Search for: All records

Dynamic trip optimization in electric rail networks is a relatively unexplored topic. In this paper, we propose a transactive controller that includes an optimization framework and a control algorithm that enable minimum cost operation of an electric rail network. The optimization framework attempts to minimize the operational costs for a given electricity price by allowing variations of the trains’ acceleration profiles and therefore their power consumption and energy costs. Constraints imposed by the train dynamics, their schedules, and power consumption are included in this framework. A control algorithm is then proposed to optimize the electricity price through an iterative procedure that combines the desired demand profiles obtained from the optimization framework together with the variations in Distributed Energy Resources (DERs) while ensuring power balance. Together, they form to an overall framework that yields the desired transactions between the railway and power grid infrastructures. This approach is validated using simulation studies of the Southbound Amtrak service along the Northeast Corridor (NEC) between Boston, MA and New Haven, CT in the United States, reducing energy costs by 10% when compared to standard trip optimization based on minimum work.