Hey everyone, we’re Thomas and Raj, cofounders of inBalance (inbalanceresearch.com).
inBalance forecasts electricity price, demand, and generation by source up to 72 hours
ahead, helping utilities and independent power producers utilize their responsive assets
such as energy storage, backup generators, flexible demand, etc more efficiently.
We met playing ultimate frisbee in Cambridge, UK, and quickly found common interests in
statistics and optimization. Thomas had previously worked on wind turbine placement
problems, providing experience with power markets, and we discussed them but didn't see
an immediate entry point, so Thomas continued his statistics PhD and Rajan worked in ML
research and GPU algorithm design at a startup.
A year ago we heard of a need for better wind power forecasts and started to look at the
market more closely. We found a gap emerging from the increase in the prevalence of
storage, especially lithium-ion, grid-scale batteries. It seemed like an interesting and useful
real-world application of machine learning, particularly with the possibility of reducing carbon
emissions, so once the business case looked tenable, we decided to go ahead!
Electrical power markets have become increasingly volatile due extreme weather events and
increased prevalence of intermittent renewables. In response to this, producers are bringing
on more flexible generation assets such as batteries to even out fluctuations in supply, and
electrical consumers are aiming to increase their ability to modulate demand to better take
advantage of cheap intermittent power. These assets don't fit into the day-ahead markets
designed for mostly traditional steam power plants, making it difficult to choose when to use
them. Our forecasts help traders better align their use with power availability, who now do so on gut feeling or low-quality coarse-grained forecasts. We hope this
will increase the value enough to make transitioning to renewables more financially
appealing.
Most standard machine learning approaches struggle in particular with price forecasting due
to the limited data, large number of factors, heavy-tails, high noise, and underlying
complexity; even given the bids for each producer and consumer, solving for the prices
across a power network taking into account transmission, energy balance, and AC power
flow constraints relies on an NP-hard mixed-integer programming problem that can take
hours to solve. Of course in reality we don't even know the bids ahead of time, and we still
haven't won the battle against the heavy tails today!
Our pilot experiences with a major East Coast utility looking to trade power, a major New
England utility managing their demand response program, a battery storage operator in
Texas, and a wind trader in Texas, have shown us that every participant has differing needs
for their particular asset collection, so we dedicate time to each of our customers to make
sure that the product is tailored to their needs. Along the way we've developed a generic
forecasting system tuned for power markets to speed up customization, but we know we
have a long way to go before we support the full range of forecast granularity, ___location,
range, risk metrics etc we've heard interest for.
With over 3000 market participants operating in open electricity markets (including Texas,
California, New York, New England, and the mid-Atlantic), we’re hoping to hit 7 figure
revenue within the next year.
We need huge amounts of storage to facilitate a transition to zero carbon grids long term, so
we hope to minimize risk and maximize the reward for building new storage assets.
We’d love to hear your thoughts, questions, and comments!
