Electrification and renewable energy nexus in developing countries; an overarching analysis of hydrogen production and electric vehicles integrality in renewable energy penetration

2021 ◽  
Vol 236 ◽  
pp. 114023 ◽  
Author(s):  
Olusola Bamisile ◽  
Akinola Babatunde ◽  
Humphrey Adun ◽  
Nasser Yimen ◽  
Mustapha Mukhtar ◽  
...  
Keyword(s):  
Developing Countries ◽  
Renewable Energy ◽  
Hydrogen Production ◽  
Electric Vehicles

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.

Conclusions

2018 ◽  
Author(s):  
Stephany Griffith-Jones ◽  
José Antonio Ocampo ◽  
Paola Arias
Keyword(s):  
Developing Countries ◽  
Renewable Energy ◽  
Exchange Rates ◽  
Interest Rates ◽  
Development Strategy ◽  
National Development ◽  
Financial Inclusion ◽  
Production Sector ◽  
New Instruments ◽  
Innovation And Entrepreneurship

Based on the seven case studies analysed in this volume, this chapter concludes that national development banks (NDBs) have been successful in many cases in supporting innovation and entrepreneurship, key new sectors like renewable energy, and financial inclusion. They have developed new instruments, such as far greater use of guarantees, equity (including venture capital) and debt funds, and new instruments for financial inclusion. The context in which they operate is key to their success. Active countercyclical policies, low inflation, fairly low real interest rates, a well-functioning financial sector, and competitive exchange rates are crucial. They are also more effective if the country has a clear development strategy, linked to production sector strategies that foster innovative sectors. Under these conditions, the chapter argues that there is great need for a larger scale of NDB activity in Latin America and in developing countries in general.

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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