Advanced Dynamic Virtual Power Plants with Electric Vehicle Integration

Electric vehicles (EVs) are the possible solution to reach for the goal of reliable and sustainable environment and electrifying the transportation system. EV integration is widely done by introducing the virtual power plant (VPP) concept in which the EVs can be clustered and controlled together. By this way one single VPP or aggregator model can be used to solve the challenges in the grid such as power quality, systems losses, and peak demand management. This paper will first analyze the conventional single VPP model and its application. The research work will then propose a new strategy to overcome its limitation for flexible use of EVs by introducing a dynamic virtual power plant (DVPP) algorithm. This algorithm is able to cluster the EVs into different virtual power plants based on the EVs' present state of charge (SOC) and plug-out time. After the formation of different VPP clusters, the EV coordination and vehicle to grid (V2G) optimization of each VPP cluster are formulated as a mixed integer nonlinear optimization model while subjected to grid constraints. The proposed methodology is evaluated by MATLAB and Open-DSS simulation and the results indicate that the proposed approach has better grid performance than the conventional single fixed VPP model.

Automated summary

This paper introduces the use of virtual power plants (VPP) for electric vehicle (EV) integration and proposes a dynamic VPP (DVPP) algorithm to overcome the limitations of the conventional single VPP model. By grouping and optimizing EVs in different ways, this method performs better in terms of grid performance compared to the traditional model.