A smart control strategy for Vehicle-to-Grid
The integration of Plug-in Electric Vehicles (PEV) into the power grid presents the opportunity to improve power grid stability and facilitate the integration of intermittent renewable energy sources. This can be achieved by aggregating large numbers of PEVs to so-called Virtual Power Plants (VPP); these VPPs can consume energy in the case of a power excess or feed electricity back to the grid in the opposite case. For PEV owners, providing this service to the power grid could create a revenue stream which may help mitigating the high investment costs of PEVs. While the technical feasibility of this concept, termed Vehicle-to-Grid (V2G), has been confirmed by both theoretical considerations [1-3] and fully functional prototypes, its economic viability is, however, still subject to controversial discussions. This is reflected in the diverging conclusions on profitability where some studies promise multiple thousand dollars of additional yearly income for PEV owners while others expect the depreciation of the battery to cause losses in a similar order of magnitude.
An essential drawback of economic analyses of the V2G concept is that calculations are based on averaged values for the involved parameters which neglect the dynamic character of the problem. In reality, however, electricity prices highly vary on an hourly scale so that revenues may be high in one time period but negligibly low in a different one. Furthermore, battery depreciation costs depend strongly on the conditions under which the battery is operated. The entity of these aspects therefore considerably limits the explanatory power of static assessments leaving the outcome highly sensitive to the choice of the input parameters. Hence, to better assess the economic viability of V2G and at the same time provide optimal control strategies for the operation of Virtual Power Plants, more dynamic approaches are required.
The purpose of the ongoing work is therefore to build a smart V2G control strategy which fully accounts for the dynamics of the problem. This is achieved by exploiting real-time price information and short-term price forecasts in order to derive an optimal charging and dispatching schedule for each individual agent. To properly account for battery depreciation costs, this includes the consideration of detailed battery ageing models which quantify battery depreciation related to a variety of operation parameters. The outcome of this work is a profit maximizing operation strategy to be executed by a PEV’s charging electronics. Ongoing research aiming at investigating the effect of the resulting and alternative charging concepts will be described in a future post.
 W. Kempton, J. Tomic, Vehicle-to-grid power implementation: From stabilizing the grid to supporting large-scale renewable energy, J. Power Sources 144 (1) (2005) 280-294.
 K. Clement-Nyns, E. Haesen, J. Driesen, The impact of vehicle-to-grid on the distribution grid, Electr. Power Syst. Res. 81 (1) (2011) 185-192. doi:http://dx.doi.org/10.1016/j.epsr.2010.08.007.
 J. Tomic, W. Kempton, Using fleets of electric-drive vehicles for grid support, J. Power Sources 168 (2) (2007) 459-468. doi:http://dx.doi.org/10.1016/j.jpowsour.2007.03.010.