The application of smart charging to battery electric buses can provide opportunities for bus operators to reduce the operational costs of their bus fleet. This research aims to create insight into the impact of different charging strategies for battery electric bus fleets on charging costs and the grid load. It proposes a novel framework to model the charging process of battery electric buses for different charging strategies: charging-on-arrival, peak-shaving, day-ahead market optimization with and without vehicle-to-grid (V2G) functions, including the provision of Frequency Containment Reserves (FCR) and automatic Frequency Restoration Reserves (aFRR) for system balancing in ancillary services markets. Model simulations are conducted to compare the charging costs and grid impact of different charging strategies, using three depots of bus operator Qbuzz in the Netherlands as a case study. Results indicate that the application of smart charging algorithms can considerably reduce charging costs for bus operators. Application of the peak-shaving strategy was found to reduce charging costs by 23–32% compared to the reference case of charging-on-arrival. Charging costs can be further reduced by 6–11% when considering day-ahead market optimization. Participation in ancillary services markets for system balancing is economically attractive for bus operators, particularly in the aFRR market, characterized by a cost reduction potential of 90–¿100% compared to the charging-on-arrival strategy. The grid impact analysis indicates that charging-on-arrival can result in high charging demand peaks, which can be drastically reduced by the application of peak-shaving or day-ahead market optimization charging strategies. However, the provision of aFRR and FCR using the battery electric bus charging process can have a severe impact on the local grid in terms of high peak demand.

将智能充电应用于纯电动公交车可为公交运营商提供降低公交车队运营成本的机会。本研究旨在深入了解电池电动公交车队的不同充电策略对充电成本和电网负载的影响。它提出了一个新的框架来模拟电池电动公交车的充电过程，用于不同的充电策略：到达充电、调峰、日前市场优化，有和没有车辆到电网 (V2G) 功能，包括规定频率控制储备 (FCR) 和自动频率恢复储备 (aFRR)，用于辅助服务市场的系统平衡。进行模型模拟以比较不同充电策略的充电成本和电网影响，使用荷兰公共汽车运营商 Qbuzz 的三个车站作为案例研究。结果表明，智能充电算法的应用可以大大降低公交运营商的充电成本。与到达时充电的参考案例相比，应用调峰策略可将充电成本降低 23-32%。考虑日前市场优化时，充电成本可进一步降低 6-11%。参与系统平衡的辅助服务市场对公交运营商具有经济吸引力，特别是在 aFRR 市场，其特点是与到达时充电策略相比，成本降低潜力为 90–100%。电网影响分析表明，到站充电会导致高充电需求高峰，这可以通过应用调峰或日前市场优化充电策略来大幅减少。然而，使用电池电动公交车充电过程提供 aFRR 和 FCR 可能会在高峰需求方面对当地电网产生严重影响。