Day-ahead resource scheduling in smart grids considering Vehicle-to-Grid and network constraints

Energy resource scheduling becomes increasingly important, as the use of distributed resources is intensified and massive gridable vehicle use is envisaged. The present paper proposes a methodology for day-ahead energy resource scheduling for smart grids considering the intensive use of distributed generation and of gridable vehicles, usually referred as Vehicle-to-Grid (V2G). This method considers that the energy resources are managed by a Virtual Power Player (VPP) which established contracts with V2G owners. It takes into account these contracts, the users’ requirements subjected to the VPP, and several discharge price steps. Full AC power flow calculation included in the model allows taking into account network constraints.

[1]  Farshid Keynia,et al.  A new spinning reserve requirement forecast method for deregulated electricity markets , 2010 .

[2]  H. Morais,et al.  Energy resource scheduling in a real distribution network managed by several virtual power players , 2012, PES T&D 2012.

[3]  Pedro Faria,et al.  Demand Response Management in Power Systems Using Particle Swarm Optimization , 2013, IEEE Intelligent Systems.

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

[5]  Zita Vale,et al.  Energy resources scheduling in competitive environment , 2011 .

[6]  Brian Vad Mathiesen,et al.  A review of computer tools for analysing the integration of renewable energy into various energy systems , 2010 .

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

[8]  Qiong Wu,et al.  Multi-objective optimization for the operation of distributed energy systems considering economic and environmental aspects , 2010 .

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

[10]  Z. Vale,et al.  Demand response in electrical energy supply: An optimal real time pricing approach , 2011 .

[11]  Mikio Kasahara,et al.  Effects of electric vehicles (EV) on environmental loads with consideration of regional differences of electric power generation and charging characteristic of EV users in Japan , 2002 .

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

[13]  Pedro Faria,et al.  Intelligent energy resource management considering vehicle-to-grid: A Simulated Annealing approach , 2012, 2012 IEEE Power and Energy Society General Meeting.

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

[15]  N. Amjady,et al.  Joint market clearing in a stochastic framework considering power system security , 2009 .

[16]  H. Morais,et al.  Technical and economic resources management in smart grids using heuristic optimization methods , 2010, IEEE PES General Meeting.

[17]  H. Morais,et al.  VPP's multi-level negotiation in smart grids and competitive electricity markets , 2011, 2011 IEEE Power and Energy Society General Meeting.

[18]  A. Daraeepour,et al.  A new self-scheduling strategy for integrated operation of wind and pumped-storage power plants in power markets , 2011 .

[19]  Bert Willems,et al.  Physical and Financial Virtual Power Plants , 2005 .