Particle Swarm Optimization for minimizing the burden of electric vehicles in active distribution networks

The concept of electrical-mobility, in opposition to the present oil-mobility, is attracting the attention of politicians and of civil society worldwide. Electrical mobility means the usage of battery powered Electric Vehicle (EV) and Plug-in Hybrid Electric Vehicle (PHEV) as the main future technology to combat greenhouse gas emissions. The burden of electric mobility will be mainly on the distribution system that, particularly during the peak hours, will be exposed to critical operation conditions by a high number of high density simultaneous loads. Vehicle-to-Grid technology by adding control capabilities to charge and discharge of cars' batteries can exalt the benefits from their whole energy storage capacity. Distributors can then be helped in the active management of the network by the services offered (e.g., VAR/volt regulation, frequency regulation, spinning reserve, integration of renewable generation). Vehicle-to-Grid is perfectly part of the emerging Smart Grid technology and is based on intelligent stations fully integrated within the distribution management system. For a full exploitation of Vehicle-to-Grid potentialities, the role of the aggregator is essential to create value to customers by offering services to the distribution system operator. In the paper, a Particle Swarm Optimization is used to define the aggregator's optimal control strategy to optimize the recharge/discharge patterns of a fleet of EVs taking into account financial contracts, driver's behavior, energy prices, etc.