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.
2022-01-01

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.
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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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