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

Abstract

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

Authors:
 [1];  [1]
  1. The Ohio State Univ., Columbus, OH (United States). Integrated Systems Engineering Dept.
Publication Date:
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)
OSTI Identifier:
1362132
Alternate Identifier(s):
OSTI ID: 1413846
Grant/Contract Number:
PI0000012; 1029337
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Transportation Research, Part B: Methodological
Additional Journal Information:
Journal Volume: 102; Journal Issue: C; Journal ID: ISSN 0191-2615
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
33 ADVANCED PROPULSION SYSTEMS; Electric vehicle; Vehicle charging; Charging control; Stochastic optimization; Sample-average approximation

Citation Formats

Wu, Fei, and Sioshansi, Ramteen. A two-stage stochastic optimization model for scheduling electric vehicle charging loads to relieve distribution-system constraints. United States: N. p., 2017. Web. doi:10.1016/j.trb.2017.05.002.
Wu, Fei, & Sioshansi, Ramteen. A two-stage stochastic optimization model for scheduling electric vehicle charging loads to relieve distribution-system constraints. United States. doi:10.1016/j.trb.2017.05.002.
Wu, Fei, and Sioshansi, Ramteen. Thu . "A two-stage stochastic optimization model for scheduling electric vehicle charging loads to relieve distribution-system constraints". United States. doi:10.1016/j.trb.2017.05.002.
@article{osti_1362132,
title = {A two-stage stochastic optimization model for scheduling electric vehicle charging loads to relieve distribution-system constraints},
author = {Wu, Fei and Sioshansi, Ramteen},
abstractNote = {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 the 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.},
doi = {10.1016/j.trb.2017.05.002},
journal = {Transportation Research, Part B: Methodological},
number = C,
volume = 102,
place = {United States},
year = {Thu May 25 00:00:00 EDT 2017},
month = {Thu May 25 00:00:00 EDT 2017}
}

Journal Article:
Free Publicly Available Full Text
This content will become publicly available on May 25, 2018
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  • Cited by 1
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  • Cited by 2
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  • This paper presents a statistical method for predicting the effect that widespread electric vehicle (EV) battery charging will have on power distribution system harmonic voltage levels. The method uses a statistical model for nonlinear load currents to generate the probabilities of specific harmonic voltage levels. The statistical model for the harmonic currents produced by a concentration of EVs accounts for partial harmonics cancellation introduced by uncertainty and variation in charger start-time and initial battery state-of-charge. A general solution technique is presented along with several examples using data from a commercially-available EV charger and an actual power distribution system. The resultsmore » show that there is a definite threshold penetration below which EV charging has negligible impact on the number of buses whose voltage total harmonic distortion (THD{sub v}) exceeds 5%. During the late evening of a summer day, the example distribution system can accommodate EV penetration levels as high as 20%. A similar analysis of the system in the spring or fall indicates that the system can accommodate a 15% EV penetration before THD{sub v} exceeds 5% at an unacceptable number of buses.« less