UC Irvine

Plug-in electric vehicles (PEVs) are considered one of the leading solutions in reducing greenhouse gas emissions since they remove carbon emissions from the tailpipe. However, as their penetration in the vehicle market increases, so will their impact on the electric grid. To minimize the impact that PEVs have on the electric grid, “smart” charging protocols are necessary to manage PEV charging. This study evaluates how two smart charging architectures, a centralized and decentralized architecture, impact both small and large-scale electric grids through real deployment of the algorithms as well as MATLAB simulations.

The “field-deployable” decentralized charging algorithm uses a telematics-based approach to create charging schedules for 10 PEVs deployed on the University of California, Irvine’s (UCI) microgrid. The results reveal that a barrier associated with this approach is the need to retrieve the vehicles’ status, referred to as “polling.” Polling affects how the algorithm creates charging schedules. To determine the effect of polling, simulations are performed on different buildings on the UCI campus using National Household Travel Survey data to simulate vehicle travel patterns. The results show that, if polling occurs frequently (e.g., once every 10 minutes), the charging schedules are not significantly altered. To determine whether or not the decentralized algorithm can provide the same emissions benefits as an ideal centralized algorithm on large-scale systems, both algorithms are simulated on the California electric grid for the year 2030. The results reveal that the decentralized algorithm provides the same emissions benefits as the centralized algorithm, but only if communication between the grid and vehicles is sufficiently frequent (e.g., 60 minutes or less).