Operating the energy system depends on critical decisions. Balancing supply and demand in real time. Managing assets and innovations across networks. Planning for a system that is becoming more distributed and less predictable. As conditions change, strategic and operational [XB2.1]decisions must be made more frequently, with greater uncertainty, and with tighter margins for error. Getting them right matters for system stability, cost, and the transition to net zero.
System and network operators must balance security of supply, cost, and decarbonisation simultaneously.
This involves integrating intermittent, low carbon generation, managing distributed assets, and responding to evolving demand patterns. The system is becoming more complex and uncertain, while tolerance for error and risk remains low.
Decisions are increasingly time-sensitive and often must be made without complete information.
In complex energy systems, outcomes depend on understanding what is driving them, how conditions may change, and how much uncertainty is involved.
Mathematical modelling, data science, and AI provide the tools to support this, when applied with rigour and in the context of real operational systems.
We have spent working at the heart of the UK energy system, supporting organisations where decisions carry operational and regulatory consequence.
We help organisations across the energy sector make better operational and strategic decisions. Our work spans forecasting, optimisation, simulation, and uncertainty quantification, supporting both real-time system operation and long-term network and asset planning.
Solving complex challenges starts with understanding the right problem. We work closely with subject matter experts to define the right challenge before selecting the most appropriate mathematical tools to address it. Combined with strong model verification and validation, this helps ensure solutions are transparent, reliable, and fit for purpose.
We work with system operators, network companies, asset owners, and policymakers. This includes predictive asset maintenance for EDF, and explainable machine learning and optimisation for organisations including NESO and SSEN to support reserve setting, demand and flexibility forecasting, and customer vulnerability assessment.
Demand is becoming less predictable. Supply is more distributed. Market structures are evolving.
This requires new approaches to forecasting, optimisation, and system design that account for uncertainty, risk, and real-world constraints.
Our work supports organisations to make these changes in practice, from forecasting demand under uncertainty to designing mechanisms that enable a low-carbon system to operate effectively.
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