The sequential construction research of regional public electric vehicle charging facilities based on data-driven analysis—Empirical analysis of Shanxi Province

Yang, Xiaoyu; Guo, Xiaopeng; Li, Yun; Yang, Kun

doi:10.1016/j.jclepro.2022.134948

Cited by 6 publications

(2 citation statements)

References 46 publications

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“…A two-stage model has also been proposed to optimize EV charging and the selection of charging piles by effectively grouping the distribution pattern of EV charging demand and various types of EVs, and by minimizing the annual investment and electricity purchasing costs of charging piles [34]. While prior studies have extensively examined the location and capacity of charging stations at the macro level of the entire city or urban area and the distribution network system [35][36][37], a systematic investigation of the configuration of charging facilities from the micro perspective of individual buildings is currently lacking in the literature.…”

Section: Battery Capacitymentioning

confidence: 99%

Electric Vehicle Charging Facility Configuration Method for Office Buildings

et al. 2023

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With the advent of advanced battery technology, EVs are gradually gaining momentum. An appropriate decision-making method for the number of charging piles is in need to meet charging needs, and concurrently, to avoid the waste of infrastructure investment. In this study, an optimal charging pile configuration method for office building parking lots is proposed. With the determination of the design period of charging facilities, a charging load prediction model is established under a collection of charging scenarios. Taking the average utilization rate of charging facilities and the average satisfaction rate of charging demand as the objective functions, the distribution of the optimal number of piles is obtained with the genetic algorithm. The benefits of the configuration method are also explored under the building demand response process. The results show that the optimal configuration of charging piles in office buildings with different volumes have similar characteristics. When the design period is 5 years and 10 years, the comprehensive indicator of the utilization rate of the charging facilities and the satisfaction rate of the charging demand can, respectively, be improved by 8.18% and 17.45%. Moreover, the reasonable scheduling strategy can realize the load regulation response with a maximum load transfer rate of 25.55%.

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Section: Battery Capacitymentioning

confidence: 99%

Electric Vehicle Charging Facility Configuration Method for Office Buildings

et al. 2023

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“…The China Electric Vehicle Charging Infrastructure Promotion Alliance (EVCIPA) released that the charging infrastructure increased up to 2.593 million units in 2022, increasing by 225.5% year-on-year (China News Network, 2023), which reflects the fierce competition in the charging station (CS) industry. In addition, the service scope could be expanded by various parameters, such as advanced charging piles, large contracts, and low-cost charging prices (CPs) (Liu et al, 2019;Yang et al, 2022;Zhou et al, 2023a). Thus, it is necessary to propose the precise division of service scope and reasonable pricing strategies to enhance the core competence among charging station operators.…”

Section: Introductionmentioning

confidence: 99%

Charging station service scope division and real-time pricing strategy based on the strongest occupation method

Yang,

Tang,

Chen

et al. 2024

Front. Energy Res.

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With the rapid construction of charging stations (CSs), charging station operators need to enhance their core competitiveness by precisely planning their service areas and formulating reasonable and effective pricing strategies. However, the regional competition among multiple charging station operators is generally ignored. In the traditional model, the service scope of CSs appears as regular circles, which is inconsistent with the market distribution law. In response to the irregular relationship between the dynamic service scope of CSs and the real-time charging price, a charging station service scope (CSSS) model is proposed by introducing the variable service field strength (SFS). First, the competitiveness of CSs is evaluated quantitatively, and the SFS of CSs is defined to describe the service scope of CSs by the strongest occupation method. Second, the impact of the charging price on the charging demand is analyzed based on the CSSS division model. In addition, the revenue of charging station operators and the stability of the power grid are considered to establish a real-time pricing optimization model. Finally, the numerical simulation is operated in Furong District of Changsha. It is shown that the proposed method effectively achieves more profits for charging stations and decreases the average power deviation of the whole region.

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Dynamic pricing based electric vehicle charging station location strategy using reinforcement learning

Wang

et al. 2023

Energy

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The sequential construction research of regional public electric vehicle charging facilities based on data-driven analysis—Empirical analysis of Shanxi Province

Cited by 6 publications

References 46 publications

Electric Vehicle Charging Facility Configuration Method for Office Buildings

Electric Vehicle Charging Facility Configuration Method for Office Buildings

Charging station service scope division and real-time pricing strategy based on the strongest occupation method

Dynamic pricing based electric vehicle charging station location strategy using reinforcement learning

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