L. Shams Ashkezari
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The electrification of heavy-duty transport requires the large-scale integration of Megawatt Charging Stations at highway rest areas and fuel stations. This infrastructure imposes significant challenges to medium-voltage (MV) distribution networks due to their high power demand, clustered charging events and limited predictability. These characteristics could lead to voltage deviations, increased line loading, and additional stress on grid assets. In this study, the grid impact of Heavy-Duty Electric Vehicle (HDEV) charging stations of varying sizes is investigated and the effectiveness of BESS-based mitigation strategies under the constant and different network configurations is evaluated. Using time-series power flow simulations in MATLAB/Simulink, the impact of unmitigated HDEV charging is analyses in a radial MV distribution network topology. The results show that the dominant limiting factors of HDEV integration are voltage deviations and increased line loading. Upstream transformer loading does not form the bottleneck in the investigated network. To address these impacts, a number of local Energy Management System strategies are evaluated, including heuristic and optimisation-based control.
The results show that with BESS-based mitigation, serving both the charging station operator and grid operator interests, effectively restores network voltage stability and reduces peak power demand at the charging station. Differences in control strategies have shown distinct charging and discharging patterns of the BESS, which affect line loading and transformer utilisation. Heuristic control provides robust peak shaving by reducing active grid power demand, whereas optimisation-based control showed an improved economic performance and smoother voltage regulation, especially when reactive power is used for voltage support. Simulation with multiple charging stations and decentralised BESS control for voltage regulation resulted in unstable network behaviour, highlighting the necessity of a centrally coordinated voltage regulation mechanism.
Overall, this study provides new insights into the trade-offs in BESS-based mitigation strategies in terms of voltage stability, congestion management and BESS operation for the integration of HDEV charging stations. The findings underscore the importance of coordinated control of decentralised assets to ensure reliable grid integration of these multi-megawatt and highly variable loads. ...
The results show that with BESS-based mitigation, serving both the charging station operator and grid operator interests, effectively restores network voltage stability and reduces peak power demand at the charging station. Differences in control strategies have shown distinct charging and discharging patterns of the BESS, which affect line loading and transformer utilisation. Heuristic control provides robust peak shaving by reducing active grid power demand, whereas optimisation-based control showed an improved economic performance and smoother voltage regulation, especially when reactive power is used for voltage support. Simulation with multiple charging stations and decentralised BESS control for voltage regulation resulted in unstable network behaviour, highlighting the necessity of a centrally coordinated voltage regulation mechanism.
Overall, this study provides new insights into the trade-offs in BESS-based mitigation strategies in terms of voltage stability, congestion management and BESS operation for the integration of HDEV charging stations. The findings underscore the importance of coordinated control of decentralised assets to ensure reliable grid integration of these multi-megawatt and highly variable loads. ...
The electrification of heavy-duty transport requires the large-scale integration of Megawatt Charging Stations at highway rest areas and fuel stations. This infrastructure imposes significant challenges to medium-voltage (MV) distribution networks due to their high power demand, clustered charging events and limited predictability. These characteristics could lead to voltage deviations, increased line loading, and additional stress on grid assets. In this study, the grid impact of Heavy-Duty Electric Vehicle (HDEV) charging stations of varying sizes is investigated and the effectiveness of BESS-based mitigation strategies under the constant and different network configurations is evaluated. Using time-series power flow simulations in MATLAB/Simulink, the impact of unmitigated HDEV charging is analyses in a radial MV distribution network topology. The results show that the dominant limiting factors of HDEV integration are voltage deviations and increased line loading. Upstream transformer loading does not form the bottleneck in the investigated network. To address these impacts, a number of local Energy Management System strategies are evaluated, including heuristic and optimisation-based control.
The results show that with BESS-based mitigation, serving both the charging station operator and grid operator interests, effectively restores network voltage stability and reduces peak power demand at the charging station. Differences in control strategies have shown distinct charging and discharging patterns of the BESS, which affect line loading and transformer utilisation. Heuristic control provides robust peak shaving by reducing active grid power demand, whereas optimisation-based control showed an improved economic performance and smoother voltage regulation, especially when reactive power is used for voltage support. Simulation with multiple charging stations and decentralised BESS control for voltage regulation resulted in unstable network behaviour, highlighting the necessity of a centrally coordinated voltage regulation mechanism.
Overall, this study provides new insights into the trade-offs in BESS-based mitigation strategies in terms of voltage stability, congestion management and BESS operation for the integration of HDEV charging stations. The findings underscore the importance of coordinated control of decentralised assets to ensure reliable grid integration of these multi-megawatt and highly variable loads.
The results show that with BESS-based mitigation, serving both the charging station operator and grid operator interests, effectively restores network voltage stability and reduces peak power demand at the charging station. Differences in control strategies have shown distinct charging and discharging patterns of the BESS, which affect line loading and transformer utilisation. Heuristic control provides robust peak shaving by reducing active grid power demand, whereas optimisation-based control showed an improved economic performance and smoother voltage regulation, especially when reactive power is used for voltage support. Simulation with multiple charging stations and decentralised BESS control for voltage regulation resulted in unstable network behaviour, highlighting the necessity of a centrally coordinated voltage regulation mechanism.
Overall, this study provides new insights into the trade-offs in BESS-based mitigation strategies in terms of voltage stability, congestion management and BESS operation for the integration of HDEV charging stations. The findings underscore the importance of coordinated control of decentralised assets to ensure reliable grid integration of these multi-megawatt and highly variable loads.
Master thesis
(2025)
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B.E.L. Lagae, G.R. Chandra Mouli, L. Shams Ashkezari, N. Yorke-Smith, M. Saeednia
The electrification of the heavy-duty freight sector requires a robust charging infrastructure that balances operational needs with grid and cost constraints. This thesis develops an integrated modeling framework to simulate charging demand and optimize the placement and configuration of high power charging stations for HDEVs. The first phase involves modeling charging demand by simulating energy depletion across real world truck trips. Using detailed vehicle specifications and regulatory driving limits, State of Charge (SoC) calculations identify when and where trucks are likely to require charging. These simulated charging events reflect realistic operational behavior across Dutch and cross border freight routes. The second phase applies a Mixed-Integer Linear Programming (MILP) model to determine optimal station locations and configurations. The optimization selects candi- date stations, either generated trough regulatory rules (AFIR) or traffic clustering, and minimizes system wide costs while accounting for wait times, charger availability, and grid connection limits. Events are assigned to specific stations and time slots to ensure feasible, cost effective infrastructure deployment. Two station placements strategies are evaluated for the year 2025 and 2030: (1) AFIR based regulatory , and (2) demand driven locations based on clustering from simulated charging events. The simulated electrification rates are 0.75% for 2025 and 7.5% for 2030. The optimization model provides the number of chargers, where to place them, and how charging events are assigned. From these results, demand curves at the station are created. The findings show that demand based placement can reduce wait times and overall installation cost, while better handling growing charging needs compared to AFIR compliant layouts. Note that the AFIR layout station locations are fixed to the regulatory minimum requirements, and only the number of chargers is adjusted to meet future demand. This thesis presents a practical method for planning charging networks using real truck data. It shows how simulated charging demand can help design better infrastructure, taking into account operational limits and policy goals to support the shift to electric freight transport in Europe.
...
The electrification of the heavy-duty freight sector requires a robust charging infrastructure that balances operational needs with grid and cost constraints. This thesis develops an integrated modeling framework to simulate charging demand and optimize the placement and configuration of high power charging stations for HDEVs. The first phase involves modeling charging demand by simulating energy depletion across real world truck trips. Using detailed vehicle specifications and regulatory driving limits, State of Charge (SoC) calculations identify when and where trucks are likely to require charging. These simulated charging events reflect realistic operational behavior across Dutch and cross border freight routes. The second phase applies a Mixed-Integer Linear Programming (MILP) model to determine optimal station locations and configurations. The optimization selects candi- date stations, either generated trough regulatory rules (AFIR) or traffic clustering, and minimizes system wide costs while accounting for wait times, charger availability, and grid connection limits. Events are assigned to specific stations and time slots to ensure feasible, cost effective infrastructure deployment. Two station placements strategies are evaluated for the year 2025 and 2030: (1) AFIR based regulatory , and (2) demand driven locations based on clustering from simulated charging events. The simulated electrification rates are 0.75% for 2025 and 7.5% for 2030. The optimization model provides the number of chargers, where to place them, and how charging events are assigned. From these results, demand curves at the station are created. The findings show that demand based placement can reduce wait times and overall installation cost, while better handling growing charging needs compared to AFIR compliant layouts. Note that the AFIR layout station locations are fixed to the regulatory minimum requirements, and only the number of chargers is adjusted to meet future demand. This thesis presents a practical method for planning charging networks using real truck data. It shows how simulated charging demand can help design better infrastructure, taking into account operational limits and policy goals to support the shift to electric freight transport in Europe.