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.
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Dynamic responses, GPS positions and environmental conditions of two light rail vehicles in Pittsburgh
We present DR-Train, the first long-term open-access dataset recording dynamic responses from in-service light rail vehicles. Specifically, the dataset contains measurements from multiple sensor channels mounted on two in-service light rail vehicles that run on a 42.2-km light rail network in the city of Pittsburgh, Pennsylvania. This dataset provides dynamic responses of in-service trains via vibration data collected by accelerometers, which enables a low-cost way of monitoring rail tracks more frequently. Such an approach will result in more reliable and economical ways to monitor rail infrastructure. The dataset also includes corresponding GPS positions of the trains, environmental conditions (including temperature, wind, weather, and precipitation), and track maintenance logs. The data, which is stored in a MAT-file format, can be conveniently loaded for various potential uses, such as validating anomaly detection and data fusion as well as investigating environmental influences on train responses.
more » « less- NSF-PAR ID:
- 10153739
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Scientific Data
- Volume:
- 6
- Issue:
- 1
- ISSN:
- 2052-4463
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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