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Title: A two-stage stochastic optimization model for scheduling electric vehicle charging loads to relieve distribution-system constraints

Electric vehicles (EVs) hold promise to improve the energy efficiency and environmental impacts of transportation. However, widespread EV use can impose significant stress on electricity-distribution systems due to their added charging loads. This paper proposes a centralized EV charging-control model, which schedules the charging of EVs that have flexibility. This flexibility stems from EVs that are parked at the charging station for a longer duration of time than is needed to fully recharge the battery. The model is formulated as a two-stage stochastic optimization problem. The model captures the use of distributed energy resources and uncertainties around EV arrival times and charging demands upon arrival, non-EV loads on the distribution system, energy prices, and availability of energy from the distributed energy resources. We use a Monte Carlo-based sample-average approximation technique and an L-shaped method to solve the resulting optimization problem efficiently. We also apply a sequential sampling technique to dynamically determine the optimal size of the randomly sampled scenario tree to give a solution with a desired quality at minimal computational cost. Here, we demonstrate the use of our model on a Central-Ohio-based case study. We show the benefits of the model in reducing charging costs, negative impacts on themore » distribution system, and unserved EV-charging demand compared to simpler heuristics. Lastly, we also conduct sensitivity analyses, to show how the model performs and the resulting costs and load profiles when the design of the station or EV-usage parameters are changed.« less
 [1] ;  [1]
  1. The Ohio State Univ., Columbus, OH (United States). Integrated Systems Engineering Dept.
Publication Date:
Grant/Contract Number:
PI0000012; 1029337
Accepted Manuscript
Journal Name:
Transportation Research, Part B: Methodological
Additional Journal Information:
Journal Volume: 102; Journal Issue: C; Journal ID: ISSN 0191-2615
Research Org:
Univ. of Michigan, Ann Arbor, MI (United States)
Sponsoring Org:
USDOE Office of Energy Efficiency and Renewable Energy (EERE); USDOE Office of International Affairs (IA); ational Science Foundation (NSF)
Country of Publication:
United States
33 ADVANCED PROPULSION SYSTEMS; Electric vehicle; Vehicle charging; Charging control; Stochastic optimization; Sample-average approximation
OSTI Identifier:
Alternate Identifier(s):
OSTI ID: 1413846