# Charging Schedule for Load Peak Minimization on Large-Scale Electric Bus Depots
## 1 Introduction
These studies are not applicable to the centralized depot concept with regular charging used in Hamburg. [PERSON] et al. propose a k-Greedy algorithm for a fleet consisting of electrical and diesel buses [16]. Their schedule reduces the usage of diesel buses and maximizes the amount of routes driven by electrical buses. [PERSON] et al. propose a constraint based mathematical model for scheduling buses on a centralized depot for a small bus fleet of up to 10 buses [17]. The model takes into account the variable electricity prices during the day and schedules the charging with the goal to minimize the costs. [PERSON] et al. combine power consumption forecasting and the scheduling of bus charging [18]. They use wavelet neural network to predict the power consumption of buses based on several external factors such as weather and historical data about passenger travelling habits. The method uses the predicted power consumption to schedule the bus charging for the next day with the objective of minimizing the charging cost. [PERSON] et al. analyze a mixed fleet of hybrid and electrical buses in the city of Luxemburg [19]. The proposed algorithm has the objective to minimize the operational costs as well as to determine the amount of electrical buses necessary to replace hybrid buses. [PERSON] et al. focus on multiobjective evolutionary algorithms for management of electric bus fleet charging [20]. The authors propose an optimal charging schedule with the goal to reduce the charging costs and battery ageing. They tested their optimization method for a scenario with one electrical bus. The mentioned studies analyze bus fleets with regular charging mode on centralized bus depots. Nevertheless, they analyze mixed fleets of electrical and diesel (or hybrid) buses or small-scale bus depots. The suggested solutions are not applicable to the large-scale bus depots in Hamburg, consisting of purely electrical buses. [PERSON] et al. propose a heuristic algorithm for scheduling electrical buses under regular charging mode [21]. They use a real example of bus fleet in the city of Shenzhen in China. The suggested approach minimizes the number of buses necessary for the scheduled routes as well as the cost of the used energy. [PERSON] et al. also analyzed the bus fleet in Shenzhen [22]. They designed a new scheduling system called bCharge and tested it on a fleet of 16,359 buses. bCharge is a real-timecharging scheduling system based on Markov Decision Process with the overall objective of reducing the charging and operation costs. They tested the system with a real-world streaming data. [PERSON] et al. consider scheduling bus charging at the depot, while predicting their arrival and departure time [23]. They propose two real-time coordinated strategies to improve the charging costs by responding to the time-of-use electricity prices. Although the studies [21; 22; 23] deal with large-scale centralized depots, their optimization is based on electricity prices and its objective is to minimize the charging costs, not to minimize the peak demand. Hochbahn purchases electricity in advance and is not affected by the electricity price changes at the market. Therefore, the suggested algorithms are not applicable to the bus depot analyzed in this paper. To the authors knowledge there are no publications concerning large-scale electric bus depots and charging schedule with the objective of pure load peak minimization.