Compensation of Charging Station Overload via On-Road Mobile Energy Storage Scheduling

Chen, Nan; Li, Mushu; Wang, Miao; Ma, Jinghuan; Shen, Xuemin

doi:10.1109/globecom38437.2019.9013285

Cited by 7 publications

(7 citation statements)

References 13 publications

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“…In this method, a TESS delivers surplus energy from a charging station with sufficient power supply to a charging station with limited capacity. The MCS operation framework developed in [ 14 , 15 ] was extended to a price-incentive scheme [ 16 ] to mitigate the overload of charging stations with limited capacity. In this case, the interaction between the distribution system operator and mobile energy storage was formulated as a Stackelberg game.…”

Section: Related Researchmentioning

confidence: 99%

Optimal Energy Management Framework for Truck-Mounted Mobile Charging Stations Considering Power Distribution System Operating Conditions

Jeon

Choi

2021

Sensors

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A high charging demand from many electric vehicles (EVs) at a fixed charging station (FCS) with a limited number of charging poles can increase the waiting time of EVs and yield an abnormal power grid condition. To resolve these challenges, this paper presents an optimization framework in which a mobile charging station (MCS) is dispatched to the overloaded FCS to reduce the number of waiting EVs while maintaining normal power grid operation. Compared to existing MCS scheduling methods that do not consider actual power distribution system operations, the proposed framework takes into account the (i) active/reactive power flow and consumption of EVs, (ii) reactive power capability of MCS, and (iii) voltage quality in power distribution systems. In coupled transportation and power distribution systems, the proposed algorithm conducts optimal operation scheduling of MCS for both road routing and charging and discharging, thereby leading to the reduction of waiting EVs within the allowable voltage range. The proposed MCS optimization algorithm was tested in IEEE 13-bus and 33-bus distribution systems coupled with 9-node and 15-node transportation systems, respectively. The test results demonstrate the effectiveness of the proposed algorithm in terms of number of waiting EVs, voltage magnitude deviation, and reactive power of the MCS.

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Section: Related Researchmentioning

confidence: 99%

Optimal Energy Management Framework for Truck-Mounted Mobile Charging Stations Considering Power Distribution System Operating Conditions

Jeon

Choi

2021

Sensors

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“…However, the SoC state at the time of each EV's arrival and necessary departure SoC differ, necessitating adjustable scheduling and incentives to encourage them to discharge [58]. As a result, the cost of battery utilization is taken into account when determining the scheduling goal [59].…”

Section: Load Flatteningmentioning

confidence: 99%

A Review on the Feasibility of Deployment of Renewable Energy Sources for Electric Vehicles under Smart Grid Environment

Jayakumar

2021

IJEER

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Nowadays, Energy consumption in the field of transportation comes next to industrial consumption worldwide. If transportation is completely powered by renewable energy, the utilization of fossil fuels can be drastically reduced, which will result in a lesser amount of greenhouse gas emissions. Electric vehicles (EVs) can act as an alternative to make transportation pollution-free. Large-scale usage of EVs causes high electricity demand on the supply system. This problem can be overcome by utilizing renewable energy sources (RESs) for Electric Vehicle charging. Due to the unpredictability of RESs, coordinating EV charging with other loads and renewable generation is problematic. By using EVs as energy units, power fluctuations in the electric grid can be compensated. This paper presents a summary of recent research in the domain of integration of renewable energy sources with electric vehicles (EVs) under the smart grid environment. Electric vehicles-smart grid integrated systems face several issues related to communication, grid infrastructure and control in the future power system. Feasibility of integration of solar and wind energy systems with electric vehicles is discussed in section 2. The existing research articles in this area are classified into two based on the purpose: EVs integration into the electric grid and Vehicle to grid services. The function of V2G in the electricity market, as well as its management concerns, are investigated in section 3. Finally, the research gaps and future scope of incorporating electric vehicles with renewable energy sources and the Smart grid are highlighted.

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“…• EV charging/discharging costs: The operator pays EVs to charge or discharge to flatten the loaf profile in terms of the energy amount EVs provide [193], [194]; • Battery degradation: When EVs are discharged upon request, additional battery degradation cost is paid as the battery degradation compensation. The cost is a function of discharging power and battery depth-of-discharge (DoD) [212]; • Mobility cost: When EVs are considered as mobile energy storages to transfer energy from energy-dense areas to energy-sparse areas, the cost incurred by EV mobility should be considered [56]. Factors such as travel distance, delay, energy loss, and on-road traffic condition need to be considered when calculating the mobility cost [204].…”

Section: B Energy Management For V2g Servicesmentioning

confidence: 99%

Energy and Information Management of Electric Vehicular Network: A Survey

Chen

Wang

Zhang

et al. 2020

IEEE Commun. Surv. Tutorials

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The connected vehicle paradigm empowers vehicles with the capability to communicate with neighboring vehicles and infrastructure, shifting the role of vehicles from a transportation tool to an intelligent service platform. Meanwhile, the transportation electrification pushes forward the electric vehicle (EV) commercialization to reduce the greenhouse gas emission by petroleum combustion. The unstoppable trends of connected vehicle and EVs transform the traditional vehicular system to an electric vehicular network (EVN), a clean, mobile, and safe system. However, due to the mobility and heterogeneity of the EVN, improper management of the network could result in charging overload and data congestion. Thus, energy and information management of the EVN should be carefully studied. In this paper, we provide a comprehensive survey on the deployment and management of EVN considering all three aspects of energy flow, data communication, and computation. We first introduce the management framework of EVN. Then, research works on the EV aggregator (AG) deployment are reviewed to provide energy and information infrastructure for the EVN. Based on the deployed AGs, we present the research work review on EV scheduling that includes both charging and vehicle-to-grid (V2G) scheduling. Moreover, related works on information communication and computing are surveyed under each scenario. Finally, we discuss open research issues in the EVN.

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Compensation of Charging Station Overload via On-Road Mobile Energy Storage Scheduling

Cited by 7 publications

References 13 publications

Optimal Energy Management Framework for Truck-Mounted Mobile Charging Stations Considering Power Distribution System Operating Conditions

Optimal Energy Management Framework for Truck-Mounted Mobile Charging Stations Considering Power Distribution System Operating Conditions

A Review on the Feasibility of Deployment of Renewable Energy Sources for Electric Vehicles under Smart Grid Environment

Energy and Information Management of Electric Vehicular Network: A Survey

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