Probabilistic Load Flow methods with high integration of Renewable Energy Sources and Electric Vehicles - case study of Greece

2011 ◽  
Author(s):  
A. G. Anastasiadis ◽  
E. Voreadi ◽  
N. D. Hatziargyriou
Keyword(s):  
Renewable Energy ◽  
Electric Vehicles ◽  
Renewable Energy Sources ◽  
Load Flow ◽  
Energy Sources ◽  
Probabilistic Load Flow ◽  
Probabilistic Load ◽  
Of Greece

scholarly journals Synergies between e-Mobility and Photovoltaic Potentials—A Case Study on an Urban Medium Voltage Grid

Energies ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 3795 ◽  
Author(s):  
Julia Vopava ◽  
Ulrich Bergmann ◽  
Thomas Kienberger
Keyword(s):  
Renewable Energy ◽  
Performance Indicators ◽  
Energy Demand ◽  
Renewable Energy Sources ◽  
Key Performance Indicators ◽  
Load Flow ◽  
Energy Sources ◽  
Grid Model ◽  
Synergy Effects

To reduce CO2 emissions, it is necessary to cover the increasing energy demand of e-mobility with renewable energy sources. Therefore, the influence of increasing e-mobility and synergy effects between e-mobility and renewable energy sources need to be investigated. The case study presented here shows results from the analysis of grid-side and energetic synergy effects between e-mobility charged only at work and photovoltaic (PV) potentials. The basis of the grid study is a simplified cell-based grid model. Following the determination of synthetic charging profiles for e-mobility, PV potential profiles, load and production profiles, we perform load flow calculations for different scenarios and a simulation period of one year using the grid model. After the grid study, the energy analyses are carried out using four key performance indicators. The grid study shows that line overloads caused by PV production are only reduced and not avoided by increasing e-mobility and vice versa. The increase in the power peak of e-mobility, by shifting the charging processes into the peak of PV potentials, leads to a reduction of the production surplus in summer, while in winter the line utilisation increases. By modelling PV potentials on real irradiation and temperature data, the investigation of key performance indicators can identify not only seasonal fluctuations but also daily fluctuations.

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.

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