scholarly journals Impact on the Spanish electricity network due to the massive incorporation of electric vehicles

2021 ◽  
Vol 19 ◽  
pp. 540-545
Author(s):  
Francisco M. Arrabal-Campos ◽  
◽  
Juan Martínez-Lao ◽  
Francisco G. Montoya ◽  
Alfredo Alcayde ◽  
...  
Keyword(s):  
Electric Vehicles ◽  
Production System ◽  
Distribution Network ◽  
Electric Energy ◽  
Electricity Demand ◽  
Electricity Production ◽  
Electric Distribution Network ◽  
Daily Data ◽  
Fast Charging ◽  
Electricity Network

Electric vehicles, along with renewable energy, are two of the most important components for achieving a more sustainable and cleaner future. This paper study the Spanish electricity demand at the Iberian Peninsula level during the eleven-year period 2007-2018 with daily data from the Spanish electricity network, calculating the monthly daily average for each year as actual data on the use of the electricity distribution network. Having in mind this information, the number of electric vehicles (EVs) that could be charged in Spain is being studied in order to reorganize the Spanish production system. Three different scenarios are analyzed (slow, accelerated and fast charging) according to the capacity conditions of the electric distribution network, previously determining the available electric energy that varies according to the electric demand. Results obtained reveals the need of a complex reorganization of the Spanish electricity production system due to the geographical seasonality of electricity demand.

Adding Electric Vehicle Modeling Capability to an Agent-Based Transport Simulation

2014 ◽  
pp. 282-318 ◽  
Author(s):  
Rashid A. Waraich ◽  
Gil Georges ◽  
Matthias D. Galus ◽  
Kay W. Axhausen
Keyword(s):  
Electric Vehicle ◽  
Electric Vehicles ◽  
Travel Demand ◽  
Interdisciplinary Approach ◽  
Electricity Demand ◽  
Policy Makers ◽  
Agent Based ◽  
Vehicle Modeling ◽  
Electricity Network ◽  
Renewable Electricity Generation

Battery-electric and plug-in hybrid-electric vehicles are envisioned by many as a way to reduce CO2 traffic emissions, support the integration of renewable electricity generation, and increase energy security. Electric vehicle modeling is an active field of research, especially with regards to assessing the impact of electric vehicles on the electricity network. However, as highlighted in this chapter, there is a lack of capability for detailed electricity demand and supply modeling. One reason for this, as pointed out in this chapter, is that such modeling requires an interdisciplinary approach and a possibility to reuse and integrate existing models. In order to solve this problem, a framework for electric vehicle modeling is presented, which provides strong capabilities for detailed electricity demand modeling. It is built on an agent-based travel demand and traffic simulation. A case study for the city of Zurich is presented, which highlights the capabilities of the framework to uncover possible bottlenecks in the electricity network and detailed fleet simulation for CO2 emission calculations, and thus its power to support policy makers in taking decisions.

Adding Electric Vehicle Modeling Capability to an Agent-Based Transport Simulation

2015 ◽  
pp. 1563-1600 ◽  
Author(s):  
Rashid A. Waraich ◽  
Gil Georges ◽  
Matthias D. Galus ◽  
Kay W. Axhausen
Keyword(s):  
Electric Vehicle ◽  
Electric Vehicles ◽  
Travel Demand ◽  
Interdisciplinary Approach ◽  
Electricity Demand ◽  
Policy Makers ◽  
Agent Based ◽  
Vehicle Modeling ◽  
Electricity Network ◽  
Renewable Electricity Generation

Battery-electric and plug-in hybrid-electric vehicles are envisioned by many as a way to reduce CO2 traffic emissions, support the integration of renewable electricity generation, and increase energy security. Electric vehicle modeling is an active field of research, especially with regards to assessing the impact of electric vehicles on the electricity network. However, as highlighted in this chapter, there is a lack of capability for detailed electricity demand and supply modeling. One reason for this, as pointed out in this chapter, is that such modeling requires an interdisciplinary approach and a possibility to reuse and integrate existing models. In order to solve this problem, a framework for electric vehicle modeling is presented, which provides strong capabilities for detailed electricity demand modeling. It is built on an agent-based travel demand and traffic simulation. A case study for the city of Zurich is presented, which highlights the capabilities of the framework to uncover possible bottlenecks in the electricity network and detailed fleet simulation for CO2 emission calculations, and thus its power to support policy makers in taking decisions.

scholarly journals Electric Vehicle Charging Coordination on Distribution Network by using Particle Swarm Optimization and Genetic Algorithm

2019 ◽  
Vol 8 (12) ◽  
pp. 5673-5676
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Electric Vehicles ◽  
Distribution Network ◽  
Particle Swarm ◽  
Combustion Method ◽  
Swarm Optimization ◽  
Electric Vehicle Charging ◽  
Electric Distribution Network ◽  
Line Overload

The Electric Vehiclesbecoming very popular in the recent years. Typically, Electric Vehicles propulsion systems come from one or more electrical motors built inside the vehicles. This motor used electricity as energy combustion method. Due to the limited energy storage capacity, Electric Vehicles need to replenish by plugging into an electrical source. The problems appear during multiple Electric Vehicles perform charging process in an Electric Distribution Network. This process willbe causing line overload and efficiency degradation of Distribution Network. In performance to evaluate the potential of different of charging coordination, a classification has been made. The new coordinated process may consider minimum power losses and acceptable voltage limit. The process also needs to define the optimal uncoordinated and coordinated charging point. Therefore, a simulation-based framework will be performed, that use two algorithms which are Particle Swarm Optimization and Genetic Algorithm.

scholarly journals Vehicle-to-Grid in Standard and Fast Electric Vehicle Charging: Comparison of Renewable Energy Source Utilization and Charging Costs

Energies ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 1510 ◽  
Author(s):  
Anamarija Falkoni ◽  
Antun Pfeifer ◽  
Goran Krajačić
Keyword(s):  
Renewable Energy ◽  
Electric Vehicles ◽  
Energy System ◽  
Electricity Production ◽  
Social Aspects ◽  
Transport Sector ◽  
Renewable Electricity ◽  
Electricity Prices ◽  
Vehicle To Grid ◽  
Fast Charging

Croatia aims to achieve 10% of its energy production from the renewable energy sources in the total energy consumption in the transport sector. One of the ways to achieve this goal is by the use of electric vehicles. This work comparatively analyses the financial and social aspects of vehicle-to-grid charging in standard and fast charging mode, their impact on the renewable electricity production and the total electricity consumption regulated through variable electricity prices. Data were taken for the wider urban area of the Dubrovnik region. The assumption is that the Dubrovnik region will be self-sufficient by the year 2050 with 100% renewable electricity production and that all conventional vehicles will be replaced by electric vehicles. This work aims to show that the fast charging based on 10 min time steps offers more opportunities for flexibility and utilization of renewable generation in the energy system than the standard charging based on hourly time step. The results of this work showed the opposite, where in most of the scenarios standard charging provided better results. Replacement of the existing two tariff model in electricity prices with variable electricity prices contributes to the stability of the energy system, providing better regulation of charging and higher opportunities for renewable electricity utilization in standard and fast charging and reduction of charging costs. According to the financial aspects, fast charging is shown to be more expensive, but for the social aspects, it provides electric vehicles with more opportunities for better competition in the market.

scholarly journals Modeling of Electric Vehicle Charging Load in Different Areas of Distribution Network

2021 ◽  
Vol 261 ◽  
pp. 01007
Author(s):  
Zhou Jiang ◽  
Hairong Zou
Keyword(s):  
Electric Vehicle ◽  
Electric Vehicles ◽  
Large Scale ◽  
Impact Load ◽  
Distribution Network ◽  
Factors Affecting ◽  
Electric Vehicle Charging ◽  
Fast Charging ◽  
Vehicle Charging ◽  
Behavior Characteristics

This paper presents a method of forecasting and modeling of electric vehicle charging load in different regions of distribution network. Firstly, the fixed factors affecting the charging load of electric vehicles are analyzed. The electric vehicles are divided into pure electric and plug-in hybrid electric vehicles, and the charging equipment is divided into ordinary charging equipment and fast charging equipment. Then, the behavior characteristics and psychological factors of electric vehicle users are considered as the random factors influencing the charging load, the investigation statistics and hypotheses are carried out. Finally, the distribution network is divided into different regions, and the charging load model of each region is established based on Monte Carlo simulation. The established model shows that with the increase of penetration rate of electric vehicles in the future, large-scale charging into the power grid will cause impact load in different regions of the distribution network at different times, which provides some reference for the suppression of voltage fluctuation in distribution network.

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.

Sign in / Sign up

Export Citation Format

Share Document