Sensitivity Analysis of Vehicle-to-Grid based Ancillary Services

Abstract

In order to decrease carbon emissions of the transport sector, the roll-out of plug-in electric vehicles (PEV) aims a replacement of conventional combustion-based vehicles. Their intended power system integration goes along with increasing the share of renewable energies. Hereby, PEV have the prerequisites for intelligent charging to support grid stability and increasing the utilisation of fluctuating renewable energies. Beside integrating PEV as an intelligent load, the approach using the PEV’s battery capacity to serve power system needs can further increase the value of PEV in the power system. Thus, for compensating additional stress on the PEV's components, ancillary services revenues are commonly measured against PEV's wearout for Vehicle-to-Grid (V2G) applications. Depending on the selected driving pattern, location, battery, regulation and revenue of ancillary services, use cases lead to different conclusions. In the scope of this thesis, it is argued that ongoing transitions in the power system and transport sector result in uncertainties how these use case and its dependencies change in the future. However, the technical prerequisites of V2G based ancillary services, which subject to physical, chemical and electrical processes, are known, whereby a broad view on use cases is essential. Hence, in this thesis, the sensitivity of these services is measured against vehicle characteristics from a technical point of view. This work identifies more than 30 technical dependencies that affect the PEV's degradation and total discharging costs. With respect to the Dutch energy market, the results demonstrate that Vehicle-to-Grid costs can lead to a significant market potential loss. However, operation conditions exists, which lead not only to a high market potential in the balancing and day-ahead market, but also in a reduction of battery degradation. Based on the sensitivity results, implications that cover the regulation, business, user and technical layers of Vehicle-to-Grid are given based on an architecture model. The presented work allows identifying use case related dependencies and therefore helps to clarify the reasons for the different results/conclusions on V2G research.