Dual battery management for renewable energy integration in EV charging stations

This paper designs a dual-battery management scheme, focusing on its charging task scheduler in EV (electric vehicle) charging stations for the sake of systematically renewable energy integration to the power grid. The scheduler tries to reduce peak load brought by concentrated charging from a large number of EVs. Basically, a dual-battery framework can overcome the intermittency of renewable energy, as even if one station battery is charged by renewable energy, the other can still discharge its electricity to an EV. A two-phase scheduler decides a time table by which the control layer selects the power source out of two station batteries and the main power line on each time slot. Based on a genetic algorithm and a heuristic for initial population selection, the first phase distributes the charging load as evenly as possible, and then the second phase selects the peaking slot and allocates renewable energy. Performance measurement results obtained from a prototype implementation show that the proposed scheduler can reduce peak load by up to 38.5% and 8.2%, compared with uncoordinated scheduling and regular genetic scheduling, respectively, while the renewable energy-based compensation efficiently works for any scheduling scheme.