Bidding and Charging Scheduling Optimization for the Urban Electric Bus Operator

With the development of electric buses (EBs) in urban areas, how to effectively utilize their charging demand flexibility and the considerable energy stored in the on-board batteries becomes an urgent issue for the electric bus operator (EBO) to consider. This paper focuses on the bidding decision in energy and reserve markets for the urban EBO owning multiple electric bus stations with energy storage systems (ESSs), and the charging scheduling of all EBs. A trip-chain based electric vehicle boundary model is proposed firstly to concisely describe the flexibility region for an EB with multiple sequential trips, which can be further aggregated via a state-based approach to adapt to large-scale problems. The uncertain boundaries are also derived taking the uncertainty of trip energy consumption into account. In order to deal with the issue that the boundary aggregation will possibly influence the feasibility of decomposing the total power to each individual EB, the stochastic bidding and EB scheduling optimization problem in a hierarchical optimization framework is proposed. Accelerated Benders decomposition is utilized to efficiently solve the problem. Case studies verify that the proposed model can dramatically reduce the computational burden, effectively consider the uncertainty and take advantage of the flexibility of both EBs and ESSs to achieve better economic performance.