Transactive Control of Electric Railways Using Dynamic Market Mechanisms

TL;DR

This paper introduces a transactive control framework for electric railways that coordinates power grid and train operations through dynamic market mechanisms, significantly reducing energy costs.

Contribution

It presents the first railway-based dynamic market mechanism (rDMM) for optimizing DER dispatch and train operations in a unified framework.

Findings

25% reduction in energy costs compared to standard trip optimization

75% reduction in energy costs compared to field dataset calculations

Effective coordination of power grid and train dispatch demonstrated in simulations

Abstract

Electricity demand of electric railways is a relatively unexplored source of flexibility in demand response applications in power systems. In this paper, we propose a transactive control based optimization framework for coordinating the power grid network and the train network. This is accomplished by coordinating dispatchable distributed energy resources and demand profiles of trains using a two-step optimization. A railway based dynamic market mechanism (rDMM) is proposed for the dispatch of distributed energy resources (DER) in the power network along the electric railway using an iterative negotiation process, and generates profiles of electricity prices, and constitutes the first step. The train dispatch attempts minimize the operational costs of trains that ply along the railway, while subject to constraints on their acceleration profiles, route schedules, and the train dynamics, and generates demand profiles of trains and constitutes the second step. The rDMM seeks to optimize the operational costs of the underlying DERs while ensuring power balance. Together, they form an overall framework that yields the desired transactions between the railway and power grid infrastructures. This overall optimization approach is validated using simulation studies of the Southbound Amtrak service along the Northeast Corridor (NEC) in the United States, which shows a 25% reduction in energy costs when compared to standard trip optimization based on minimum work, and a 75% reduction in energy costs when compared to the train cost calculated using a field dataset.