An Operational Digital Twin for Electric Bus Depot Charging Infrastructure: Validation and Scenario Analysis at a Sub-Saharan African Fleet

Charging infrastructure for battery electric bus (BEB) fleets must be sized against the timing of demand, not only its magnitude: an accurate per-trip energy estimate cannot on its own establish whether a depot’s chargers, contracted grid connection, and dispatch schedule can recover the fleet’s energy deficit without breaching demand-based tariff thresholds. This is especially consequential in resource-constrained settings, where notified-maximum-demand penalties and steep time-of-use differentials turn a mistimed charging load into a direct commercial liability rather than an operational inconvenience. Yet pre-investment planning tools typically treat vehicle energy consumption, dispatch scheduling, and charging infrastructure as separate problems, so a validated demand estimate rarely translates into a validated depot load profile. This paper presents an operational digital twin that converts the per-trip arrival state-of-charge predicted by a companion rolling stock twin into a depot-level charging load profile for the Golden Arrow Bus Services (GABS) electric fleet at the Arrowgate depot in Cape Town, South Africa. The twin reconstructs each bus’s daily duty chain from scheduling data to determine its available charging window, then simulates session initiation, power draw, and duration against these windows using charging behaviour parametrised from depot Open Charge Point Protocol (OCPP) records, including the smart charging constraints already enforced at the depot. Each bus’s state of charge is carried from one depot visit to the next, so an undercharged departure reappears as a lower arrival rather than disappearing from the model. The simulated load profile is validated against 968 OCPP session records from two periods with different depot hardware, achieving energy-per-session agreement of 4.5 to 7.3% and load-shape correlation of 0.95 to 0.99; on a held-out night not used for calibration, with every recorded session as input, the twin reproduces the depot’s measured overnight peak to within 3.3%, and on a July day with no site pool, with nothing calibrated, its mean overnight load to within 0.8%. The simulated load profile is evaluated against the depot’s contracted notified maximum demand of 3000 kVA. Scenario analysis quantifies how the charging schedule, charger rate, and site pool limit shift the load profile relative to this threshold and to time-of-use cost exposure, showing that scheduling charging into off-peak windows without a site-wide limit can raise peak demand above uncontrolled charging and beyond the contracted threshold (3167 against 1832 kW), that a site pool limit controls peak demand and monthly cost together, and that a 30 kW per-charger cap leaves buses unable to complete up to 15% of their trips; grown to 80 buses, the fleet would exceed the depot’s 2025 connector count before its pool limit, which the twin shows as overnight queueing and stranded trips. By coupling a validated, condition-aware demand layer to depot dispatch and charging logic, this work supports capital-efficient charging-infrastructure decisions for transit operators facing demand-based tariffs and constrained grid capacity, as at Arrowgate, where undersized infrastructure risks service disruption and oversized infrastructure diverts capital from wider electrification.