Dynamic Time-Of-Use Pricing Strategy for Electric Vehicle Charging Considering User Satisfaction Degree

Zhang, Qian; Hu, Yue; Tan, Weiyu; Li, Chunyan; Ding, Zhuwei

doi:10.3390/app10093247

Cited by 31 publications

(12 citation statements)

References 18 publications

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“…In [11], the authors address the operation cost of the EV charging infrastructure, and the simulation results show that it is difficult for the EV charging infrastructure to gain profits. In [12], the authors give a pricing range of the EV charging station, which can not only ensure the profit of station operators but also improve the profit of consumers compared to using the internal-combustionengine vehicle. In [13], the authors present a methodology to assess the coat-benefit and develop the service pricing strategy of electric taxies in Shanghai, China.…”

Section: Introductionmentioning

confidence: 99%

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Comprehensive Pricing Scheme of the EV Charging Station considering Consumer Differences Based on Integrated AHP/DEA Methodology

Yin

Zhang

et al. 2020

Mathematical Problems in Engineering

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Scientific pricing of the electric vehicle charging station is closely related to consumer behavior inevitably. Existing studies have not considered the impacts of consumer differences on the charging price, which will fail to meet the interests of various types of consumers. This paper proposes a novel pricing method based on consumer classification and comprehensive evaluation strategies. First, the basis for consumer classification is established according to a single factor sensitivity analysis of the consumer benefit model; then, the nonlinear expression of the basis is piecewise linearized. Additionally, with the principle of least fitting error to determine consumers’ classification, the initial charging price schemes for various types of consumers are formulated. Second, this paper defines evaluation indices and establishes the hierarchy model for comprehensive evaluation schemes. Finally, the integrated analytic hierarchy process and data envelopment analysis are adopted for comprehensive evaluation of schemes. Simulations results illustrate that the proposed method can formulate the comprehensive optimal charging price considering consumer differences, and the method can reflect the impacts of both subjective and objective factors conveniently and accurately.

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Section: Introductionmentioning

confidence: 99%

“…In principle, the pricing of the EVs charging station should consider the tripartite interests of the government, operators, and consumers [28]. In [12], based on the Game theory, the authors construct the benefit models of EV operators and consumers in terms of the standard charging stations and the standard EV.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Pricing Scheme of the EV Charging Station considering Consumer Differences Based on Integrated AHP/DEA Methodology

Yin

Zhang

et al. 2020

Mathematical Problems in Engineering

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“…Likewise, the EVs integration in micro-grids also have been studied [24,25]. In [26,27], charging methodologies are proposed based on EV users' preferences.…”

Section: Related Workmentioning

confidence: 99%

Participation of an EV Aggregator in the Reserve Market through Chance-Constrained Optimization

et al. 2020

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The adoption of Electric Vehicles (EVs) will revolutionize the storage capacity in the power system and, therefore, will contribute to mitigate the uncertainty of renewable generation. In addition, EVs have fast response capabilities and are suitable for frequency regulation, which is essential for the proliferation of intermittent renewable sources. To this end, EV aggregators will arise as a market representative party on behalf of EVs. Thus, this player will be responsible for supplying the power needed to charge EVs, as well as offering their flexibility to support the system. The main goal of EV aggregators is to manage the potential participation of EVs in the reserve market, accounting for their charging and travel needs. This work follows this trend by conceiving a chance-constrained model able to optimize EVs participation in the reserve market, taking into account the uncertain behavior of EVs and their charging needs. The proposed model, includes penalties in the event of a failure in the provision of upward or downward reserve. Therefore, stochastic and chance-constrained programming are used to handle the uncertainty of a small fleet of EVs and the risk profile of the EV aggregator. Two different relaxation approaches, i.e., Big-M and McCormick, of the chance-constrained model are tested and validated for different number of scenarios and risk levels, based on an actual test case in Denmark with actual driving patterns. As a final remark, the McCormick relaxation presents better performance when the uncertainty budget increases, which is appropriated for large-scale problems.

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“…Compared with the greedy algorithm, the SDP effectively improves the economic benefits of charging service providers. Given that static peak-valley electricity prices cannot reflect the EV charging demands, a dynamic pricing strategy that considers user satisfaction is developed in [8] to optimize the economic benefits of EVs and power grids. The work presented in [9] proposes a nonlinear optimization model for EV charging scheduling.…”

Section: Introductionmentioning

confidence: 99%

A Modified Rainbow-Based Deep Reinforcement Learning Method for Optimal Scheduling of Charging Station

et al. 2022

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To improve the operating efficiency and economic benefits, this article proposes a modified rainbow-based deep reinforcement learning (DRL) strategy to realize the charging station (CS) optimal scheduling. As the charging process is a real-time matching between electric vehicles ‘(EVs) charging demand and CS equipment resources, the CS charging scheduling problem is duly formulated as a finite Markov decision process (FMDP). Considering the multi-stakeholder interaction among EVs, CSs, and distribution networks (DNs), a comprehensive information perception model was constructed to extract the environmental state required by the agent. According to the random behavior characteristics of the EV charging arrival and departure times, the startup of the charging pile control module was regarded as the agent’s action space. To tackle this issue, the modified rainbow approach was utilized to develop a time-scale-based CS scheme to compensate for the resource requirements mismatch on the energy scale. Case studies were conducted within a CS integrated with the photovoltaic and energy storage system. The results reveal that the proposed method effectively reduces the CS operating cost and improves the new energy consumption.

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Dynamic Time-Of-Use Pricing Strategy for Electric Vehicle Charging Considering User Satisfaction Degree

Cited by 31 publications

References 18 publications

Comprehensive Pricing Scheme of the EV Charging Station considering Consumer Differences Based on Integrated AHP/DEA Methodology

Comprehensive Pricing Scheme of the EV Charging Station considering Consumer Differences Based on Integrated AHP/DEA Methodology

Participation of an EV Aggregator in the Reserve Market through Chance-Constrained Optimization

A Modified Rainbow-Based Deep Reinforcement Learning Method for Optimal Scheduling of Charging Station

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