There are sectors of the transport system, such as buses and heavy goods vehicles, which have proven challenging to electrify due to their high energy demands. The expected adoption of EVs is a challenge to networks across the globe as they expect to plan network to facilitate the demand growth followed by the transport electrification. Megawatt charging system of eTrucks has the potential to help plan the charging of eTrucks. This proposal aligns with the focus areas of our Innovation strategy. This project will investigate charging infrastructure and its impact on our network. The project will produce models that will accurately assess the impact of a roll out of EV Trucks across our license areas. The key deliverables will be shared with all RIIO licensed UK DNOs for their own use.
Benefits
This project will:
- Develop an understanding of charge demand patterns producing eTrucks fleet charging load profiles.
- Evaluate the mitigation systems to reduce concentrated peak demand.
- Validate chargers and battery models developed in simulations and laboratory with a potential of scaling up.
- Provide the basis of understanding of the capabilities of this technology and its limitations.
- Guide in developing our strategy around eTrucks charging and delivering the power effectively by managing peak loads.
Learnings
Outcomes
- Developed modelling tools for probabilistic Monte Carlo BEHGVs charging and BEHGV Charging Load
- Simulator developed in Python to evaluate the impact of BEHGV depot charging strategies. These tools can be applied to any location where relevant data is available, making it a versatile tool for planning charging infrastructure and their impact on the grid.
- Modelling studies of the charging demand patterns for different charge types, generating electric trucks fleet charging load profiles.
- Forecast power demand and peak-demand from MCS of electric trucks for different future scenarios up to 2040.
Recommendations for further work
None at this stage
Lessons Learnt
As the project is still progressing, it is not yet applicable to discuss lessons learned in detail. However, there area few lessons learned from previous deliverables:
- Flexible Methodology: A versatile methodology for load profile analysis can accommodate various scenarios and operational conditions, aiding in infrastructure planning.
- Probabilistic Approach: Utilising Monte Carlo simulations enhances the accuracy of load forecasting and operational characteristics of charging stations.
- Charging Strategies: Different charging strategies (Immediate, Delayed, Constant) have distinct impacts on grid load, highlighting the need for careful planning to mitigate peak demand.
- Infrastructure Planning: Identifying critical zones in the transmission network is essential for anticipating capacity constraints and necessary reinforcements.
- Stochastic Algorithms: Developing stochastic algorithms for fleet charging can help grid operators prepare for future electrification demands effectively.
Dissemination
The research results were presented at Universities Power Engineering Conference 2024 in Cardiff 2-6
September.
Khan, K. S., Alharbi, F., Shaban, M., Albano, M. and Cipcigan, L. M. 2025. Methodology for assessing load demands of megawatt charging stations for electric HGVs. Presented at: 59th International Universities Power Engineering Conference, Cardiff, UK, 2-6 Sept. 2024 59th International Universities Power Engineering Conference (UPEC)
- The research results were presented at London EV Show 26 November 2024, the presentation title "Grid Challenges of Electric Trucks Megawatt Charging System.
- A conference paper was submitted to The 60th Universities' Power Engineering Conference (UPEC 2025) London.
Chandima D. Pathiranage, Liana Cipcigan, Fahd Alharbi, Manu Haddad, Aisha Ali, Optimising Fleet Charging Loads at Depots for Battery Electric Heavy Goods Vehicles, submitted to UPEC 2025.
- A journal paper was submitted to eTransportation: Elsevier journal and it is under review.
Chandima D. Pathiranage, Liana Cipcigan, Fahd Alharbi, Manu Haddad, Aisha Ali, Megawatt Charging Systems for Enroute Charging of Battery Electric Trucks: Load Forecast and Grid Requirements in Great Britain using a Monte Carlo Approach.
