Electrical Loads in Automotive Systems

The European Union strives for sharp reductions in both CO2 emissions as well as primary energy use. Electricity consuming technologies are becoming increasingly important in this context, due to the ongoing electrification of transport and heating services. To correctly evaluate these technologies, conversion factors are needed—namely CO2 intensities and primary energy factors (PEFs). However, this evaluation is hindered by the unavailability of a high-quality database of conversion factor values. Ideally, such a database has a broad geographical scope, a high temporal resolution and considers cross-country exchanges of electricity as well as future evolutions in the electricity mix. In this paper, a state-of-the-art unit commitment economic dispatch model of the European electricity system is developed and a flow-tracing technique is innovatively applied to future scenarios (2025–2040)—to generate such a database and make it publicly available. Important dynamics are revealed, including an overall decrease in conversion factor values as well as considerable temporal variability at both the seasonal and hourly level. Furthermore, the importance of taking into account imports and carefully considering the calculation methodology for PEFs are both confirmed. Future estimates of the CO2 emissions and primary energy use associated with individual electrical loads can be meaningfully improved by taking into account these dynamics.

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Machine learning roles in advancing the power network stability due to deployments of renewable energies and electric vehicles

International Journal of Emerging Electric Power Systems ◽

10.1515/ijeeps-2019-0270 ◽

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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