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    Studies from Thapar Institute of Engineering & Technology Update Current Data on Machine Learning (Pets: P2p Energy Trading Scheduling Scheme for Electric Vehicles In Smart Grid Systems)


    January 20, 2022 - Robotics & Machine Learning Daily News

     

      2022 JAN 19 (NewsRx) -- By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News -- Data detailed on Machine Learning have been presented. According to news reporting out of Punjab, India, by NewsRx editors, research stated, “Due to the lack of improper access control policies and decentralized access controllers, security and privacy-aware peer-to-peer (P2P) energy trading among electric vehicles (EVs) and the smart grid is challenging. Most of the solutions reported in the literature for P2P energy trading are based upon centralized controllers having various security flaws resulting in their limited applicabilities in real-world scenarios.”

      Financial support for this research came from TCS Innovation Laboratory, New Delhi.

      Our news journalists obtained a quote from the research from the Thapar Institute of Engineering & Technology, “To handle these issues, in this paper, we propose a P2P energy trading scheduling scheme called as P2P Energy Trading Scheduling (PETS) using blockchain technology. PETS is based on real-time energy consumption monitoring for balancing the energy gap between service providers (SPs), i.e., smart grids and service consumers, i.e., EVs. In PETS, the Stackelberg game theory-based 1-leader multiple-followers scheme is proposed to depict the interactions between EVs and the SP. The selection of the leader among all SPs is made using a second-price reverse auction. As per the announced energy price by the leader, EVs manage energy consumption by minimizing their energy bills. In PETS, on the leader’s side, we propose the Genetic algorithm to maximize its profit. In contrast, on the followers’ side, i.e., EVs, we use the Stackelberg Equilibrium to minimize their energy bills. Simulation results demonstrate that the proposed PETS scheme outperforms the existing state-of-the-art schemes using various performance evaluation metrics.”

      According to the news editors, the research concluded: “Specifically, it reduces the peak-to-average ratio (PAR) by 12.5% of EVs’ energy load in comparison to the existing state-of-the-art scheme.”

      This research has been peer-reviewed.

      For more information on this research see: Pets: P2p Energy Trading Scheduling Scheme for Electric Vehicles In Smart Grid Systems. IEEE Transactions on Intelligent Transportation Systems, 2021. IEEE Transactions on Intelligent Transportation Systems can be contacted at: Ieee-inst Electrical Electronics Engineers Inc, 445 Hoes Lane, Piscataway, NJ 08855-4141, USA. (Institute of Electrical and Electronics Engineers - http://www.ieee.org/; IEEE Transactions on Intelligent Transportation Systems - http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979)

      Our news journalists report that additional information may be obtained by contacting Neeraj Kumar, Thapar Institute of Engineering & Technology, Dept. of Computer Sciences and Engineering, Patiala 147004, Punjab, India.

      The direct object identifier (DOI) for that additional information is: https://doi.org/10.1109/TITS.2021.3127577. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

      (Our reports deliver fact-based news of research and discoveries from around the world.)

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