Identification of the Best Charging Time of Electric Vehicles in Fast Charging Stations Connected to Smart Grid Based on Q-Learning

2018 ◽  
Author(s):  
Morteza Rahimi Shaarbaf ◽  
Mohsen Ghayeni
Keyword(s):  
Smart Grid ◽  
Electric Vehicles ◽  
Q Learning ◽  
Charging Time ◽  
Fast Charging ◽  
Charging Stations ◽  
Grid Based

scholarly journals Perceived Usage Potential of Fast-Charging Locations

2018 ◽  
Vol 9 (1) ◽  
pp. 14 ◽  
Author(s):  
Julia Krause ◽  
Stefan Ladwig ◽  
Lotte Saupp ◽  
Denis Horn ◽  
Alexander Schmidt ◽  
...  
Keyword(s):  
Electric Vehicles ◽  
Point Of View ◽  
Charging Infrastructure ◽  
Charging Time ◽  
Points Of Interest ◽  
Fast Charging ◽  
Business Perspective ◽  
Charging Stations ◽  
User Perspectives ◽  
Model Point

Fast-charging infrastructure with charging time of 20–30 min can help minimizing current perceived limitations of electric vehicles, especially considering the unbalanced and incomprehensive distribution of charging options combined with a long perceived charging time. Positioned on optimal location from user and business perspective, the technology is assumed to help increasing the usage of an electric vehicle (EV). Considering the user perspectives, current and potential EV users were interviewed in two different surveys about optimal fast-charging locations depending on travel purposes and relevant location criteria. The obtained results show that customers prefer to rather charge at origins and destinations than during the trip. For longer distances, charging locations on axes with attractive points of interest are also considered as optimal. From the business model point of view, fast-charging stations at destinations are controversial. The expensive infrastructure and the therefore needed large number of charging sessions are in conflict with the comparatively time consuming stay.

Machine learning roles in advancing the power network stability due to deployments of renewable energies and electric vehicles

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Mohamad Nassereddine
Keyword(s):  
Machine Learning ◽  
Renewable Energy ◽  
Electric Vehicles ◽  
Renewable Energy Sources ◽  
Energy Sources ◽  
Power Network ◽  
Network Stability ◽  
Fast Charging ◽  
Electrical Loads ◽  
Charging Stations

AbstractRenewable energy sources are widely installed across countries. In recent years, the capacity of the installed renewable network supports large percentage of the required electrical loads. The relying on renewable energy sources to support the required electrical loads could have a catastrophic impact on the network stability under sudden change in weather conditions. Also, the recent deployment of fast charging stations for electric vehicles adds additional load burden on the electrical work. The fast charging stations require large amount of power for short period. This major increase in power load with the presence of renewable energy generation, increases the risk of power failure/outage due to overload scenarios. To mitigate the issue, the paper introduces the machine learning roles to ensure network stability and reliability always maintained. The paper contains valuable information on the data collection devises within the power network, how these data can be used to ensure system stability. The paper introduces the architect for the machine learning algorithm to monitor and manage the installed renewable energy sources and fast charging stations for optimum power grid network stability. Case study is included.

SURVEY OF HARMONIC AND SUPRAHARMONIC EMISSION OF FAST CHARGING STATIONS FOR ELECTRIC VEHICLES IN CHINA AND GERMANY

2021 ◽  
Author(s):  
F. Chen ◽  
Q. Zhong ◽  
H. Zhang ◽  
M. Zhu ◽  
S. Müller ◽  
...  
Keyword(s):  
Electric Vehicles ◽  
Fast Charging ◽  
Charging Stations

scholarly journals Congestion Control in Charging Stations Allocation with Q-Learning

2019 ◽  
Vol 11 (14) ◽  
pp. 3900
Author(s):  
Zhang ◽  
Gong ◽  
Xu
Keyword(s):  
Travel Time ◽  
Learning Algorithm ◽  
Navigation Systems ◽  
Routing Problem ◽  
Q Learning ◽  
The Road ◽  
Adaptive Allocation ◽  
Charging Time ◽  
Convergence Performance ◽  
Charging Stations

Navigation systems can help in allocating public charging stations to electric vehicles (EVs) with the aim of minimizing EVs’ charging time by integrating sufficient data. However, the existing systems only consider their travel time and transform the allocation as a routing problem. In this paper, we involve the queuing time in stations as one part of EVs’ charging time, and another part is the travel time on roads. Roads and stations are easily congested resources, and we constructed a joint-resource congestion game to describe the interaction between vehicles and resources. With a finite number of vehicles and resources, there exists a Nash equilibrium. To realize a self-adaptive allocation work, we applied the Q-learning algorithm on systems, defining sets of states and actions in our constructed environment. After being allocated one by one, vehicles concurrently requesting to be charged will be processed properly. We collected urban road network data from Chongqing city and conducted experiments. The results illustrate the proposed method can be used to solve the problem, and its convergence performance was better than the genetic algorithm. The road capacity and the number of EVs affected the initial of Q-value, and not the convergence trends.

Aggregated electric vehicles load profiles with fast charging stations

2014 ◽  
Author(s):  
G. Celli ◽  
G. G. Soma ◽  
F. Pilo ◽  
F. Lacu ◽  
S. Mocci ◽  
...  
Keyword(s):  
Electric Vehicles ◽  
Fast Charging ◽  
Charging Stations

scholarly journals Latvian Electric Vehicle Fast Charging Infrastructure: Results of the First Year of Operation

2019 ◽  
Vol 23 (2) ◽  
pp. 9-21
Author(s):  
Aivars Rubenis ◽  
Aigars Laizans ◽  
Andra Zvirbule
Keyword(s):  
Electric Vehicles ◽  
Capacity Utilization ◽  
Skewed Distribution ◽  
First Year ◽  
Charging Infrastructure ◽  
Large Dispersion ◽  
Fast Charging ◽  
Total Capacity ◽  
Charging Stations ◽  
Initial Results

Abstract This article presents preliminary analysis of the Latvian national EV fast - charging network after the first year of operation. The first phase of Latvian national EV fast-charging network was launched in 2018 with 70 charging stations on the TEN-T roads and in the largest towns and cities. The article looks at the initial results, both looking at the total capacity utilization for individual charging stations, determining the hourly charging distribution; and to the utilization of the network as a whole. The results present that there is a very large dispersion of the data, most of the charging events happening in a few charging stations in and around the capital of Latvia. However, there have been charging events in all charging stations, even in the most remote ones. Even more skewed distribution was observed analyzing the charging habits of the EV users, with 10 % of users accounting for more than half of the charging events. This should be taken into account when considering applying the results for the future, expecting larger number of electric vehicles in Latvia.

scholarly journals A Brief Survey on Important Interconnection Standards for Photovoltaic Systems and Electric Vehicles

2021 ◽  
Vol 12 (3) ◽  
pp. 117
Author(s):  
Suvetha Poyyamani Poyyamani Sunddararaj ◽  
Shriram S. Rangarajan ◽  
Subashini Nallusamy ◽  
E. Randolph Collins ◽  
Tomonobu Senjyu
Keyword(s):  
Electric Vehicles ◽  
Large Scale ◽  
Reliable Operation ◽  
Charging Infrastructure ◽  
Grid Systems ◽  
Pv Systems ◽  
Charging Time ◽  
Cost Distance ◽  
Smart Charging ◽  
Charging Stations

The consumer adoption of electric vehicles (EVs) has become most popular. Numerous studies are being carried out on the usage of EVs, the challenges of EVs, and their benefits. Based on these studies, factors such as battery charging time, charging infrastructure, battery cost, distance per charge, and the capital cost are considered factors in the adoption of electric vehicles and their interconnection with the grid. The large-scale development of electric vehicles has laid the path to Photovoltaic (PV) power for charging and grid support, as the PV panels can be placed at the top of the smart charging stations connected to a grid. By proper scheduling of PV and grid systems, the V2G connections can be made simple. For reliable operation of the grid, the ramifications associated with the PV interconnection must be properly addressed without any violations. To overcome the above issues, certain standards can be imposed on these systems. This paper mainly focuses on the various standards for EV, PV systems and their interconnection with grid-connected systems.

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