Minimization of energy transmission cost and CO2 emissions using coordination of electric vehicle and wind power (W2V)

In recent years, the electrical energy distribution network is at the heart of energy and environmental changes that affect our society. In fact, many applications use the electric vector in order to increase their energy efficiency. This is particularly the case of electric vehicles (EVs), wind and solar power generation. However, these means of production are often intermittent and not in conjunction with power consumption. Therefore, the lack of control of these evolutions may lead to technical and financial problems. This strategy would be a real obstacle to the development of EVs. So, the paper deals with an approach based on fuzzy logic control that allows EV peaks power shaving, and coordination of wind power excess and EVs load. This will enable the Distribution System Operator (DSO) to limit its energy transmission costs and CO2 emissions. The supervision strategy is applied to a real test system, and its performance is shown with the help of simulations using Matlab/Simulink.

[1]  Wenxin Liu,et al.  Stable Multi-Agent-Based Load Shedding Algorithm for Power Systems , 2011, IEEE Transactions on Power Systems.

[2]  P Frías,et al.  Assessment of the Impact of Plug-in Electric Vehicles on Distribution Networks , 2011, IEEE Transactions on Power Systems.

[3]  Canbing Li,et al.  An Optimized EV Charging Model Considering TOU Price and SOC Curve , 2012, IEEE Transactions on Smart Grid.

[4]  Vincent Courtecuisse,et al.  A methodology to design a fuzzy logic based supervision of Hybrid Renewable Energy Systems , 2010, Math. Comput. Simul..

[5]  Peter Vas,et al.  Artificial-Intelligence-Based Electrical Machines and Drives: Application of Fuzzy, Neural, Fuzzy-neural, and Genetic-Algorithm-based Techniques , 1999 .

[6]  Yue Yuan,et al.  Load model for prediction of electric vehicle charging demand , 2010, 2010 International Conference on Power System Technology.

[7]  S. Albayrak,et al.  Wind power-aware vehicle-to-grid algorithms for sustainable EV energy management systems , 2012, 2012 IEEE International Electric Vehicle Conference.

[8]  E. Alishahi.  Scheduling of wind power and electrical vehicles considering the technical and economical aspects , 2012, 2012 Proceedings of 17th Conference on Electrical Power Distribution.

[9]  Mohammad Kazem Sheikh-El-Eslami,et al.  Wind farm power management by high penetration of PHEV , 2011, 2011 IEEE Power and Energy Society General Meeting.

[10]  Melaine Rousselle.  Impact of the Electric Vehicle on the Electric System , 2009 .

[11]  Alireza Khaligh,et al.  Minimum charging-cost tracking based optimization algorithm with dynamic programming technique for plug-in hybrid electric vehicles , 2011, 2011 IEEE Vehicle Power and Propulsion Conference.

[12]  Benoit Robyns,et al.  Variable Speed Wind Generators Participation in Primary Frequency Control , 2008 .

[13]  Shuang Gao,et al.  Modeling and coordinated control for integrating electric vehicles into the power grid , 2011, 2011 International Conference on Electrical Machines and Systems.

[14]  Walid G. Morsi,et al.  A novel demand side management program using water heaters and particle swarm optimization , 2010, 2010 IEEE Electrical Power & Energy Conference.