EnerSwap: Large-Scale, Privacy-First Automated Market Maker for V2G Energy Trading

• URL: http://arxiv.org/abs/2508.18942v1
• Date: Tue, 26 Aug 2025 11:31:05 GMT
• Title: EnerSwap: Large-Scale, Privacy-First Automated Market Maker for V2G Energy Trading
• Authors: Ahmed Mounsf Rafik Bendada, Yacine Ghamri-Doudane,
• Abstract summary: This work proposes a secure, decentralized exchange market built on blockchain technology.<n>It uses a privacy-preserving Automated Market Maker (AMM) model to offer open and fair, and equal access to traders.
• Score: 3.6654007813492258
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: With the rapid growth of Electric Vehicle (EV) technology, EVs are destined to shape the future of transportation. The large number of EVs facilitates the development of the emerging vehicle-to-grid (V2G) technology, which realizes bidirectional energy exchanges between EVs and the power grid. This has led to the setting up of electricity markets that are usually confined to a small geographical location, often with a small number of participants. Usually, these markets are manipulated by intermediaries responsible for collecting bids from prosumers, determining the market-clearing price, incorporating grid constraints, and accounting for network losses. While centralized models can be highly efficient, they grant excessive power to the intermediary by allowing them to gain exclusive access to prosumers \textquotesingle price preferences. This opens the door to potential market manipulation and raises significant privacy concerns for users, such as the location of energy providers. This lack of protection exposes users to potential risks, as untrustworthy servers and malicious adversaries can exploit this information to infer trading activities and real identities. This work proposes a secure, decentralized exchange market built on blockchain technology, utilizing a privacy-preserving Automated Market Maker (AMM) model to offer open and fair, and equal access to traders, and mitigates the most common trading-manipulation attacks. Additionally, it incorporates a scalable architecture based on geographical dynamic sharding, allowing for efficient resource allocation and improved performance as the market grows.

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