Abstract
Abstract Electrified railways are large users of electrical power at a time when grid supply conversion to renewable energy production is making supply to the grid less predictable and environmental concerns demand reduction in energy use. These developments make it desirable to control and reduce both total energy usage and peak power demand of railway systems. While AC systems have a well-developed ability to regenerate power to the grid, high transmission losses in DC systems make local storage of energy a more attractive option. A model has been created integrating a versatile and configurable database-driven generic rail network model with a power supply network representative of DC electric railways. The work is intended as a high-level design tool to explore system wide behaviors prior to detailed final design modelling of specific technologies. To validate our method, predictions of train motion and power demand have been compared with data from the Merseyrail network in the UK. Simulating a full day of traffic for the Wirral Line of Merseyrail (237 services on two routes) with the assumption of energy storage being available at each electrical sub-station revealed the dependence of storage effectiveness on the timetable and traffic density at specific locations. The model is combined with a genetic algorithm to optimise system parameters (storage size, charge/discharge power limits, timetable, train driving style/trajectory) and also enables identification of cases in which poorly specified storage technology would have little impact on peak power and energy consumption.
Highlights
Railway electricity demand in systems internationally is rising because of (a) electriication of diesel services [1], (b) longer and more frequent trains in response to increased passenger demand, and (c) the higher power demands of modern rolling stock (Smulders, 2005)
The model is agnostic to the energy storage technology and this is referred to as “storage” in the discussion below, but to ensure realistic capacity and charge rates (Table 2) these were based on battery storage typical of grid scale application [36]
At substation 8, which supports a higher traic density, storage is more successful at reducing net energy consumption, which is reduced for all storage sizes relative to the case without storage
Summary
Railway electricity demand in systems internationally is rising because of (a) electriication of diesel services [1], (b) longer and more frequent trains in response to increased passenger demand, and (c) the higher power demands of modern rolling stock (improved acceleration and interior comfort, e.g. air conditioning) (Smulders, 2005) With these demand-side changes, power supply networks are changing. The simpliied model and heuristic search provides a good estimate/design without pre-empting the outcome by embedding existing component behaviors It can help deine required component behavior, or the targets of research to develop components able to achieve that behavior such as the converter design research of Zhang et al [11]. This work focuses on providing a plausible model for this “quick & dirty” phase of this process
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