A Scalable Approach for Energy Optimisation in Urban Rail Systems

Urban railway systems produce regenerative braking energy, which, if properly managed, can significantly reduce carbon emissions. This paper evaluates the ability of deep reinforcement learning (DRL) to optimise the use of regenerative energy from urban railway systems stored in an energy storage system, aiming to maximise CO2 emission savings. Unlike conventional approaches focused on economic optimisation, environmental impact is prioritised, addressing a harder control setting shaped by the uncertainty of train braking events and fluctuating grid emission intensities.