High-power electric vehicle charging: Low-carbon grid integration pathways with stationary lithium-ion battery systems and renewable generation

Abstract

The energy transition in the mobility sector is well underway. The electrification of road transport is resulting in a shift of the energy demand from the oil and gas sector to the electricity grid. Increasingly aggressive targets for low charging times for Electric Vehicles (EVs) are slated to raise the demand for High-Power Charging (HPC). This is likely to lead to bottlenecks and overloading in vulnerable sections of the electricity grid. Battery Assistance (BA) is a promising grid integration measure for High-Power Charging (HPC) to mitigate these problems. As decarbonization is the primary objective of the energy transition, the determination and comparison of the Global Warming Potential (GWP) footprints for HPC stations with BA is crucial. A comprehensive mathematical framework for the modelling and quantification of GWP footprints for HPC has been developed. The Levelized Emissions of Energy Supply (LEES) methodology has been extended and generalized to handle energy from the grid. A new state variable for the Battery Energy Storage System (BESS) — the State of Carbon Intensity (SOCI) has been introduced to calculate the operation phase GWP footprint of the BESS. The energy consumption GWP footprint for the load is also described by a new quantity — the Load Energy Consumption (LEC) emissions. The effect of incorporation of local Photovoltaic Solar (PV) generation in the energy flows is also investigated. An optimized Energy Management System (EMS) strategy with rolling horizon optimization to minimize emissions has been implemented to regulate energy flows in scenarios with BA and local PV generation. The Levelized Emissions of Energy Supply (LEES) values are obtained for all simulated scenarios and compared against a baseline rule-based EMS strategy. In combination with on-site PV generation, BA could achieve a reduction of 24% in the LEES vis-á-vis the baseline strategy. For reference, two scenarios with Grid Reinforcement (GR) for the grid section with and without local PV generation have also been simulated. With Grid Reinforcement (GR), a reduction of over 2% can be achieved with respect to the baseline EMS strategy for BA. Grid Reinforcement (GR) in conjunction with local PV generation can bring about a further reduction of about 6% with respect to the baseline EMS strategy for BA.