Energy-storage configuration for EV fast charging stations considering characteristics of charging load and wind-power fluctuation

Liu, Xiaoyi; Feng, Tianyuan

doi:10.1016/j.gloei.2021.03.005

Cited by 15 publications

(10 citation statements)

References 16 publications

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“…The combination of these five business logics can constitute the serverside application business logic of the whole real-time monitoring system, and complete the functions of the system. Data management business logic is the basic module between system server programs, which is responsible for data transmission among business logics; Configuration business logic is mainly responsible for monitoring interpersonal picture drawing; The monitoring business logic is responsible for interacting with customers to present monitoring data [12].…”

Section: Distribution Real-time Monitoring System Modelmentioning

confidence: 99%

Research and development of distribution monitoring configuration software based on network

Zhang,

Xu,

Zhao

et al. 2023

5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023)

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In order to solve the problem of network real-time data transmission and the difficult requirements of monitoring system for network data real-time presentation, this paper designs a power monitoring configuration software with Web service function. It is based on B/S architecture, which can expand the equipment primitives for power monitoring applications, and has strong universality and transplant reliability. By using its configuration function, users can quickly generate the power monitoring software system, which can realize the scientific management of power grid monitoring in power enterprises and achieve the purpose of improving work efficiency and automation management level.

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Section: Distribution Real-time Monitoring System Modelmentioning

confidence: 99%

Research and development of distribution monitoring configuration software based on network

Zhang,

Xu,

Zhao

et al. 2023

5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023)

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“…Considering the charging requirements of electric vehicles and the impact of power fluctuations at fast charging stations, an optimal configuration model is proposed. This general configuration method can be applied to different distributed generation scenarios and different charging requirements for electric vehicles [31]. Another approach is the use of bidirectional chargers for V2G electric vehicle batteries with a direct current control strategy, where bidirectional chargers allow for fast charging, fast discharging, slow charging, and slow discharging.…”

Section: Research On Electric Vehicles As Energy Storagementioning

confidence: 99%

Study on the interaction of vehicle-to-grid and its impact on power quality of electric grid

Cao

2023

J. Phys.: Conf. Ser.

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With the continuous increase in the number of electric vehicles (EVs) in the market, both EVs and charging stations are rapidly developing. This paper provides an overview of the negative impacts of EV charging on the distribution grid at the current stage. It summarizes specific solutions proposed by scholars both domestically and internationally to address the effects on voltage, harmonics, and power quality. These solutions mainly involve the addition of filters to the distribution grid, the use of 12-pulse rectifiers, and series resonance. Furthermore, the paper analyzes how Vehicle-to-Grid (V2G) technology can balance the grid and regulate power quality issues caused by EV charging, ensuring stable system operation. V2G technology also enables EVs to function as mobile energy storage devices and allows for rational energy distribution using bidirectional charging stations. The integration of charging stations with the grid, forming a connected vehicle network, is considered the future direction of EV development.

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“…Table 1 lists the environment parameters. In addition, charging time characteristics are detailed in reference [28], and the duration of EV users arrived at the CS obeys N (58.32, 12.44) [29]. The rainbow algorithm consists of two hidden layers with 120 and 80 neurons, respectively.…”

Section: Case Study Setupmentioning

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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Energy-storage configuration for EV fast charging stations considering characteristics of charging load and wind-power fluctuation

Cited by 15 publications

References 16 publications

Research and development of distribution monitoring configuration software based on network

Research and development of distribution monitoring configuration software based on network

Study on the interaction of vehicle-to-grid and its impact on power quality of electric grid

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

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