jochen L. Cremer and Adrian Kelly and Ricardo J.
Bessa and Milos Subasic and Panagiotis N.
Papadopoulos and Samuel Young and Amar Sagar
and Antoine Marot, “A Pioneering Roadmap for ML-
Driven Algorithmic Advancements in Electrical
Networks”, https://arxiv.org/pdf/2405.17184, 2024

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Presentatie 4. Jochen Cremer - TU Delft 28 mei 2024

  • 1. Thank you The role of AI to accelerate the energy transition from research perspective Dr. Jochen Cremer Assistant Professor j.l.cremer@tudelft.nl Dutch Power Workshop 28-05-2024
  • 2. Topic Mission & objective Education Research Key innovations Team Dynamic Sustainable Energy Systems • combine groundbreaking ML with the reliable theory of the physical energy system • make energy systems sustainable, reliable, effective ▪ EE4C12 ML for Electrical Engineering ▪ SC42150 Statistical Signal Processing ▪ SC42110 Dynamic Programming and Stochastic Control ▪ MOOC Digitalization of Intelligent and Integrated Energy Systems ▪ Crash course of “Data-science” • Supervised learning for real-time grid assessment • Distributed learning for power system congestion management • Data-driven grid models for electricity load and weather forecasts • Characterizing healthy/normal trajectories of complex dynamical systems using dictionary learning • From fast Fourier transform to fast reinforcement learning ▪ AI-based algorithms for grid operation ▪ Real-time security assessment and anomaly detection ▪ Real-time learning algorithms for control and security of complex dynamical systems Haiwei Xie Mert Karaçelebi Ali Rajaei Demetris Chrysostomou Peyman Mohajerin Esfahani Shabnam Khodakaramzadeh Mohammad Boveiri Viktor Zobernig Olayiwola Arowolo Basel Morsy Jochen Stiasny Team https://www.tudelft.nl/ai/delft-ai-energy-lab DAIEnergyLab DAI-Energy-Lab@tudelft.nl Jochen Cremer Delft AI Energy Lab Benjamin Habib Runyao Yu Perine Cunat
  • 3. Challenge: Highly complex systems AI: Decentralised multi-agent algorithms Challenge: Standard software slow AI: Real-time-fast algorithms 3 Why AI for Electrical Power Systems (EPS)?
  • 4. Houston, Texas 07 Feb 2021 Houston, Texas 16 Feb 2021 • Damages from the blackouts were estimated at $195 billion • Seconds away from a total power blackout in Texas 4 Software for control room: Predict Blackouts
  • 5. 5 design an AI-based algorithm that can predict power blackouts Define requirements Design Test & analyse Product Our goal today:
  • 6. 6 • Data • AI method & model • Engineers What do we need for an AI-based algorithm to predict power blackouts?
  • 7. 7 • Temporal • Discrete • Continuous Different types of energy data
  • 8. 8 • Example: Load profile, renewable generation Load Profile Wind generation https://dataviewer.pjm.com/ Temporal data
  • 9. 10 • What pattern do you see in the electricity consumption data? https://williamkoehrsen.medium.com/building-energy-data-analysis-part-two-7861b0c6a2d6 Quiz
  • 10. 11 • day of week • business day vs weekend • time of day, season Patterns in energy consumption
  • 11. 12 Sometimes outliers. What happened? https://blog.scienceandindustrymuseum.org.uk/electricity-half-time-peak/ North West England
  • 12. 13 • Power blackouts happen very rarely! Define requirements Design Test & analyse Product Houston, Texas 07 Feb 2021 Houston, Texas 16 Feb 2021 Is this also a problem for our design?
  • 13. •Data •AI method & model •Engineers 14 What do we need to design an AI-algorithm to predict power blackouts? that includes power blackouts
  • 14. Decision Trees as a model? 𝑋𝑋1 insecure insecure secure secure 𝑋𝑋 2 Two-dimensional example Decision trees: • Limited expressive power • Fantastic interpretability 15
  • 15. Metrics for classification Predicted and actual security limits are different! 16
  • 16. Metrics for classification TP FN FP TN Predicted Class Positive Negative Negative Positive True Class Predicted and actual security limits are different! Confusion Matrix 17
  • 17. Metrics for classification TP FN FP TN Predicted Class Positive Negative Negative Positive True Class Predicted and actual security limits are different! Two types of accurate predictions: TN: Is secure and we think it is secure (GOOD) TP: Is insecure and we think it is insecure (VERY GOOD!) 18
  • 18. Metrics for classification TP FN FP TN Predicted Class Positive Negative Negative Positive True Class This can have a severe effect! Two types of wrong predictions: FP: Is secure but we think it is insecure (BAD) FN: Is insecure but we think it is secure (VERY BAD!) Predicted and actual security limits are different! Two types of accurate predictions: TN: Is secure and we think it is secure (GOOD) TP: Is insecure and we think it is insecure (VERY GOOD!) 19
  • 19. Metrics for classification TP FN FP TN TP FN FP TN Ratio of correct predictions Accuracy = 𝑁𝑁𝑇𝑇𝑇𝑇+𝑁𝑁𝑇𝑇𝑇𝑇 𝑁𝑁𝐹𝐹𝐹𝐹+𝑁𝑁𝑇𝑇𝑇𝑇+𝑁𝑁𝐹𝐹𝐹𝐹+𝑁𝑁𝑇𝑇𝑇𝑇 Predicted Class True Class 20
  • 20. Metrics for classification Accuracy = 𝑁𝑁𝑇𝑇𝑇𝑇+𝑁𝑁𝑇𝑇𝑇𝑇 𝑁𝑁𝐹𝐹𝐹𝐹+𝑁𝑁𝑇𝑇𝑇𝑇+𝑁𝑁𝐹𝐹𝐹𝐹+𝑁𝑁𝑇𝑇𝑇𝑇 TP FN FP TN TP FN FP TN Ratio of correctly found insecure cases to predicted insecure predictions Precision = 𝑁𝑁𝑇𝑇𝑇𝑇 𝑁𝑁𝑇𝑇𝑇𝑇+𝑁𝑁𝐹𝐹𝐹𝐹 Predicted Class True Class 21
  • 21. Metrics for classification Accuracy = 𝑁𝑁𝑇𝑇𝑇𝑇+𝑁𝑁𝑇𝑇𝑇𝑇 𝑁𝑁𝐹𝐹𝐹𝐹+𝑁𝑁𝑇𝑇𝑇𝑇+𝑁𝑁𝐹𝐹𝐹𝐹+𝑁𝑁𝑇𝑇𝑇𝑇 Precision = 𝑁𝑁𝑇𝑇𝑇𝑇 𝑁𝑁𝑇𝑇𝑇𝑇+𝑁𝑁𝐹𝐹𝐹𝐹 Recall = 𝑁𝑁𝑇𝑇𝑇𝑇 𝑁𝑁𝑇𝑇𝑇𝑇+𝑁𝑁𝐹𝐹𝐹𝐹 TP FN FP TN Ratio of correctly found insecure cases to all insecure cases Predicted Class True Class 22
  • 22. Duality: Precision vs Recall When do we observe the highest performance? Precision Recall 23
  • 23. Blackout predictions: Precision or Recall? Houston, Texas 07 Feb 2021 Houston, Texas 16 Feb 2021 24
  • 24. Designed algorithm for predicting blackouts Data with power blackouts 25 Recall = 𝑁𝑁𝑇𝑇𝑇𝑇 𝑁𝑁𝑇𝑇𝑇𝑇+𝑁𝑁𝐹𝐹𝐹𝐹 Decision Trees Ratio of correctly found insecure cases to all insecure cases
  • 25. How can we move from ideas to implementation? 26 • Interviewed 110 engineers in power systems (with focus on transmission system operators) [1] Jochen L. Cremer and Adrian Kelly and Ricardo J. Bessa and Milos Subasic and Panagiotis N. Papadopoulos and Samuel Young and Amar Sagar and Antoine Marot, “A Pioneering Roadmap for ML-Driven Algorithmic Advancements in Electrical Networks”, https://arxiv.org/pdf/2405.17184, 2024 [2] CIGRE C2.42 Working Group, “The impact of the growing use of ML-AI in the operation and control of power networks from an operational Perspective”
  • 26. Findings 27 • AI-innovations are often rooted in open-source developments • High testing quality and safety is needed • Example PowSyBl N. Omont, Artelys, “Continental-wide coordination of the power grid powered by PowSyBl,” in Linux Foundation Energy Summit, Paris, 2023.
  • 27. Need to adapt to changing innovation environment 28 Academic and Applied Research Centres Open-Source Development AI Testing and Experimentation Facility Operators Operators
  • 28.  Collaboration between knowledge institutes and private and/or public parties  Shared fundamental research agenda determined together  At least 5 PhD students, a lab manager, and scientific directors  Plus custom made possibilities (postdocs, external PhDs, etc.)  Five year collaboration based on open knowledge development  External focus  PhD students work part of their time at the location of the partner  Knowledge transfer based on the needs (e.g., meetups, internships) ICAI Labs 29
  • 29. AI for Energy Grids Lab Flin Verhaasdonk Decentral AI & Control Charlotte Cambier van Nooten Graph Neural Networks Eva De Winkel Explainable AI Shaohong Shi Risk-based invesments and operation Mission & objective • enhance the transport capability of distribution grids • increase the share of renewable energy • universal access to affordable, reliable and modern energy services • combine groundbreaking ML with the reliable theory of the physical grid • open-source code and data
  • 30.  ICAI is a national network in The Netherlands aimed at technology and talent development between knowledge institutes, industry, and government in the area of artificial intelligence.  The Netherlands has the talent, the world-class research and the longstanding tradition in AI education to be one of the world’s top ranked countries in terms of innovation power.  ICAI brings these forces together in a unique national initiative. ICAI Innovation Center for AI 31
  • 32. Thank you for your attention Jochen Cremer j.l.cremer@tudelft.nl Reference: Jochen L. Cremer and Adrian Kelly and Ricardo J. Bessa and Milos Subasic and Panagiotis N. Papadopoulos and Samuel Young and Amar Sagar and Antoine Marot, “A Pioneering Roadmap for ML- Driven Algorithmic Advancements in Electrical Networks”, https://arxiv.org/pdf/2405.17184, 2024