Fabian Heymann

Abstract
The uptake of electric vehicles (EV) will require important modifications in traditional grid planning and load forecasting techniques. Existing literature suggests that the integration of EVs will be more adversarial to elements of the existing electricity infrastructure in terms of power supply (kW) than energy (kWh) delivery. While several studies analyzed the grid impact of electric vehicle fleets, few consider the adoption process itself which may lead to strong spatial variations of the utilization of charging infrastructure. The presented approach extends spatial load forecasting, introducing diffusion theory elements to analyze spatio-temporal clustering of EV charging demand. Using open-access census and grid data, this work develops a deterministic framework to forecast spatial patterns of EV charging applied to a real-world environment. Outcomes suggest substantial spatial clustering of EV adoption patterns, showing substation overrating for EV penetration rates of 25% and above with 7.4kW charging power.

Abstract
Power-to-Gas can contribute with valuable balancing power and seasonal storage capacity to future power systems. In Portugal, forecasts for 2020 show significant excess of renewable energy generation that can be transformed by power-to-gas technology and fed into the natural gas infrastructure. This work suggests an innovative approach to assess future power-togas integration potentials at the national level, focusing on wind power. Following a geographical distance analysis, a first economical estimation of future energy transformation costs is made with the help of Levelized Costs of Energy (LCOE). © 2015 IEEE.