De-risk scenario planning and identify trading bottlenecks
Integrate isolated component models to enable end-to-end forecasting
- Resilient scenario generation, planning and scheduling is required to meet ambitious carbon reduction targets. Top-down strategic planning often fails to account for short-term scheduling and pricing constraints due to siloed modelling systems, coordination overheads and manual feedback loops.
- Use Quaisr to integrate existing supply, demand and pricing systems and establish end-to-end planning that can be updated daily automatically, to replace manual synchronisation once per year. Empower robust and resilient scenario planning using real-time scheduling and pricing data. Identify system bottlenecks that are typically missed without integrated simulations and sensitivity analysis.
- Make effective CAPEX decisions under uncertainty.
Offshore wind modelling and optimisation
Cost-effective structural engineering with multi-objective optimisation.
- Robust modelling for offshore-wind installation is challenging as multiple structural engineering and financial modelling tools must be integrated.
- Use Quaisr to automate thousands of structural simulations for statistical analysis and uncertainty quantification, accounting for environmental conditions and rare events.
- Optimise offshore foundation designs by integrating structural and financial simulations to increase structural performance while minimising material use.
Faster screening of potential CO₂ injection sites
Accelerate feasibility studies for CO₂ injection
- Candidate reservoirs for CO₂ sequestration are assessed using high-performance computing (HPC) simulations and multiple models in the pipeline (e.g. geomechanical models, reservoir models, thermal models). Each detailed simulation generates large amounts of characterisation data that can take days of engineer time to process.
- Use Quaisr to connect automate and orchestrate simulations for faster scenario generation. Faster processing of results, including text summarisation and alerts for outputs that are outside of normal ranges. Apply active learning techniques to select the next input parameters to explore, and run simulation campaigns automatically from one integration platform.
- Accelerate carbon reduction targets by speeding up model driven decision making.
- 2x - 3x
- Faster carbon reduction goals
- 20% - 25%
- Reduction in structural costs
- 120% - 150%
- Decreased CAPEX under uncertainty