Distribution Grid Modelling and Particle Swarm Optimization for Electric Vehicles Flexibility

Electrical vehicles and renewable energy resources have increased penetration in the market. Consumers have redefined themselves as “prosumers” because they are no longer consuming energy but also are producing energy. This creates problems like congestion and rebound effects on the distribution grid. However, controlling them with appropriate frameworks helps the distribution grid by providing services like voltage control, congestion management or compensating losses. These parameters are restructuring or creating new markets at the distribution level such that prosumers can participate and aid the distribution grid operators. This paper aims to perform a literature review on flexibility concepts at the distribution grid. Various types of flexibility, market formulation and methods to solve operational problems are summarized in this paper. Furthermore, a distribution grid with G2V (Grid-to-Vehicle) flexibility is modelled to demonstrate local electric vehicle flexi-bility and analyze the impacts on the distribution grid. A meta-heuristic algorithm called Particle Swarm Optimization is used to optimize the electric vehicle schedule and minimize the impacts on electricity grid. This coordination between the real-time grid loads and energy needs for electric vehicles directly affects the charging and discharging patterns of electric vehicles. The results from the simulations show a great reduction in impacts on the voltage of the grid and reduction in loads for grids.