Resolving Uncertainty with Decisions for Electric Vehicle Aggregators

The integration of Electric Vehicles (EVs) into the power market is expected to grow significantly, with a key challenge being the creation of a charging strategy that reduces costs while meeting drivers' needs. EV charging is flexible, as vehicles are parked for long periods, allowing them to be charged at optimal times. This flexibility presents several benefits like charging can be scheduled during off-peak hours to reduce costs, enhance grid stability and increase the utilization of renewable energy sources. Simultaneously, it ensures that vehicles are adequately charged to meet driving needs. This research focuses on investigating the auction policy framework of an aggregator managing a fleet of electric vehicles. The methodology of the research exploration authorises formulating the auction scenario implying a deterministic and stochastic programming method with Mixed Integer Linear Programming approach. The crucial uniqueness of this research work is that the aggregator comprehends the charging of these EVs with the purpose of reducing the charging values as well as safeguarding that the driving necessities of EV operatives are fulfilled. It is revealed from this research paper findings that the virtual battery on behalf of the EV aggregation is chosen selectively to charge at those specific time spans with the fairly lowest market values and continuously conforming with the constraints of energy and power bounds.