What is the role of ChatGPT and Generative AI technologies in improving resilience and reliability of utilities. In this presentation, Dr. Sayonsom Chanda, dives deep into the innovative ways in which ChatGPT and Generative AI technologies are being leveraged to revolutionize the utilities sector. Dr. Sayonsom Chanda, an esteemed expert in both AI and utilities infrastructure, explores the challenges faced by modern utilities and showcases how these cutting-edge technologies provide sustainable solutions. In this detailed presentation, attendees can expect to: Gain insights into the current landscape of utilities and the pressing need for increased resilience and reliability. Understand the foundational concepts of ChatGPT and Generative AI, and their potential applications in various industries, with a specific focus on utilities. Discover real-world case studies where these technologies have been successfully integrated into utilities operations to predict failures, automate customer interactions, and optimize resource allocation. Learn about the transformative benefits, including enhanced operational efficiency, reduced costs, and improved customer satisfaction. Engage in a thoughtful discussion on the potential ethical considerations and best practices for implementing such technologies. Throughout the presentation, Dr. Chanda will weave in his extensive research, firsthand experiences, and vision for the future, ensuring that attendees leave with a comprehensive understanding of the subject and practical takeaways to consider for their own organizations.

1. Role of Generative AI in advanced scenario
planning for enhancing distribution system
resilience, reliability, and customer satisfaction
Dr. Sayonsom Chanda,
Senior Scientist,
National Renewable Energy Laboratory
(United States Department of Energy)
Boulder, Colorado
USA
1

2. History of Adopting Innovation in the Utility Industry
2

3. What is Generative AI?
3
“Prompt”

4. History of Generative AI
1950s - Markov Chain, a statistical model that could be used to generate new
sequences of data based on input.
1990s - Neural Networks
2014 - Generative Adversarial Network (Ian Goodfellow)
2015-16 - DeepMind, VAE, RNN
2017-18 - NVIDIA Progressive GANs - generates images. 2019, 2020 - GPT-2, GPT-3
2022 - Dall-E, GPT-3.5 (Open AI)
2023 - Google BARD, OpenAI (Chat-GPT powered by GPT-4)
4

5. Common Use-cases of Generative AI Tools in the Market
5
Text input in Natural
Language
Text Generation
Multimedia
Generation
Customer
Conversations
Synthetic Images,
Music and Videos
DOCUMENT
GENERATION: Email,
RFPs, Conversations
CODE GENERATION: Models,
Data

6. Examples of
commercially available
Generative AI tools
today
2,500+ Products available
for general public to
try/buy.
Most Common: ChatGPT
6
Image Credit: RapidOps

7. So, what it means for the Utility/Energy
Sector Industries?
7

9. Key aspects of Resilience and Reliability - review
RESILIENCE:
- Enable customers to be informed
during inevitable power outages
- Reduce outage duration during
extreme events
- Not trigger cascading outages
and blackouts
- Staff coordination during
emergencies
- Customer requires faster support
9
RELIABILITY
- Avoid faults and power outages
as much as possible
- Acceptable voltage level and
power quality to all customers
- Dependability of service
- Customer requires justification
and support
- Documentation

10. Generative AI can help keep up with rapid changes in
energy landscape.
10

11. Traditional AI/ML/Data Science Architecture at Smart
Grid Companies
11

12. 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 12

13. Consumer Experience Enhancement
What Utilities Have Today, and
have made significant
investments in:
Yet, Common Issues in most
Utilities Worldwide:
- Call Centers
- ChatBots
- WhatsApp based interaction
- Long on-hold time
- Incorrect tagging of
complaints to
officer/locations
- Long resolution time,
- Non-optimum complaint
handling procedure,
- Manual intervention etc.
13

14. What Generative AI can do?
- Automatic generated email responses and explanations to billing
questions
- Automatically escalate & classify cases using sensitivity & domain
expertise analytics
- Handle multiple queries at once, analyse huge data & convert it into
reports etc.
Outcomes:
- Optimize agent availability & wait time
- Require less number of manual intervention and call center agents
- Avoid manual mistakes
14

15. 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.
15
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.

16. Fill the gap of Primary and Secondary Distribution
Feeder Models: Identify ways to reduce T&D losses
16

17. 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
17

18. Value-Added Services based on Smart Meter Data
18

19. Employee Experience Enhancement - Can improve data
governance & cybersecurity
19
Generative AI
(Custom GPT
Model)
Managers from
different
business orgs
New Employees
Retiring or
exiting
employees

20. Renewable Energy Integration
20

21. Determine Upstream Utility network capacity/loading
from incomplete behind-the-meter customer data
Outcomes:
- re-design overall tariff structures
- enhanced incentive to consumers,
- better plan power procurement,
- upstream NTW capacity design
21

22. Caveats & Risks of using Generative A.I
22

23. Potential Pitfalls and Risks of using Generative AI
23
Misinformation & Disinformation:
Generative AI can produce fake images, videos, or text, leading to spread of
falsehoods and challenging the credibility of genuine content.
Over-reliance & Loss of Skills:
As tasks get automated, there's a risk of reduced human expertise in critical
areas, potentially making it harder to detect AI-generated errors or anomalies.
Ethical & Privacy Concerns:
Generative AI models, particularly those that use vast amounts of data, might
unintentionally reproduce, amplify or leak sensitive or biased information,
posing risks to privacy and fairness.

24. Explainability, Accountability, Ethics, Accuracy
- It is not possible (till today) to
develop an unbiased AI language
model
24

25. Getting Started with Generative AI
25

26. Some options
> Try ChatGPT, Large Language Models and other AI tools shown in slide
> For Smart Grid specific Generative AI tasks - consider one or two specific
tasks, and choose a dedicated platform for smart grid applications. Example
platform: https:/
/GridLeaf.org
> Customize and self-host a customized Large Language Model for your
company.
26

27. Let’s Imagine the Future
27
Generative AI + 3D Manufacturing + Quantum Computing
→ Create anything we need at record speed and efficiency.
“The solution to climate change is innovation, not
activism.”

28. 28
Thank you for your time. Please stay in touch with me:
Email: chanda.sayonsom@gmail.com (Personal) or, sayonsom.chanda@nrel.gov (Official)
Whatsapp: +1 (509) 432-9525
If I can add you to my free research update newsletter, please let me know