Two-Tier Energy Compensation Framework Based on Mobile Vehicular Electric Storage

Plug-in electric vehicle (PEV) commercialization propels an extensive charging station deployment in a power distribution system to satisfy fast growing PEV charging demands. Considering the power distribution, some stations deployed at limited capacity feeders may undergo power overload at peak hours due to time-varying traffic and PEV demands. The potential power overload could lead to severe transformer degradation or even black-out on the aged power infrastructure. To avoid power overload without excessive expenditure on the infrastructure upgrade, proper energy compensation at limited capacity station is highly effective. In this paper, we investigate an energy compensation problem based on utility-owned mobile vehicular electric storage (MVES), aiming to mitigate the overload issues among a group of charging stations (GCS). First, a Markov Chain based energy capacity model is developed to estimate the energy statuses among GCS and a graph theory based GCS transportation model is developed to facilitate on-road MVES allocation. Then, a two-tier energy compensation framework is introduced to efficiently schedule MVESs to minimize the scheduling cost. Simulations are conducted based on real traffic data on California highway collected by California department of transportation, and the results validate the effectiveness of the introduced framework.