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. The authors use a Monte Carlo-based sample-average approximation technique and an L-shaped method to solve the resulting optimization problem efficiently. The authors 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. The authors demonstrate the use of their model on a Central-Ohio-based case study. The authors 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. The authors 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.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/01912615
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Supplemental Notes:
- Abstract reprinted with permission of Elsevier.
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Authors:
- Wu, Fei
- Sioshansi, Ramteen
- Publication Date: 2017-8
Language
- English
Media Info
- Media Type: Web
- Features: Appendices; Figures; References; Tables;
- Pagination: pp 55-82
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Serial:
- Transportation Research Part B: Methodological
- Volume: 102
- Publisher: Elsevier
- ISSN: 0191-2615
- Serial URL: http://www.sciencedirect.com/science/journal/01912615
Subject/Index Terms
- TRT Terms: Case studies; Electric vehicle charging; Electric vehicles; Loads; Monte Carlo method; Optimization; Sensitivity analysis; Stochastic programming
- Geographic Terms: Ohio
- Subject Areas: Energy; Highways; Planning and Forecasting; Vehicles and Equipment;
Filing Info
- Accession Number: 01642049
- Record Type: Publication
- Files: TRIS
- Created Date: Jul 26 2017 5:02PM