Sutubra Research is an energy system modelling practice working across North America's electricity system and the sectors reshaping it.

System Planning

Long-term planning models that determine the right mix of generation, storage, and transmission over a 20- to 30-year horizon. No single set of assumptions about the future is reliable—costs shift, policy changes, demand surprises, weather varies. So rather than producing one optimal plan, the models generate hundreds of near-cost-optimal alternatives: plans that look structurally different but cost about the same. This reveals which investments are robust regardless of how the future unfolds, which decisions can wait, and where the genuine trade-offs lie. The approach is the basis of a co-authored paper in Nature Communications.

The models are built on high-resolution wind and solar resource characterization and tested against multiple historical weather years. The same framework evaluates specific technologies and projects at the system level: what long-duration storage, interregional transmission, hydrogen, or synthetic fuels are actually worth to a given system under different futures.

Cross-Sector Energy Modelling

The hardest planning questions often live at the boundaries between sectors—and getting them wrong is expensive. For instance, when buildings electrify, it reshapes electricity demand curves. The choice between heat pumps, gas, and hybrid systems depends on electric system costs, gas cost recovery, and household economics simultaneously. Sutubra builds sector-coupled models that capture these interdependencies across electricity, buildings, transportation, and industry.

A co-authored paper in Environmental Research Letters examined exactly this—finding that fully electrifying peak heating loads in cold climates carries extreme marginal abatement costs compared to hybrid approaches, using least-cost optimization across all three systems.

Forecasting & Reliability

As renewable penetration grows and heating electrifies, both supply and demand become highly weather-sensitive—making traditional point-estimate planning increasingly unreliable. Sutubra provides resource adequacy assessments and probabilistic load forecasting using deep historical weather datasets. The result is a forecast calibrated to reality: full probability distributions with honest confidence intervals, not point estimates. The methods draw on extreme value theory and Monte Carlo simulation to characterize tail risks—the low-probability, high-consequence events that drive capacity planning decisions. This covers peak demand, load shapes, energy forecasting, and the structural shifts in consumption driven by electrification, distributed generation, and emerging loads.

Behind-the-Meter Modelling

At the facility level, optimizing the sizing, scheduling, and dispatch of on-site resources—storage, solar, flexible loads—to minimize costs and capture value. This also includes clean energy target setting for companies and institutions: what's achievable, what it costs, and what the realistic timeline looks like. The work means navigating uncertain load profiles, variable generation, evolving tariff structures, and grid service revenue opportunities—the same optimization and uncertainty quantification that applies at the grid scale, pointed at a different problem.