Performance evaluation of a PHEV parking station using Particle Swarm Optimization

There is expected to be a large penetration of Plug-in Hybrid Electric Vehicles (PHEVs) into the market in the near future. As a result, many technical problems related to the impact of this technology on the power grid need to be addressed. The anticipating large penetration of PHEV into our societies will add a substantial energy load to power grids, as well as add substantial energy resources that can be utilized. There is also a need for in-depth study on PHEVs in term of Smart Grid environment. In this paper, we propose an algorithm for optimally managing a large number of PHEVs (i.e., 500) charging at a municipal parking station. We used Particle Swarm Optimization (PSO) to intelligently allocate energy to the PHEVs. We considered constraints such as energy price, remaining battery capacity, and remaining charging time. A mathematical framework for the objective function (i.e., maximizing the average State-of-Charge at the next time step) is also given. We characterized the performance of our PSO algorithm using a MATLAB simulation, and compared it with other techniques.

[1]  Salman Mohagheghi,et al.  Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems , 2008, IEEE Transactions on Evolutionary Computation.

[2]  A.A. Abido,et al.  Particle swarm optimization for multimachine power system stabilizer design , 2001, 2001 Power Engineering Society Summer Meeting. Conference Proceedings (Cat. No.01CH37262).

[3]  Suet Yee Chu.  Particle swarm optimization , 2009 .

[4]  Willett Kempton,et al.  Using fleets of electric-drive vehicles for grid support , 2007 .

[5]  Russell C. Eberhart,et al.  Multiobjective optimization using dynamic neighborhood particle swarm optimization , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[6]  Mo-Yuen Chow,et al.  Intelligent energy management system simulator for PHEVs at municipal parking deck in a smart grid environment , 2009, 2009 IEEE Power & Energy Society General Meeting.

[7]  Sekyung Han,et al.  Development of an Optimal Vehicle-to-Grid Aggregator for Frequency Regulation , 2010, IEEE Transactions on Smart Grid.

[8]  Benjamin K. Sovacool,et al.  Beyond Batteries: An Examination of the Benefits and Barriers to Plug-In Hybrid Electric Vehicles (PHEVs) and a Vehicle-to-Grid (V2G) Transition , 2009 .

[9]  C.A. Coello Coello,et al.  MOPSO: a proposal for multiple objective particle swarm optimization , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[10]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[11]  Srdjan Lukic,et al.  Evaluation of ZigBee communication platform for controlling the charging of PHEVs at a municipal parking deck , 2009, 2009 IEEE Vehicle Power and Propulsion Conference.

[12]  Zoran Filipi,et al.  Environmental assessment of plug-in hybrid electric vehicles using naturalistic drive cycles and vehicle travel patterns: A Michigan case study , 2013 .

[13]  Tony Markel,et al.  Costs and Emissions Associated with Plug-In Hybrid Electric Vehicle Charging in the Xcel Energy Colorado Service Territory , 2007 .

[14]  Carlos A. Coello Coello,et al.  Handling multiple objectives with particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.