Qatalyst will present its latest energy optimisation research at IEEE Quantum Week 2026, focusing on robust battery energy storage siting under changing future grid conditions.
The work addresses a practical infrastructure-planning problem: battery-storage investments are made today, but the electricity system they operate in can change substantially over the lifetime of those assets.
Why a single forecast is not enough
Conventional planning can optimise against one assumed future. But demand levels change, renewable output varies, network bottlenecks move and individual assets can become unavailable.
A storage location that looks attractive under one scenario may therefore perform poorly under another. Qatalyst's research evaluates candidate storage decisions across multiple conditions rather than relying on one deterministic forecast.
A network-constrained optimisation problem
The work uses a standard power-network benchmark to test strategic battery siting under realistic network constraints. Candidate storage portfolios are assessed using power-flow representations and contingency screening so that optimisation reflects the physical structure of the electricity network rather than treating each investment location independently.
This creates a combinatorial decision problem: as candidate sites, network constraints and future scenarios increase, the number of possible infrastructure portfolios grows rapidly.
From the best answer to the most defensible decision
The objective is not simply to find the portfolio that performs best under one set of assumptions. It is to identify decisions that remain strong when those assumptions change.
That distinction matters for long-lived infrastructure. Decision makers need to understand not only which option is mathematically attractive, but also how sensitive it is to uncertainty and how much value is lost if the future develops differently from the central forecast.
Classical first, quantum ready
The research also reflects Qatalyst's broader approach to optimisation technology. Classical methods provide the baseline. Robust, heuristic, hybrid and quantum-ready approaches are then considered selectively and benchmarked under controlled conditions.
The purpose is not to assume that quantum computing should solve every optimisation problem. The purpose is to build evidence about which computational approach is appropriate for a given decision.
Where classical optimisation is sufficient, that result matters. Where a hybrid or quantum approach improves the decision under fair benchmarking, that should also be visible.
From research to SOLA
The work contributes to the technical foundations of SOLA, Qatalyst's decision tool for energy-infrastructure planning under uncertainty.
SOLA is being developed to support questions such as:
- Where should battery energy storage be located?
- Which locations remain valuable as demand and network conditions change?
- When should additional capacity be added?
- How sensitive is an investment decision to the assumptions behind it?
- When does a more advanced optimisation method genuinely improve the decision?
IEEE Quantum Week 2026
IEEE Quantum Week brings together researchers, practitioners and industry participants working across quantum computing and engineering through technical presentations, workshops, tutorials and exhibitions.
Qatalyst's contribution sits at the intersection of energy-system optimisation, uncertainty, network-constrained decision making and the practical evaluation of emerging computing methods.
The formal paper title is retained here for scientific accuracy. Qatalyst's wider commercial focus is energy-infrastructure optimisation: helping organisations make robust decisions about storage, grid constraints and future capacity under uncertainty.
Classical first. Quantum ready. Evidence either way.