An Energy Trading Strategy Considering the Priority of Renewable Energy and Electric Vehicles

2019 ◽  
Author(s):  
Hong Yuan ◽  
Huadong Zhang ◽  
Yong Hu ◽  
Dujiahao Fan ◽  
Chengfu Wang ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Electric Vehicles ◽  
Trading Strategy ◽  
Energy Trading

Design of a Management Algorithm for Energy Trading in Microgrids

2020 ◽  
Vol 13 (7) ◽  
pp. 1028-1040
Author(s):  
Dimosthenis Verginadis ◽  
Athanasios Karlis
Keyword(s):  
Renewable Energy ◽  
Electric Vehicles ◽  
Low Voltage ◽  
Renewable Energy Sources ◽  
Distribution Networks ◽  
Power Grids ◽  
Energy Sources ◽  
Future Research ◽  
Energy Trading ◽  
Management Algorithm

Background: The scope of this paper is to study the energy trading in microgrids. Microgrids are low voltage or medium voltage distribution networks, which consist of energy storage systems, electric loads, e.g. electric vehicles and Renewable Energy Sources (RES). Methods: Legacy energy grids are being transformed by the introduction of small to medium sized individual or cooperative, mostly RES invested energy producers and prosumers. Electric vehicles penetrate the market and modern power grids integrate them as ancillary services providers when there are peak domestic loads, as well as in order to balance grid voltage aiming to increase system reliability, compensating for renewable energy sources’ intermittency and volatility in energy production. Results: An elaborate management algorithm is proposed in this paper, to balance demand and local renewable energy sources microgrid supply. Conclusion: Finally, the results of simulations of different scenarios, including economic parameters and proposals for future research are presented.

scholarly journals Optimal Energy Trading for Renewable Energy Integrated Building Microgrids Containing Electric Vehicles and Energy Storage Batteries

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 106092-106101 ◽  
Author(s):  
Abinet Tesfaye Eseye ◽  
Matti Lehtonen ◽  
Toni Tukia ◽  
Semen Uimonen ◽  
R. John Millar
Keyword(s):  
Renewable Energy ◽  
Energy Storage ◽  
Electric Vehicles ◽  
Energy Trading ◽  
Optimal Energy ◽  
Storage Batteries

Energy After COVID

2020 ◽  
Vol 119 (820) ◽  
pp. 317-322
Author(s):  
Michael T. Klare
Keyword(s):  
Renewable Energy ◽  
Electric Vehicles ◽  
Fossil Fuels ◽  
Fossil Fuel ◽  
White Collar ◽  
Peak Oil ◽  
Business Travel ◽  
Reduce Energy Consumption ◽  
The Cost ◽  
Energy Companies

By transforming patterns of travel and work around the world, the COVID-19 pandemic is accelerating the transition to renewable energy and the decline of fossil fuels. Lockdowns brought car commuting and plane travel to a near halt, and the mass experiment in which white-collar employees have been working from home may permanently reduce energy consumption for business travel. Renewable energy and electric vehicles were already gaining market share before the pandemic. Under pressure from investors, major energy companies have started writing off fossil fuel reserves as stranded assets that are no longer worth the cost of extracting. These shifts may indicate that “peak oil demand” has arrived earlier than expected.

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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