Impact of Increased Market Penetration of Electric Vehicles on the PJM Regional Electric Grid

Open Access
Humenik, Dylan James
Area of Honors:
Energy Engineering
Bachelor of Science
Document Type:
Thesis Supervisors:
  • Dr. Mort D Webster, Thesis Supervisor
  • Dr. Sarma V Pisupati, Honors Advisor
  • Electric Vehicle
  • Electric Car
  • Economic Dispatch
  • unit commitment
  • electricity
  • PJM
  • electric grid
  • EV
  • charging
  • IEEE
  • market
  • market penetration
  • regional grid
  • grid
  • energy
  • power
  • engineering
  • EME
  • generation
  • generator
  • wind
  • solar
  • nuclear
  • coal
  • gas
  • biomass
  • control algorithm
  • constraint
  • charging window
This paper analyzes an increased market penetration of electric vehicles in the Classic PJM regional electric grid, and its effect on the dispatch of generators. A unit commitment model was used to create an hour-by-hour generator dispatch schedule for a 168-hour week in PJM. The additional demand placed on the electric grid by increased usage of electric vehicles was modeled using two extreme scenarios and an intermediate scenario. Electric vehicles were assumed to charge within a 13-hour charging window during nighttime hours. Scenario 1 assumed no control algorithm to manage EV demand, and thus resulted in a large spike in demand at the onset of the charging window. Scenario 2 assumed that a perfect control algorithm was present to spread out all EV charging demand across the 13-hour charging window. In reality, a control algorithm implemented through a future smart grid would likely result in an hourly demand curve somewhere in between these two extreme scenarios, thus, an intermediate scenario (Scenario 3) where the EV demand is ramped up and down around the beginning and end of the charging window, respectively, was also analyzed. Three EV market penetrations were analyzed: 0.2, 0.5, and 1.0. When EV demand was added to existing demand in the 168-hour week, the unit commitment model produced an economic dispatch schedule that met demand but raised costs, as expected. Scenario 1 resulted in the highest costs, while significant savings were achieved in Scenario 2 and Scenario 3. As EV market penetration increased, these cost savings became even more pronounced. Scenario 3 had a slight advantage over Scenario 2 in cost savings, but both cut costs significantly from Scenario 1.