Browsing by Author "Xu, Yiming"

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    An anti-fraud double auction model in vehicle-to-vehicle energy trading with the k-factor approach
    (IEEE, 2024-05-01) Xu, Yiming; Zhang, Lu; Ozkan, Nazmiye; Long, Chao
    The rise in electric vehicle adoption has reduced greenhouse gas emissions in transportation but overloads the power grid due to charging demands. This paper introduces a Double Auction (DA) model in Vehicle-to-Vehicle (V2V) energy trading with the K-factor approach. The novel approach defines unique market clearing prices for each successfully matched V2V transaction pairs, robustly counteracts potential economic fraud. It overcomes shortcoming of some other models of sacrificing participants who could have conducted V2V transactions in order to prevent economic fraud. Meanwhile, the model ensures transactional economic benefits, transparency and fairness. This work facilitates EV adoption across the UK and globally, by increasing confidence and convenience in energy trading mechanisms.
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    Vehicle-to-vehicle energy trading framework: a systematic literature review
    (MDPI, 2024-06-02) Xu, Yiming; Alderete Peralta, Ali; Balta-Ozkan, Nazmiye
    As transportation evolves with greater adoption of electric vehicles (EVs), vehicle-to-vehicle (V2V) energy trading stands out as an important innovation for managing energy resources more effectively as it reduces dependency on traditional energy infrastructures and, hence, alleviates the pressure on the power grid during peak demand times. Thus, this paper conducts a systematic review of the V2V energy trading frameworks. Through the included article analysis (n = 61), this paper discusses the state-of-the-art energy trading frameworks’ structure, employed methodologies, encountered challenges, and potential directions for future research. To the best of the authors’ knowledge, this is the first review explicitly focused on V2V energy trading. We detail four critical challenges to face while establishing the framework in current research, providing an overview of various methodologies, including auctions, blockchain, game theory, optimisation, and demand forecasting, that are used to address these challenges and explore their integration within the research landscape. Additionally, this paper forecasts the evolution of V2V energy trading, highlighting the potential incorporation of advanced and established technologies like artificial intelligence (AI), digital twins, and smart contracts. This review aims to encapsulate the existing state of V2V energy trading research and stimulate future advancements and technological integration within the field.