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Solving Overstay and Stochasticity in PEV Charging Station Planning with Real Data
Zeng, Teng1; Zhang, Hongcai2; Moura, Scott1,3
2020-05
Source PublicationIEEE Transactions on Industrial Informatics
ISSN1551-3203
Volume16Issue:5Pages:3504-3514
Abstract

This article studies optimal plug-in electric vehicle (PEV) charging station planning, with consideration for the 'overstay' problem. Today, public PEV charging station utilization is typically around 15%. When un-utilized, the chargers are either idle or occupied by a fully charged PEV that has not departed. We call this 'overstay.' This motivates a strategy for increasing utilization by interchanging fully charged PEVs with those waiting for service-an issue which is not well addressed in the existing literature. Thus, this article studies the PEV charging station planning problem taking strategic interchange into account. To our best understanding, this has not been studied in the literature. With interchange, the objective is to enhance the charger utilization rate and, thus, reduce the number of chargers. This potentially reduces the capital investment and operational cost. A novel power/energy aggregation model is proposed, and a chance-constrained stochastic programming planning model with interchange is developed for a public charging station to incorporate customer demand uncertainties. Numerical experiments are conducted to illustrate the performance of the proposed method. Simulation results show that incorporating strategic interchange operation can significantly decrease the number of chargers, enhance utilization and economic efficiency.

KeywordChance Constraints Charging Station Planning Interchange Plug-in Electric Vehicle (Pev) Stochastic Programming (Sp)
DOI10.1109/TII.2019.2955997
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaAutomation & Control Systems ; Computer Science ; Engineering
WOS SubjectAutomation & Control Systems ; Computer Science, Interdisciplinary Applications ; Engineering, Industrial
WOS IDWOS:000519588700059
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 445 HOES LANE, PISCATAWAY, NJ 08855-4141
Scopus ID2-s2.0-85079763649
Fulltext Access
Citation statistics
Document TypeJournal article
CollectionTHE STATE KEY LABORATORY OF INTERNET OF THINGS FOR SMART CITY (UNIVERSITY OF MACAU)
Corresponding AuthorZhang, Hongcai
Affiliation1.Department of Civil and Environmental Engineering, University of California, Berkeley, 94720, United States
2.State Key Lab. of Internet of Things for Smart City, Dept. of Elec. and Computer Engineering, University of Macau, 999078, Macao
3.Smart Grid and Renewable Energy Laboratory, Tsinghua-Berkeley Shenzhen Institute, Shenzhen, 518055, China
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Zeng, Teng,Zhang, Hongcai,Moura, Scott. Solving Overstay and Stochasticity in PEV Charging Station Planning with Real Data[J]. IEEE Transactions on Industrial Informatics, 2020, 16(5), 3504-3514.
APA Zeng, Teng., Zhang, Hongcai., & Moura, Scott (2020). Solving Overstay and Stochasticity in PEV Charging Station Planning with Real Data. IEEE Transactions on Industrial Informatics, 16(5), 3504-3514.
MLA Zeng, Teng,et al."Solving Overstay and Stochasticity in PEV Charging Station Planning with Real Data".IEEE Transactions on Industrial Informatics 16.5(2020):3504-3514.
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