Kevala: Grid Intelligence

I had a chat with Troy, DS manager @ Kevala.

A few things I got out of the chat:

On Kevala

  • key mission: make energy data more useful and drive decisions in energy policy and utility decisions
  • customers: They work a lot with utilities as their main stakeholders, as well as some state and federal regulatory bodies
  • offerings:
    • They have a user platform that shows granular data on the electric grid, including high voltage transmission lines and lower voltage distribution lines
    • A key data asset is their granular data on the lower voltage distribution lines that feed customers and EV chargers
  • example works:
    • One of their data science teams models different energy resources like rooftop solar, batteries, EVs, and EV charging infrastructure
    • They help utilities plan their grids and identify where new energy needs will arise at a granular level
    • They can help utilities understand impacts on revenue and customer bills from technology adoption


DS projects in energy

  • Forecasting:
    • Forecasting energy demand
    • Forecasting electricity prices
  • Optimization:
    • Controlling/optimizing different energy assets like batteries, EV chargers
  • Other Applications:
    • Image recognition (e.g. analyzing satellite imagery)
    • LLMs (e.g. parsing regulations/zoning PDFs)
    • Graph analysis (representing utility grid network as a graph)

What he looks for when hiring

  • clean, documented, production-quality code that can be collaborated
  • Familiarity with version control, cloud platforms (AWS, GCP)
  • Already thinking about evaluation metrics and how to assess model performance
  • Able to discuss next steps - other modeling techniques, deployment considerations
  • Thinking holistically about the entire solution pipeline beyond just modeling on laptop, i.e. big-picture thinking about full lifecycle of a data science solution

projects he would work on

  • EV Charging Analysis
    • Analyze data on locations of EV charging stations
    • Look at characteristics of neighborhoods that lead to more charger deployments
    • Examine speed of charger rollout in different areas
  • Transportation/Mobility Analysis
    • Use datasets on people's travel patterns and destinations
    • Analyze bike networks and most used routes/locations
  • Battery/EV Charger Optimization Modeling
    • Model integrated battery and EV charger systems
    • Optimize battery usage alongside EV charging patterns
    • Factor in pricing signals, grid carbon intensity by time of day
  • New York City Subway/Transit Analysis
    • Mentioned NYC has open data on subway schedules that could enable analysis

what keeps him up at night

  • The massive scale of EV charger deployment needed to support transportation electrification goals
  • He cites a target of 1.2 million chargers needed in California by 2035/2040, but currently only around 100,000 public ports
  • Acknowledges utilities were not designed for this magnitude of new electric loads
  • But he finds it exciting that utilities are open to changing their processes to accommodate this transition

advice for a master's student

  • Make the most of being in the Bay Area for networking and events outside just your masters program
  • Attend energy/sustainability groups and events to learn from other professionals
  • This exposure helped give him a better understanding of why energy is used/deployed in certain ways