Electric Grid Integration Costs for Plug-In Electric Vehicles

2014 ◽  
Vol 3 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Jeff Berkheimer ◽  
Jeff Tang ◽  
Bill Boyce ◽  
Deepak J. Aswani
Keyword(s):  
Electric Vehicles ◽  
Grid Integration ◽  
Electric Grid

Different Penetration of Electric Vehicles and Impact on Developments in the Electric Grid

2020 ◽  
Author(s):  
Enrico Mancini ◽  
Michela Longo ◽  
Federica Foiadelli ◽  
Giovanni Parrotta ◽  
Gabriele Montinaro
Keyword(s):  
Electric Vehicles ◽  
Electric Grid

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.

Electric Vehicles in Smart Grids

2022 ◽  
pp. 1192-1211
Author(s):  
Cosmin Darab
Keyword(s):  
Electric Vehicles ◽  
Smart Grids ◽  
New Technologies ◽  
Combustion Engine ◽  
Power Grid ◽  
Rapid Development ◽  
Energy Crisis ◽  
Grid Integration ◽  
Vehicle To Grid ◽  
Smart Grid Technology

Electric vehicles were proposed as a good solution to solving energy crisis and environmental problems caused by the traditional internal combustion engine vehicles. In the last years due to the rapid development of the electric vehicles, the problem of power grid integration was addressed. In order to not put additional pressure onto the power grid several new technologies were developed. This chapter presents the smart grid technology, vehicle-to-grid concept, and electric vehicles grid integration. These technologies made possible the integration of electric vehicles without any major changes in the power grid. Moreover, electric vehicles integration brought new benefits to the power grid like better integration of renewable energy.

scholarly journals The future of electric vehicles to grid integration in Indonesia

2019 ◽  
Vol 158 ◽  
pp. 4592-4597 ◽  
Author(s):  
Muhammad Huda ◽  
Muhammad Aziz ◽  
Koji Tokimatsu
Keyword(s):  
Electric Vehicles ◽  
Grid Integration ◽  
The Future

scholarly journals A General Parameter Identification Procedure Used for the Comparative Study of Supercapacitors Models

Energies ◽  
2019 ◽  
Vol 12 (9) ◽  
pp. 1776 ◽  
Author(s):  
Henry Miniguano ◽  
Andrés Barrado ◽  
Cristina Fernández ◽  
Pablo Zumel ◽  
Antonio Lázaro
Keyword(s):  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Parametric Identification ◽  
Grid Integration ◽  
Current Profile ◽  
Model Accuracy ◽  
Complex Processes ◽  
General Parameter ◽  
Identification Parameter ◽  
The Comparative Study

Supercapacitors with characteristics such as high power density, long cycling life, fast charge, and discharge response are used in different applications like hybrid and electric vehicles, grid integration of renewable energies, or medical equipment. The parametric identification and the supercapacitor model selection are two complex processes, which have a critical impact on the system design process. This paper shows a comparison of the six commonly used supercapacitor models, as well as a general and straightforward identification parameter procedure based on Simulink or Simscape and the Optimization Toolbox of Matlab®. The proposed procedure allows for estimating the different parameters of every model using a different identification current profile. Once the parameters have been obtained, the performance of each supercapacitor model is evaluated through two current profiles applied to hybrid electric vehicles, the urban driving cycle (ECE-15 or UDC) and the hybrid pulse power characterization (HPPC). The experimental results show that the model accuracy depends on the identification profile, as well as the robustness of each supercapacitor model. Finally, some model and identification current profile recommendations are detailed.

Sign in / Sign up

Export Citation Format

Share Document