Co-Optimized Parking Lot Placement and Incentive Design for Promoting PEV Integration Considering Decision-Dependent Uncertainties

This article proposes a new planning framework for optimal allocation of parking lot (PL)-based charging infrastructures to facilitate the efficient integration of plug-in electric vehicles (PEVs). Unlike existing works, the present article explicitly considers the uncertain implications of incentiv...

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Vydáno v:IEEE transactions on industrial informatics Ročník 17; číslo 3; s. 1863 - 1872
Hlavní autoři: Zeng, Bo, Feng, Jiahuan, Liu, Nian, Liu, Yixian
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.03.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Batteries
Charging
Contracts
Decision theory
Decision-dependent uncertainty
Electric vehicles
Genetic algorithms
incentive
Parking facilities
parking lot
Planning
plug-in electric vehicle (PEV)
Randomness
regret-matching
Resource management
Stochastic processes
Uncertainty
Vehicle-to-grid
ISSN:1551-3203, 1941-0050
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Shrnutí:This article proposes a new planning framework for optimal allocation of parking lot (PL)-based charging infrastructures to facilitate the efficient integration of plug-in electric vehicles (PEVs). Unlike existing works, the present article explicitly considers the uncertain implications of incentive policy on PEV owners' charging behaviors and its effects in PL planning. For this aim, a regret-matching technique is introduced to model the bounded rationality of PEV owners in deciding the choice to use different charging options for recharging their vehicle, as a dependency with respect to the incentive value and the accessibility of the charging service in long-term horizon. Such endogenous uncertainties are considered simultaneously with the inherent exogenous randomness of PEV demand and captured by the proposed PL planning model using a proper scenario generation method. The resulting model turns out to be a two-stage stochastic programming with decision-dependent uncertainties and it is solved by using the genetic algorithm. Numerical studies based on an illustrative test system verify the effectiveness of the proposed model and the approach.
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ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2020.2993815