A data decomposition approach to design a dynamic pricing mechanism for residence-based plug-in electric vehicles in wind energy-rich grids

Abstract

Abstract Grid-level energy storage presents a potential mechanism for storing excess wind energy in wind energy-rich grids. However, the current economic infeasibility of grid-level storage hinders widespread practical application. Alternatively, plug-in electric vehicles (PEVs) present a potentially adaptable load that can facilitate managing excess wind energy. Our previous research justifies the need for a pricing mechanism that can coordinate wind energy and PEVs charging. In this paper, we employ a data decomposition approach (i.e., the dynamic mode decomposition) to design a dedicated pricing mechanism for PEVs. We use data from a city with relatively high current and projected PEV adoption rates (Austin, Texas) located in a wind-rich electricity grid (ERCOT). Our results indicate that a dedicated seasonal time of use pricing for PEVs can reduce wind energy curtailments, reduce emissions from electricity generation and PEV charging, and potentially avoid wind energy output curtailments.