Modelling of Multi-Agent Systems for the Scheduling of Multi-EV Charging from Power Limited Sources
Authors: Manan’Iarivo Rasolonjanahary, Chris Bingham, Nigel Schofield, Masoud Bazargan
Abstract:
This paper presents the research and application of model predictive scheduled charging of electric vehicles (EV) subject to limited available power resource. To focus on algorithm and operational characteristics, the EV interface to the source is modelled as a battery state equation during the charging operation. The researched methods allow for the priority scheduling of EV charging in a multi-vehicle regime and when subject to limited source power availability. Priority attribution for each connected EV is described. The validity of the developed methodology is shown through the simulation of different scenarios of charging operation of multiple connected EVs including non-scheduled and scheduled operation with various numbers of vehicles. Performance of the developed algorithms is also reported with the recommendation of the choice of suitable parameters.
Keywords: Model predictive control, non-scheduled, power limited sources, scheduled and stop-start battery charging.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 516References:
[1] S. Ohtani, Y. Shirasawa, O. Mori and J. Kawaguchi, "Power-Peak-Curbing Switching Schedule for a Multiagent System," Transactions of the Japan Society for Aeronautical and Space Sciences, Aerospace Technology Japan, vol. 14, 2016, pp. 25-30
[2] A. Bemporad, M. Morari, and N. L Ricker, Model Predictive Control Toolbox, 2005.
[3] D. E. Seborg, Process Dynamics and Control, John Wiley & Sons, Incorporated, 2012.
[4] J. Currie, A. Prince-Pike, and D. I. Wilson, "Auto-code generation for fast embedded Model Predictive Controllers," in 19th International Conference on Mechatronics and Machine Vision in Practice (M2VIP), 2012, pp. 116-122.
[5] A. Jain, F. Smarra, M. Behl, and R. Mangharam, "Data-Driven Model Predictive Control with Regression Trees—An Application to Building Energy Management," ACM Trans. Cyber-Phys. Syst., vol. 2, p. Article 4, 2018.
[6] S. Strand and J. R. Sagli, "MPC in Statoil – Advantages with In-House Technology," IFAC Proceedings Volumes, vol. 37, 2004, pp. 97-103.
[7] T. Geyer, G. Papafotiou, and M. Morari, "Model Predictive Control in Power Electronics: A Hybrid Systems Approach," in Proceedings of the 44th IEEE Conference on Decision and Control, 2005, pp. 5606-5611.
[8] https://batteryuniversity.com/learn/article/electric_vehicle_ev(dated 19/03/20
[9] https://www.evspecifications.com/ (dated 19/03/20)
[10] https://www.evspecifications.com/en/model/e4f07b (dated 19/03/20)
[11] https://autotechreview.com/technology/propulsion-system-of-the-new-jaguar-i-pace (dated 19/03/20)
[12] https://ev-database.uk/ (dated 20/03/20)
[13] https://www.zap-map.com/charge-points/ev-energy-tariffs/ (03/12/20)
[14] http://www.zap.map.com/charge-point/public-charging-point-networks/ (dated 03/12/20)