This paper presents a simulator for electric vehicles in the context of smart grids and distribution networks. It aims to support network operators’ planning and operations but can be used by other entities for related studies. The paper describes the parameters supported by the current version of the Electric Vehicle Scenario Simulator (EVeSSi) tool and its current algorithm. EVeSSi enables the definition of electric vehicles scenarios on distribution networks using a built-in movement engine. The scenarios created with EVeSSi can be used by external tools (e.g., power flow) for specific analysis, for instance grid impacts. Two scenarios are briefly presented for illustration of the simulator capabilities.

The ever-increasing penetration level of renewable energy and electric vehicles threatens the operation of the power grid. Dealing with uncertainty in smart grids is critical in order to mitigate possible issues. This paper proposes a two-stage stochastic model for a large-scale energy resource scheduling problem of aggregators in a smart grid. The idea is to address the challenges brought by the variability of demand, renewable energy, electric vehicles, and market price variations while minimizing the total operation cost. Benders' decomposition approach is implemented to improve the tractability of the original model and its computational burden. A realistic case study is presented using a real distribution network in Portugal with high penetration of renewable energy and electric vehicles. The results show the effectiveness of the proposed approach when compared with a deterministic model. They also reveal that demand response and storage systems can mitigate the uncertainty.

The increase of variable renewable energy generation has brought several new challenges to power and energy systems. Solutions based on storage systems and consumption flexibility are being proposed to balance the variability from generation sources that depend directly on environmental conditions. The widespread use of electric vehicles is seen as a resource that includes both distributed storage capabilities and the potential for consumption (charging) flexibility. However, to take advantage of the full potential of electric vehicles’ flexibility, it is essential that proper incentives are provided and that the management is performed with the variation of generation. This paper presents a research study on the impact of the variation of the electricity prices on the behavior of electric vehicle’s users. This study compared the benefits when using the variable and fixed charging prices. The variable prices are determined based on the calculation of distribution locational marginal pricing, which are recalculated and adapted continuously accordingly to the users’ trips and behavior. A travel simulation tool was developed for simulating real environments taking into account the behavior of real users. Results show that variable-rate of electricity prices demonstrate to be more advantageous to the users, enabling them to reduce charging costs while contributing to the required flexibility for the system.