Deep-Reinforcement-Learning-Based Energy Management Strategy for Supercapacitor Energy Storage Systems in Urban Rail Transit

[1]  Xiaoqing Han,et al.  Review on the research and practice of deep learning and reinforcement learning in smart grids , 2018, CSEE Journal of Power and Energy Systems.

[2]  Philippe Delarue,et al.  The Ultracapacitor-Based Controlled Electric Drives With Braking and Ride-Through Capability: Overview and Analysis , 2011, IEEE Transactions on Industrial Electronics.

[3]  Philippe Delarue,et al.  Modeling and Control of the Ultracapacitor-Based Regenerative Controlled Electric Drives , 2011, IEEE Transactions on Industrial Electronics.

[4]  Hanmin Lee,et al.  Capacity optimization of the supercapacitor energy storages on DC railway system using a railway powerflow algorithm , 2011 .

[5]  Ion Etxeberria-Otadui,et al.  Optimal Energy Management Strategy of an Improved Elevator With Energy Storage Capacity Based on Dynamic Programming , 2014, IEEE Transactions on Industry Applications.

[6]  Tianshu Chu,et al.  Multi-Agent Deep Reinforcement Learning for Large-Scale Traffic Signal Control , 2019, IEEE Transactions on Intelligent Transportation Systems.

[7]  Flavio Ciccarelli,et al.  Control of metro-trains equipped with onboard supercapacitors for energy saving and reduction of power peak demand , 2012 .

[8]  Flavio Ciccarelli,et al.  Improvement of Energy Efficiency in Light Railway Vehicles Based on Power Management Control of Wayside Lithium-Ion Capacitor Storage , 2014, IEEE Transactions on Power Electronics.

[9]  M. Péra,et al.  Review of characterization methods for supercapacitor modelling , 2014 .

[10]  Hanmin Lee,et al.  Energy Storage Application Strategy on DC Electric Railroad System using a Novel Railroad Analysis Algorithm , 2010 .

[11]  F. Ciccarelli,et al.  A Novel energy management control of wayside Li-Ion capacitors-based energy storage for urban mass transit systems , 2012, International Symposium on Power Electronics Power Electronics, Electrical Drives, Automation and Motion.

[12]  Donald E. Kirk,et al.  Optimal control theory : an introduction , 1970 .

[13]  Shane Legg,et al.  Human-level control through deep reinforcement learning , 2015, Nature.

[14]  Srdjan M. Lukic,et al.  Energy Storage Systems for Transport and Grid Applications , 2010, IEEE Transactions on Industrial Electronics.

[15]  Junqiang Xi,et al.  Real-Time Energy Management Strategy Based on Velocity Forecasts Using V2V and V2I Communications , 2017, IEEE Transactions on Intelligent Transportation Systems.

[16]  P. Tricoli,et al.  Energy loss minimisation by optimal design of stationary supercapacitors for light railways , 2015, 2015 International Conference on Clean Electrical Power (ICCEP).

[17]  Flavio Ciccarelli,et al.  Line-Voltage Control Based on Wayside Energy Storage Systems for Tramway Networks , 2016, IEEE Transactions on Power Electronics.

[18]  Luis M. Fernández,et al.  Predictive Control for the Energy Management of a Fuel-Cell–Battery–Supercapacitor Tramway , 2014, IEEE Transactions on Industrial Informatics.

[19]  Christian Schroer,et al.  Deep Reinforcement Learning for Advanced Energy Management of Hybrid Electric Vehicles , 2018, ICAART.

[20]  Demis Hassabis,et al.  Mastering the game of Go without human knowledge , 2017, Nature.

[21]  Pietro Tricoli,et al.  Recent developments and applications of energy storage devices in electrified railways , 2014 .

[22]  Alex Graves,et al.  Asynchronous Methods for Deep Reinforcement Learning , 2016, ICML.

[23]  Philippe Delarue,et al.  The Ultracapacitor-Based Regenerative Controlled Electric Drives With Power-Smoothing Capability , 2012, IEEE Transactions on Industrial Electronics.

[24]  P. Pozzobon.  Transient and steady-state short-circuit currents in rectifiers for DC traction supply , 1998 .

[25]  J. Van Mierlo,et al.  Improving energy efficiency in public transport: Stationary supercapacitor based Energy Storage Systems for a metro network , 2008, 2008 IEEE Vehicle Power and Propulsion Conference.

[26]  R Barrero,et al.  Stationary or onboard energy storage systems for energy consumption reduction in a metro network , 2010 .

[27]  Felix Schmid,et al.  An assessment of available measures to reduce traction energy use in railway networks , 2015 .

[28]  Paul Batty,et al.  Sustainable urban rail systems: strategies and technologies for optimal management of regenerative braking energy , 2013 .

[29]  Flavio Ciccarelli,et al.  Supercapacitors-based energy storage for urban mass transit systems , 2011, Proceedings of the 2011 14th European Conference on Power Electronics and Applications.

[30]  Andreas Poullikkas,et al.  Overview of current and future energy storage technologies for electric power applications , 2009 .

[31]  Demis Hassabis,et al.  Mastering the game of Go with deep neural networks and tree search , 2016, Nature.

[32]  Alex Graves,et al.  Playing Atari with Deep Reinforcement Learning , 2013, ArXiv.