The mass-scale integration of electric vehicles into the power system is a key pillar of the European energy transition agenda. Yet, the extent to which such integration would represent a burden for the power system of each member country is still an unanswered question. This is mainly due to a lack of accurate and context-specific representations of aggregate mobility and charging patterns for large electric vehicle fleets. Here, we develop and validate against empirical data an open-source model that simulates such patterns at high (1-min) temporal resolution, based on easy-to-gather, openly accessible data. We hence apply the model – which we name RAMP-mobility – to 28 European countries, showing for the first time the existence of marked differences in mobility and charging patterns across those, due to a combination of weather and socio-economic factors. We hence quantify the impact that fully-electric car fleets would have on the demand to be met by each country's power system: an uncontrolled deployment of electric vehicles would increase peak demand in the range 35–51%, whilst a plausible share of adoption of smart charging strategies could limit the increase to 30–41%. On the contrary, plausible technology (battery density) and infrastructure (charging power) developments would not provide substantial benefits. Efforts for electric vehicles integration should hence primarily focus on mechanisms to support smart vehicle-to-grid interaction. The approach is applicable generally beyond Europe and can provide policy makers with quantitatively reliable insights about electric vehicles impact on the power system.@en

Hybrid microgrids represent a cost-effective and viable option to ensure access to energy in rural areas located far from the main grid. Nonetheless, the sizing of rural microgrids is complicated by the lack of models capable of accounting for the evolution of the energy demand over time, which is likely to occur in such contexts as a result of the modification of users' lifestyles. To tackle this issue, the present study aims at developing a novel, long-term optimisation model formulation, capable of accounting for load evolution and performing suitable investment decisions for capacity expansion along the time horizon. Multiple scenarios of load evolution are considered to evaluate the beneficial effects of this novel approach, through the coupling of the model with a tool for stochastic load profiles generation. The results show how this implementation brings lower Net Present Cost to the project and improved correspondence between actual electricity demand and microgrid sizing. Finally, a sensitivity analysis evaluates the robustness of the approach with respect to input data variability and the Loss of Load parameter.

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The relevance of sector coupling is increasing when shifting from the current highly centralised and mainly fossil fuel-based energy system to a more decentralized and renewable energy system. Cross-sectoral linkages are already recognized as a cost-effective decarbonisation strategy that provides significant flexibility to the system. Modelling such cross-sectoral interconnections is thus highly relevant. In this work, these interactions are considered in a long-term perspective by uni-directional soft-linking of two models: JRC-EU-TIMES, a long term planning multisectoral model, and Dispa-SET, a unit commitment and optimal dispatch model covering multiple energy sectors such as power, heating & cooling, transportation etc. The impact of sector coupling in future Europe-wide energy systems with high shares of renewables is evaluated through five scenarios. Results show that the contributions of individual sectors are quite diverse. The transport sector provides the highest flexibility potential in terms of power curtailment, load shedding, congestion in the interconnection lines and resulting greenhouse gas emissions reduction. Nevertheless, allowing combinations of multiple flexibility options such as hydro for the long-term, electric vehicles and flexible thermal units for the short-term provides the best solution in terms of system adequacy, greenhouse gas emissions and operational costs.@en