Plug-in hybrid electric vehicles (PHEVs) have been identified as a mode of personal transportation that can facilitate CO2 emissions reductions while allowing users to maintain their typical travel patterns. PHEVs couple an electric motor and internal combustion engine together in order to travel on energy from either battery electricity, or gasoline. By demanding electricity to charge its battery the PHEV imposes a new load on the grid, thereby impacting the shape of the grid’s load curve. This, in turn, influences the types of electricity generators that will meet demand, causing changes to the amount of CO2 emitted by the grid. This study examines the aggregate load imposed by a large number of PHEVs on the electric grid. It assumes this load to be dispatchable, and seeks a dispatch policy that simultaneously minimizes: (i) PHEV CO2 emissions, (ii) total unmet PHEV electricity demand, and (iii) a load leveling objective. We compute these objectives using 2009 National Household Travel Survey (NHTS) vehicle trip data combined with previously-developed models of on-road PHEV power management, grid and PHEV CO2 emissions, non-PHEV grid loads, and generation asset dispatch. We focus on the Electric Reliability Council of Texas (ERCOT) as a representative power grid, and repeat the optimization for various PHEV market penetration levels. Because the above optimization objectives are mutually conflicting, we trade them off using a simple multi-objective particle swarm optimization algorithm. This furnishes a Pareto front of feedforward PHEV dispatch policies. The Pareto front elucidates some fundamental tradeoffs in aggregate PHEV load dispatch, and can form the foundation for future research on more robust dispatch policies employing feedback control. The findings further suggest that Pareto-optimal CO2 emissions are less sensitive to the policy variables than the unmet demand, and load leveling objectives.
CSS Publication Number:
Hosam Fathy, Jarod Kelly, Saeid Bashash and Gregory Keoleian. “Plug-In Hybrid Electric Vehicle Charging Policy Optimization Using Particle Swarms.” 6th International Conference of the International Society for Industrial Ecology (ISIE) Proceedings. Berkeley, CA, June 7-10 2011, Abstract #659.