Abstract
Many hopes lie on the successful introduction of electric vehicles (EV): reduction of transportation-related emissions, reduced dependence on oil imports, electricity storage provision for the grid, or improved integration of renewable energy sources. Meeting these goals will not only require a significant number of EVs on the streets, but it will also require intelligent decision making with respect to their charging schedules. Through dynamic rates or local energy trading smart grids incentivize load flexibility required for taking advantage of renewable generation availability. For EVs to respond to these incentives intelligent charging protocols are required. These protocols should aim to minimize electricity costs and/or emissions while at the same time securing the customers’ driving requirements. We describe and characterize the relevant problems and solution concepts on how to achieve smart charging behavior. Currently discussed smart charging concepts are not directly applicable for practical decision support system. To address this shortcoming we develop relaxed and heuristic optimization approaches. We evaluate these solutions approaches using simulations based on empirical mobility and electricity price data.
Recommended Citation
Flath, Christoph; Ilg, Jens; and Weinhardt, Christof, "Decision Support for Electric Vehicle Charging" (2012). AMCIS 2012 Proceedings. 14.
https://aisel.aisnet.org/amcis2012/proceedings/GreenIS/14
Decision Support for Electric Vehicle Charging
Many hopes lie on the successful introduction of electric vehicles (EV): reduction of transportation-related emissions, reduced dependence on oil imports, electricity storage provision for the grid, or improved integration of renewable energy sources. Meeting these goals will not only require a significant number of EVs on the streets, but it will also require intelligent decision making with respect to their charging schedules. Through dynamic rates or local energy trading smart grids incentivize load flexibility required for taking advantage of renewable generation availability. For EVs to respond to these incentives intelligent charging protocols are required. These protocols should aim to minimize electricity costs and/or emissions while at the same time securing the customers’ driving requirements. We describe and characterize the relevant problems and solution concepts on how to achieve smart charging behavior. Currently discussed smart charging concepts are not directly applicable for practical decision support system. To address this shortcoming we develop relaxed and heuristic optimization approaches. We evaluate these solutions approaches using simulations based on empirical mobility and electricity price data.