Charging Pricing in Power-Traffic Systems With Price-Elastic Demand: A Quasi-Variational Inequality Approach

The rise of electric vehicles (EVs) fosters closer integration between the power and transportation sectors. While implementing a fair EV charging pricing strategy optimizes the system economic performance, modeling EV users' behaviors and their elastic demand in response to charging prices rem...

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Bibliographic Details
Published in:IEEE transactions on power systems Vol. 40; no. 6; pp. 5013 - 5026
Main Authors: Xie, Shiwei, Xie, Longtao, Wu, Qiuwei, Shu, Shengwen, Chen, Yuanyi, Yang, Qiang, Shao, Zhenguo
Format: Journal Article
Language:English
Published: New York IEEE 01.11.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Costs
Elasticity
Electric vehicle charging
Electric vehicles
EV user behavior
Game theory
Load shedding
market regulation
Optimization
Price-elastic demand
Pricing
projection gradient algorithm
quasi-variational inequality
Reactive power
Regulation
Sensitivity analysis
Transportation networks
Transportation systems
Upper bound
ISSN:0885-8950, 1558-0679
Online Access:Get full text
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Summary:The rise of electric vehicles (EVs) fosters closer integration between the power and transportation sectors. While implementing a fair EV charging pricing strategy optimizes the system economic performance, modeling EV users' behaviors and their elastic demand in response to charging prices remains a significant challenge. This paper proposes a novel pricing scheme for EV charging within power-transportation systems using a tri-level framework that considers the interactions among the power distribution network (PDN), charging network operator (CNO), and EVs. To capture the responsive behaviors of EVs, a user equilibrium (UE) model with price-elastic demand is formulated as a quasi-variational inequality (QVI). This approach reduces the tri-level pricing problem to a bi-level optimization problem by merging the middle and lower levels into an optimization problem with QVI constraints, thereby achieving mathematical tractability. The outer level optimizes energy dispatch in the PDN, while the inner level focuses on the CNO's pricing optimization in response to elastic EV demand. To solve the problem, a projection gradient algorithm and a tailored fixed-point algorithm are developed. Simulation results confirm the effectiveness and superiority of the proposed model and algorithms. Sensitivity analysis further shows that elasticity and regulation significantly affect system efficiency, demonstrating the model's robustness.
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ISSN:0885-8950
1558-0679
DOI:10.1109/TPWRS.2025.3566413