Stochastic optimal charging of electric-drive vehicles with renewable energy

The paper presents the stochastic optimization algorithm that may eventually be used by electric energy suppliers to coordinate charging of electric-drive vehicles (EDVs) in order to maximize the use of renewable energy in transportation. Due to the stochastic nature of transportation patterns, the Monte Carlo simulation is applied to model uncertainties presented by numerous scenarios. To reduce the problem complexity, the simulated driving patterns are not individually considered in the optimization but clustered into fleets using the GAMS/SCENRED tool. Uncertainties of production of renewable energy sources (RESs) are presented by statistical central moments that are further considered in Hong’s 2-point+1 estimation method in order to define estimate points considered in the optimization. Case studies illustrate the application of the proposed optimization in achieving maximal exploitation of RESs in transportation by EDVs.

[1]  Filip Johnsson,et al.  Plug-in hybrid electric vehicles as a mean to reduce CO2 emissions from electricity production , 2009 .

[2]  M. Shahidehpour,et al.  Stochastic Security-Constrained Unit Commitment , 2007, IEEE Transactions on Power Systems.

[3]  M. Shahidehpour,et al.  Market-Based Generation and Transmission Planning With Uncertainties , 2009, IEEE Transactions on Power Systems.

[4]  Soo Hee Han,et al.  Optimal decision on contract size for V2G aggregator regarding frequency regulation , 2010, 2010 12th International Conference on Optimization of Electrical and Electronic Equipment.

[5]  G. Gross,et al.  Design of a Conceptual Framework for the V2G Implementation , 2008, 2008 IEEE Energy 2030 Conference.

[6]  Ganesh Kumar Venayagamoorthy,et al.  Unit commitment with vehicle-to-Grid using particle swarm optimization , 2009, 2009 IEEE Bucharest PowerTech.

[7]  Robert C. Green,et al.  The impact of plug-in hybrid electric vehicles on distribution networks: a review and outlook , 2010, PES 2010.

[8]  M. Shahidehpour,et al.  Network planning in unbundled power systems , 2006, IEEE Transactions on Power Systems.

[9]  Willett Kempton,et al.  Vehicle-to-grid power fundamentals: Calculating capacity and net revenue , 2005 .

[10]  K. Shimizu,et al.  Effect of autonomous distributed vehicle-to-grid (V2G) on power system frequency control , 2010, 2010 5th International Conference on Industrial and Information Systems.

[11]  Willett Kempton,et al.  Integration of renewable energy into the transport and electricity sectors through V2G , 2008 .

[12]  H. Hong.  An efficient point estimate method for probabilistic analysis , 1998 .

[13]  Willett Kempton,et al.  Vehicle-to-grid power implementation: From stabilizing the grid to supporting large-scale renewable energy , 2005 .

[14]  E. Rosenblueth.  Point estimates for probability moments. , 1975, Proceedings of the National Academy of Sciences of the United States of America.

[15]  J. Dupacová,et al.  Scenario reduction in stochastic programming: An approach using probability metrics , 2000 .

[16]  Akihiko Yokoyama,et al.  Load Frequency Control in power system using Vehicle-to-Grid system considering the customer convenience of Electric Vehicles , 2010, 2010 International Conference on Power System Technology.

[17]  Emilio Rosenblueth,et al.  Two-point estimates in probabilities , 1981 .

[18]  Karsten Emil Capion,et al.  Optimal charging of electric drive vehicles in a market environment , 2011 .

[19]  P. Lombardi,et al.  Vehicles to grid (V2G) concept applied to a Virtual Power Plant structure , 2010, The XIX International Conference on Electrical Machines - ICEM 2010.

[20]  J. Morales,et al.  Point Estimate Schemes to Solve the Probabilistic Power Flow , 2007, IEEE Transactions on Power Systems.

[21]  J. Christian,et al.  The point‐estimate method with large numbers of variables , 2002 .

[22]  Jitka Dupacová,et al.  Scenario reduction in stochastic programming , 2003, Math. Program..

[23]  C. Cañizares,et al.  Probabilistic Optimal Power Flow in Electricity Markets Based on a Two-Point Estimate Method , 2006, IEEE Transactions on Power Systems.