Broad classifications of Electric vehicle charging methodologies
The great challenge and potential of EV charing problems have attracted plenty of attention to this area. The subject can be probed from different angles. An EV owner wants to minimize the total charging cost with respect to dynamic electricity prices . The objective of a aggregator or a charging station operator is to maximize the total charging profit, facing stochastic EV arrivals, random charging demands, dynamic electricity prices, fluctuating ancillary service requirements and other system constraints. The distribution network operator is concerned about the stability and reliability of the grid and manages the charging of EV taking into account voltage deviation, efficient dispatch, demand deviation, system losses etc.
EV charge scheduling policies can be classified into two categories, static( offline) and dynamic(online) algorithms, by the information available. Static scheduling algorithms require all information about EVs and the power system within the scheduling horizons, e.g., the charging demand, arrival and departure time of all EVs, electricity price and so on. Dynamic scheduling algorithms only know the information about the power system up to the time the scheduling is made and the states of the EVs that have already arrived at the station.
Instead of knowing the future electricity price and charging requirement of EVs, dynamic scheduling algorithms may assume that statistics of this information is available, such as the distribution of the EV arrivals. From the perspective of aggregators or a charging station operator, the centralized control problem may be reasonable. That is, the scheduler has direct access to control the charging of multiple EVs in the charging station. The scheduler can determine when to activate or deactivate the chargers and control the charging rate of each individual EV. As an independent system operator (ISO), it may be more reasonable to consider the decentralized framework. The ISO does not have direct control on the charging of each individual EV. Instead, it can affect the charging behavior of EVs via adjusting the charging price or broadcasting similar signals. The individual EV owner and charging station operator would respond to the signal by modifying their charging profiles.
