A Systematic Review of Uncertainty Handling Approaches for Electric Grids Considering Electrical Vehicles

This paper systematically reviews the techniques and dynamics to study uncertainty modelling in the electric grids considering electric vehicles with vehicle-to-grid integration. Uncertainty types and the most frequent uncertainty modelling approaches for electric vehicles are outlined. The modelling approaches discussed in this paper are Monte Carlo, probabilistic scenarios, stochastic, point estimate method and robust optimisation. Then, Scopus is used to search for articles, and according to these categories, data from articles are extracted. The findings suggest that the probabilistic techniques are the most widely applied, with Monte Carlo and scenario analysis leading. In particular, 19% of the cases benefit from Monte Carlo, 15% from scenario analysis, and 10% each from robust optimisation and the stochastic approach, respectively. Early articles consider robust optimisation relatively more frequent, possibly due to the lack of historical data, while more recent articles adopt the Monte Carlo simulation approach. The uncertainty handling techniques depend on the uncertainty type and human resource availability in aggregate but are unrelated to the generation type. Finally, future directions are given.

Automated summary

This paper reviews the techniques and dynamics of uncertainty modelling in electric grids with vehicle-to-grid integration for electric vehicles. The most frequently used uncertainty modelling approaches are Monte Carlo, probabilistic scenarios, stochastic, point estimate method, and robust optimization. The findings show that probabilistic techniques, specifically Monte Carlo and scenario analysis, are the most widely applied. Early articles tend to use robust optimization due to the lack of historical data, while more recent articles favor Monte Carlo simulation. The uncertainty handling techniques depend on the type of uncertainty and the availability of human resources, and are unrelated to the generation type.