The emergence of nanogrid has led to active local energy markets. Renewable energy sources and flexible energy storages in nanogrids provide favorable conditions for establishing a sustainable bilateral trading market. However, due to the diversity of energy sources in nanogrids, it is difficult to establish a uniform scheduling scheme. To this end, this paper first establishes an effective peer-to-peer energy block contract market to provide a scheme for users to exchange energy at any time interval. The goal is to maximize contract volume while ensuring the economic profitability of all users. Beyond that, a garbled circuit-based price comparison mechanism is proposed based on a secure multi-party computation to achieve price comparison without revealing any individual user's data to other users for privacy preserving. Finally, a fully distributed algorithm is designed by constructing a local tracker to track the inequality amount in iteration. The algorithm also subtly parses the input data, which weakens the intertwined relationship in peer-to-peer transactions and improves computation efficiency. Case studies verify the privacy of the described method and the effectiveness of the energy block contract market.

Energy Block-Based Peer-to-Peer Contract Trading with Secure Multi-Party Computation in Nanogrid

Siano P.
2022

Abstract

The emergence of nanogrid has led to active local energy markets. Renewable energy sources and flexible energy storages in nanogrids provide favorable conditions for establishing a sustainable bilateral trading market. However, due to the diversity of energy sources in nanogrids, it is difficult to establish a uniform scheduling scheme. To this end, this paper first establishes an effective peer-to-peer energy block contract market to provide a scheme for users to exchange energy at any time interval. The goal is to maximize contract volume while ensuring the economic profitability of all users. Beyond that, a garbled circuit-based price comparison mechanism is proposed based on a secure multi-party computation to achieve price comparison without revealing any individual user's data to other users for privacy preserving. Finally, a fully distributed algorithm is designed by constructing a local tracker to track the inequality amount in iteration. The algorithm also subtly parses the input data, which weakens the intertwined relationship in peer-to-peer transactions and improves computation efficiency. Case studies verify the privacy of the described method and the effectiveness of the energy block contract market.
2022
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4812337
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