An Improved Genetic Algorithm for Optimal Stationary Energy Storage System Locating and Sizing

The application of a stationary ultra-capacitor energy storage system (ESS) in urban rail transit allows for the recuperation of vehicle braking energy for increasing energy savings as well as for a better vehicle voltage profile. This paper aims to obtain the best energy savings and voltage profile by optimizing the location and size of ultra-capacitors. This paper firstly raises the optimization objective functions from the perspectives of energy savings, regenerative braking cancellation and installation cost, respectively. Then, proper mathematical models of the DC (direct current) traction power supply system are established to simulate the electrical load-flow of the traction supply network, and the optimization objections are evaluated in the example of a Chinese metro line. Ultimately, a methodology for optimal ultra-capacitor energy storage system locating and sizing is put forward based on the improved genetic algorithm. The optimized result shows that certain preferable and compromised schemes of ESSs’ location and size can be obtained, acting as a compromise between satisfying better energy savings, voltage profile and lower installation cost.

[1]  Hanmin Lee,et al.  A Study on the Effects of Energy Storage System , 2009, 2009 International Conference on Information and Multimedia Technology.

[2]  Peng Lou,et al.  Genetic simulated annealing algorithm-based assembly sequence planning , 2006 .

[3]  Lei-yin Jiao,et al.  The application of improved genetic algorithm in fitting the spatial variogram , 2011, Proceedings of 2011 International Conference on Computer Science and Network Technology.

[4]  Ogasa Masamichi.  Onboard storage in Japanese electrified lines , 2010, Proceedings of 14th International Power Electronics and Motion Control Conference EPE-PEMC 2010.

[5]  Masamichi Ogasa.  Energy Saving and Environmental Measures in Railway Technologies: Example with Hybrid Electric Railway Vehicles , 2008 .

[6]  Pinaki Mazumder,et al.  SAGA : a unification of the genetic algorithm with simulated annealing and its application to macro-cell placement , 1994, Proceedings of 7th International Conference on VLSI Design.

[7]  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.

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

[9]  H. Ibrahima,et al.  Energy storage systems — Characteristics and comparisons , 2008 .

[10]  An Analytical Solution for Optimal Design of Stationary Lithium-Ion Capacitor Storage Device in Light Electrical Transportation Networks , 2013 .

[11]  Flavio Ciccarelli,et al.  Stationary ultracapacitors storage device for improving energy saving and voltage profile of light transportation networks , 2012 .

[12]  Hitoshi Hayashiya,et al.  Application examples of energy saving measures in Japanese DC feeding system , 2014, 2014 International Power Electronics Conference (IPEC-Hiroshima 2014 - ECCE ASIA).

[13]  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.

[14]  X. Yao.  Evolving Artificial Neural Networks , 1999 .

[15]  Davide Lauria,et al.  Optimal design of stationary supercapacitors storage devices for light electrical transportation systems , 2011 .

[16]  Alfred Rufer,et al.  Energy storage for railway systems, energy recovery and vehicle autonomy in Europe , 2010, The 2010 International Power Electronics Conference - ECCE ASIA -.

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

[18]  Carlos García-Martínez,et al.  A GA-based multiple simulated annealing , 2010, IEEE Congress on Evolutionary Computation.

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

[20]  R. Barrero,et al.  Energy savings in public transport , 2008, IEEE Vehicular Technology Magazine.

[21]  Hanmin Lee,et al.  A Study on the Application of ESS on SeoulMetro Line 2 , 2009, 2009 International Conference on Information and Multimedia Technology.

[22]  Diego Iannuzzi,et al.  The Use of Energy Storage Systems for Supporting the Voltage Needs of Urban and Suburban Railway Contact Lines , 2013 .

[23]  Jian Wang,et al.  A simulated annealing- new genetic algorithm and its application , 2011, Proceedings of 2011 International Conference on Electronics and Optoelectronics.

[24]  Flavio Ciccarelli,et al.  Comparison of energy management control strategy based on wayside ESS for LRV application , 2013, IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society.

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

[26]  D. Iannuzzi,et al.  Energy management of electrified mass transit systems with Energy Storage devices , 2012, International Symposium on Power Electronics Power Electronics, Electrical Drives, Automation and Motion.

[27]  Hossein Iman-Eini,et al.  Stationary super-capacitor energy storage system to save regenerative braking energy in a metro line , 2012 .

[28]  Yih-Kuen Jan,et al.  Using Artificial Neural Network to Determine Favorable Wheelchair Tilt and Recline Usage in People with Spinal Cord Injury: Training ANN with Genetic Algorithm to Improve Generalization , 2011, 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence.

[29]  Yong-gi Kim,et al.  Calculation of regenerative energy in DC 1500V electric railway substations , 2007, 2007 7th Internatonal Conference on Power Electronics.

[30]  M. Steiner,et al.  Energy storage system with ultracaps on board of railway vehicles , 2007, 2007 European Conference on Power Electronics and Applications.

[31]  Pengfei Guo,et al.  The enhanced genetic algorithms for the optimization design , 2010, 2010 3rd International Conference on Biomedical Engineering and Informatics.