Explore the cutting-edge: Quantum computing and generative AI solutions for accelerating the clean energy transition. Discover applications for optimization, simulation, and innovation in the clean energy and utility sector.

1. How to leverage Quantum
Computing and Generative AI for
Clean Energy Transition
Sayonsom Chanda, Ph.D.
Senior Scientist
National Renewable Energy Laboratory
Boulder, Colorado, USA

2. History of Adopting Innovation in the Utility Industry
2

3. Some typical heavy computation use-cases in Utilities
Use Case Size of
study
Data
Generated
Resourc
es used
Time taken
for solution
Quantum
Worthy?
Monte-carlo
for a few
defined
scenario
planning
3,000
transmission
nodes
25 TB daily Fourteen
192GB EC2s
on AWS
48 hours ✅
Hourly Load
Forecast with
multiple DERs
2,000 Feeders
for NY
11 TB Ten 96GB
EC2 on AWS
10-12 days per
scenario
✅
Unit
Commitment
28,000 nodes depends Cloud-
deployed
PowerWorld
Minutes-hours ⛔
Power Flow 50,000 buses 500 GB PSSE Few Minutes ⛔

4. Five unsolved / over-looked / emerging challenges in
Power Systems?
There is no way to perform “Gain
of Function” research on cyber-
vulnerabilities
Scenario planning using all
customers’ smart meter data is
not even considered
Too many devices to manage and
optimize
Grid partitioning, Microgrid
islanding
Customer Level Energy behavior or
usage modeling

5. Examples of
commercially available
Generative AI tools
today
2,500+ Products available
for general public to
try/buy.
Most Common:
ChatGPT
5
Image Credit: RapidOps

6. What is Generative AI?
6
“Prompt”

7. Generative AI can help keep up with rapid changes in
energy landscape.
7

8. Traditional AI/ML/Data Science Architecture at Smart
Grid Companies
8

9. Vegetation Management
Situation:
In some areas of DISCOM, overhead lines are passing through
forest area wherein vegetation on the OH lines are causing
frequent interruptions. DISCOM officials are able to check the
vegetation encroachments only during regular maintenance.
Initiative based on conventional AI:
DISCOM is interested to carry out the assessment of vegetation
encroachment with integration of IT systems such as Image
based Vegetation Management. Past 1 year data is available and
any other relevant information → Based on that a AI/ML model
will be built.
BUT, the limitations of this costly model will be:
- will be always data-hungry
- satellite or drone imagery is expensive
- data for all regions will not be available
- Seasonal variations 9

10. Asset Inspection Needs
Situation:
Live monitoring for all assets is economically infeasible and impractical.
At present, the health condition of the major equipments like PTRs and CBs
in 33/11kV Substations are analysed by DISCOM officials during the
scheduled inspection which happens once-every-2-weeks or once-a-
month through thermal imagery by visiting the substation physically.
10
Traditional A.I.
(Under Development at
many places)
Probability of
Failure
Generative AI
What would the
thermal imagery be?
Engineer inputs a given
scenario, or verbally
reports somethings that
may have happened in
the circuit.

11. Fill the gap of Primary and Secondary Distribution
Feeder Models: Identify ways to reduce T&D losses
11

12. Advanced Monte Carlo Simulation for Day-ahead
Demand Forecasting
Situation:
The accuracy of day ahead forecast is very much
essential to plan for the requirement of Day ahead
Power Purchase and planning.
Limitations:
Forecasting is influenced by various meteorological and
socio-economic factors which can lead to a mismatch
between Actual vs Projected demand.
It is difficult for a team to think of many different
scenarios
12

13. What is Quantum Computing
Dictionary of common terms used in Quantum Computing: https://blog.learn-
quantum.org/quantum-computing-jargon/

14. An oft-forgotten fact in the history of computing
1885 - Charles Babbage
- Electric switches
drove mechanical
relays to perform the
calculation
… and over the next
100 years, we just
improved the switches
[Classical Computing]

15. How does a quantum computer look like?

16. A mindset shift
But, nature, universe, the world around us,
don’t have any switches - yet they do
amazing computations and chemical
processes all the time.
Operation of the universe cannot be
modeled using binary - 0 or 1 - ON or OFF.

17. Whatever will be
outright valuable:
Grid Partitioning,
Cybersecurity,
Customer Modeling
Whatever can be easily validated
Large-scale Optimization Problems
Noisy
Intermediate
Scale
Quantum
(NISQ)

18. Some Energy-sector relevant use-cases
● Development of new materials
● Convert methane to methanol

19. Developing Advanced Materials for Energy Storage
- Solving Navier-Stokes Equation - Molecular Vibrational
Calculations

20. Quantum Sensing
Faster, more accurate, more reliable geolocation
Image sources are attributed in https://chanda.io/acknowledgments

21. Quantum Secure Power Grids
Quantum Key Distribution
National Institute of Standards and
Technology (NIST) is identifying
quantum-resistant (or post-
quantum) algorithms for
standardization.
Following the conclusion of NIST's
selection process, updated guidance
will be issued through CNSSP-15 by
the NSA.
- There are still challenges to be
resolved related to secret key rate,
distance, size, cost and practical
security

22. It’s a good time to build your AI &
Quantum Knowledge Stack

23. Learning Roadmap or building a Quantum-ready
Workforce for applied fields (such as power & energy)
✅ Linear Algebra
✅ Understanding the variety of
hardware a little bit
✅ Deconstructing the famous algorithms
- Grover, Shor’s, HHL
Gruelling
hard work
Get a flavor
Low Value High Value
✅ Copy-pasting code
✅ QKD
📛 Getting stuck on which framework to start with

24. Let’s continue the conversation . . .
Email me:
chanda.sayonsom@gmail.com
sayonsom.chanda@nrel.gov