Intelligent Energy Resource Management Considering Vehicle-to-Grid: A Simulated Annealing Approach

This paper proposes a simulated annealing (SA) approach to address energy resources management from the point of view of a virtual power player (VPP) operating in a smart grid. Distributed generation, demand response, and gridable vehicles are intelligently managed on a multiperiod basis according to V2G users' profiles and requirements. Apart from using the aggregated resources, the VPP can also purchase additional energy from a set of external suppliers. The paper includes a case study for a 33 bus distribution network with 66 generators, 32 loads, and 1000 gridable vehicles. The results of the SA approach are compared with a methodology based on mixed-integer nonlinear programming. A variation of this method, using ac load flow, is also used and the results are compared with the SA solution using network simulation. The proposed SA approach proved to be able to obtain good solutions in low execution times, providing VPPs with suitable decision support for the management of a large number of distributed resources.

[1]  Anjan Bose,et al.  Smart Transmission Grid Applications and Their Supporting Infrastructure , 2010, IEEE Transactions on Smart Grid.

[2]  Malcolm McCulloch,et al.  Modeling the prospects of plug-in hybrid electric vehicles to reduce CO2 emissions , 2011 .

[3]  Zita Vale,et al.  Computational Intelligence Applications for Future Power Systems , 2011 .

[4]  Steven E. Widergren,et al.  An Orderly Transition to a Transformed Electricity System , 2010, IEEE Transactions on Smart Grid.

[5]  Pedro Faria,et al.  An optimal scheduling problem in distribution networks considering V2G , 2011, 2011 IEEE Symposium on Computational Intelligence Applications In Smart Grid (CIASG).

[6]  Eberhard Meissner,et al.  Vehicle electric power systems are under change! Implications for design, monitoring and management of automotive batteries , 2001 .

[7]  Tony Markel,et al.  Communication and control of electric drive vehicles supporting renewables , 2009, 2009 IEEE Vehicle Power and Propulsion Conference.

[8]  C. C. Chan,et al.  The state of the art of electric and hybrid vehicles , 2002, Proc. IEEE.

[9]  Felix F. Wu,et al.  Network Reconfiguration in Distribution Systems for Loss Reduction and Load Balancing , 1989, IEEE Power Engineering Review.

[10]  Pedro Faria,et al.  Demsi — A demand response simulator in the context of intensive use of distributed generation , 2010, 2010 IEEE International Conference on Systems, Man and Cybernetics.

[11]  J. Driesen,et al.  The Impact of Charging Plug-In Hybrid Electric Vehicles on a Residential Distribution Grid , 2010, IEEE Transactions on Power Systems.

[12]  H. Morais,et al.  Towards a future SCADA , 2009, 2009 IEEE Power & Energy Society General Meeting.

[13]  H. Morais,et al.  Intelligent multi-player smart grid management considering distributed energy resources and demand response , 2010, IEEE PES General Meeting.

[14]  Ahmed Yousuf Saber,et al.  Intelligent unit commitment with vehicle-to-grid —A cost-emission optimization , 2010 .

[15]  Mohamed A. El-Sharkawi,et al.  Optimal Charging Strategies for Unidirectional Vehicle-to-Grid , 2011, IEEE Transactions on Smart Grid.

[16]  Ahmed Yousuf Saber,et al.  Efficient Utilization of Renewable Energy Sources by Gridable Vehicles in Cyber-Physical Energy Systems , 2010, IEEE Systems Journal.

[17]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[18]  Zita Vale,et al.  Optimal scheduling of a renewable micro-grid in an isolated load area using mixed-integer linear programming , 2010 .

[19]  Marco Dorigo,et al.  Swarm intelligence: from natural to artificial systems , 1999 .

[20]  Emile H. L. Aarts,et al.  Simulated annealing and Boltzmann machines - a stochastic approach to combinatorial optimization and neural computing , 1990, Wiley-Interscience series in discrete mathematics and optimization.

[21]  Amit Kumar Tamang Coordinated Charging of Plug-in Hybrid Electric Vehicles to Minimize Distribution System Losses , 2013 .