Energy Demand Forecasting and Optimizing Electric Systems for Developing Countries

Arnob, Saadman S.; Arefin, Abu Isha Md. Sadot; Saber, Ahmed Yousuf; Mamun, Khondaker A.

doi:10.1109/access.2023.3250110

Cited by 3 publications

References 117 publications

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Robust Vehicle-to-Grid Energy Trading Method Based on Smart Forecast and Multi-Blockchain Network

Liang,

Wang,

Abdallah

2024

IEEE Access

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In the present era, energy issues are a significant concern, and the energy trading market is the crucial sector to facilitate supply-demand balance and sustainable development. For better demand response and grid balancing, vehicle-to-grid (V2G) technology is rapidly gaining importance in energy markets. To narrow the gap between ideal V2G goals and actual applications needs, energy trading system has to overcome the challenges of over-centralized structure, inflexible timeline adaptation, limited market scale and energy efficiency, excessive feedback time costs, and low rate of economic return. To address these issues and ensure a secure energy market, we propose a decentralized intelligent V2G system called V2G Forecasting and Trading Network (V2GFTN) to achieve efficient and robust energy trading in campus EV networks. A multiple blockchain structure is proposed in V2GFTN to ensure trading security and data privacy between energy requests and offers. V2GFTN also integrates energy forecasting functions for EVs with a smart energy trading and EV allocation mechanism called SRET so that the EVs with driving tasks can supply their extra power back to the grid and achieve higher energy efficiency and economic profit. Through rigorous experimentation and compared with equivalent studies, V2GFTN system has demonstrated higher economic profit and energy demand fill rate by up to 1.6 times and 1.9 times than the state-of-the-art V2G approaches.

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