This project aims to create the most realistic within day profiles as possible for the future GB electric heavy-goods vehicles (HGVs) fleet. This project will output a model, which can produce NESO’s required inputs to Plexos, and enable periodic updates/sensitivity testing to enabling lasting value to the NESO’s processes and stakeholders.
Benefits
A step change in understanding of HGVs system impacts, moving the NESO towards its strategic goal of being GB leaders in the within day system impacts and flexibility of future demand.
Outputs which can quickly be captured in NESOs network investment assessment processes (FES, ETYS & NOA – Soon to be CSNP).
Through engagement with the Department for Energy Security and Net Zero (DESNZ) and NGED (National Grid Electricity Distribution), it is likely that outputs will inform the modelling of other infrastructure planners
Learnings
Outcomes
The project delivered a step change in NESO’s understanding of the future electricity system impacts of electric Heavy Goods Vehicles (HGVs), moving from high‑level assumptions to evidence‑based, system‑relevant insight on the scale, timing and flexibility of HGV charging demand.
A central outcome was the development of a modular, Python‑based demand model capable of producing realistic within‑day charging profiles and aggregating impacts to national and Grid Supply Point level. Using this capability, the project demonstrated that electric HGV demand could exceed 160 GWh per winter day by mid‑century under high uptake scenarios, representing a material new electricity demand. The analysis also showed that smart charging could reduce peak demand by over 17% nationally and by up to 35% at some Grid Supply Points, relative to unmanaged charging scenarios.
Extensive stakeholder engagement generated new behavioural evidence, demonstrating that while smart charging offers theoretical system benefits, achievable flexibility is constrained by operational requirements which varies per HGV segment, grid capacity and fleet utilisation. This materially improved how flexibility and uptake are represented in system analysis and reduced the risk of overstating demand‑side response.
The project progressed the analytical capability from early research (TRL 4) to a validated, large‑scale modelling approach suitable for strategic system planning (TRL 6). Key learning includes the value of combining modelling with stakeholder evidence, explicitly representing operational constraints, and adopting modular model designs to support future reuse across NESO planning and scenario development activities.
Lessons Learnt
The project demonstrated the importance of integrating technical modelling with early and targeted stakeholder engagement when addressing emerging demand types where empirical data is limited. Behavioural insight proved critical to validating assumptions and ensuring that modelling outputs reflected operational reality rather than theoretical potential.
The work reinforced the need to distinguish clearly between technical flexibility and operational feasibility. While smart charging offers material theoretical benefits, the project showed that factors such as grid connection capacity, vehicle utilisation patterns and risk tolerance place practical limits on achievable flexibility. Explicitly representing these constraints improved the credibility of outputs and reduced the risk of overstating system benefits.
The project also highlighted the value of iterative assumption refinement as evidence emerges. Desk‑based assumptions provided a necessary starting point, but stakeholder feedback enabled more realistic treatment of charging windows, parking behaviour and uptake rates. Future research projects should plan for structured assumption review points rather than fixed parameters.
From a delivery perspective, the modular modelling approach was effective. Separating fleet evolution, charging behaviour and spatial aggregation enabled targeted updates and reduced rework as evidence evolved. Finally, the project confirmed the importance of clearly positioning research outputs as decision‑support evidence, with appropriate communication of uncertainty, limitations and intended use to inform downstream planning and policy discussions.
