scholarly journals Market Interfaces for Electric Vehicle Charging

2017 ◽  
Vol 59 ◽  
pp. 175-227 ◽  
Author(s):  
Sebastian Stein ◽  
Enrico H. Gerding ◽  
Adrian Nedea ◽  
Avi Rosenfeld ◽  
Nicholas R. Jennings
Keyword(s):  
Electric Vehicles ◽  
Market Mechanism ◽  
Electric Vehicle Charging ◽  
Learning Agent ◽  
Performance Trends ◽  
Different Types ◽  
Secondary Objective ◽  
Key Features ◽  
Ev Charging ◽  
Desirable Effect

We consider settings where owners of electric vehicles (EVs) participate in a market mechanism to charge their vehicles. Existing work on such mechanisms has typically assumed that participants are fully rational and can report their preferences accurately via some interface to the mechanism or to a software agent participating on their behalf. However, this may not be reasonable in settings with non-expert human end-users.Thus, our overarching aim in this paper is to determine experimentally if a fully expressive market interface that enables accurate preference reports is suitable for the EV charging domain, or, alternatively, if a simpler, restricted interface that reduces the space of possible options is preferable. In doing this, we measure the performance of an interface both in terms of how it helps participants maximise their utility and how it affects deliberation time. Our secondary objective is to contrast two different types of restricted interfaces that vary in how they restrict the space of preferences that can be reported. To enable this analysis, we develop a novel game that replicates key features of an abstract EV charging scenario. In two experiments with over 300 users, we show that restricting the users' preferences significantly reduces the time they spend deliberating (by up to half in some cases). An extensive usability survey confirms that this restriction is furthermore associated with a lower perceived cognitive burden on the users. More surprisingly, at the same time, using restricted interfaces leads to an increase in the users' performance compared to the fully expressive interface (by up to 70%). We also show that some restricted interfaces have the desirable effect of reducing the energy consumption of their users by up to 20% while achieving the same utility as other interfaces. Finally, we find that a reinforcement learning agent displays similar performance trends to human users, enabling a novel methodology for evaluating market interfaces.

scholarly journals Energy Trading with Electric Vehicles in Smart Campus Parking Lots

2018 ◽  
Vol 8 (10) ◽  
pp. 1749 ◽  
Author(s):  
Mohamed Ahmed ◽  
Young-Chon Kim
Keyword(s):  
Electric Vehicles ◽  
Market Mechanism ◽  
University Campus ◽  
Energy Trading ◽  
Control Center ◽  
Parking Lots ◽  
Electric Vehicle Charging ◽  
Parking Lot ◽  
Demand Information ◽  
Knapsack Algorithm

Energy trading with electric vehicles provides opportunities to eliminate the high peak demand for electric vehicle charging while providing cost saving and profits for all participants. This work aims to design a framework for local energy trading with electric vehicles in smart parking lots where electric vehicles are able to exchange energy through buying and selling prices. The proposed architecture consists of four layers: the parking energy layer, data acquisition layer, communication network layer, and market layer. Electric vehicles are classified into three different types: seller electric vehicles (SEVs) with an excess of energy in the battery, buyer electric vehicles (BEVs) with lack of energy in the battery, and idle electric vehicles (IEVs). The parking lot control center (PLCC) plays a major role in collecting all available offer/demand information among parked electric vehicles. We propose a market mechanism based on the Knapsack Algorithm (KPA) to maximize the PLCC profit. Two cases are considered: electric vehicles as energy sellers and the PLCC as an energy buyer, and electric vehicles as energy buyers and the PLCC as an energy seller. A realistic parking pattern of a parking lot on a university campus is considered as a case study. Different scenarios are investigated with respect to the number of electric vehicles and amount of energy trading. The proposed market mechanism outperforms the conventional scheme in view of costs and profits.

scholarly journals Day-Ahead Forecast of Electric Vehicle Charging Demand with Deep Neural Networks

2021 ◽  
Vol 12 (4) ◽  
pp. 178
Author(s):  
Gilles Van Van Kriekinge ◽  
Cedric De De Cauwer ◽  
Nikolaos Sapountzoglou ◽  
Thierry Coosemans ◽  
Maarten Messagie
Keyword(s):  
Neural Network ◽  
Electric Vehicles ◽  
Negative Impact ◽  
Absolute Error ◽  
Electric Grid ◽  
Electric Vehicle Charging ◽  
The Neural Network ◽  
The Mean ◽  
Ev Charging ◽  
Charging Demand

The increasing penetration rate of electric vehicles, associated with a growing charging demand, could induce a negative impact on the electric grid, such as higher peak power demand. To support the electric grid, and to anticipate those peaks, a growing interest exists for forecasting the day-ahead charging demand of electric vehicles. This paper proposes the enhancement of a state-of-the-art deep neural network to forecast the day-ahead charging demand of electric vehicles with a time resolution of 15 min. In particular, new features have been added on the neural network in order to improve the forecasting. The forecaster is applied on an important use case of a local charging site of a hospital. The results show that the mean-absolute error (MAE) and root-mean-square error (RMSE) are respectively reduced by 28.8% and 19.22% thanks to the use of calendar and weather features. The main achievement of this research is the possibility to forecast a high stochastic aggregated EV charging demand on a day-ahead horizon with a MAE lower than 1 kW.

scholarly journals Selecting Locations of Electric Vehicle Charging Stations Based on the Traffic Load Eliminating Method

Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1650 ◽  
Author(s):  
Bong-Gi Choi ◽  
Byeong-Chan Oh ◽  
Sungyun Choi ◽  
Sung-Yul Kim
Keyword(s):  
Electric Vehicle ◽  
Electric Vehicles ◽  
Traffic Load ◽  
Queueing Model ◽  
Traffic Data ◽  
Electric Vehicle Charging ◽  
Supply Equipment ◽  
Charging Stations ◽  
Ev Charging ◽  
Charging Demand

Establishing electric vehicle supply equipment (EVSE) to keep up with the increasing number of electric vehicles (EVs) is the most realistic and direct means of promoting their spread. Using traffic data collected in one area; we estimated the EV charging demand and selected priority fast chargers; ranging from high to low charging demand. A queueing model was used to calculate the number of fast chargers required in the study area. Comparison of the existing distribution of fast chargers with that suggested by the traffic load eliminating method demonstrated the validity of our traffic-based location approach.

scholarly journals Investigation of THD Analysis in Residential Distribution Systems with Different Penetration Levels of Electric Vehicles

2018 ◽  
Vol 9 (1) ◽  
pp. 21-27
Author(s):  
Jayababu Badugu ◽  
Y.P. Obulesu ◽  
Ch. Sai Babu
Keyword(s):  
Electric Vehicles ◽  
Distribution System ◽  
Distribution Systems ◽  
Harmonic Distortion ◽  
Voltage Distortion ◽  
Electric Vehicle Charging ◽  
Private And Public ◽  
Residential Distribution ◽  
Charging Stations ◽  
Ev Charging

Electric Vehicles (EVs) are becoming a viable transportation option because they are environmentally friendly and provide solutions to high oil prices. This paper investigates the impacts of electric vehicles on harmonic distortions in urban radial residential distribution systems. The accomplishment of EV innovation relies on the accessibility of EV charging stations. To meet the power demand of growing EVs, utilities are introducing EV charging stations in private and public areas; this led to a change in the residential distribution system infrastructure. In this paper, an urban radial residential distribution system with the integration of an electric vehicle charging facility is considered for investigation. An impact of different EV penetration levels on voltage distortion is analysed. Different penetration levels of EVs into the residential distribution system are considered. Simulation results are presented to validate the work carried out in this paper. An attempt has been made to establish the relationship between the level of penetration of the EVs and voltage distortion in terms of THD (Total Harmonic Distortion)

scholarly journals Self-Sustainable off grid Electric Vehicle (EV) Charging Station with Integration of Renewable Sources

2020 ◽  
Vol 9 (3) ◽  
pp. 2669-2674
Keyword(s):  
Electric Vehicle ◽  
Electric Vehicles ◽  
Large Scale ◽  
Transport Systems ◽  
Diesel Generator ◽  
Charging Station ◽  
Electric Vehicle Charging ◽  
Power Generation Technology ◽  
Charging Stations ◽  
Ev Charging

Electric Vehicles (EV) are the world’s future transport systems. With the rise in pollutions and its effects on the environment, there has been a large scale movetowards electrical vehicles. But the plug point availability for charging is the serious problem faced by the mostof Electric Vehicle consumers. Therefore, there is a definite need to move from the GRID based/connected charging stations to standalone off-grid stations for charging the Electric Vehicles. The objective of this paper is to arrive at the best configuration or mix of the renewable resources and energy storage systems along with conventional Diesel Generator set which together works in offgrid for Electric Vehicle charging. As aconclusion, by utilizing self-sustainable off-grid power generation technology, the availability of EV charging stations in remote localities at affordable price can be made and mainly it reduces burden on the existing electrical infrastructure.

scholarly journals Impacts of Electric Vehicle Charging On Distribution Grid

2019 ◽  
Vol 18 (3) ◽  
pp. 88-96
Author(s):  
Nazmul Haque ◽  
Ahmed Mortuza Saleque
Keyword(s):  
Electric Vehicle ◽  
Electric Vehicles ◽  
Distribution Grid ◽  
Vehicle To Grid ◽  
Grid Analysis ◽  
Electric Vehicle Charging ◽  
Vehicle Charging ◽  
Battery Technology ◽  
Ev Charging ◽  
Modern Era

Electric Vehicle is one of the most emerging technology in modern era. Different type of latest technologies are used in today’s electric vehicles as well as the battery technology is also developed. Besides many advantages of electric vehicles there are some bad impacts of electric vehicles charging on electric grid. Analysis of Electric Vehicle charging impacts on distribution grid are highly importance for the development of electric vehicles. In this paper a very simplified model is used by MATLAB/Simulink to analyze the Electric vehicle charging impacts on distribution grid. In this model Vehicle to grid (V2G) technology is also used to analyze the grid power. The active power of distribution grid was measured while EVs were charging for both V2G on and off cases and the differences between this two conditions were measured from the simulation and the results were compared.  In this paper the impacts of EV charging on other grid connected loads are also analyzed.

Comparative Study on Electric Vehicle Charging Standards at Home and Abroad

2012 ◽  
Vol 608-609 ◽  
pp. 1553-1559
Author(s):  
Wu Wu Tang ◽  
Yu Ming Wu ◽  
Jian Qin
Keyword(s):  
Comparative Study ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Future Development ◽  
Harmonious Development ◽  
Charging Infrastructure ◽  
Electric Vehicle Charging ◽  
Ev Charging ◽  
S Application ◽  
At Home

Charging infrastructure is the fundamental conditions of electric vehicles(EV)’s application and dissemination, and advanced charging standards can guide and regulate the harmonious development of EV and infrastructure. In this paper, plenty of and latest EV charging standards were collected at home and abroad, which were compared in different classifications, then the standards differences were analyzed in term of relative merits to provide reference for the future development of EV charging standards in China.

Analysis of Transformer Overload Problem with EV Charging Load

2014 ◽  
Vol 1070-1072 ◽  
pp. 1648-1651
Author(s):  
Zong Feng Li ◽  
Chun Lin Guo
Keyword(s):  
Energy Conservation ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Rapid Development ◽  
Load Models ◽  
Electric Vehicle Charging ◽  
The Future ◽  
Vehicle Charging ◽  
Original Grid ◽  
Ev Charging

As a promising transport in the future, electric vehicles plays an important role in people's lives and energy conservation. In recent years, the rapid development of electric vehicles, inevitably causes the overload problems to the original grid. This paper presents a transformer load models with the consideration of electric vehicle charging load. We analyze the transformer overload problem by the results obtained by simulation of the model.

scholarly journals Research on charging strategy of electric vehicle considering user and load curve

2021 ◽  
Vol 236 ◽  
pp. 02003
Author(s):  
Wang Jun ◽  
Li Xincong ◽  
Xia Minhao ◽  
Xu Lin ◽  
Wang Bing
Keyword(s):  
Electric Vehicle ◽  
Electric Vehicles ◽  
Large Scale ◽  
Regional Distribution ◽  
Time Sharing ◽  
Load Curve ◽  
Electric Vehicle Charging ◽  
Proposed Model ◽  
Time Division ◽  
Ev Charging

With the increasing popularity of electric vehicles, the disordered charging of large-scale electric vehicles will have a great impact on the safe operation of regional distribution network. In order to solve the security problems that may occur in the power grid, this paper uses the time-sharing pricing time division method for EV charging to meet the needs of EV users. Based on this method, a multi-objective optimization model is established, which takes the electric vehicle charging capacity and power as the constraints, and based on the minimum user charging cost and the minimum load curve variance. Then, the model is solved by non-dominated sorting genetic algorithm (NSGA -Ⅱ), and the optimal compromise solution is extracted by using fuzzy set theory. Finally, the correctness of the proposed model is verified by the example.

scholarly journals Optimized Strategic Planning of Future Norwegian Low-Voltage Networks with a Genetic Algorithm Applying Empirical Electric Vehicle Charging Data

Electricity ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 91-109
Author(s):  
Julian Wruk ◽  
Kevin Cibis ◽  
Matthias Resch ◽  
Hanne Sæle ◽  
Markus Zdrallek
Keyword(s):  
Genetic Algorithm ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Low Voltage ◽  
Network Planning ◽  
Voltage Regulation ◽  
Electric Vehicle Charging ◽  
Vehicle Charging ◽  
The Impact

This article outlines methods to facilitate the assessment of the impact of electric vehicle charging on distribution networks at planning stage and applies them to a case study. As network planning is becoming a more complex task, an approach to automated network planning that yields the optimal reinforcement strategy is outlined. Different reinforcement measures are weighted against each other in terms of technical feasibility and costs by applying a genetic algorithm. Traditional reinforcements as well as novel solutions including voltage regulation are considered. To account for electric vehicle charging, a method to determine the uptake in equivalent load is presented. For this, measured data of households and statistical data of electric vehicles are combined in a stochastic analysis to determine the simultaneity factors of household load including electric vehicle charging. The developed methods are applied to an exemplary case study with Norwegian low-voltage networks. Different penetration rates of electric vehicles on a development path until 2040 are considered.

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